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Monteiro, Natalia Kondo. "Síntese e caracterização de manganita-cromita de lantânio dopada com rutênio para anodos de células a combustível de óxidos sólidos." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/85/85134/tde-18112011-160321/.
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Diversos anodos para célula a combustível de óxido sólido (SOFC) têm sido estudados devido aos problemas de deterioração dos anodos tradicionalmente usados, os compósitos à base de zircônia estabilizada/Ni (YSZ/Ni). Estudos prévios evidenciaram que a perovskita La0,75Sr0,25Cr0,50Mn0,50O3 (LSCM) possui desempenho similar em SOFCs usando hidrogênio e metano como combustível, tornando essa cerâmica um possível substituto dos compósitos à base de níquel. No presente estudo, foram sintetizados compostos La0,75Sr0,25Cr0,50-xMn0,50- yRux,yO3 (LSCM-Ru) pelo método dos precursores poliméricos. Análises termogravimétrica e térmica diferencial (TG/ATD) simultâneas e difração de raios X (DRX) foram utilizadas para monitorar a evolução térmica das resinas precursoras e a formação de fase dos compostos. As propriedades elétricas de amostras sinterizadas foram estudadas pela técnica de 4 pontas de prova dc na faixa de temperatura entre 25 °C e 800 °C. Os resultados experimentais indicaram a formação de fase única dos compostos LSCM-Ru calcinados a ~1200 °C. Os parâmetros de rede, calculados a partir dos dados de DRX, revelaram que a substituição parcial dos íons Cr ou Mn pelo Ru não altera significativamente a estrutura cristalina do LSCM até x,y ~ 0,10; uma característica consistente com os raios iônicos similares dos cátions Cr, Mn e Ru com número de coordenação seis. Medidas de resistividade elétrica ao ar mostraram que o mecanismo de transporte não é alterado e que o efeito da substituição de Ru nas propriedades elétricas do composto depende do íon substituído (Cr ou Mn) de maneira consistente com suas substituições parciais. Os testes de SOFCs unitárias revelaram que células com os anodos constituídos por uma camada coletora de corrente do anodo cerâmico LSCM-Ru e uma camada funcional de YSZ/Ni têm desempenho superior a células contendo apenas o anodo cerâmico. As células contendo os anodos cerâmicos LSCM-Ru foram testadas em hidrogênio e etanol, entre 800 °C e 950 °C, e mostraram desempenho em etanol superior ao em hidrogênio; uma característica que foi associada às propriedades de transporte eletrônico dos compostos LSCM-Ru em atmosfera redutora. Os resultados sugerem que os compostos LSCM com substituição parcial de Ru são anodos promissores para SOFC operando com etanol.Several anodes for solid oxide fuel cell (SOFC) have been studied because of serious degradation exhibited by the traditionally used yttria-stabilized zirconia/Ni cermets (YSZ/Ni). Previous studies showed that the perovskite La0.75Sr0.25Cr0.50Mn0.50O3 (LSCM) has similar performance in SOFCs running on hydrogen and methane fuels, making such a ceramic a potential alternative to YSZ/Ni cermets. In the present study, compounds La0.75Sr0.25Cr0.50- xMn0.50-yRux,yO3 (LSCM-Ru) were synthesized by the polymeric precursor method. Simultaneous thermogravimetric and differential thermal analysis (TG/DTA) and X-ray diffraction (XRD) were used to monitor the thermal evolution of the precursor resins and the formation of crystalline phases. The electrical properties of sintered samples were studied by the 4-probe dc technique in the temperature range between 25 °C and 800 °C with controlled atmosphere. The experimental results showed the formation of single phase LSCM-Ru compounds after heat treatment at ~ 1200 °C. Lattice parameters, calculated from the XRD data, revealed that the partial substitution of Cr or Mn by Ru has no significant effect on the crystal structure of LSCM up to Ru x,y ~ 0.10; in agreement with the similar ionic radius of Cr, Mn and Ru with coordination number six. Electrical resistivity measurements showed that the transport mechanism is unchanged and that the effect of Ru addition on the electrical properties of the compound depends on the substituted ion (Cr or Mn). Electrochemical tests of SOFCs demonstrated that single cells comprised of a current collector layer of LSCM-Ru ceramic anode and a functional layer for YSZ/Ni have superior performance when compared to single cells with only one layer of the ceramic anode. Single cells with the LSCM-Ru ceramic anode layer were tested under both hydrogen and ethanol fuels, in the 800 °C - 950 °C temperature range. The main results showed that the single fuel cells exhibited higher performance under ethanol than under hydrogen; a feature that was related to the enhanced electronic transport properties of LSCM-Ru in reducing atmosphere. The experimental results suggest that the LSCM-Ru compounds are promising anodes for ethanol fueled SOFCs.
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Farber, Elliott. "A new method to achieve lithic use-wear discrimination using laser scanning confocal microscopy (LSCM)." Thesis, Florida Atlantic University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1524500.
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My study sought to acquire quantitative data from the surface of lithic tools and use that data to discriminate tools used on different contact materials. An experimental archaeological wear production method was conceived, whereby I and several volunteers produced wear on chert, heat-treated chert, and obsidian flakes by using those flakes on several contact materials. The flakes were then analyzed using a laser scanning confocal microscope, which recorded three-dimensional surface data from each tool.
The data was analyzed using cluster analysis to find the ideal combination of parameters which correctly discriminated the flakes based on use-wear data. After finding acceptable parameters which grouped flakes appropriately through cluster analysis, those groups were subjected to a discriminant analysis. Each analysis returned a p-value under .05, meaning that the clustering based on the parameters Sq and Sfd produced by the cluster analysis was not random, but indicative of these variables’ ability to discriminate lithic use-wear. The major advantage of the approach developed in this study is that it can quantitatively discriminate use-wear produced by different contact materials on flakes with no a priori information at all.
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Smith, Shea C. "LASER SCANNING CONFOCAL MICROSCOPY (LSCM): AN APPLICATION FOR THE DETECTION OF MORPHOLOGICAL ALTERATIONS IN SKIN STRUCTURE." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/198.
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Laser scanning confocal microscopy (LSCM) is an optical imaging technique that provides improved resolution and sensitivity over conventional methods of optical microscopy. However, the cost of most commercial LSCM systems exceeds the financial limitations of many smaller laboratories. The design of a custom LSCM created at a fraction of the cost of a commercial model is discussed in this paper.
The increase in the incidence rate of skin cancer in the world today is alarming, as such, it is essential to provide an early, rapid and effective method for in vivo diagnostics of human skin tissue. LSCM is capable of detecting alterations in skin morphology and configuration, as well as providing chemical composition information which may be indicative of the development of skin cancer. If developed successfully, LSCM could replace the current invasive biopsy procedures performed today with a quick, non-invasive optical scanning method that would prove beneficial for both patients and physicians alike.
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Oliveira, Caio Vinícius Mazaro de. "Um diagnóstico do elo curtume da cadeia do couro do Oeste Paulista baseado na Lean Supply Chain Management (LSCM)." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/153901.
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O Brasil está entre os países que mais exportam couro. O segmento de couro possui importante papel na contribuição do desenvolvimento econômico de várias cidades brasileiras. O estado de São Paulo é destaque na produção de couros, juntamente como o estado do Rio Grande do Sul. Neste processo ocorre o beneficiamento do couro, sendo repassado para indústrias que elaboram roupas, sapatos, acessórios, e diversos outros produtos. Essas organizações, denominadas de agroindústrias processadoras de couro, estão em busca de uma gestão eficiente, que contemple toda produção, visando o aumento do resultado financeiro, possibilitando a continuação da atividade. Para tanto, a redução de desperdícios e ganho na qualidade são requisitos fundamentais para essa gestão. Diante da carência de estudos, a presente pesquisa tem como objetivo geral diagnosticar por meio da Lean Supply Chain Management (LSCM) aspectos de melhoria na gestão do elo curtume da cadeia do couro do Oeste Paulista. A abordagem da LSCM sendo aplicada corretamente cumpre os requisitos buscados pelas organizações. Para atingir o objetivo da pesquisa, utilizou-se como método de pesquisa o estudo de caso do tipo múltiplo, com a condução de visitas in loco e aplicação de formulário em cinco Unidades de Pesquisa, caracterizando os portes das organizações, os tipos de processos que elas fazem, quais os elementos e pilares que mais precisam de suporte referente à abordagem empregada, quais as ferramentas utilizadas que se enquadram na LSCM e os benefícios e dificuldades da aplicação. Destacam-se como resultados da coleta de dados que os pilares da Gestão da Tecnologia de Informação, Gestão Logística e Melhoria Contínua são os que mais carecem de melhorias, necessitando empregar a tecnologia da informação na comunicação com os clientes, planejar a rede logística de distribuição, direcionar equipes de trabalho para melhoria contínua. Os planos de ação para elaborar como serão realizadas as ações supramencionadas são oportunidades de futuras pesquisas.
Brazil is among the countries that export the most leather. The leather segment plays an important role in the contribution of the economic development of several Brazilian cities. The state of São Paulo is prominent in the production of leather, along with the state of Rio Grande do Sul. In this process the leather is processed, being passed on to industries that manufacture clothes, shoes, accessories, and various other products. These organizations, called leather processing agroindustries, are in search of an efficient management that contemplates all production, aiming to increase the financial result, allowing the continuation of the activity. Therefore, waste reduction and quality gain are fundamental requirements for this management. In view of the lack of studies, the present research has as general objective to diagnose, through the Lean Supply Chain Management (LSCM), aspects of improvement in the management of the tannery chain link of the leather chain of Oeste Paulista. The LSCM approach being applied correctly meets the requirements sought by organizations. In order to reach the research objective, a multiple-case study was used as the research method, with the conduction of on-site visits and application of the form in five Research Units, characterizing the organizations' sizes, the types of processes that they do, which elements and pillars most need support regarding the approach employed, which tools are used that fit the LSCM, and the benefits and difficulties of the application. It is highlighted as results of data collection that the pillars of Information Technology Management, Logistics Management and Continuous Improvement are the ones that need the most improvement, needing to use information technology in communication with customers, to plan the distribution logistics network, to direct work teams for continuous improvement. The action plans to elaborate how the aforementioned actions will be carried out are opportunities for future research.
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Raymundo, Juliana Delgado. "Análise da gestão da cadeia de suprimentos do leite a partir de pequenos produtores da região de Tupã/SP /." Tupã, 2019. http://hdl.handle.net/11449/191198.
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Orientador: Eduardo Guilherme SatoloResumo: A cadeia de produção do leite é composta por diversos agentes que integram a cadeia de suprimentos desde a aquisição da matéria-prima até a distribuição do produto para o cliente final. Esta cadeia tem importância em termos de contribuição econômica e social para o país ou região. Desta forma, a obtenção de produtividade não está relacionada apenas aos benefícios para o produtor, como aumento de lucratividade, competitividade no mercado, eficiência na gestão das propriedades, mas em aspectos que geram impactos na sociedade, como por exemplo geração de novos postos de trabalho. Este trabalho tem como objetivo geral, analisar quais os principais gargalos na gestão da cadeia de suprimentos leiteira de pequenos produtores da região de Tupã/SP. Desta forma, é indispensável explorar os impactos sofridos pelos produtores, a falta de apoio e orientação para que os mesmos consigam conduzir e realizar uma gestão eficiente em sua propriedade. Tal estudo apresenta a metodologia aplicada em forma de pesquisa de campo de caráter descritiva, com abordagem qualitativa, do tipo survey com aplicação de questionário com questões predominantemente fechadas com uso da escala Likert. O resultado da coleta foi apresentado por meio da análise de correspondência. Tais resultados demostraram os gargalos na cadeia de suprimentos, baseado nos oito pilares da abordagem LSCM, sendo: Gestão da Tecnologia da Informação, Gestão de Fornecedores de Insumos, Eliminação de desperdícios, Produção, Gestão de Relac... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The milk supply chain is made up of products that integrate the supply chain from the raw material to the final distribution of the product to the end customer. This organization has an economic and social building base for the country or region. Thus, a strategy option is not only an advantage for the producer, such as the increase of profitability, market competitiveness, management performance of companies, but also the managerial impact on society, such as the generation of new jobs. This paper aims to analyze what are the main bottlenecks of the milk and milk industry of small farmers in the Tupã / SP region. Therefore, is necessary It is necessary to explore the impacts suffered by the producers, the lack of support and guidance for them to be able to conduct and efficiently manage their property. The study presents the applied method in the search fields, descriptive, with a qualitative approach, on the survey form, of general studies predominated closed to use the scale of Likert. The result of the collection was presented through the correspondence analysis. These results demonstrated bottlenecks in the supply chain, based on the eight pillars of the LSCM approach, namely: Information Technology Management, Input Supplier Management, Waste Disposal, Production, Customer Relationship Management, Logistics Management, Owners Commitment, and Continuous Improvement. Stands out the result of data collection for the need to improve quality, the implementation of technologi... (Complete abstract click electronic access below)
Mestre
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Carneiro, Dias André Eduardo. "Study of RBC shape transitions induced by nanoparticles." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/668080.
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Aquesta tesi descriu l'estudi de les propietats del medi extracel·lular sobre la criopreservació de glòbuls vermells i la possible aplicació de nanopartícules de sílice com a co-agents per al lliurament intracel·lular de trehalosa, un crioprotector natural. La primera part de l™estudi es va centrar en les condicions de congelació i descongelació i en les propietats del medi extracel·lular per a la congelació. Es van analitzar diferents propietats segons la seva influència en la taxa de supervivència dels glòbuls vermells, que es va avaluar mitjançant l™assaig d™hemòlisi i es va analitzar l™efecte de la congelació mitjançant anàlisi morfològica d™imatges de glòbuls vermells. La segona part de l'estudi investiga la interacció de nanopartícules de sílice carregades de manera diferent amb els glòbuls vermells per a futures aplicacions com a co-agent per al lliurament de la trehalosa. La toxicitat de les nanopartícules de sílice es va determinar mitjançant un assaig d™hemòlisi i la seva distribució espacial es va estudiar mitjançant l™examen de glòbuls vermells que flotaven lliurement mitjançant microscòpia confocal d™escaneig làser (LSCM). Es va desenvolupar un nou mètode de visualització 3D de gran rendiment i aplicat a les imatges LSCM per tal de corregir la deriva al llarg de la z-stack permetent l'anàlisi de les imatges. Els resultats es van confirmar interactuant les nanopartícules de sílice amb vesícules gegants unilamellars (GUV) com a sistema experimental.Esta tesis describe el estudio de las propiedades del medio extracelular en la crioconservación de los glóbulos rojos y la posible aplicación de nanopartículas de sílice como coagentes para la entrega intracelular de trehalose, un crioprotector natural. La primera parte del estudio se centró en las condiciones de congelación y descongelación, y en las propiedades del medio extracelular para la congelación. Se analizaron diferentes propiedades de acuerdo con su influencia en la tasa de supervivencia de los glóbulos rojos, según se evaluó mediante el ensayo de hemólisis, y se analizó el efecto de la congelación mediante el análisis morfológico de las imágenes de los glóbulos rojos. La segunda parte del estudio investiga la interacción de nanopartículas de sílice, cargadas de manera diferente, con glóbulos rojos para su futura aplicación como coagente para la entrega de trehalose. La toxicidad de la nanopartícula de sílice se determinó mediante un ensayo de hemólisis y su distribución espacial se estudió mediante la obtención de imágenes de los glóbulos rojos que flotan libremente usando microscopía confocal (LSCM). Se desarrolló un nuevo método de visualización 3D de alto rendimiento que se aplicó a las imágenes LSCM para corregir la deriva en toda la pila z permitiendo el análisis de las imágenes. Los resultados se confirmaron mediante la interacción de las nanopartículas de sílice con vesículas unilamelares gigantes (GUV) como un sistema de modelo experimental.
This thesis describes the study of the properties of extracellular medium on the cryopreservation of red blood cells and the potential application of silica nanoparticles as co-agents for the intracellular delivery of trehalose, a natural cryoprotectant. The first part of the study focused on the freezing and thawing conditions, and on the properties of the extracellular medium for freezing. Different properties were analyzed according to their influence on the survival rate of red blood cells as assessed by hemolysis assay and the effect of freezing was analyzed by morphological analysis of images of red blood cells. The second part of the study investigates the interaction of differently charged silica nanoparticles with red blood cells for future application as co-agent for trehalose delivery. Silica nanoparticle toxicity was determined by hemolysis assay and their spatial distribution was studied by imaging freely floating red blood cells using laser scanning confocal microscopy (LSCM). A novel high-throughput 3D visualization method was developed and applied to LSCM images in order to correct the drift throughout the z-stack allowing the analysis of the images. Results were confirmed by interacting the silica nanoparticles with giant unilamellar vesicles (GUV) as an experimental model system.
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Selvaraj, Ranjith Karthick. "A Study on the Implementation of Green Supply Chain- A Comparative Analysis between Small Scale Industries in India and Developed Nations." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-13275.
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Environmental pollution is the major problem that mankind faces in present state, the major emission of toxic gases is from vehicles and manufacturing industries. The thesis study focuses on three different types of Small Scale Industries (SSI) in India that are bumper manufacturing industry, dyeing industry and food processing industry. The product life cycles of the process for each industry are identified and their final green waste disposal methods are investigated. The industries are identified with more lean wastes within their product life cycle process. The major green wastes from their disposal methods have high influence on environment. These wastes have to be reduced or eliminated by practicing a suitable supply chain. In present the companies doesn’t practice any supply chain in their organization. The implement of supply chain could reduce the environmental pressures and wastes of the companies to some extent. The lean wastes identified in the process could be eliminated by practicing suitable lean tools and methods. The final disposal wastes are considered to be the green wastes. The method of disposal practiced by the SSIs in India shows an evidence of how much they concern towards the environment. The research tries to explain some suitable waste management techniques for the industries and discusses about importance of government role on making this techniques possible. The small scale industries experiences both wastes, so it has to integrate lean for practicing green supply chain, the implementation of lean would pay a way for green supply chain management. As a result of it a comprehensive lean and green model is suggested for the industries because the model is composed of both lean and green waste reduction techniques and it also helps in achieving both lean and green business results.
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Thommy, Léonard. "Développement de nouveaux matériaux d’électrodes pour convertisseurs électrochimiques à haute température : piles à combustible et électrolyseurs." Nantes, 2015. http://archive.bu.univ-nantes.fr/pollux/show.action?id=7f5a6fca-209c-49d4-b02e-4d6eef28f439.
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L’objectif de la thèse est le développement de nouveaux matériaux d’électrode négative pour piles à combustible et électrolyseurs à oxydes solide (SOFC et SOEC), présentant une bonne activité catalytique à température intermédiaire. Dans une première partie de ce travail, des composés dérivés de l'électrolyte BaIn0,3Ti0,7O3-δ,Æ Ba0. 5La0. 5Ti0. 3Mn0. 7O3-δ (BLTM) et Ba0. 5La0. 5In0. 3Ti0. 1Mn0. 6O3-δ (BLITIM) ont été développés. Des cellules symétriques Ni-BLTM/BIT07 et Ni-BLITIM/BIT07 ont été préparées par coulage en bande et co-frittées. Une résistance de polarisation (Rp) de 0,20 Ω cm2 à 700°C a été observée sous hydrogène, pour une teneur initiale de 40% en NiO. Dans une deuxième partie, une nouvelle famille de composés MIEC dérivées du matériau La0. 75Sr0. 25Cr0. 5Mn0. 5O3-δ (LSCM) par substitution de Ru dans la phase a été préparée. L’introduction de ruthénium a provoqué une amélioration de la conductivité totale des composés obtenus sous air et sous Ar/H2 5%. Les performances de La0. 75Sr0. 25Cr0. 4Mn0. 5Ru0. 1O3-δ (LSC0. 4MRu0. 1) ont été évaluées en tant qu’anode de cellule symétrique avec Ce0. 9Gd0. 1O1. 95 comme matériau d’électrolyte, et comparées à celles de La0. 75Sr0. 25Cr0. 5Mn0. 3Ni0. 2O3-δ (LSCM0. 3Ni0. 2), LSCM et LSCM imprégné avec du Ni. Des particules métalliques de Ru et Ni ont été obtenues par exsolution à la surface des matériaux LSC0. 4MRu0. 1 et LSCM0. 3Ni0. 2 et ont provoqué une amélioration de la part de la Rp liée à l’adsorption de H2. Les meilleures performances sont obtenues avec le matériau LSC0. 4MRu0. 1. La comparaison des résultats obtenus sous Ar/H2 5% et sous méthane ainsi qu’une comparaison du vieillissement des cellules a permis d’évaluer l’intérêt de l’exsolutionThe aim of this work is the development of new materials for the negative electrode of solid oxide fuel cells and electrolysers (SOFC and SOEC), showing a good electrocatalytic activity at intermediate temperatures. New BaIn0,3Ti0,7O3±δ-derived compounds Ba0. 5La0. 5Ti0. 3Mn0. 7O3 (BLTM) and Ba0. 5La0. 5In0. 3Ti0. 1Mn0. 6O3 (BLITIM) were developed in a first part of this work. Ni-BLTM/BIT07 et Ni-BLITIM/BIT07 symmetrical cells were fabricated by tape casting and co-sintering, and were optimised. A polarisation resistance (Rp) value of 0,20 Ω cm2 has been measured at 700°C under Ar/H2 (5%), for a nominal NiO-content of 40%m. In a second part of this work, new MIEC compound family derived from La0. 75Sr0. 25Cr0. 5-xMn0. 5O3-δ (LSCM) by substitution of ruthenium have been prepared. The introduction of ruthenium increased the total conductivity of the compound under both air and reducing atmosphere. The performances of La0. 75Sr0. 25Cr0. 4Mn0. 5Ru0. 1O3-δ (LSC0. 4MRu0. 1) as an anode material have been investigated in symmetrical cells with Ce0. 9Gd0. 1O1. 95 as electrolyte material, and compared to that of La0. 75Sr0. 25Cr0. 5Mn0. 3Ni0. 2O3-δ (LSCM0. 3Ni0. 2), LSCM, and LSCM impregnated with nickel. A metallic particle dispersion has been obtained at the surface of LSCM0. 3Ni0. 2 et LSC0. 4MRu0. 1 and it has been shown to improve a the part of the Rp linked to gas adsorption. The best performances have been obtained for LSC0. 4MRu0. 1. The comparison of the results obtained under Ar/H2 5% and under methane along with a comparison of the cell ageing allowed us to evaluate and discuss the interest of exsolution
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Lay, Elisa. "Nouveaux matériaux d'électrode de cellule SOFC." Phd thesis, Université Joseph Fourier (Grenoble), 2009. http://tel.archives-ouvertes.fr/tel-00461152.
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Ce travail est consacré à l'étude des influences de deux cations, le cérium et le baryum, sur les propriétés structurales, physico-chimiques, électriques et électrochimiques de l'oxyde (La,Sr)(Cr,Mn)O3 (LSCM). L'effet de l'état d'oxydation du cérium a été déterminé en substituant les sites A de LSCM et d'un oxyde de composition proche, CexSr1-xCr0,5Mn0,5O3 (CeSCM). L'influence des propriétés de basicité du baryum a été examinée. Les matériaux sont stables en conditions de fonctionnement d'anode pour SOFC. La conductivité est de type p pour CeLSCM et CeSCM. Les composés LBCM sont des semi-conducteurs de type n pour des pressions partielles comprises entre 1 et 10-4 atm, et de type p pour des pressions plus faibles. Sous atmosphère neutre, la conductivité électrique totale augmente avec la teneur en cérium dans LSCM, et la conductivité des matériaux CeSCM est similaire à celle de CeLSCM substitué par 25% de cérium (36 S.cm-1 à 900 °C). Sous atmosphère réductrice, la conductivité des matériaux CeLSCM est de l'ordre de 1 S.cm-1. La quantité de baryum n'a pas d'influence sur la conductivité de LSBCM. La caractérisation d'électrodes ponctuelles denses a permis de montrer que les performances anodiques augmentent avec la teneur en cérium substitué au lanthane dans LSCM. La nature des processus impliqués n'est pas modifiée lorsque le strontium est substitué par le cérium, même si l'absence de lanthane pénalise le comportement anodique. Des performances intéressantes pour une application comme matériau d'anode pour SOFC ont été atteintes pour le composé La0,75Ba0,25Cr0,5Mn0,5O3. Les origines des contributions élémentaires des caractéristiques d'électrode sont discutées.
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Mirzababaei, Jelvehnaz. "LSCF Synthesis and Syngas Reactivity over LSCF-modified Ni/YSZ Anode." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1312225644.
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Siengchum, Tritti. "Electrochemical Oxidation of Methane on Ni-Doped Perovskite Anode Solid Oxide Fuel Cell." University of Akron / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1248205545.
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Edholm, Gustav, and Xuechen Zuo. "A comparison between aconventional LSTM network and agrid LSTM network applied onspeech recognition." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230173.
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In this paper, a comparision between the conventional LSTM network and the one-dimensionalgrid LSTM network applied on single word speech recognition is conducted. The performanceof the networks are measured in terms of accuracy and training time. The conventional LSTMmodel is the current state of the art method to model speech recognition. However, thegrid LSTM architecture has proven to be successful in solving other emperical tasks such astranslation and handwriting recognition. When implementing the two networks in the sametraining framework with the same training data of single word audio files, the conventionalLSTM network yielded an accuracy rate of 64.8 % while the grid LSTM network yielded anaccuracy rate of 65.2 %. Statistically, there was no difference in the accuracy rate betweenthe models. In addition, the conventional LSTM network took 2 % longer to train. However,this difference in training time is considered to be of little significance when tralnslating it toabsolute time. Thus, it can be concluded that the one-dimensional grid LSTM model performsjust as well as the conventional one.
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Yue, Xiangling. "The development of alternative cathodes for high temperature solid oxide electrolysis cells." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/6531.
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This study mainly explores the development of alternative cathode materials for the electrochemical reduction of CO₂ by high temperature solid oxide electrolysis cells (HTSOECs), which operate in the reverse manner of solid oxide fuel cells (SOFCs). The conventional Ni-yttria stabilized zirconia (YSZ) cermets cathode suffered from coke formation, whereas the perovskite-type (La, Sr)(Cr, Mn)O₃ (LSCM) oxide material displayed excellent carbon resistance. Initial CO₂ electrolysis performance tests from different cathode materials prepared by screen-printing showed that LSCM based cathode performed poorer than Ni-YSZ cermets, due to non-optimized microstructure. Efforts were made on microstructure modification of LSCM based cathodes by means of various fabrication methods. Among the LSCM/YSZ graded cathode, extra catalyst (including Pd, Ni, CeO₂, and Pt) aided LSCM/GDC (Gd₀.₁Ce₀.₉O₁.₉₅) cathode, LSCM impregnated YSZ cathode, and GDC impregnated LSCM cathode, the GDC impregnated LSCM cathode, with porous LSCM as backbone for finely dispersed GDC nanoparticles, was found to possess the desired microstructure for CO₂ splitting reaction via SOEC. Incorporating of 0.5wt% Pd into GDC impregnated LSCM cathode gave rise to an Rp of 0.24 Ω cm² at open circuit voltage (OCV) at 900°C in CO₂-CO 70-30 mixture, comparable with the Ni/YSZ cermet cathode operated in the identical conditions. Meanwhile, the cathode kinetics and possible mechanisms of the electrochemical reduction of CO₂ were studied, and factors including CO₂/CO composition, operation temperature and potential were taken into account. The current-to-chemical efficiency of CO₂ electrolysis was evaluated with gas chromatography (GC). The high performance Pd and GDC co-impregnated LSCM cathode was also applied for CO₂ electrolysis without protective CO gas in feed. This cathode also displayed superb performance towards CO₂ electrochemical reduction under SOEC operation condition in CO₂/N₂ mixtures, though it had OCV as low as 0.12V at 900°C. The LSCM/GDC set of SOEC cathode materials were investigated in the application of steam electrolysis and H₂O-CO₂ co-electrolysis as well. For the former, adequate supply of steam was essential to avoid the appearance of S-shaped I-V curves and limited steam transport. The 0.5wt% Pd and GDC co-infiltrated LSCM material has been found to be a versatile cathode with high performance and good durability in SOEC operations.
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Fu, Reid J. "CCG Realization with LSTM Hypertagging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534236955413883.
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Nordin, Stensö Isak. "Predicting Tropical Thunderstorm Trajectories Using LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231613.
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Thunderstorms are both dangerous as well as important rain-bearing structures for large parts of the world. The prediction of thunderstorm trajectories is however difficult, especially in tropical regions. This is largely due to their smaller size and shorter lifespan. To overcome this issue, this thesis investigates how well a neural network composed of long short-term memory (LSTM) units can predict the trajectories of thunderstorms, based on several years of lightning strike data. The data is first clustered, and important features are extracted from it. These are used to predict the mean position of the thunderstorms using an LSTM network. A random search is then carried out to identify optimal parameters for the LSTM model. It is shown that the trajectories predicted by the LSTM are much closer to the true trajectories than what a linear model predicts. This is especially true for predictions of more than 1 hour. Scores commonly used to measure forecast accuracy are applied to compare the LSTM and linear model. It is found that the LSTM significantly improves forecast accuracy compared to the linear model.Åskväder är både farliga och livsviktiga bärare av vatten för stora delar av världen. Det är dock svårt att förutsäga åskcellernas banor, främst i tropiska områden. Detta beror till större delen på deras mindre storlek och kortare livslängd. Detta examensarbete undersöker hur väl ett neuralt nätverk, bestående av long short-term memory-lager (LSTM) kan förutsäga åskväders banor baserat på flera års blixtnedlslagsdata. Först klustras datan, och viktiga karaktärsdrag hämtas ut från den. Dessa används för att förutspå åskvädrens genomsnittliga position med hjälp av ett LSTMnätverk. En slumpmässig sökning genomförs sedan för att identifiera optimala parametrar för LSTM-modellen. Det fastslås att de banor som förutspås av LSTM-modellen är mycket närmare de sanna banorna, än de som förutspås av en linjär modell. Detta gäller i synnerhet för förutsägelser mer än 1 timme framåt. Värden som är vanliga för att bedöma prognosers träffsäkerhet beräknas för att jämföra LSTM-modellen och den linjära. Det visas att LSTM-modellen klart förbättrar förutsägelsernas träffsäkerhet jämfört med den linjära modellen.
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Rogers, Joseph. "Effects of an LSTM Composite Prefetcher." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396842.
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Recent work in computer architecture and machine learning has seen various groups begin exploring the viability of using neural networks to augment conventional processor designs. Of particular interest is using the predictive capabilities of techniques in natural language processing to assist traditional CPU memory prefetching methods. This work demonstrates one of these proposed techniques, and examines some of the challenges associated with producing satisfactory and consistently reproducible results. Special attention is given to data acquisition and preprocessing as different methods. This is important since the handling training data can enormously influence on the final prediction accuracy of the network. Finally, a number of changes to improve these methods are suggested. These include ways to raise accuracy, reduce network overhead, and to improve the consistency of results. This work shows that augmenting an LSTM prefetcher with a simple stream prefetcher leads to moderate improvements in prediction accuracy. This could be a way to start reducing the size of neural networks so they are usable in real hardware.
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Schelhaas, Wietze. "Predicting network performancein IoT environments using LSTM." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454062.
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There are still many problems that need to be solved with Internet of Things (IoT) technology, one of them being performance assurance. To ensure a certain quality of service in an IoT environment, the network has to be monitored and actively measured. However, Due to the limited computational recourses Internet of things nodes have, active measurement is difficult to achieve without also inducing energy and network overhead. A potential solution to this problem is to apply a machine-learning algorithm to predict network performance metrics such as round- trip time or packet loss. By substituting active performance measurements with a machine-learning algorithm, you reduce the overhead created by active performance measurements Previous research has revolved around applying traditional machine learning algorithms to wireless sensor network features such as packet statistics and topological information of the network to predict round-trip time. The purpose of this thesis is to use a more advanced deep learning algorithm namely Long short-term memory (LSTM) to try and exploit time dependencies in the data Three different datasets containing network statistics are used in three different experiments. In every experiment, LSTM models with different configurations are created, and their predictioncapabilities are compared to traditional neural networks with equivalent configurations. In all experiments, both the LSTM model and its corresponding equivalent neural network model produced similar results, meaning that a time dependency in the data could not be proven.
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Nilson, Erik, and Arvid Renström. "LSTM-nätverk för generellt Atari 2600 spelande." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17174.
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I detta arbete jämfördes ett LSTM-nätverk med ett feedforward-nätverk för generellt Atari 2600 spelande. Prestandan definierades som poängen agenten får för ett visst spel. Hypotesen var att LSTM skulle prestera minst lika bra som feedforward och förhoppningsvis mycket bättre. För att svara på frågeställningen skapades två olika agenter, en med ett LSTM-nätverk och en med ett feedforward-nätverk. Experimenten utfördes på Stella emulatorn med hjälp av ramverket the Arcade Learning Environment (ALE). Hänsyn togs till Machado råd om inställningar för användning av ALE och hur agenter borde tränas och evalueras samtidigt. Agenterna utvecklades med hjälp av en genetisk algoritm. Resultaten visade att LSTM var minst lika bra som feedforward men båda metoderna blev slagna av Machados metoder. Toppoängen i varje spel jämfördes med Granfelts arbete som har varit en utgångspunkt för detta arbete.
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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.
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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level parallelism options as blocks are instantiated based on parameters. The internal structure of the design implements pipelined, parallel or serial solutions depending on which is optimal in every case. The implications concerning these decisions are discussed in detail in the report. The design process is described in detail and the evaluation of the design is also presented to measure accuracy and error of the design output.This thesis work resulted in a complete synthesizable ASIC design implementing an LSTM layer, a Fully Connected layer and a Softmax layer which can perform classification of data based on trained weight matrices and bias vectors. The design primarily uses 16-bit fixed point format with 5 integer and 11 fractional bits but increased precision representations are used in some blocks to reduce error output. Additionally, a verification environment has also been designed and is capable of performing simulations, evaluating the design output by comparing it with results produced from performing the same operations with 64-bit floating point precision on a SystemVerilog testbench and measuring the encountered error. The results concerning the accuracy and the design output error margin are presented in this thesis report. The design went through Logic and Physical synthesis and successfully resulted in a functional netlist for every tested configuration. Timing, area and power measurements on the generated netlists of various configurations of the design show consistency and are reported in this report.LSTM neurala nätverk har använts för taligenkänning, bildigenkänning och andra artificiella intelligensapplikationer i många år. De flesta applikationer utför LSTM-algoritmen och de nödvändiga beräkningarna i digitala moln. Offline lösningar inkluderar användningen av FPGA och GPU men de mest lovande lösningarna inkluderar ASIC-acceleratorer utformade för endast dettaändamål. Denna rapport presenterar en ASIC-design som kan utföra multipla iterationer av LSTM-algoritmen på en enkelriktad neural nätverksarkitetur utan peepholes. Den föreslagna designed ger aritmetrisk nivå-parallellismalternativ som block som är instansierat baserat på parametrar. Designens inre konstruktion implementerar pipelinerade, parallella, eller seriella lösningar beroende på vilket anternativ som är optimalt till alla fall. Konsekvenserna för dessa beslut diskuteras i detalj i rapporten. Designprocessen beskrivs i detalj och utvärderingen av designen presenteras också för att mäta noggrannheten och felmarginal i designutgången. Resultatet av arbetet från denna rapport är en fullständig syntetiserbar ASIC design som har implementerat ett LSTM-lager, ett fullständigt anslutet lager och ett Softmax-lager som kan utföra klassificering av data baserat på tränade viktmatriser och biasvektorer. Designen använder huvudsakligen 16bitars fast flytpunktsformat med 5 heltal och 11 fraktions bitar men ökade precisionsrepresentationer används i vissa block för att minska felmarginal. Till detta har även en verifieringsmiljö utformats som kan utföra simuleringar, utvärdera designresultatet genom att jämföra det med resultatet som produceras från att utföra samma operationer med 64-bitars flytpunktsprecision på en SystemVerilog testbänk och mäta uppstådda felmarginal. Resultaten avseende noggrannheten och designutgångens felmarginal presenteras i denna rapport.Designen gick genom Logisk och Fysisk syntes och framgångsrikt resulterade i en funktionell nätlista för varje testad konfiguration. Timing, area och effektmätningar på den genererade nätlistorna av olika konfigurationer av designen visar konsistens och rapporteras i denna rapport.
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Valluru, Aravind-Deshikh. "Realization of LSTM Based Cognitive Radio Network." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538697/.
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This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.
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Li, Edwin. "LSTM Neural Network Models for Market Movement Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.
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Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisions. Experiments are made on different setups of the S&P-500 stock index, and two distinct models are built, each one being an improvement of the previous model. The first model is a multivariate regression model, and the second model is a multivariate binary classifier. The output of each model is used to reason about the future behavior of the index. The experiment shows for the configuration provided that LSTM RNNs are unsuitable for predicting exact values of daily returns, but gives satisfactory results when used to predict the direction of the movement.Att förstå och kunna förutsäga hur index varierar med tiden och andra parametrar är ett viktigt problem inom kapitalmarknader. Tidsserieanalys med autoregressiva metoder har funnits sedan årtionden tillbaka, och har oftast gett goda resultat. Dessa metoder saknar dock möjligheten att förklara trender och cykliska variationer i tidsserien, något som kan karaktäriseras av tidsvarierande samband, men även samband mellan parametrar som indexet beror utav. Syftet med denna studie är att undersöka om recurrent neural networks (RNN) med long short-term memory-celler (LSTM) kan användas för att fånga dessa samband, för att slutligen användas som en modell för att komplettera indexhandel. Experimenten är gjorda mot en modifierad S&P-500 datamängd, och två distinkta modeller har tagits fram. Den ena är en multivariat regressionsmodell för att förutspå exakta värden, och den andra modellen är en multivariat klassifierare som förutspår riktningen på nästa dags indexrörelse. Experimenten visar för den konfiguration som presenteras i rapporten att LSTM RNN inte passar för att förutspå exakta värden för indexet, men ger tillfredsställande resultat när modellen ska förutsäga indexets framtida riktning.
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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.
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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone.Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
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Tang, Hao. "Bidirectional LSTM-CNNs-CRF Models for POS Tagging." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362823.
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In order to achieve state-of-the-art performance for part-of-speech(POS) tagging, the traditional systems require a significant amount of hand-crafted features and data pre-processing. In this thesis, we present a discriminative word embedding, character embedding and byte pair encoding (BPE) hybrid neural network architecture to implement a true end-to-end system without feature engineering and data pre-processing. The neural network architecture is a combination of bidirectional LSTM, CNNs, and CRF, which can achieve a state-of-the-art performance for a wide range of sequence labeling tasks. We evaluate our model on Universal Dependencies (UD) dataset for English, Spanish, and German POS tagging. It outperforms other models with 95.1%, 98.15%, and 93.43% accuracy on testing datasets respectively. Moreover, the largest improvements of our model appear on out-of-vocabulary corpora for Spanish and German. According to statistical significance testing, the improvements of English on testing and out-of-vocabulary corpora are not statistically significant. However, the improvements of the other more morphological languages are statistically significant on their corresponding corpora.
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Andréasson, David, and Blomquist Jesper Mortensen. "Forecasting the OMXS30 - a comparison between ARIMA and LSTM." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413793.
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Machine learning is a rapidly growing field with more and more applications being proposed every year, including but not limited to the financial sector. In this thesis, historical adjusted closing prices from the OMXS30 index are used to forecast the corresponding future values using two different approaches; one using an ARIMA model and the other using an LSTM neural network. The forecasts are made on three different time intervals: 90, 30 and 7 days ahead. The results showed that the LSTM model performs slightly better when forecasting 90 and 30 days ahead, whereas the ARIMA model has comparable accuracy on the seven day forecast.
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Cavallie, Mester Jon William. "Using LSTM Neural Networks To Predict Daily Stock Returns." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106124.
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Long short-term memory (LSTM) neural networks have been proven to be effective for time series prediction, even in some instances where the data is non-stationary. This lead us to examine their predictive ability of stock market returns, as the development of stock prices and returns tend to be a non-stationary time series. We used daily stock trading data to let an LSTM train models at predicting daily returns for 60 stocks from the OMX30 and Nasdaq-100 indices. Subsequently, we measured their accuracy, precision, and recall. The mean accuracy was 49.75 percent, meaning that the observed accuracy was close to the accuracy one would observe by randomly selecting a prediction for each day and lower than the accuracy achieved by blindly predicting all days to be positive. Finally, we concluded that further improvements need to be made for models trained by LSTMs to have any notable predictive ability in the area of stock returns.
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Pokhrel, Abhishek <1996>. "Stock Returns Prediction using Recurrent Neural Networks with LSTM." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/22038.
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Research in asset pricing has, until recently, side-stepped the high dimensionality problem by focusing on low-dimensional models. Work on cross-sectional stock return prediction, for example, has focused on regressions with a small number of characteristics. Given the background of an enormously large number of variables that could potentially be relevant for predicting returns, focusing on such a small number of factors effectively means that the researchers are imposing a very high degree of sparsity on these models. This research studies the use of the recurrent neural network (RNN) method to deal with the “curse of dimensionality” challenge in the cross-section of stock returns. The purpose is to predict the daily stock returns. Compared with the traditional method of returns, namely the CAPM model, the focus will be on using the LSTM model to do the prediction. LSTM is very powerful in sequence prediction problems because they’re able to store past information. Thus, we compare the forecast of returns from the LSTM model with the traditional CAPM model. The comparison will be made using the out-of-sample R2 along with the Sharpe Ratio and Sortino Ratio. Finally, we conclude with the further improvements that need to be made for models trained by LSTMs to have any notable predictive ability in the area of stock returns.
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Reijns, Martin. "An analysis of Lsm protein complexes." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/12856.
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Recombinant yeast Lsm proteins were purified from Escherichia coli and tested for their ability to promote annealing of the U4 and U6 snRNAs and to unwind a DNA/RNA duplex resembling the 3’ stem-loop of U6 snRNA. For comparison, Hfq, the Sm-like protein from E. coli, and recombinant human Lsm complexes were used in the same in vitro assays. The results are consistent with their ability to promote RNA/RNA annealing and to modulate RNA secondary structure, which, in vivo, may also allow them to affect RNA/protein interactions. The function of LSM4 was studied by overexpression and depletion of wild-type Lsm4p, and by expression of an Lsm4p C-terminal deletion mutant. Results suggest that Lsm4p affects cell morphology and that its C-terminus promotes efficient recruitment of Lsm1-7p to P-bodies and may promote P-body formation. LSM5 was shown to be dispensable for cell visibility, and its depletion was shown to affect levels of U4, U6 and U4/U6 RNAs similar to effects in Ism6Δ and Ism7Δ strains. The involvement of the different domains of Lsm1p and Lsm8p in localisation of these proteins to the cytoplasm (to P-bodies under stress conditions) and nucleus respectively was investigated by creating (deletion) mutants and hybrids of various domains of these proteins. Results suggest that the N-termini of both proteins play a central role in targeting them to their respective cellular locations. The in vitro studies reveal that the RNA chaperone function of Sm-like proteins appears to have been conserved from bacteria to eukaryotes. Presumably, gene duplication and formation of hetereo-multimeric complexes in higher organisms has allowed functional diversification.
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Gualandi, Giacomo. "Analisi di dataset in campo finanziario mediante reti neurali LSTM." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19623/.
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Con il presente elaborato si è esplorato il campo della data analytics.
È stato analizzato un dataset relativo all' andamento storico del titolo di borsa di una società, i cui dati sono stati manipolati in modo tale da renderli compatibili per un loro utilizzo in una applicazione di Machine Learning. Si sono approfondite le reti neurali artificiali LSTM e con esse si è creato un modello che permettesse di effettuare delle predizioni sui valori futuri del titolo. Infine sono state valutate le differenze tra i valori predetti e quelli reali assunti dal titolo di borsa.
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Larsson, Joel. "Optimizing text-independent speaker recognition using an LSTM neural network." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26312.
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In this paper a novel speaker recognition system is introduced. Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Here, a recurrent neural network approach is used to learn to identify ten speakers within a set of 21 audio books. Audio signals are processed via spectral analysis into Mel Frequency Cepstral Coefficients that serve as speaker specific features, which are input to the neural network. The Long Short-Term Memory algorithm is examined for the first time within this area, with interesting results. Experiments are made as to find the optimum network model for the problem. These show that the network learns to identify the speakers well, text-independently, when the recording situation is the same. However the system has problems to recognize speakers from different recordings, which is probably due to noise sensitivity of the speech processing algorithm in use.
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Wolpher, Maxim. "Anomaly Detection in Unstructured Time Series Datausing an LSTM Autoencoder." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231368.
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An exploration of anomaly detection. Much work has been done on the topic of anomalyd etection, but what seems to be lacking is a dive into anomaly detection of unstructuredand unlabeled data. This thesis aims to determine the efctiveness of combining recurrentneural networks with autoencoder structures for sequential anomaly detection. The use of an LSTM autoencoder will be detailed, but along the way there will also be backgroundon time-independent anomaly detection using Isolation Forests and Replicator Neural Networks on the benchmark DARPA dataset. The empirical results in this thesis show that Isolation Forests and Replicator Neural Networks both reach an F1-score of 0.98. The RNN reached a ROC AUC score of 0.90 while the Isolation Forest reached a ROC AUC of 0.99. The results for the LSTM Autoencoder show that with 137 features extracted from the unstructured data, it can reach an F1 score of 0.8 and a ROC AUC score of 0.86En undersökning av anomalitetsdetektering. Mycket arbete har gjorts inom ämnet anomalitetsdetektering, men det som verkar saknas är en fördjupning i anomalitetsdetektering av ostrukturerad och omärktdata. Denna avhandling syftar till att bestämma effektiviteten av att kombinera Recurrent Neural Networks med Autoencoder strukturer för sekventiell anomalitetsdetektion. Användningen av en LSTM autoencoder kommeratt beskrivas i detalj, men bakgrund till tidsoberoende anomalitetsdetektering med hjälp av Isolation Forests och Replicator Neural Networks på referens DARPA dataset kommer också att täckas. De empiriska resultaten i denna avhandling visar att Isolation Forestsoch Replicator Neural Networks (RNN) båda når en F1-score på 0,98. RNN nådde en ROC AUC-score på 0,90 medan Isolation Forest nådde en ROC-AUC på 0,99. Resultaten för LSTM Autoencoder visar att med 137 features extraherade från ostrukturerad data kan den nå en F1-score på 0,80 och en ROC AUC-score på 0,86.
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Berenji, Ardestani Sarah. "Time Series Anomaly Detection and Uncertainty Estimation using LSTM Autoencoders." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281354.
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The goal of this thesis is to implement an anomaly detection tool using LSTM autoencoder and apply a novel method for uncertainty estimation using Bayesian NeuralNetworks (BNNs) based on a paper from Uber research group [1]. Having a reliable anomaly detection tool and accurate uncertainty estimation is critical in many fields. At Telia, such a tool can be used in many different data domains like device logs to detect abnormal behaviours. Our method uses an autoencoder to extract important features and learn the encoded representation of the time series. This approach helps to capture testing data points coming from a different population. We then train a prediction model based on this encoder’s representation of data. An uncertainty estimation algorithm is used to estimate the model’s uncertainty, which breaks it down to three different sources: model uncertainty, model misspecification, and inherent noise. To get the first two, a Monte Carlo dropout approach is used which is simple to implement and easy to scale. For the third part, a bootstrap approach that estimates the noise level via the residual sum of squares on validation data is used. As a result, we could see that our proposed model can make a better prediction in comparison to our benchmarks. Although the difference is not big, yet it shows that making prediction based on encoding representation is more accurate. The anomaly detection results based on these predictions also show that our proposed model has a better performance than the benchmarks. This means that using autoencoder can improve both prediction and anomaly detection tasks. Additionally, we conclude that using deep neutral networks would show bigger improvement if the data has more complexity.Målet med den här uppsatsen är att implentera ett verktyg för anomaliupptäckande med hjälp av LSTM autoencoders och applicera en ny metod för osäkerhetsestimering med hjälp av Bayesian Neural Networks (BNN) baserat på en artikel från Uber research group [1]. Pålitliga verktyg för att upptäcka anomalier och att göra precisa osäkerhetsestimeringar är kritiskt i många fält. På Telia kan ett sådant verktyg användas för många olika datadomäner, som i enhetsloggar för att upptäcka abnormalt beteende. Vår metod använder en autoencoder för att extrahera viktiga egenskaper och lära sig den kodade representationen av tidsserierna. Detta tillvägagångssätt hjälper till med att ta in testdatapunker som kommer in från olika grundmängder. Sedan tränas en förutsägelsemodell baserad på encoderns representation av datan. För att uppskatta modellens osäkerhet används en uppskattningsalgoritm som delar upp osäkerheten till tre olika källor. Dessa tre källor är: modellosäkerhet, felspeciferad model, och naturligt brus. För att få de första två används en Monte Carlo dropout approach som är lätt att implementera och enkel att skala. För den tredje delen används en enkel anfallsvikel som uppskattar brusnivån med hjälp av felkvadratsumman av valideringsdatan. Som ett resultat kunde vi se att vår föreslagna model kan göra bättre förutsägelser än våra benchmarks. Även om skillnaden inte är stor så visar det att att använda autoencoderrepresentation för att göra förutsägelser är mer noggrant. Resulaten för anomaliupptäckanden baserat på dessa förutsägelser visar också att vår föreslagna modell har bättre prestanda än benchmarken. Det betyder att användning av autoencoders kan förbättra både förutsägelser och anomaliupptäckande. Utöver det kan vi dra slutsatsen att användning av djupa neurala nätverk skulle visa en större förbättring om datan hade mer komplexitet.
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Singh, J. P., A. Kumar, Nripendra P. Rana, and Y. K. Dwivedi. "Attention-based LSTM network for rumor veracity estimation of tweets." Springer, 2020. http://hdl.handle.net/10454/17942.
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YesTwitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.
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Backer-Meurke, Henrik, and Marcus Polland. "Predicting Road Rut with a Multi-time-series LSTM Model." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37599.
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Road ruts are depressions or grooves worn into a road. Increases in rut depth are highly undesirable due to the heightened risk of hydroplaning. Accurately predicting increases in road rut depth is important for maintenance planning within the Swedish Transport Administration. At the time of writing this paper, the agency utilizes a linear regression model and is developing a feed-forward neural network for road rut predictions. The aim of the study was to evaluate the possibility of using a Recurrent Neural Network to predict road rut. Through design science research, an artefact in the form of a LSTM model was designed, developed, and evaluated.The dataset consisted of multiple-multivariate short time series where research was limited. Case studies were conducted which inspired the conceptual design of the model. The baseline LSTM model proposed in this paper utilizes the full dataset in combination with time-series individualization through an added index feature. Additional features thought to correlate with rut depth was also studied through multiple training set variations. The model was evaluated by calculating the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) for each training set variation. The baseline model predicted rut depth with a MAE of 0.8110 (mm) and a RMSE of 1.124 (mm) outperforming a control set without the added index. The feature with the highest correlation to rut depth was curvature with a MAEof 0.8031 and a RMSE of 1.1093. Initial finding shows that there is a possibility of utilizing an LSTM model trained on multiple-multivariate time series to predict rut depth. Time series individualization through an added index feature yielded better results than control, indicating that it had the desired effect on model performance.
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Ärlemalm, Filip. "Harbour Porpoise Click Train Classification with LSTM Recurrent Neural Networks." Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215088.
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The harbour porpoise is a toothed whale whose presence is threatened in Scandinavia. Onestep towards preserving the species in critical areas is to study and observe the harbourporpoise population growth or decline in these areas. Today this is done by using underwateraudio recorders, so called hydrophones, and manual analyzing tools. This report describes amethod that modernizes the process of harbour porpoise detection with machine learning. Thedetection method is based on data collected by the hydrophone AQUAclick 100. The data isprocessed and classified automatically with a stacked long short-term memory recurrent neuralnetwork designed specifically for this purpose.Vanlig tumlare är en tandval vars närvaro i Skandinavien är hotad. Ett steg mot att kunnabevara arten i utsatta områden är att studera och observera tumlarbeståndets tillväxt ellertillbakagång i dessa områden. Detta görs idag med hjälp av ljudinspelare för undervattensbruk,så kallade hydrofoner, samt manuella analysverktyg. Den här rapporten beskriver enmetod som moderniserar processen för detektering av vanlig tumlare genom maskininlärning.Detekteringen är baserad på insamlad data från hydrofonen AQUAclick 100. Bearbetning ochklassificering av data har automatiserats genom att använda ett staplat återkopplande neuraltnätverk med långt korttidsminne utarbetat specifikt för detta ändamål.
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Bergström, Carl, and Oscar Hjelm. "Impact of Time Steps on Stock Market Prediction with LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262221.
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Machine learning models as tools for predicting time series have in recent years proven to perform exceptionally well. With financial time series in the form of stock indices being inherently complex and subject to noise and volatility, the prediction of stock market movements has proven to be especially difficult throughout extensive research. The objective of this study is to thoroughly analyze the LSTM architecture for neural networks and its performance when applied to the S&P 500 stock index. The main research question revolves around quantifying the impact of varying the number of time steps in the LSTM model on predictive performance when applied to the S&P 500 index. The data used in the model is of high reliability downloaded from the Bloomberg Terminal, where the closing price has been used as feature in the model. Other constituents of the model have been based in previous research, where satisfactory results have been reached. The results indicate that among the evaluated time steps, ten steps provided the superior performance. However, the impact of varying time steps is not all too significant for the overall performance of the model. Finally, the implications of the results for the field of research present themselves as good basis for future research, where parameters are varied and fine-tuned in pursuit of optimal performance.Maskininlärningsmodeller som redskap för att förutspå tidsserier har de senaste åren visat sig prestera exceptionellt bra. Vad gäller finansiella tidsserier i formen av aktieindex, som har en inneboende komplexitet, och är föremål för störningar och volatilitet, har förutsägelse av aktiemarknadsrörelser visat sig vara särskilt svårt igenom omfattande forskning. Målet med denna studie är att grundligt undersöka LSTM-arkitekturen för neurala nätverk och dess prestanda när den appliceras på aktieindexet S&P 500. Huvudfrågan kretsar kring att kvantifiera inverkan som varierande av antal tidssteg i LTSM-modellen har på prediktivprestanda när den appliceras på aktieindexet S&P 500. Data som använts i modellen är av hög pålitlighet, nedladdad frånBloomberg-terminalen, där stängningskurs har använts som feature i modellen. Andra beståndsdelar av modellen har baserats i tidigare forskning, där tillfredsställande resultat har uppnåtts. Resultaten indikerar att bland de testade tidsstegen så producerartio tidssteg bäst resultat. Dock verkar inte påverkan av antalet tidssteg vara särskilt signifikant för modellens övergripandeprestanda. Slutligen så presenterar sig implikationerna av resultaten för forskningsområdet som god grund för framtida forskning, där parametrar kan varieras och finjusteras i strävan efter optimal prestanda.
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Poormehdi, Ghaemmaghami Masoumeh. "Tracking of Humans in Video Stream Using LSTM Recurrent Neural Network." Thesis, KTH, Teoretisk datalogi, TCS, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217495.
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In this master thesis, the problem of tracking humans in video streams by using Deep Learning is examined. We use spatially supervised recurrent convolutional neural networks for visual human tracking. In this method, the recurrent convolutional network uses both the history of locations and the visual features from the deep neural networks. This method is used for tracking, based on the detection results. We concatenate the location of detected bounding boxes with high-level visual features produced by convolutional networks and then predict the tracking bounding box for next frames. Because a video contain continuous frames, we decide to have a method which uses the information from history of frames to have a robust tracking in different visually challenging cases such as occlusion, motion blur, fast movement, etc. Long Short-Term Memory (LSTM) is a kind of recurrent convolutional neural network and useful for our purpose. Instead of using binary classification which is commonly used in deep learning based tracking methods, we use a regression for direct prediction of the tracking locations. Our purpose is to test our method on real videos which is recorded by head-mounted camera. So our test videos are very challenging and contain different cases of fast movements, motion blur, occlusions, etc. Considering the limitation of the training data-set which is spatially imbalanced, we have a problem for tracking the humans who are in the corners of the image but in other challenging cases, the proposed tracking method worked well.I detta examensarbete undersöks problemet att spåra människor i videoströmmar genom att använda deep learning. Spårningen utförs genom att använda ett recurrent convolutional neural network. Input till nätverket består av visuella features extraherade med hjälp av ett convolutional neural network, samt av detektionsresultat från tidigare frames. Vi väljer att använda oss av historiska detektioner för att skapa en metod som är robust mot olika utmanande situationer, som t.ex. snabba rörelser, rörelseoskärpa och ocklusion. Long Short- Term Memory (LSTM) är ett recurrent convolutional neural network som är användbart för detta ändamål. Istället för att använda binära klassificering, vilket är vanligt i många deep learning-baserade tracking-metoder, så använder vi oss av regression för att direkt förutse positionen av de spårade subjekten. Vårt syfte är att testa vår metod på videor som spelats in med hjälp av en huvudmonterad kamera. På grund av begränsningar i våra träningsdataset som är spatiellt oblanserade har vi problem att spåra människor som befinner sig i utkanten av bildområdet, men i andra utmanande fall lyckades spårningen bra.
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Singh, Akash. "Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215723.
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We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on LSTM prediction and detection performance. LSTMs are also compared to feed-forward neural networks with fixed size time windows over inputs. Our experiments, with three real-world datasets, show that while LSTM RNNs are suitable for general purpose time series modeling and anomaly detection, maintaining LSTM state is crucial for getting desired results. Moreover, LSTMs may not be required at all for simple time series.Vi undersöker Long short-term memory (LSTM) för avvikelsedetektion i tidsseriedata. På grund av svårigheterna i att hitta data med etiketter så har ett oövervakat an-greppssätt använts. Vi tränar rekursiva neuronnät (RNN) med LSTM-noder för att lära modellen det normala tidsseriemönstret och prediktera framtida värden. Vi undersö-ker olika sätt av att behålla LSTM-tillståndet och effekter av att använda ett konstant antal tidssteg på LSTM-prediktionen och avvikelsedetektionsprestandan. LSTM är också jämförda med vanliga neuronnät med fasta tidsfönster över indata. Våra experiment med tre verkliga datasetvisar att även om LSTM RNN är tillämpbara för generell tidsseriemodellering och avvikelsedetektion så är det avgörande att behålla LSTM-tillståndet för att få de önskaderesultaten. Dessutom är det inte nödvändigt att använda LSTM för enkla tidsserier.
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Zambezi, Samantha. "Predicting social unrest events in South Africa using LSTM neural networks." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33986.
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This thesis demonstrates an approach to predict the count of social unrest events in South Africa. A comparison is made between traditional forecasting approaches and neural networks; the traditional forecast method selected being the Autoregressive Integrated Moving Average (ARIMA model). The type of neural network implemented was the Long Short-Term Memory (LSTM) neural network. The basic theoretical concepts of ARIMA and LSTM neural networks are explained and subsequently, the patterns of the social unrest time series were analysed using time series exploratory techniques. The social unrest time series contained a significant number of irregular fluctuations with a non-linear trend. The structure of the social unrest time series suggested that traditional linear approaches would fail to model the non-linear behaviour of the time series. This thesis confirms this finding. Twelve experiments were conducted, and in these experiments, features, scaling procedures and model configurations are varied (i.e. univariate and multivariate models). Multivariate LSTM achieved the lowest forecast errors and performance improved as more explanatory features were introduced. The ARIMA model's performance deteriorated with added complexity and the univariate ARIMA produced lower forecast errors compared to the multivariate ARIMA. In conclusion, it can be claimed that multivariate LSTM neural networks are useful for predicting social unrest events.
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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.
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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.
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Sarika, Pawan Kumar. "Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.
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Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques to analyze data. One such kind of data which is needed to be analyzed are text reviews given for movies. Rather than classifying the reviews as positive or negative, we will classify the sentiment of the reviews on the scale of one to ten. In doing so, we will compare two recurrent neural network algorithms Long short term memory(LSTM) and Gated recurrent unit(GRU). The main objective of this study is to compare the accuracies of LSTM and GRU models. For training models, we collected data from two different sources. For filtering data, we used porter stemming and stop words. We coupled LSTM and GRU with the convolutional neural networks to increase the performance. After conducting experiments, we have observed that LSTM performed better in predicting border values. Whereas, GRU predicted every class equally. Overall GRU was able to predict multiclass text data of movie reviews slightly better than LSTM. GRU was computationally expansive when compared to LSTM.
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Kindbom, Hannes. "LSTM vs Random Forest for Binary Classification of Insurance Related Text." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252748.
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The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The models’ performance were not significantly improved after optimization and it was also dependent on which corpus the models were trained on. An investigation of how a chatbot would affect Hedvig’s adoption rate was also conducted, mainly by reviewing previous studies about chatbots’ effects on user experience. The potential effects on the innovation’s five attributes, relative advantage, compatibility, complexity, trialability and observability were analyzed to answer the problem statement. The results showed that the adoption rate of Hedvig could be positively affected, by improving the first two attributes. The effects a chatbot would have on complexity, trialability and observability were however suggested to be negligible, if not negative.Det vetenskapliga området språkteknologi har fått ökad uppmärksamhet den senaste tiden, men mindre fokus riktas på att jämföra modeller som skiljer sig i komplexitet. Den här kandidatuppsatsen jämför Random Forest med LSTM, genom att undersöka hur väl modellerna kan användas för att klassificera ett meddelande som fråga eller icke-fråga. Jämförelsen gjordes genom att träna och optimera modellerna på historisk chattdata från det svenska försäkringsbolaget Hedvig. Olika typer av word embedding, så som Word2vec och Bag of Words, testades också. Resultaten visade att LSTM uppnådde något högre F1 och accuracy än Random Forest. Modellernas prestanda förbättrades inte signifikant efter optimering och resultatet var också beroende av vilket korpus modellerna tränades på. En undersökning av hur en chattbot skulle påverka Hedvigs adoption rate genomfördes också, huvudsakligen genom att granska tidigare studier om chattbotars effekt på användarupplevelsen. De potentiella effekterna på en innovations fem attribut, relativ fördel, kompatibilitet, komplexitet, prövbarhet and observerbarhet analyserades för att kunna svara på frågeställningen. Resultaten visade att Hedvigs adoption rate kan påverkas positivt, genom att förbättra de två första attributen. Effekterna en chattbot skulle ha på komplexitet, prövbarhet och observerbarhet ansågs dock vara försumbar, om inte negativ.
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Gessle, Gabriel, and Simon Åkesson. "A comparative analysis of CNN and LSTM for music genre classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260138.
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The music industry has seen a great influx of new channels to browse and distribute music. This does not come without drawbacks. As the data rapidly increases, manual curation becomes a much more difficult task. Audio files have a plethora of features that could be used to make parts of this process a lot easier. It is possible to extract these features, but the best way to handle these for different tasks is not always known. This thesis compares the two deep learning models, convolutional neural network (CNN) and long short-term memory (LSTM), for music genre classification when trained using mel-frequency cepstral coefficients (MFCCs) in hopes of making audio data as useful as possible for future usage. These models were tested on two different datasets, GTZAN and FMA, and the results show that the CNN had a 56.0% and 50.5% prediction accuracy, respectively. This outperformed the LSTM model that instead achieved a 42.0% and 33.5% prediction accuracy.Musikindustrin har sett en stor ökning i antalet sätt att hitta och distribuera musik. Det kommer däremot med sina nackdelar, då mängden data ökar fort så blir det svårare att hantera den på ett bra sätt. Ljudfiler har mängder av information man kan extrahera och därmed göra den här processen enklare. Det är möjligt att använda sig av de olika typer av information som finns i filen, men bästa sättet att hantera dessa är inte alltid känt. Den här rapporten jämför två olika djupinlärningsmetoder, convolutional neural network (CNN) och long short-term memory (LSTM), tränade med mel-frequency cepstral coefficients (MFCCs) för klassificering av musikgenre i hopp om att göra ljuddata lättare att hantera inför framtida användning. Modellerna testades på två olika dataset, GTZAN och FMA, där resultaten visade att CNN:et fick en träffsäkerhet på 56.0% och 50.5% tränat på respektive dataset. Denna utpresterade LSTM modellen som istället uppnådde en träffsäkerhet på 42.0% och 33.5%.
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Vitali, Greta <1995>. "“Forecasting Stock Index Volatility: A comparison between GARCH and LSTM models”." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/15933.
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The financial world is characterized by the uncertainty of events and this phenomenon can expose operators to huge financial risks. Thus, there is a need to measure this uncertainty, with the aim to predict it and to make adequate plans of action. The concept of uncertainty is often associated with the definition of volatility, which is a measure of the variation of stock prices of a financial instrument during the time. But modelling volatility is not a trivial task, because of the essence of financial stock prices, which usually present volatility clusters, fat tails, nonnormality and structural breaks in the distribution. A popular class of models able to capture many of these stylized facts is the ARCH/GARCH family. As a matter of fact, a GARCH model is able to explain the time-varying variance and the presence of clusters in the series of the returns. Nevertheless, it requires some constraints on both parameters and distributions of returns to obtain satisfactory results. An attractive solution is given by some mathematical models based on artificial intelligence. Indeed, the artificial neural networks, resembling the human brain, are able to make predictions of future volatility due to their ability to be self-adaptive and to be a universal approximator of any underlying nonlinear function of financial data. The aim of this thesis is to make a comparison between the forecasting capabilities of a GARCH(1,1) model and a Long Short-Term Memory network. In particular, the objective is to predict the volatility of the Dow Jones Industrial Average Index, demonstrating the superiority of the neural network with respect to the well-established GARCH model.
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Lin, Yu-Chun, and 林祐群. "Fabrication, Characterization and Electrochemical Measurements of LCVO/LSGM/LSCF Single Cell as Intermediate Temperature Solid Oxide Fuel Cell." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7at53b.
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碩士國立交通大學
跨領域分子科學國際碩士學位學程
106
In this thesis, La2(Ce2V0.2)2O7-δ (LCVO),developed by our lab, was synthesized by glycine-nitrate method. LCVO combines with commercial electrolyte material La0.8Sr0.2Ga0.8Mg0.2O3-(LSGM) and commercial cathode material (La0.60Sr0.40)0.95Co0.20Fe0.80O3-δ to study the performance of single cell at new device. At the same time, we study the fabrication of ceramics to improve the quality of single cells. For the fabrication, we used Tape casting method to fabricate electrolyte-supported cells. And we found out the best ratio of solvents and milling program of slurry which was milled by Planetary Mills, and we can produce green tape without the bubbles. TGA told us there were endothermic reaction at two ranges: from 178.2°C to 197.4°C and from 358.8°C to 394.7°C. We modified the sintering program based on this result. Last step, we pressed the ceramic by the alumina plates and sintered at target temperature with 200°C /h to flatter the ceramics. We used Pyrex glass sealant, made by our lab, to replace the previous ceramics sealant, and found this glass sealant can tolerate up to 0.09L/min flow rate at 800°C. For anode half cell, we found that the major open circuit potential (OCP) was provided by anode cite. For cathode half cell, ionic conductivity of LSGM was higher than pure LSGM from 600°C to 750°C. Instead, ionic conductivity of pure LSGM was higher than LSGM of cathode half cell at 800°C. For LCVO-LSGM/LSGM/LSCF-LSGM and LCVO/LSGM/LSCF, OCP of both cells were bewtween 1.08 V and 1.14 V, and the cells were considered to be sealed well between 600°C and 800°C. However, the former’s power density is lower than the latter one. Repeating experiments and based on SEM, the LCVO content of the former is less than the latter. LSGM, mixed with LCVO, made the porosity of anode less than pure LCVO anode. Therefore, the power density of LCVO/LSGM/LSCF could reach to 0.389 W/cm2。
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Kuo, Shih-Chun, and 郭士鈞. "LSTM-Based Vehicle Trajectory Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q7qwdc.
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碩士國立清華大學
通訊工程研究所
107
Future trajectory prediction of objects is a very important technical link for self-driving cars and navigation systems. In order to be safe, efficient, and to avoid collisions, self-driving cars should be able to anticipate what will happen in a changeable environment and predict the future location of surrounding objects in advance. There have been many significant technical advances in autonomous cars, such as Google’s self-driving cars and Tesla’s Autopilot. In the past research of object trajectory prediction, the interaction between objects was simulated by considering the object distances and learning functions using the Long Short-Term Memory model. However, the results of future predictions should not only depend on the distance of the surrounding objects but also related to their own inertial trajectories and the relative importance between target and other objects. The forecasting system should look at all past trajectories and establish an important relationship between the input trajectories and the prediction results. The forecasting system also needs to aware which surrounding objects is important to target, thereby improving the prediction performance. In addition, considering more object information, such as heading, how fast, and object class will improve the prediction results. In this paper, our main goal is to improve the effectiveness of object trajectory prediction in dashcam videos. First, a temporal attention model is built to focus on the motion characteristics of moving objects from past trajectories. Our approach is to calculate the importance relationship value between future position and all past trajectories. Furthermore, we build a spatial attention model to understand the relative importance relationship between itself and surrounding objects information, thereby reducing errors and error propagation of predicted results. Finally, combining the direction, speed information and object class of the input trajectory will provide more object information and reduce the misjudgment of prediction. We apply the experimental results to the Kitti tracking database of real driving dashcam videos and New York Grand Central of pedestrian trajectory prediction database. The results show that our method still has quite good results compared to the previous method.
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XIAO, HUNG-JIE, and 蕭宏杰. "LSTM-based Parking Space Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/z88u44.
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碩士國立中正大學
電機工程研究所
107
In this research, we propose the LSTM-based parking lot detection method architecture. This framework divides to two parts, one is “Status ConvNet”, and another is “Action ConvNet”. Frist, we will separate individual frame from sequence of image to become the spatial stream. And then, we will calculate optical flow to be moving information to become the temporal stream. For spatial stream, we input an image to Convolutional Neural Network (CNN) to detect the status of parking space, called the network “Status ConvNet”. At the same time, input extracted high-level feature to LSTM that could consider the information of historical status to avoid wrong detection from single frame. The classes of space status are “Occupy” and “Vacant”. For temporal stream, we stack multiple images of optical flow, and input them to 3-dimension CNN to detection parking status of driver. The network is similar as Status ConvNet called “Action ConvNet”. Action ConvNet uses optical flow as short-term information to detect parking status of driver. In order to increase the accuracy, we also introduce LSTM in network to consider historical information of optical flow as long-term moving information. The classes of parking status are “Drop off”, “Pick up”, and “No action”. Finally, we design the two-stream architecture to fuse spatial and temporal information.
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Mendes, João Filipe Batista. "Forecasting bitcoin prices: ARIMA vs LSTM." Master's thesis, 2019. http://hdl.handle.net/10071/19724.
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Bitcoin has recently received special attention in economics and finance as the most popular blockchain technology. This dissertation aims to discuss whether newly machine-leaning models perform better than traditional models in forecasting. Particularly, this study compares the accuracy of the prediction of bitcoin prices using two different models: Long-Short Term Memory (LSTM) versus Auto Regressive Integrated Moving Average (ARIMA), in terms of forecasting errors, and Python routines were used for such purpose. Bitcoin price time series ranges from 2017-06-18 to 2019-08-07, in a daily basis, sourced from the Federal Reserve Economic Data. To compare the results of both models, data was divided into two subsets: training (83.5%) and testing (16.5%). The literature usually indicates that LSTM outperforms ARIMA. In this dissertation, the results do confirm that LSTM forecasts of bitcoin prices improve on average ARIMA predictions by 92% and 94%, according to RMSE and MAE.A Bitcoin tem recebido recentemente especial atenção em áreas como a economia e finanças por ser a mais popular tecnologia de blockchain. Esta dissertação tem como objetivo verificar se os novos modelos de machine-learning apresentam melhores resultados que os modelos tradicionais em previsões. Este estudo compara, em particular, a precisão da previsão do preço da Bitcoin usando dois modelos diferentes: Long-Short Term Memory (LSTM) versus Auto Regressive Integrated Moving Average (ARIMA), em termos de erros de previsão e aplicando rotinas do Python. A análise teve como base os preços diários da Bitcoin entre 18 de junho de 2016 e 7 de agosto de 2019, retirados da base de dados da Reserva Federal. Para comparar os resultados dos dois modelos, os dados foram divididos em duas secções: o treino (83.5%) e o teste (16.5%). A literatura indica que o modelo LSTM tem uma melhor precisão que o ARIMA e nesta dissertação os resultados confirmam que o modelo LSTM melhora em média 92% e 94% a previsão do ARIMA, de acordo com o RMSE e o MAE.
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Chen, Wei-Rui, and 陳維睿. "Applying LSTM to Bitcoin price prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/67y8s7.
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碩士國立政治大學
資訊科學系
106
This thesis focuses on applying Long Short-Term Memory (LSTM) technique to predict Bitcoin price direction. Features including internal and external features are extracted from Bitcoin blockchain and exchange center respectively. Cryptocurrency is a new type of currency that is traded over the infrastructure of Internet. Bitcoin (BTC) is the first cryptocurrency and ranks first in the market capitalization among all the other cryptocurrencies. Predicting Bitcoin price is a novel topic because of its differences with traditional financial assets and its volatility. As contributions, this thesis provides a guide of processing Bitcoin blockchain data and serves as an empirical study on applying LSTM to Bitcoin price prediction.
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Fonseca, José Pedro Castro. "FPGA implementation of a LSTM Neural Network." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/90359.
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Este trabalho pretende fazer uma implementação customizada, em Hardware, duma Rede Neuronal Long Short-Term Memory. O modelo python, assim como a descrição Verilog, e síntese RTL, encontram-se terminadas. Falta apenas fazer o benchmarking e a integração de um sistema de aprendizagem.
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Yen-LiangLin and 林彥良. "PM 2.5 Prediction based on LSTM Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8gayas.
Full textAbstract:
碩士國立成功大學
工程科學系
106
Recently, pollution conditions of particulate matter 2.5 in Taiwan have become more severe day by day. Several other cities in Asia such as Beijing and Delhi are also facing the same pollution problem, which draws attention to government and experts. Due to the human activities in Asia such as industrialization and animal husbandry, air pollution condition has been getting worse, increases the possibility of population suffering from cardiovascular disease. Particular matter pollution has become a problem we cannot ignore in modern society. Currently, official meteorological department applies traditional statistic model to predict meteorology trend. Traditional statistic model such like ARIMA has certain accuracy on time series data. However, nowadays along with the calculate ability of computer and chips progressing, application field of neural network and deep learning has become much more extensive. Recurrent neural network had been developed to deal with time sequence data. Long short term memory model has a longer time range memorize ability than recurrent neural network, meanwhile has been frequently applied on forecasting and analyzation. This thesis utilizes the long short term memory model to predict future particular matter hourly average concentration, in hope that government and the departments concerned could take actions on the pollution phenomenon, improve the air pollution problem.