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Relevant bibliographies by topics / Optimization of LSPM

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Dissertations / Theses
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Academic literature on the topic 'Optimization of LSPM'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Optimization of LSPM"

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Peibo, Duan, Zhang Changsheng, and Zhang Bin. "A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/734975.

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This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of in

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Saeed Al-khayyt, Saad Zaghlul. "Creating Through Points in Linear Function with Parabolic Blends Path by Optimization Method." Al-Khwarizmi Engineering Journal 14, no. 1 (2018): 77–89. http://dx.doi.org/10.22153/https://doi.org/10.22153/kej.2018.10.005.

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The linear segment with parabolic blend (LSPB) trajectory deviates from the specified waypoints. It is restricted to that the acceleration must be sufficiently high. In this work, it is proposed to engage modified LSPB trajectory with particle swarm optimization (PSO) so as to create through points on the trajectory. The assumption of normal LSPB method that parabolic part is centered in time around waypoints is replaced by proposed coefficients for calculating the time duration of the linear part. These coefficients are functions of velocities between through points. The velocities are obtain

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Victor, Nancy, and Daphne Lopez. "sl-LSTM." International Journal of Grid and High Performance Computing 12, no. 3 (2020): 1–16. http://dx.doi.org/10.4018/ijghpc.2020070101.

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The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state.

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Lin, Jin Lan, and Jian Hong Fan. "Research on the Theory of the Laser Shock Processing Technology." Applied Mechanics and Materials 610 (August 2014): 1021–28. http://dx.doi.org/10.4028/www.scientific.net/amm.610.1021.

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In this paper the laser shock processing technology (LSPT) is investigated theoretically. A one-dimensional theoretical model is presented to express analytically the transmission coefficient of the incident laser beam through four different layers, i.e., the air layer, the constrained layer, the plasma layer, and the absorbing coating. Based on this model, the key parameters of LSPT can be further optimized to obtain the maximum transmission coefficient and the best surface-hardening effect. This one-dimensional theoretical model presented can be further used in guiding the parameter optimiza

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Yin, Fengyu, Jin Liu, Haima Yang, Aleksey Kudreyko, and Bo Huang. "Design and Optimization of Plasmon Resonance Sensor Based on Micro–Nano Symmetrical Localized Surface." Symmetry 12, no. 5 (2020): 841. http://dx.doi.org/10.3390/sym12050841.

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Surface Plasma resonance (SPR) sensors combined with biological receptors are widely used in biosensors. Due to limitations of measurement techniques, small-scale, low accuracy, and sensitivity to the refractive index of solution in traditional SPR prism sensor arise. As a consequence, it is difficult to launch commercial production of SPR sensors. The theory of localized surface plasmon resonance (LSPR) developed based on SPR theory has stronger coupling ability to near-field photons. Based on the LSPR sensing theory, we propose a submicron-sized golden-disk and graphene composite structure.

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Parvez, Iram, and Jianjian Shen. "Algorithms of approximate dynamic programming for hydro scheduling." E3S Web of Conferences 144 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202014401001.

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In hydro scheduling, unit commitment is a complex sub-problem. This paper proposes a new approximate dynamic programming technique to solve unit commitment. A new method called Least Square Policy Iteration (LSPI) algorithm is introduced which is efficient and faster in convergence. This algorithm takes the properties of widely used algorithm least square temporal difference (LSTD), enhance it further and make it useful for optimization problems. First value function is to find a fixed policy by using least square temporal difference Q (LSTDQ) algorithm which is similar to LSTD, then LSPI is i

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Rodrigues, Marco S., Joel Borges, and Filipe Vaz. "Enhancing the Sensitivity of Nanoplasmonic Thin Films for Ethanol Vapor Detection." Materials 13, no. 4 (2020): 870. http://dx.doi.org/10.3390/ma13040870.

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Nanoplasmonic thin films, composed of noble metal nanoparticles (gold) embedded in an oxide matrix, have been a subject of considerable interest for Localized Surface Plasmon Resonance (LSPR) sensing. Ethanol is one of the promising materials for fuel cells, and there is an urgent need of a new generation of safe optical sensors for its detection. In this work, we propose the development of sensitive plasmonic platforms to detect molecular analytes (ethanol) through changes of the LSPR band. The thin films were deposited by sputtering followed by a heat treatment to promote the growth of the g

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Chang, Qiu Xiang. "Flexible Module Optimization of Hydraulic Press Based on LSRM." Advanced Materials Research 308-310 (August 2011): 2297–301. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.2297.

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Many scholars have developed a lot of flexible modules used on Hydraulic press, but to reduce costs such as the producing cost and the transportation cost, the above modules, formed through continuation, should be optimized before put into the module library. This paper introduces a method which optimizes the continuation body of type YH30 by means of LSRM. And the contrast shows the new nodule has a well effect on optimizing structure and reducing costs and materials.

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Massaro, Alessandro, Vincenzo Maritati, Daniele Giannone, Daniele Convertini, and Angelo Galiano. "LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction." Applied Sciences 9, no. 17 (2019): 3532. http://dx.doi.org/10.3390/app9173532.

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The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is fo

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Zhang, Qiang, Tianze Gao, Xueyan Liu, and Yun Zheng. "Public Environment Emotion Prediction Model Using LSTM Network." Sustainability 12, no. 4 (2020): 1665. http://dx.doi.org/10.3390/su12041665.

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Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile “impression ecology” p

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Dissertations / Theses on the topic "Optimization of LSPM"

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Kumar, Jha Amit. "Optimization of Line Start Permanent Magnet Synchronous Motor for Magnet Cost Reduction." Thesis, KTH, Elektrisk energiomvandling, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124550.

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In this thesis different methods of optimizing line start permanent magnet motor (LSPM) for magnet cost reduction is studied. Influence of different parameters has been studied by simulating magneto-static and transient FEM models of the machine. Finally a motor design of a LSPM with high rotor saliency has been proposed. The first method investigated is the use of flux barriers in LSPM and its effect on the magnetic flux leakage. The flux barriers reduce the flux leakage and hence help in reducing magnet volume. The second method studied is the use of two different grades of magnets. Using lo

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Andersson, Aron, and Shabnam Mirkhani. "Portfolio Performance Optimization Using Multivariate Time Series Volatilities Processed With Deep Layering LSTM Neurons and Markowitz." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273617.

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The stock market is a non-linear field, but many of the best-known portfolio optimization algorithms are based on linear models. In recent years, the rapid development of machine learning has produced flexible models capable of complex pattern recognition. In this paper, we propose two different methods of portfolio optimization; one based on the development of a multivariate time-dependent neural network,thelongshort-termmemory(LSTM),capable of finding lon gshort-term price trends. The other is the linear Markowitz model, where we add an exponential moving average to the input price data to c

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Garige, Naga Siddhardha. "A Distributed Routing Algorithm for ER-LSP Setup in MLPS Networks." [Tampa, Fla. : s.n.], 2003. http://purl.fcla.edu/fcla/etd/SFE0000086.

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Ujihara, Rintaro. "Multi-objective optimization for model selection in music classification." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298370.

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With the breakthrough of machine learning techniques, the research concerning music emotion classification has been getting notable progress combining various audio features and state-of-the-art machine learning models. Still, it is known that the way to preprocess music samples and to choose which machine classification algorithm to use depends on data sets and the objective of each project work. The collaborating company of this thesis, Ichigoichie AB, is currently developing a system to categorize music data into positive/negative classes. To enhance the accuracy of the existing system, thi

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Zhang, Jiahui. "Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning based Prediction and Particle Swarm Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP on both economic cost(EC) and power quality (PQ).Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solvin

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Botha, Marlene. "Online traffic engineering for MPLS networks." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50049.

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Thesis (MSc) -- Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: The Internet is fast evolving into a commercial platform that carries a mixture of narrow- and broadband applications such as voice, video, and data. Users expect a certain level of guaranteed service from their service providers and consequently the need exists for efficient Internet traffic engineering to enable better Quality of Service (QoS) capabilities. Multi-protocol Label Switching (MPLS) is a label switching protocol that has emerged as an enabling technology to achieve efficient traffic engineering for QoS mana

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Oliveira, Hugo Luiz. "Modelos numéricos aplicados à análise viscoelástica linear e à otimização topológica probabilística de estruturas bidimensionais: uma abordagem pelo Método dos Elementos de Contorno." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18134/tde-27042017-093145/.

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O presente trabalho trata da formulação e implementação de modelos numéricos baseados no Método dos Elementos de Contorno (MEC). Inspirando-se em problemas de engenharia, uma abordagem multidisciplinar é proposta como meio de representação numérica mais realista. Há materiais de uso corrente na engenharia que possuem resposta dependente do tempo. Nesta tese os fenômenos dependentes do tempo são abordados por meio da Mecânica Viscoelástica Linear associada a modelos reológicos. Neste trabalho, se apresenta a dedução do modelo constitutivo de Maxwell para ser utilizado via MEC. As equações deduz

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Tsai, Ching-Wen, and 蔡晴雯. "Optimization of Chemical Immobilization of the Au-LSPR Biosensor." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/59055218560117926651.

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碩士<br>國立中興大學<br>化學系所<br>100<br>The thesis provides a method to make Au NPs substrates at the short time by using homemade-microwave plasma bombardment, and optimize the previous detection method to improve the detecting sensitivity. The method not only decrease the experimental time but also speed up the follow-up experimental steps. For application to clinical medicine development and understand the expiration dates of GA-immobilized and IgG-immobilized substrates, we divided the expiration date into five stages to test how long theses substrates could preserve. After 6 weeks, we found that G

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Soman, Akhil. "Short Term Energy Forecasting for a Microgird Load using LSTM RNN." 2020. https://scholarworks.umass.edu/masters_theses_2/994.

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Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting. In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for

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Book chapters on the topic "Optimization of LSPM"

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Adam, Kazybek, Kamilya Smagulova, and Alex Pappachen James. "Memristive LSTM Architectures." In Modeling and Optimization in Science and Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14524-8_12.

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Bakalos, Nikolaos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou, and Matthaios Bimpas. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM." In Cyber-Physical Security for Critical Infrastructures Protection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_6.

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AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.

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Shailesh, S., M. Anantha Krishnan, and M. V. Judy. "Classification of Sequence Data Using LSTM: An Application on Chaotic Sequences." In Modeling, Simulation and Optimization. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9829-6_20.

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Wu, JingRong, DingCheng Wang, ZhuoYing Huang, JiaLe Qi, and Rui Wang. "Weather Temperature Prediction Based on LSTM-Bayesian Optimization." In Advances in Artificial Intelligence and Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78615-1_39.

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Benkerroum, Houda, Walid Cherif, and Mohamed Kissi. "Optimization of LSTM Algorithm Through Outliers – Application to Financial Time Series Forecasting." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45183-7_16.

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Nelikanti, Arjun, G. Venkata Rami Reddy, and G. Karuna. "An Optimization Based deep LSTM Predictive Analysis for Decision Making in Cricket." In Innovative Data Communication Technologies and Application. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9651-3_59.

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Jeong, Wang-Boo, Dong-Won Park, and Young-Ho Sohn. "Optimization of LSPL Algorithm for Data Transfer in Sensor Networks Based on LEACH." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0281-6_111.

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Vavra, Jan, and Martin Hromada. "Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment." In Intelligent Systems Applications in Software Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30329-7_28.

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Kim, Tae-Young, and Sung-Bae Cho. "Particle Swarm Optimization-Based CNN-LSTM Networks for Anomalous Query Access Control in RBAC-Administered Model." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29859-3_11.

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Arulmozhivarman, M., and Gerard Deepak. "OWLW: Ontology Focused User Centric Architecture for Web Service Recommendation Based on LSTM and Whale Optimization." In Artificial Intelligence Systems and the Internet of Things in the Digital Era. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77246-8_32.

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Conference papers on the topic "Optimization of LSPM"

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Wei, Xinlong, Jianxin Zhou, and Xiang Ling. "Optimization of Residual Stresses Induced by Multiple Laser Shock Processing." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97193.

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Laser shock processing (LSP), also known as laser peening (LP), proves to be superior to conventional surface treatments such as shot peening, including deeper penetration of the residual stresses. The LSP treatment, which uses a very short pulse (ns) of intense (GW cm−2) laser beam to generate compressive residual stresses near the surface of the metallic samples, demonstrates a significant improvement of fatigue life and stress corrosion cracking resistance. In this paper, finite element analysis (FEA) combined with particle swarm optimization (PSO) method to predict the magnitude and distri

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Wang, Yifeng, Yuying Liu, Meiqing Wang, and Rong Liu. "LSTM Model Optimization on Stock Price Forecasting." In 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2018. http://dx.doi.org/10.1109/dcabes.2018.00052.

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Hu, Weifei, Yihan He, Zhenyu Liu, Jianrong Tan, Ming Yang, and Jiancheng Chen. "A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16500.

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Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-

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Wang, Fuyong, Yun Zai, Jiuyu Zhao, and Siyi Fang. "Field Application of Deep Learning for Flow Rate Prediction with Downhole Temperature and Pressure." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21364-ms.

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Abstract Well real-time flow rate is one of the most important production parameters in oilfield and accurate flow rate information is crucial for production monitoring and optimization. With the wide application of permanent downhole gauge (PDG), the high-frequency and large volume of downhole temperature and pressure make applying of deep learning technique to predict flow rate possible. Flow rate of production well is predicted with long short-term memory (LSTM) network using downhole temperature and pressure production data. The specific parameters of LSTM neural network are given, as well

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Fu, Anrui, and Bo Wang. "Portfolio Optimization based on LSTM Neural Network Prediction." In 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2020. http://dx.doi.org/10.1109/icnsc48988.2020.9238089.

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Bidwai, Sandeep, Nikhil Joshi, and Saylee Bidwai. "LSTM model for Channel Occupation Prediction in GSM Band." In 2018 Third International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). IEEE, 2018. http://dx.doi.org/10.1109/iceeccot43722.2018.9001366.

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Yu, Wennian, Chris K. Mechefske, and Il Yong Kim. "Cutting Tool Wear Estimation Using a Genetic Algorithm Based Long Short-Term Memory Neural Network." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85253.

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On-line cutting tool wear monitoring plays a critical role in industry automation and has the potential to significantly increase productivity and improve product quality. In this study, we employed the long short-term memory neural network as the decision model of the tool condition monitoring system to predict the amount of cutting tool wear. Compared with the traditional recurrent neural networks, the long short-term memory (LSTM) network can capture the long-term dependencies within a time series. To further decrease the training error and enhance the prediction performance of the network,

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Sakinah, Nur, Muhlis Tahir, Tessy Badriyah, and Iwan Syarif. "LSTM With Adam Optimization-Powered High Accuracy Preeclampsia Classification." In 2019 International Electronics Symposium (IES). IEEE, 2019. http://dx.doi.org/10.1109/elecsym.2019.8901536.

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Gorgolis, Nikolaos, Ioannis Hatzilygeroudis, Zoltan Istenes, and Lazlo n. Grad Gyenne. "Hyperparameter Optimization of LSTM Network Models through Genetic Algorithm." In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2019. http://dx.doi.org/10.1109/iisa.2019.8900675.

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Ashok, Darkunde Mayur, Agrawal Nidhi Ghanshyam, Sayed Saniya Salim, Dungarpur Burhanuddin Mazahir, and Bhushan S. Thakare. "Sarcasm Detection using Genetic Optimization on LSTM with CNN." In 2020 International Conference for Emerging Technology (INCET). IEEE, 2020. http://dx.doi.org/10.1109/incet49848.2020.9154090.

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