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Dissertations / Theses on the topic 'Energy Efficient Machine Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Azmat, Freeha. "Machine learning and energy efficient cognitive radio." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/85990/.

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With an explosion of wireless mobile devices and services, system designers are facing a challenge of spectrum scarcity and high energy consumption. Cognitive radio (CR) is a promising solution for fulfilling the growing demand of radio spectrum using dynamic spectrum access. It has the ability of sensing, allocating, sharing and adapting to the radio environment. In this thesis, an analytical performance evaluation of the machine learning and energy efficient cognitive radio systems has been investigated while taking some realistic conditions into account. Firstly, bio-inspired techniques, in

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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data s

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Le, Borgne Yann-Aël. "Learning in wireless sensor networks for energy-efficient environmental monitoring." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210334.

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Wireless sensor networks form an emerging class of computing devices capable of observing the world with an unprecedented resolution, and promise to provide a revolutionary instrument for environmental monitoring. Such a network is composed of a collection of battery-operated wireless sensors, or sensor nodes, each of which is equipped with sensing, processing and wireless communication capabilities. Thanks to advances in microelectronics and wireless technologies, wireless sensors are small in size, and can be deployed at low cost over different kinds of environments in order to monitor both

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Yurur, Ozgur. "Energy Efficient Context-Aware Framework in Mobile Sensing." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4797.

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The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environ

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Zayene, Mariem. "Cooperative data exchange for wireless networks : Delay-aware and energy-efficient approaches." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0033/document.

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Avec le nombre croissant d’appareils intelligents à faible puissance, au cours ces dernières années, la question de l’efficacité énergétique a joué un rôle de plus en plus indispensable dans la conception des systèmes de communication. Cette thèse vise à concevoir des schémas de transmission distribués à faible consommation d’énergie pour les réseaux sans fil, utilisant la théorie des jeux et le codage réseau instantanément décodable (IDNC), qui est une sous-classe prometteuse du codage réseau. En outre, nous étudions le modèle de l'échange coopératif de donnée (CDE) dans lequel tous les périp

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Kheffache, Mansour. "Energy-Efficient Detection of Atrial Fibrillation in the Context of Resource-Restrained Devices." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76394.

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eHealth is a recently emerging practice at the intersection between the ICT and healthcare fields where computing and communication technology is used to improve the traditional healthcare processes or create new opportunities to provide better health services, and eHealth can be considered under the umbrella of the Internet of Things. A common practice in eHealth is the use of machine learning for a computer-aided diagnosis, where an algorithm would be fed some biomedical signal to provide a diagnosis, in the same way a trained radiologist would do. This work considers the task of Atrial Fibr

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Zorello, Ligia Maria Moreira. "Dynamic CPU frequency scaling using machine learning for NFV applications." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-30012019-100044/.

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Growth in the Information and Communication Technology sector is increasing the need to improve the quality of service and energy efficiency, as this industry has already surpassed 12% of global energy consumption in 2017. Data centers correspond to a large part of this consumption, accounting for about 15% of energy expenditure on the Information and Communication Technology domain; moreover, the subsystem that generates the most costs for data center operators is that of servers and storage. Many solutions have been proposed to reduce server consumption, such as the use of dynamic voltage an

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Guss, Herman, and Linus Rustas. "Applying Machine Learning Algorithms for Anomaly Detection in Electricity Data : Improving the Energy Efficiency of Residential Buildings." Thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415507.

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The purpose of this thesis is to investigate how data from a residential property owner can be utilized to enable better energy management for their building stock. Specifically, this is done through the development of two machine learning models with the objective of detecting anomalies in the existing data of electricity consumption. The dataset consists of two years of residential electricity consumption for 193 substations belonging to the residential property owner Uppsalahem. The first of the developed models uses the K-means method to cluster substations with similar consumption pattern

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Seeker, Volker Günter. "User experience driven CPU frequency scaling on mobile devices : towards better energy efficiency." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29583.

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With the development of modern smartphones, mobile devices have become ubiquitous in our daily lives. With high processing capabilities and a vast number of applications, users now need them for both business and personal tasks. Unfortunately, battery technology did not scale with the same speed as computational power. Hence, modern smartphone batteries often last for less than a day before they need to be recharged. One of the most power hungry components is the central processing unit (CPU). Multiple techniques are applied to reduce CPU energy consumption. Among them is dynamic voltage and f

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Collard, Sophie. "Assessing and Predicting the Impact of Energy Conservation Measures Using Smart Meter Data." Thesis, KTH, Kraft- och värmeteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150352.

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Buildings account for around 40 percent of the primary energy consumption in Europe and in the United States. They also hold tremendous energy savings potential: 15 to 29 percent by 2020 for the European building stock according to a 2009 study from the European Commission. Verifying and predicting the impact of energy conservation measures in buildings is typically done through energy audits. These audits are costly, time-consuming, and may have high error margins if only limited amounts of data can be collected. The ongoing large-scale roll-out of smart meters and wireless sensor networks in

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Chakraborty, Debaditya. "Detection of Faults in HVAC Systems using Tree-based Ensemble Models and Dynamic Thresholds." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582336141076.

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Bambini, Alberto. "Combining Active Learning and Mathematical Programming: a hybrid approach for Transprecision Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19664/.

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This paper explores the possibility of applying a hybrid approach between Active Learning and Mathematical Programming to Transprecision Computing. This would entail embedding a machine learning model trained by means of an Active Learning approach into an optimization model to automatically and intelligently tweak the representation of floating-point numerical data. This project aims to lower the energetic expenditure of every single intermediate computation in a given program, while also avoiding errors that are systematically introduced when manipulating variables using this technique, and

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Sala, Cardoso Enric. "Advanced energy management strategies for HVAC systems in smart buildings." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668528.

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The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning i

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Ahmed, Omar W. "Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5407.

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Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics

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Ahmed, Omar Wahab. "Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5407.

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Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, imag

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Ruan, Yongshao. "Efficient inference : a machine learning approach /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/7009.

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Geras, Krzysztof Jerzy. "Exploiting diversity for efficient machine learning." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28839.

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A common practice for solving machine learning problems is currently to consider each problem in isolation, starting from scratch every time a new learning problem is encountered or a new model is proposed. This is a perfectly feasible solution when the problems are sufficiently easy or, if the problem is hard when a large amount of resources, both in terms of the training data and computation, are available. Although this naive approach has been the main focus of research in machine learning for a few decades and had a lot of success, it becomes infeasible if the problem is too hard in propor

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El, Amine Ali. "Radio resource allocation in 5G cellular networks powered by the smart grid and renewable energies." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0167/document.

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Nous vivons une révolution numérique où l’Internet est devenu un élément essentiel de notre vie quotidienne. Avec plus de 750 millions de foyers connectés et plus de 6,8 milliards d'abonnés à la téléphonie mobile, les réseaux cellulaires dominent le secteur des Technologies de l'Information et de la Communication (TIC) avec plus de 75%. La tendance est à la hausse et ne semble pas avoir de signe de ralentissement dans un avenir proche en raison des nouveaux services et applications en cours. Cependant, cette augmentation radicale des appareils et services TIC a poussé la consommation d'énergie

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Ferrer, Martínez Claudia. "Machine Learning for Solar Energy Prediction." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27423.

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This thesis consists of the study of different Machine Learning models used to predict solar power data in photovoltaic plants. The process of implement a model of Machine Learning will be reviewed step by step: to collect the data, to pre-process the data in order to make it able to use as input for the model, to divide the data into training data and testing data, to train the Machine Learning algorithm with the training data, to evaluate the algorithm with the testing data, and to make the necessary changes to achieve the best results. The thesis will start with a brief introduction to sola

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Wang, Gang. "Solution path algorithms : an efficient model selection approach /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANGG.

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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a pr

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Wu, Zhan-Yuan. "Energy efficient PWM induction machine drives for electric vehicles." Thesis, University of Sheffield, 2000. http://etheses.whiterose.ac.uk/14622/.

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The viability of any electric vehicle is critically dependent on it having an acceptable range between charges, a feature which is ultimately dictated by the capacity of the battery energy store. Considerable improvements in vehicle range are possible, however, by ensuring the most effective use of this limited energy resource through the minimisation of the losses in the electric drive-train, i.e. the combined machine and power electronic controller. A particular consideration is that, for the majority of the time, the electric drive-train will be operating at part load. The thesis investigat

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Walter, David Porter III. "Efficient fixed-radius near neighbors for machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123119.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-63).<br>Deep learning has enabled artificial intelligence systems to move away from manual feature engineering and toward feature learning and better performance. Convolutional neural networks (CNNs) have especially demonst

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Moore, Andrew William. "Efficient memory-based learning for robot control." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385715.

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Predojev, Tatiana. "Energy-efficient wireless communication schemes and real-time middleware for machine-to-machine networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/284213.

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Esta tesis estudia sistemas Machine-to-Machine (M2M) en los que se ejecutan tareas de manera autónoma sin, o con mínima intervención humana. Los sistemas M2M están formados por dispositivos desplegados en un entorno que recolectan información relacionada con una tarea y la envían a aplicaciones para su proceso. Las aplicaciones optimizan estas tareas y responden a los dispositivos con comandos de control. Idealmente, después de configurar las políticas de tareas, los humanos son excluidos del lazo de control. Un importante caso de uso en M2M es la automatización de la red eléctrica, también co

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Vandehzad, Mashhood. "Efficient flight schedules with utilizing Machine Learning prediction algorithms." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20663.

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While data is becoming more and more pervasive and ubiquitous in today’s life, businesses in modern societies prefer to take advantage of using data, in particular Big Data, in their decision-making and analytical processes to increase their product efficiency. Software applications which are being utilized in the airline industry are one of the most complex and sophisticated ones for which conducting of data analyzing techniques can make many decision making processes easier and faster. Flight delays are one of the most important areas under investigation in this area because they cause a lot

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Snelson, Edward Lloyd. "Flexible and efficient Gaussian process models for machine learning." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445855/.

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Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several limitations, which can be divided into two broad categories: computational difficulties for large data sets, a

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Aijazi, Arfa N. (Arfa Nawal). "Machine learning paradigms for building energy performance simulations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111280.

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Thesis: S.M. in Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 134-138).<br>This research seeks to overcome a technical limitation of building energy performance simulations, the computation time, by using surrogate modeling, a class of supervised machine learning techniques where the output is a

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Harmer, Keith. "An energy efficient brushless drive system for a domestic washing machine." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265571.

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Liu, Fang. "Efficient Online Learning with Bandit Feedback." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587680990430268.

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Takouna, Ibrahim. "Energy-efficient and performance-aware virtual machine management for cloud data centers." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/texte_eingeschraenkt_verlag/2014/7239/.

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Virtualisierte Cloud Datenzentren stellen nach Bedarf Ressourcen zur Verfügu-ng, ermöglichen agile Ressourcenbereitstellung und beherbergen heterogene Applikationen mit verschiedenen Anforderungen an Ressourcen. Solche Datenzentren verbrauchen enorme Mengen an Energie, was die Erhöhung der Betriebskosten, der Wärme innerhalb der Zentren und des Kohlendioxidausstoßes verursacht. Der Anstieg des Energieverbrauches kann durch ein ineffektives Ressourcenmanagement, das die ineffiziente Ressourcenausnutzung verursacht, entstehen. Die vorliegende Dissertation stellt detaillierte Modelle und neue V

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Anderlini, Enrico. "Control of wave energy converters using machine learning strategies." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/31112.

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Wave energy converters are devices that are designed to extract power from ocean waves. Existing wave energy converter technologies are not financially viable yet. Control systems have been identified as one of the areas that can contribute the most towards the increase in energy absorption and reduction of loads acting on the structure, whilst incurring only minimal extra hardware costs. In this thesis, control schemes are developed for wave energy converters, with the focus on single isolated devices. Numerical models of increasing complexity are developed for the simulation of a point absor

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Ogunniyi, Samuel. "Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/13308.

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Includes bibliographical references.<br>In this thesis the author investigated the use of a Q-learning based path planning algorithm to investigate how effective it is in saving energy. It is important to pursue any means to save energy in this day and age, due to the excessive exploitation of natural resources and in order to prevent drops in production in industrial environments where less downtime is necessary or other applications where a mobile robot running out of energy can be costly or even disastrous, such as search and rescue operations or dangerous environment navigation. The study

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Knauth, Thomas. "Energy Efficient Cloud Computing: Techniques and Tools." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-164391.

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Data centers hosting internet-scale services consume megawatts of power. Mainly for cost reasons but also to appease environmental concerns, data center operators are interested to reduce their use of energy. This thesis investigates if and how hardware virtualization helps to improve the energy efficiency of modern cloud data centers. Our main motivation is to power off unused servers to save energy. The work encompasses three major parts: First, a simulation-driven analysis to quantify the benefits of known reservation times in infrastructure clouds. Virtual machines with similar expiratio

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Wressnegger, Christian Verfasser], and Konrad [Akademischer Betreuer] [Rieck. "Efficient Machine Learning for Attack Detection / Christian Wressnegger ; Betreuer: Konrad Rieck." Braunschweig : Technische Universität Braunschweig, 2019. http://d-nb.info/117867178X/34.

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Lin, Tsung-Han. "Stable and Efficient Sparse Recovery for Machine Learning and Wireless Communication." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11572.

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Recent theoretical study shows that the sparsest solution to an underdetermined linear system is unique, provided the solution vector is sufficiently sparse, and the operator matrix has sufficiently incoherent column vectors. In addition, efficient algorithms have been discovered to find such solutions. This intriguing result opens a new door for many potential applications. In this thesis, we study the design of a class of greedy algorithms that are extremely efficient, e.g., Orthogonal Matching Pursuit (OMP). These greedy algorithms suffer from a stability issue that the greedy selection app

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Westphal, Florian. "Efficient Document Image Binarization using Heterogeneous Computing and Interactive Machine Learning." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16797.

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Large collections of historical document images have been collected by companies and government institutions for decades. More recently, these collections have been made available to a larger public via the Internet. However, to make accessing them truly useful, the contained images need to be made readable and searchable. One step in that direction is document image binarization, the separation of text foreground from page background. This separation makes the text shown in the document images easier to process by humans and other image processing algorithms alike. While reasonably well worki

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Wressnegger, Christian [Verfasser], and Konrad [Akademischer Betreuer] Rieck. "Efficient Machine Learning for Attack Detection / Christian Wressnegger ; Betreuer: Konrad Rieck." Braunschweig : Technische Universität Braunschweig, 2019. http://d-nb.info/117867178X/34.

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Kanumuri, Sai Srilakshmi. "ON EVALUATING MACHINE LEARNING APPROACHES FOR EFFICIENT CLASSIFICATION OF TRAFFIC PATTERNS." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14985.

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Context. With the increased usage of mobile devices and internet, the cellular network traffic has increased tremendously. This increase in network traffic has led to increased occurrences of communication failures among the network nodes. Each communication failure among the nodes is defined as a bad event and occurrence of one such bad event acts as a source of origin for several consecutive bad events. These bad events as a whole may eventually lead to node failures (not being able to respond to any data requests). But it requires a lot of human effort and cost to be invested in by the tele

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Reddi, Sashank Jakkam. "New Optimization Methods for Modern Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1116.

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Modern machine learning systems pose several new statistical, scalability, privacy and ethical challenges. With the advent of massive datasets and increasingly complex tasks, scalability has especially become a critical issue in these systems. In this thesis, we focus on fundamental challenges related to scalability, such as computational and communication efficiency, in modern machine learning applications. The underlying central message of this thesis is that classical statistical thinking leads to highly effective optimization methods for modern big data applications. The first part of the

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Riggelsen, Carsten. "Approximation methods for efficient learning of Bayesian networks /." Amsterdam ; Washington, DC : IOS Press, 2008. http://www.loc.gov/catdir/toc/fy0804/2007942192.html.

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Wong, Shun Him. "Valuing energy storage in electricity grids : a machine learning approach." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117879.

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Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2018.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 83-87).<br>Meeting climate change mitigation targets likely requires the integration of large amounts of renewable energy generation, as well as energy storage systems, into the electric grid. H

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Immaneni, Raghu Nandan. "An efficient approach to machine learning based text classification through distributed computing." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1603338.

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<p> Text Classification is one of the classical problems in computer science, which is primarily used for categorizing data, spam detection, anonymization, information extraction, text summarization etc. Given the large amounts of data involved in the above applications, automated and accurate training models and approaches to classify data efficiently are needed. </p><p> In this thesis, an extensive study of the interaction between natural language processing, information retrieval and text classification has been performed. A case study named “keyword extraction” that deals wit

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Dobler, Markus [Verfasser]. "Bordersearch: Efficient Characterization of Automotive Electronic Systems Through Machine Learning / Markus Dobler." München : Verlag Dr. Hut, 2018. http://d-nb.info/1168534615/34.

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Cui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.

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Large scale machine learning has many characteristics that can be exploited in the system designs to improve its efficiency. This dissertation demonstrates that the characteristics of the ML computations can be exploited in the design and implementation of parameter server systems, to greatly improve the efficiency by an order of magnitude or more. We support this thesis statement with three case study systems, IterStore, GeePS, and MLtuner. IterStore is an optimized parameter server system design that exploits the repeated data access pattern characteristic of ML computations. The designed op

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Marr, Bo. "Learning, probabilistic, and asynchronous technologies for an ultra efficient datapath." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31724.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Paul Hasler; Committee Co-Chair: David V. Anderson. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Shahdi, Arya. "Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103603.

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In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contribution

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Habib, Shahroz. "Machine Learning to identify aberrant energy use to detect property failures." Thesis, KTH, Energiteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286306.

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The digitalization of energy sector has provided immense amount of data about buildings which created an untapped opportunity for energy savings using energy data analytics. In recent years, there has been significant research on energy optimization using machine learning. With the advancement in deep neural networks, researchers have investigated the potential of using time series machine learning algorithms to develop sophisticated energy prediction and proactive alert systems for energy management. In this thesis, we aim to explore utility of time series machine learning algorithm for anoma

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Bui, Thang Duc. "Efficient deterministic approximate Bayesian inference for Gaussian process models." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273833.

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Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by analytic and computational intractabilities. It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approxima

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Xie, Zhongliu. "Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/44567.

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Three studies have been carried out to investigate new approaches to efficient image segmentation and anomaly detection. The first study investigates the use of deep learning in patch based segmentation. Current approaches to patch based segmentation use low level features such as the sum of squared differences between patches. We argue that better segmentation can be achieved by harnessing the power of deep neural networks. Currently these networks make extensive use of convolutional layers. However, we argue that in the context of patch based segmentation, convolutional layers have little ad

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