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Статті в журналах з теми "Energy Efficient Machine Learning System"
Reddy, V. Sandeep Kumar, Saravanan T., N. T. Velusudha, and T. Sunder Selwyn. "Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution." E3S Web of Conferences 387 (2023): 02005. http://dx.doi.org/10.1051/e3sconf/202338702005.
Повний текст джерелаHusainy, Avesahemad S. N., Sairam A. Patil, Atharva S. Sinfal, Vasim M. Mujawar, and Chandrashekhar S. Sinfal. "Parameter Optimization of Refrigeration Chiller by Machine Learning." Asian Journal of Electrical Sciences 12, no. 1 (June 22, 2023): 39–45. http://dx.doi.org/10.51983/ajes-2023.12.1.3684.
Повний текст джерелаWu, Qingying, Benjamin K. Ng, and Chan-Tong Lam. "Energy-Efficient Cooperative Spectrum Sensing Using Machine Learning Algorithm." Sensors 22, no. 21 (October 27, 2022): 8230. http://dx.doi.org/10.3390/s22218230.
Повний текст джерелаZhang, Huanhuan, Jigeng Li, and Mengna Hong. "Machine Learning-Based Energy System Model for Tissue Paper Machines." Processes 9, no. 4 (April 9, 2021): 655. http://dx.doi.org/10.3390/pr9040655.
Повний текст джерелаNour, Samar, Shahira Habashy, and Sameh Salem. "Energy-Efficient Cache Partitioning Using Machine Learning for Embedded Systems." Jordan Journal of Electrical Engineering 9, no. 3 (2023): 285. http://dx.doi.org/10.5455/jjee.204-1669909560.
Повний текст джерелаIsmail, Mahmoud M. "A Machine Learning Approach for Energy-Efficient IoT Systems." Journal of Intelligent Systems and Internet of Things 1, no. 1 (2020): 61–69. http://dx.doi.org/10.54216/jisiot.010105.
Повний текст джерелаWaqas Khan, Prince, Yung-Cheol Byun, Sang-Joon Lee, and Namje Park. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting." Energies 13, no. 11 (May 26, 2020): 2681. http://dx.doi.org/10.3390/en13112681.
Повний текст джерелаDixit, Abhishek, and Santosh Kumar. "Machine Learning Based Efficient Protection Scheme for AC Microgrid." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 10, no. 4 (December 31, 2022): 18–23. http://dx.doi.org/10.55083/irjeas.2022.v10i04009.
Повний текст джерелаKhan, Murad, Junho Seo, and Dongkyun Kim. "Towards Energy Efficient Home Automation: A Deep Learning Approach." Sensors 20, no. 24 (December 15, 2020): 7187. http://dx.doi.org/10.3390/s20247187.
Повний текст джерелаLee, Jin-Hyun, Hye-In Lee, Kyoung-Hwan Ji, and Young-Hum Cho. "Optimal Economizer Control of VAV System using Machine Learning." E3S Web of Conferences 396 (2023): 03034. http://dx.doi.org/10.1051/e3sconf/202339603034.
Повний текст джерелаДисертації з теми "Energy Efficient Machine Learning System"
OSTA, MARIO. "Energy-efficient embedded machine learning algorithms for smart sensing systems." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997732.
Повний текст джерелаAzmat, Freeha. "Machine learning and energy efficient cognitive radio." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/85990/.
Повний текст джерелаGarcía-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.
Повний текст джерелаScalable resource-efficient systems for big data analytics
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.
Повний текст джерелаCui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.
Повний текст джерелаLe, 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.
Повний текст джерелаIn environmental monitoring studies, many applications are expected to run unattended for months or years. Sensor nodes are however constrained by limited resources, particularly in terms of energy. Since communication is one order of magnitude more energy-consuming than processing, the design of data collection schemes that limit the amount of transmitted data is therefore recognized as a central issue for wireless sensor networks.
An efficient way to address this challenge is to approximate, by means of mathematical models, the evolution of the measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model may be used in place of the true measurements, significant gains in communications may be obtained by only transmitting the parameters of the model instead of the set of real measurements. Since in most cases there is little or no a priori information about the variations taken by sensor measurements, the models must be identified in an automated manner. This calls for the use of machine learning techniques, which allow to model the variations of future measurements on the basis of past measurements.
This thesis brings two main contributions to the use of learning techniques in a sensor network. First, we propose an approach which combines time series prediction and model selection for reducing the amount of communication. The rationale of this approach, called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data.
The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis (PCA). The proposed method allows to transform along a routing tree the measurements taken in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed, which in turn extends the overall network lifetime. The framework can be seen as a truly distributed approach for the principal component analysis, and finds applications not only for approximated data collection tasks, but also for event detection or recognition tasks.
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Les réseaux de capteurs sans fil forment une nouvelle famille de systèmes informatiques permettant d'observer le monde avec une résolution sans précédent. En particulier, ces systèmes promettent de révolutionner le domaine de l'étude environnementale. Un tel réseau est composé d'un ensemble de capteurs sans fil, ou unités sensorielles, capables de collecter, traiter, et transmettre de l'information. Grâce aux avancées dans les domaines de la microélectronique et des technologies sans fil, ces systèmes sont à la fois peu volumineux et peu coûteux. Ceci permet leurs deploiements dans différents types d'environnements, afin d'observer l'évolution dans le temps et l'espace de quantités physiques telles que la température, l'humidité, la lumière ou le son.
Dans le domaine de l'étude environnementale, les systèmes de prise de mesures doivent souvent fonctionner de manière autonome pendant plusieurs mois ou plusieurs années. Les capteurs sans fil ont cependant des ressources limitées, particulièrement en terme d'énergie. Les communications radios étant d'un ordre de grandeur plus coûteuses en énergie que l'utilisation du processeur, la conception de méthodes de collecte de données limitant la transmission de données est devenue l'un des principaux défis soulevés par cette technologie.
Ce défi peut être abordé de manière efficace par l'utilisation de modèles mathématiques modélisant l'évolution spatiotemporelle des mesures prises par les capteurs. En effet, si un tel modèle peut être utilisé à la place des mesures, d'importants gains en communications peuvent être obtenus en utilisant les paramètres du modèle comme substitut des mesures. Cependant, dans la majorité des cas, peu ou aucune information sur la nature des mesures prises par les capteurs ne sont disponibles, et donc aucun modèle ne peut être a priori défini. Dans ces cas, les techniques issues du domaine de l'apprentissage machine sont particulièrement appropriées. Ces techniques ont pour but de créer ces modèles de façon autonome, en anticipant les mesures à venir sur la base des mesures passées.
Dans cette thèse, deux contributions sont principalement apportées permettant l'applica-tion de techniques d'apprentissage machine dans le domaine des réseaux de capteurs sans fil. Premièrement, nous proposons une approche qui combine la prédiction de série temporelle avec la sélection de modèles afin de réduire la communication. La logique de cette approche, appelée sélection de modèle adaptive, est de permettre aux unités sensorielles de determiner de manière autonome un modèle de prédiction qui anticipe correctement leurs mesures, tout en réduisant l'utilisation de leur radio.
Deuxièmement, nous avons conçu une méthode permettant de modéliser de façon distribuée les mesures collectées, qui se base sur l'analyse en composantes principales (ACP). La méthode permet de transformer les mesures le long d'un arbre de routage, de façon à ce que (i) la majeure partie des variations dans les mesures des capteurs soient conservées, et (ii) la charge réseau soit réduite et mieux distribuée, ce qui permet d'augmenter également la durée de vie du réseau. L'approche proposée permet de véritablement distribuer l'ACP, et peut être utilisée pour des applications impliquant la collecte de données, mais également pour la détection ou la classification d'événements.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Yurur, Ozgur. "Energy Efficient Context-Aware Framework in Mobile Sensing." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4797.
Повний текст джерелаWestphal, 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.
Повний текст джерелаScalable resource-efficient systems for big data analytics
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.
Повний текст джерелаSala, 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.
Повний текст джерелаL’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupants
Книги з теми "Energy Efficient Machine Learning System"
The design and analysis of efficient learning algorithms. Cambridge, Mass: MIT Press, 1992.
Знайти повний текст джерелаAwad, Mariette. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Springer Nature, 2015.
Знайти повний текст джерелаKhanna, Rahul, and Mariette Awad. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Apress, 2015.
Знайти повний текст джерелаKumar, C. Daniel Nesa, 1st. Performance Measure and Analysis on Machine Learning Techniques for Energy Efficient Secured Multipath Multicast Routing in MANET. Selfypage Developers Pvt Ltd, 2022.
Знайти повний текст джерелаGershman, Samuel. What Makes Us Smart. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691205717.001.0001.
Повний текст джерелаDelgado Martín, Jordi, Andrea Muñoz-Ibáñez, and Ismael Himar Falcón-Suárez. 6th International Workshop on Rock Physics: A Coruña, Spain 13 -17 June 2022: Book of Abstracts. 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.000005.
Повний текст джерелаShengelia, Revaz. Modern Economics. Universal, Georgia, 2021. http://dx.doi.org/10.36962/rsme012021.
Повний текст джерелаЧастини книг з теми "Energy Efficient Machine Learning System"
Berral, Josep Ll, Iñigo Goiri, Ramon Nou, Ferran Julià, Josep O. Fitó, Jordi Guitart, Ricard Gavaldá, and Jordi Torres. "Toward Energy-Aware Scheduling Using Machine Learning." In Energy-Efficient Distributed Computing Systems, 215–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118342015.ch8.
Повний текст джерелаKruglov, Artem, Giancarlo Succi, and Gcinizwe Dlamini. "System Energy Consumption Measurement." In Developing Sustainable and Energy-Efficient Software Systems, 27–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11658-2_3.
Повний текст джерелаChakraborty, Indrasis, Aritra Dasgupta, Javier Rubio-Herrero, Sai Pushpak Nandanoori, Soumya Kundu, and Vikas Chandan. "Application of Machine Learning for Energy-Efficient Buildings." In Handbook of Smart Energy Systems, 837–58. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-97940-9_102.
Повний текст джерелаChakraborty, Indrasis, Aritra Dasgupta, Javier Rubio-Herrero, Sai Pushpak Nandanoori, Soumya Kundu, and Vikas Chandan. "Application of Machine Learning for Energy-Efficient Buildings." In Handbook of Smart Energy Systems, 1–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72322-4_102-1.
Повний текст джерелаWang, Xi, Xiangbin Yu, Tao Teng, and Guangying Wang. "Energy-Efficient Power Allocation Scheme Based on Discrete-Rate Adaptive Modulation in Distributed Antenna System." In Machine Learning and Intelligent Communications, 284–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00557-3_29.
Повний текст джерелаTrenz, André, Christoph Hoffmann, Christopher Lange, and Richard Öchsner. "Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning." In Lecture Notes in Mechanical Engineering, 449–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_50.
Повний текст джерелаLoni, Mohammad, Ali Zoljodi, Sima Sinaei, Masoud Daneshtalab, and Mikael Sjödin. "NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems." In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation, 208–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30487-4_17.
Повний текст джерелаIoshchikhes, Borys, Daniel Piendl, Henrik Schmitz, Jasper Heiland, and Matthias Weigold. "Development of a Holistic Framework for Identifying Energy Efficiency Potentials of Production Machines." In Lecture Notes in Mechanical Engineering, 431–39. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_48.
Повний текст джерелаBehura, Aradhana, and Manas Ranjan Kabat. "Energy-Efficient Optimization-Based Routing Technique for Wireless Sensor Network Using Machine Learning." In Advances in Intelligent Systems and Computing, 555–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_56.
Повний текст джерелаAraghi, Farhang Motallebi, Aaron Rabinwoitz, Chon Chia Ang, Sachin Sharma, Parth Kadav, Richard T. Meyer, Thomas Bradley, and Zachary D. Asher. "Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-Driving." In Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems, 759–86. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28016-0_27.
Повний текст джерелаТези доповідей конференцій з теми "Energy Efficient Machine Learning System"
Hussein, Ramy, Rabab Ward, Z. Jane Wand, and Amr Mohamed. "Energy Efficient EEG Monitoring System for Wireless Epileptic Seizure Detection." In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016. http://dx.doi.org/10.1109/icmla.2016.0055.
Повний текст джерелаRamkumar, S., G. Emayavaramban, K. Sathesh Kumar, K. Shankar, M. Ilayaraja, P. Sriramakrishnan, and J. Macklin Abraham Navamani. "Designing Communication System for Person with Locked in Syndrome Using Machine Learning Technique." In 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES). IEEE, 2019. http://dx.doi.org/10.1109/incces47820.2019.9167686.
Повний текст джерелаZhou, Shiyang, Yufan Cheng, and Xia Lei. "Model-Based Machine Learning for Energy-Efficient UAV Placement." In 2022 7th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2022. http://dx.doi.org/10.1109/icccs55155.2022.9846781.
Повний текст джерелаMurthy, Akshay, Curtis Green, Radu Stoleru, Suman Bhunia, Charles Swanson, and Theodora Chaspari. "Machine Learning-based Irrigation Control Optimization." In BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3360322.3360854.
Повний текст джерелаWissing, J., and S. Scheele. "A4.1 - Boosting Energy Efficient Machine Learning in Smart Sensor Systems." In SMSI 2023. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023. http://dx.doi.org/10.5162/smsi2023/a4.1.
Повний текст джерелаGryzlov, Anton, Liliya Mironova, Sergey Safonov, and Muhammad Arsalan. "Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates." In SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry. SPE, 2021. http://dx.doi.org/10.2118/208648-ms.
Повний текст джерелаOsta, Mario, Mohamad Alameh, Hamoud Younes, Ali Ibrahim, and Maurizio Valle. "Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms." In 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2019. http://dx.doi.org/10.1109/icecs46596.2019.8965157.
Повний текст джерелаJiang, Shixiong, Sheena Ratnam Priya, Naveena Elango, James Clay, and Ramalingam Sridhar. "An Energy Efficient In-Memory Computing Machine Learning Classifier Scheme." In 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID). IEEE, 2019. http://dx.doi.org/10.1109/vlsid.2019.00046.
Повний текст джерелаHerzog, Benedict, Stefan Reif, Fabian Hügel, Timo Hönig, and Wolfgang Schröder-Preikschat. "Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning." In e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447555.3466566.
Повний текст джерелаFayzrakhmanov, Rustam Abubakirovich, Polina Yurievna Fominykh, Daniil Sergeevich Kurushin, Ekaterina Dmitrievna Orlova, Olga Vladimirovna Soboleva, and Denis Vladimirovich Yarullin. "Machine Learning for Building Literary Mapping Geoinformation System." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280665.
Повний текст джерелаЗвіти організацій з теми "Energy Efficient Machine Learning System"
Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.
Повний текст джерелаAguiar, Brandon, Paul Bianco, and Arvind Agarwal. Using High-Speed Imaging and Machine Learning to Capture Ultrasonic Treatment Cavitation Area at Different Amplitudes. Florida International University, October 2021. http://dx.doi.org/10.25148/mmeurs.009773.
Повний текст джерелаYang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, July 2023. http://dx.doi.org/10.31979/mti.2023.2240.
Повний текст джерела