Relevant bibliographies by topics / Energy Efficient Machine Learning System

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Energy Efficient Machine Learning System'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Energy Efficient Machine Learning System"

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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.

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This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved
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Husainy, 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 (2023): 39–45. http://dx.doi.org/10.51983/ajes-2023.12.1.3684.

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The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are w
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Wu, Qingying, Benjamin K. Ng, and Chan-Tong Lam. "Energy-Efficient Cooperative Spectrum Sensing Using Machine Learning Algorithm." Sensors 22, no. 21 (2022): 8230. http://dx.doi.org/10.3390/s22218230.

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Cognitive Radio (CR) is a practical technique for overcoming spectrum inefficiencies by sensing and utilizing spectrum holes over a wide spectrum. In particular, cooperative spectrum sensing (CSS) determines the state of primary users (PUs) by cooperating with multiple secondary users (SUs) distributed around a Cognitive Radio Network (CRN), further overcoming various noise and fading issues in the radio environment. But it’s still challenging to balance energy efficiency and good sensing performances in the existing CSS system, especially when the CRN consists of battery-limited sensors. This
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Zhang, Huanhuan, Jigeng Li, and Mengna Hong. "Machine Learning-Based Energy System Model for Tissue Paper Machines." Processes 9, no. 4 (2021): 655. http://dx.doi.org/10.3390/pr9040655.

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With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consu
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Nour, 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.

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Nowadays, embedded device applications have become partially correlated and can share platform resources. Cross-execution and sharing resources can cause memory access conflicts, especially in the Last Level Cache (LLC). LLC is a promising candidate for improving system performance on multicore embedded systems. It leads to a reduction in the number of high-latency main memory accesses. Currently, commercial devices can use cache partitioning. The software could better utilize the LLC and conserve energy by caching. This paper proposes a new energy-optimization model for embedded multicore sys
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Ismail, 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.

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The energy challenge in IoT refers to the significant energy consumption of IoT devices, which can lead to sustainability issues, shorter battery life, and increased operating costs. IoT devices are known for their high energy consumption, and optimizing their energy usage can have a significant impact on sustainability and cost. Machine learning (ML) can learn from data and patterns to predict and control energy consumption in IoT systems, making them more energy efficient. The main contribution of this paper is the establishment of a novel deep learning framework for enhanced predictive mode
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Waqas 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 (2020): 2681. http://dx.doi.org/10.3390/en13112681.

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The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature eng
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Dixit, Abhishek, and Santosh Kumar. "Machine Learning Based Efficient Protection Scheme for AC Microgrid." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 10, no. 4 (2022): 18–23. http://dx.doi.org/10.55083/irjeas.2022.v10i04009.

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Micro grids have become popular as a way to reduce carbon emissions and use nonrenewable energy sources to produce power. Microgrids allow users to generate and regulate energy as needed, reducing their reliance on the utility grid. They may also sell excess electricity to the grid and make money. Due to its simple design, fast installation, and easy maintenance, photovoltaic systems are a vital microgrid resource. Microgrids threaten the reliability and optimum functioning of major power grids. It's crucial to discover defects early and fix them before catastrophic system breakdown. This rese
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Khan, Murad, Junho Seo, and Dongkyun Kim. "Towards Energy Efficient Home Automation: A Deep Learning Approach." Sensors 20, no. 24 (2020): 7187. http://dx.doi.org/10.3390/s20247187.

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Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a sys
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Lee, 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.

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Energy efficiency of the HVAC system can be improved through system renovation and operating method improvement. Economizer control, one of the energy efficient measures through improvement of operating method, introduces outdoor air when outdoor air is sufficient for cooling. There are high/low limit that determine the range of economizer control and mixed air temperature as control set-points. Economizer is controlled with the user's or manager's experience, and the set-point is operated fixed. This causes problems energy waste because it does not consider indoor and outdoor environments. Th
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Dissertations / Theses on the topic "Energy Efficient Machine Learning System"

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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.

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Embedded autonomous electronic systems are required in numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical systems. Embedded electronic systems usually host sensors, and each sensor hosts multiple input channels (e.g., tactile, vision), tightly coupled to the electronic computing unit (ECU). The ECU extracts information by often employing sophisticated methods, e.g., Machine Learning. However, embedding Machine Learning algorithms poses essential challenges in terms of hardware resources and energy consumption because of: 1) the high amount of data t
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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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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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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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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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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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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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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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Books on the topic "Energy Efficient Machine Learning System"

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The design and analysis of efficient learning algorithms. MIT Press, 1992.

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Awad, Mariette. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Springer Nature, 2015.

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Khanna, Rahul, and Mariette Awad. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Apress, 2015.

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Kumar, 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.

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Gershman, Samuel. What Makes Us Smart. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691205717.001.0001.

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At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. This book makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory—in other words, data and resource limita
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Delgado 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.

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[Abstract] The 6th International Workshop on Rock Physics (6IWRP) was held A Coruña, Spain, between 13th and 17th of June, 2022. This meeting follows the track of the five successful encounters held in Golden (USA, 2011), Southampton (UK, 2013), Perth (Australia, 2015), Trondheim (Norway, 2017) and Hong Kong (China, 2019). The aim of the workshop was to bring together experiences allowing to illustrate, discuss and exchange recent advances in the wide realm of rock physics, including theoretical developments, in situ and laboratory scale experiments as well as digital analysis. While rock phys
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Shengelia, Revaz. Modern Economics. Universal, Georgia, 2021. http://dx.doi.org/10.36962/rsme012021.

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Economy and mankind are inextricably interlinked. Just as the economy or the production of material wealth is unimaginable without a man, so human existence and development are impossible without the wealth created in the economy. Shortly, both the goal and the means of achieving and realization of the economy are still the human resources. People have long ago noticed that it was the economy that created livelihoods, and the delays in their production led to the catastrophic events such as hunger, poverty, civil wars, social upheavals, revolutions, moral degeneration, and more. Therefore, the
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Book chapters on the topic "Energy Efficient Machine Learning System"

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Berral, Josep Ll, Iñigo Goiri, Ramon Nou, et al. "Toward Energy-Aware Scheduling Using Machine Learning." In Energy-Efficient Distributed Computing Systems. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118342015.ch8.

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Kruglov, Artem, Giancarlo Succi, and Gcinizwe Dlamini. "System Energy Consumption Measurement." In Developing Sustainable and Energy-Efficient Software Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11658-2_3.

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AbstractOver the years, the task to reduce energy consumed by a system has been mainly assigned to computer hardware developers. This is mainly because it is believed that the hardware is the principal component that consumes more electrical energy. However, the software also plays a vital role in power usage. Hardware works hand in hand with software programs. It has become equally important to estimate the energy consumed as a whole using artificial intelligence-based approaches. Machine learning is presented as one of the scalable approaches toward efficiently and accurately estimating energy consumed in the software development domain.
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Chakraborty, 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. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-97940-9_102.

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Chakraborty, 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. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72322-4_102-1.

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Wang, 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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00557-3_29.

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Trenz, 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. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_50.

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AbstractThe ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.
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Loni, 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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30487-4_17.

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Ioshchikhes, 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. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_48.

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AbstractA prerequisite to identify energy efficiency potentials and to improve energy efficiency is the measurement and analysis of the energy demand. However, in industrial practice, approaches to identify energy efficiency measures of production machines are associated with high costs for metering equipment and time consuming analysis requiring expertise. Against this background, this paper describes a comprehensive and cost-efficient framework from acquisition to analysis of energy data to serve as a starting point to increase energy efficiency in manufacturing. For this purpose, an energy transparency and analysis system is being developed that can measure, record and analyze electrical quantities. The validity of the data acquisition can be verified by utilizing a Raspberry Pi as a low-cost edge analyzer device. Measurement data is stored with associated metadata in a SQLite database for subsequent processing in a Python-based web application, in which machine learning algorithms can be deployed. The algorithms can be used to process vast amounts of data and to provide a basis for calculating energy performance indicators to reveal energy efficiency potentials. The overall workflow is validated using a lathe and a cleaning machine within the ETA Research Factory at the Technical University of Darmstadt.
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Behura, 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_56.

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Araghi, Farhang Motallebi, Aaron Rabinwoitz, Chon Chia Ang, et al. "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. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28016-0_27.

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Conference papers on the topic "Energy Efficient Machine Learning System"

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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.

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Ramkumar, S., G. Emayavaramban, K. Sathesh Kumar, et al. "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.

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Zhou, 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.

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Murthy, 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. ACM, 2019. http://dx.doi.org/10.1145/3360322.3360854.

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Wissing, 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.

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Gryzlov, 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.

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Abstract Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flo
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Osta, 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.

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Jiang, 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.

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Herzog, 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. ACM, 2021. http://dx.doi.org/10.1145/3447555.3466566.

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Fayzrakhmanov, 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.

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Reports on the topic "Energy Efficient Machine Learning System"

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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM.
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Aguiar, 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, 2021. http://dx.doi.org/10.25148/mmeurs.009773.

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The ultrasonic treatment process strengthens metals by increasing nucleation and decreasing grain size in an energy efficient way, without having to add anything to the material. The goal of this research endeavor was to use machine learning to automatically measure cavitation area in the Ultrasonic Treatment process to understand how amplitude influences cavitation area. For this experiment, a probe was placed into a container filled with turpentine because it has a similar viscosity to liquid aluminum. The probe gyrates up and down tens of micrometers at a frequency of 20 kHz, which causes c
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Yang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2240.

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California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered
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