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Dissertation
Bistable Recurrent Cells and Belief Filtering for Q-learning in Partially Observable Markov Decision Processes
Authors: --- --- --- ---
Year: 2021 Publisher: Liège Université de Liège (ULiège)

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In this master's thesis, reinforcement learning (RL) methods are used to learn (near-)optimal policies to act in several Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). More precisely, Q-learning and recurrent Q-learning techniques are used. Some of the considered POMDPs require a high-memorisation ability in order to achieve optimal decision making. In POMDPs, RL techniques usually rely on approximating functions that take as input sequences of observations with variable length. Recurrent neural networks (RNNs) are thus a clever choice of such approximators. This work is based on the recently introduced bistable recurrent cells, which have been empirically shown to provide a significantly better long term memory than standard cells, such as the long short-term memory (LSTM) and the gated recurrent unit (GRU). These cells are named the bistable recurrent cell (BRC) and the recurrently neuromodulated BRC (nBRC). First, by importing these cells for the first time in the RL setting, it is empirically shown that they also provide a significant advantage in memory-demanding POMDPs, in comparison to LSTM and GRU. Second, the ability of the RNN to represent a belief distribution over the states of the POMDP is studied. It is achieved by evaluating the mutual information between the hidden states of the RNN and the belief filtered on the successive observations. This analysis is thus strongly anchored in the theory of information and the theory of optimal control for POMDPs. Third, as a complement to this research project, a new target update is proposed for Q-learning algorithms with target networks, for both reactive and recurrent policies. This new update speeds up learning, especially in environments with sparse rewards.


Book
Energy-Efficient Computing and Communication
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Year: 2020 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Information and communication technology (ICT) is reponsible for up to 10% of world power consumption. In particular, communications and computing systems are indispensable elements in ICT; thus, determining how to improve the energy efficiency in communications and computing systems has become one of the most important issues for realizing green ICT. Even though a number of studies have been conducted, most of them focused on one aspect—either communications or computing systems. However, salient features in communications and computing systems should be jointly considered, and novel holistic approaches across communications and computing systems are strongly required to implement energy-efficient systems. In addition, emerging systems, such as energy-harvesting IoT devices, cyber-physical systems (CPSs), autonomous vehicles (AVs), and unmanned aerial vehicles (UAVs), require new approaches to satisfy their strict energy consumption requirements in mission-critical situations. The goal of this Special Issue is to disseminate the recent advances in energy-efficient communications and computing systems. Review and survey papers on these topics are welcome. Potential topics include, but are not limited to, the following: • energy-efficient communications: from physical layer to application layer; • energy-efficient computing systems; • energy-efficient network architecture: through SDN/NFV/network slicing; • energy-efficient system design; • energy-efficient Internet of Things (IoT) and Industrial IoT (IIoT); • energy-efficient edge/fog/cloud computing; • new approaches for energy-efficient computing and communications (e.g., AI/ML and data-driven approaches); • new performance metrics on energy efficiency in emerging systems; • energy harvesting and simultaneous wireless information and power transfer (SWIPT); • smart grid and vehicle-to-grid (V2G); and • standardization and open source activities for energy efficient systems.


Book
Energy-Efficient Computing and Communication
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Year: 2020 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Information and communication technology (ICT) is reponsible for up to 10% of world power consumption. In particular, communications and computing systems are indispensable elements in ICT; thus, determining how to improve the energy efficiency in communications and computing systems has become one of the most important issues for realizing green ICT. Even though a number of studies have been conducted, most of them focused on one aspect—either communications or computing systems. However, salient features in communications and computing systems should be jointly considered, and novel holistic approaches across communications and computing systems are strongly required to implement energy-efficient systems. In addition, emerging systems, such as energy-harvesting IoT devices, cyber-physical systems (CPSs), autonomous vehicles (AVs), and unmanned aerial vehicles (UAVs), require new approaches to satisfy their strict energy consumption requirements in mission-critical situations. The goal of this Special Issue is to disseminate the recent advances in energy-efficient communications and computing systems. Review and survey papers on these topics are welcome. Potential topics include, but are not limited to, the following: • energy-efficient communications: from physical layer to application layer; • energy-efficient computing systems; • energy-efficient network architecture: through SDN/NFV/network slicing; • energy-efficient system design; • energy-efficient Internet of Things (IoT) and Industrial IoT (IIoT); • energy-efficient edge/fog/cloud computing; • new approaches for energy-efficient computing and communications (e.g., AI/ML and data-driven approaches); • new performance metrics on energy efficiency in emerging systems; • energy harvesting and simultaneous wireless information and power transfer (SWIPT); • smart grid and vehicle-to-grid (V2G); and • standardization and open source activities for energy efficient systems.


Book
Energy-Efficient Computing and Communication
Author:
Year: 2020 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Information and communication technology (ICT) is reponsible for up to 10% of world power consumption. In particular, communications and computing systems are indispensable elements in ICT; thus, determining how to improve the energy efficiency in communications and computing systems has become one of the most important issues for realizing green ICT. Even though a number of studies have been conducted, most of them focused on one aspect—either communications or computing systems. However, salient features in communications and computing systems should be jointly considered, and novel holistic approaches across communications and computing systems are strongly required to implement energy-efficient systems. In addition, emerging systems, such as energy-harvesting IoT devices, cyber-physical systems (CPSs), autonomous vehicles (AVs), and unmanned aerial vehicles (UAVs), require new approaches to satisfy their strict energy consumption requirements in mission-critical situations. The goal of this Special Issue is to disseminate the recent advances in energy-efficient communications and computing systems. Review and survey papers on these topics are welcome. Potential topics include, but are not limited to, the following: • energy-efficient communications: from physical layer to application layer; • energy-efficient computing systems; • energy-efficient network architecture: through SDN/NFV/network slicing; • energy-efficient system design; • energy-efficient Internet of Things (IoT) and Industrial IoT (IIoT); • energy-efficient edge/fog/cloud computing; • new approaches for energy-efficient computing and communications (e.g., AI/ML and data-driven approaches); • new performance metrics on energy efficiency in emerging systems; • energy harvesting and simultaneous wireless information and power transfer (SWIPT); • smart grid and vehicle-to-grid (V2G); and • standardization and open source activities for energy efficient systems.


Book
AI Applications to Power Systems
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Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.


Book
Edge/Fog Computing Technologies for IoT Infrastructure
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies.


Book
AI Applications to Power Systems
Author:
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.


Book
AI Applications to Power Systems
Author:
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

Keywords

Technology: general issues --- History of engineering & technology --- self-healing grid --- machine-learning --- feature extraction --- event detection --- optimization techniques --- manta ray foraging optimization algorithm --- multi-objective function --- radial networks --- optimal power flow --- automatic P2P energy trading --- Markov decision process --- deep reinforcement learning --- deep Q-network --- long short-term delayed reward --- inter-area oscillations --- modal analysis --- reduced order modeling --- dynamic mode decomposition --- machine learning --- artificial neural networks --- steady-state security assessment --- situation awareness --- cellular computational networks --- load flow prediction --- contingency --- fuzzy system --- change detection --- data analytics --- data mining --- filtering --- optimization --- power quality --- signal processing --- total variation smoothing --- self-healing grid --- machine-learning --- feature extraction --- event detection --- optimization techniques --- manta ray foraging optimization algorithm --- multi-objective function --- radial networks --- optimal power flow --- automatic P2P energy trading --- Markov decision process --- deep reinforcement learning --- deep Q-network --- long short-term delayed reward --- inter-area oscillations --- modal analysis --- reduced order modeling --- dynamic mode decomposition --- machine learning --- artificial neural networks --- steady-state security assessment --- situation awareness --- cellular computational networks --- load flow prediction --- contingency --- fuzzy system --- change detection --- data analytics --- data mining --- filtering --- optimization --- power quality --- signal processing --- total variation smoothing


Book
Edge/Fog Computing Technologies for IoT Infrastructure
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies.

Keywords

Information technology industries --- cloud computing --- container orchestration --- custom metrics --- Docker --- edge computing --- Horizontal Pod Autoscaling (HPA) --- Kubernetes --- Prometheus --- resource metrics --- fog computing --- task allocation --- multi-objective optimization --- evolutionary genetics --- hyper-angle --- crowding distance --- containers --- leader election --- load balancing --- stateful --- multi-access edge computing --- orchestrator --- task offloading --- fuzzy logic --- 5G --- fog/edge computing --- service provisioning --- service placement --- service offloading --- Internet of Things (IoT) --- task scheduling --- markov decision process (MDP) --- deep reinforcement learning (DRL) --- resource management --- algorithm classification --- evaluation framework --- web --- Web Assembly --- OpenCL --- LWC --- fast implementation --- Internet of things --- IoT actor --- data manager --- GDPR --- computing --- computational offloading --- dynamic offloading threshold --- minimizing delay --- minimizing energy consumption --- maximizing throughputs --- cloud computing --- container orchestration --- custom metrics --- Docker --- edge computing --- Horizontal Pod Autoscaling (HPA) --- Kubernetes --- Prometheus --- resource metrics --- fog computing --- task allocation --- multi-objective optimization --- evolutionary genetics --- hyper-angle --- crowding distance --- containers --- leader election --- load balancing --- stateful --- multi-access edge computing --- orchestrator --- task offloading --- fuzzy logic --- 5G --- fog/edge computing --- service provisioning --- service placement --- service offloading --- Internet of Things (IoT) --- task scheduling --- markov decision process (MDP) --- deep reinforcement learning (DRL) --- resource management --- algorithm classification --- evaluation framework --- web --- Web Assembly --- OpenCL --- LWC --- fast implementation --- Internet of things --- IoT actor --- data manager --- GDPR --- computing --- computational offloading --- dynamic offloading threshold --- minimizing delay --- minimizing energy consumption --- maximizing throughputs


Book
Plug-in Hybrid Electric Vehicle (PHEV)
Author:
ISBN: 3039214543 3039214535 Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This book is based on the Special Issue of the journal Applied Sciences on “Plug-In Hybrid Electric Vehicles (PHEVs)”. This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems.

Keywords

hybrid energy storage system --- plug-in hybrid electric vehicle --- Li-ion battery --- emerging electric machines --- lithium-ion capacitor --- electric vehicles (EVs) --- efficient energy management strategies for hybrid propulsion systems --- plug-in hybrid --- attributional --- electric vehicle --- energy system --- energy efficiency --- modified one-state hysteresis model --- air quality --- adaptive neuron-fuzzy inference system (ANFIS) --- Markov decision process (MDP) --- simulated annealing --- Paris Agreement --- mobility needs --- interleaved multiport converte --- dynamic programming --- state of health estimation --- strong track filter --- LCA --- modelling --- consequential --- losses model --- voltage vector distribution --- parallel hybrid electric vehicle --- electricity mix --- time-delay input --- convex optimization --- lifetime model --- artificial neural network (ANN) --- Li(Ni1/3Co1/3Mn1/3)O2 battery --- battery power --- CO2 --- capacity degradation --- regenerative braking --- open-end winding --- novel propulsion systems --- group method of data handling (GMDH) --- state of charge --- Well-to-Wheel --- energy storage systems --- including wide bandgap (WBG) technology --- wide bandgap (WBG) technologies --- marginal --- lithium polymer battery --- life-cycle assessment (LCA) --- energy management --- dual inverter --- lithium-ion battery --- measurements --- plug-in hybrid electric vehicles (PHEVs) --- emerging power electronics --- Q-learning (QL) --- fuel consumption characteristics --- Plugin Hybrid electric vehicle --- Energy Storage systems --- meta-analysis --- range-extender --- engine-on power --- reinforcement learning (RL) --- multi-objective genetic algorithm --- power sharing --- energy management strategy --- power distribution --- hybrid electric vehicles --- system modelling

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