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Knowledge Graphs and Big Data Processing

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Book Series: Lecture Notes in Computer Science; Information Systems and Applications, incl. Internet/Web, and HCI ISBN: 9783030531997 Year: Pages: 209 DOI: 10.1007/978-3-030-53199-7 Language: English
Publisher: Springer Nature
Subject: Computer Science --- Political Science --- Business and Management
Added to DOAB on : 2020-09-01 00:02:02
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Abstract

This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Plug-in Hybrid Electric Vehicle (PHEV)

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ISBN: 9783039214532 9783039214549 Year: Pages: 230 DOI: 10.3390/books978-3-03921-454-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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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

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

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