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Clinical Text Mining: Secondary Use of Electronic Patient Records

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ISBN: 9783319785028 9783319785035 Year: Pages: 181 DOI: https://doi.org/10.1007/978-3-319-78503-5 Language: English
Publisher: Springer Grant: Riksbankens Jubileumsfond; Stockholm University
Subject: Medicine (General)
Added to DOAB on : 2018-06-22 15:52:54
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This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records.It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters.The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

Language Technologies for the Challenges of the Digital Age: 27th International Conference, GSCL 2017, Berlin, Germany, September 13-14, 2017, Proceedings

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Book Series: Lecture Notes in Artificial Intelligence ISSN: 0302-9743 ISBN: 9783319737058 9783319737065 Year: Volume: 10713 Pages: 310 DOI: https://doi.org/10.1007/978-3-319-73706-5 Language: English
Publisher: Springer Grant: Bundesministerium für Bildung und Forschung (BMBF)
Subject: Linguistics --- Education --- Technology (General)
Added to DOAB on : 2018-06-29 12:25:05
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This open access volume constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceedings were carefully selected from 36 submissions. Topics covered include text processing of the German language, online media and online content, semantics and reasoning, sentiment analysis, and semantic web description languages.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.

Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

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ISBN: 9783038972921 9783038972938 Year: Pages: 186 DOI: 10.3390/books978-3-03897-293-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-22 10:01:53
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The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy.

Short-Term Load Forecasting by Artificial Intelligent Technologies

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ISBN: 9783038975823 / 9783038975830 Year: Pages: 444 DOI: 10.3390/books978-3-03897-583-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-01-29 10:55:39
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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

Applications of Computational Intelligence to Power Systems

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ISBN: 9783039217601/9783039217618 Year: Pages: 116 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering
Added to DOAB on : 2019-11-08 11:31:56
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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

Google Earth Engine Applications

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ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

Applications of Power Electronics

Authors: --- ---
ISBN: 9783038979746 / 9783038979753 Year: Volume: 1 Pages: 476 DOI: 10.3390/books978-3-03897-975-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering --- Technology (General)
Added to DOAB on : 2019-06-26 08:44:06
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Power electronics technology is still an emerging technology, and it has found its way into many applications, from renewable energy generation (i.e., wind power and solar power) to electrical vehicles (EVs), biomedical devices, and small appliances, such as laptop chargers. In the near future, electrical energy will be provided and handled by power electronics and consumed through power electronics; this not only will intensify the role of power electronics technology in power conversion processes, but also implies that power systems are undergoing a paradigm shift, from centralized distribution to distributed generation. Today, more than 1000 GW of renewable energy generation sources (photovoltaic (PV) and wind) have been installed, all of which are handled by power electronics technology. The main aim of this book is to highlight and address recent breakthroughs in the range of emerging applications in power electronics and in harmonic and electromagnetic interference (EMI) issues at device and system levels as discussed in ?robust and reliable power electronics technologies, including fault prognosis and diagnosis technique stability of grid-connected converters and ?smart control of power electronics in devices, microgrids, and at system levels.

Keywords

energy storage --- lithium-ion battery --- battery management system BMS --- battery modeling --- state of charge SoC --- grid-connected inverter --- power electronics --- multi-objective optimization --- switching frequency --- total demand distortion --- switching losses --- EMI filter --- power converter --- power density --- optimal design --- electrical drives --- axial flux machines --- magnetic equivalent circuit --- torque ripple --- back EMF --- permanent-magnet machines --- five-phase permanent magnet synchronous machine --- five-leg voltage source inverter --- multiphase space vector modulation --- sliding mode control --- extended Kalman filter --- voltage source inverters (VSI) --- voltage control --- current control --- digital control --- predictive controllers --- advanced controllers --- stability --- response time --- lithium-ion batteries --- electric vehicles --- battery management system --- electric power --- dynamic PV model --- grid-connected VSI --- HF-link MPPT converter --- nanocrystalline core --- SiC PV Supply --- DC–DC converters --- multi-level control --- renewable energy resources control --- electrical engineering communications --- microgrid control --- distributed control --- power system operation and control --- variable speed pumped storage system --- droop control --- vector control --- phasor model technique --- nine switch converter --- synchronous generator --- digital signal controller --- static compensator, distribution generation --- hybrid converter --- multi-level converter (MLC) --- series active filter --- power factor correction (PFC) --- field-programmable gate array --- particle swarm optimization --- selective harmonic elimination method --- voltage source converter --- plug-in hybrid electric vehicles --- power management system --- renewable energy sources --- fuzzy --- smart micro-grid --- five-phase machine --- fault-tolerant control --- induction motor --- one phase open circuit fault (1-Ph) --- adjacent two-phase open circuit fault (A2-Ph) --- volt-per-hertz control (scalar control) --- current-fed inverter --- LCL-S topology --- semi-active bridge --- soft switching --- voltage boost --- wireless power transfer --- DC–DC conversion --- zero-voltage switching (ZVS) --- transient control --- DC–DC conversion --- bidirectional converter --- power factor correction --- line frequency instability --- one cycle control --- non-linear phenomena --- bifurcation --- boost converter --- converter --- ice melting --- modular multilevel converter (MMC) --- optimization design --- transmission line --- static var generator (SVG) --- hardware-in-the-loop --- floating-point --- fixed-point --- real-time emulation --- field programmable gate array --- slim DC-link drive --- VPI active damping control --- total harmonic distortion --- cogging torque --- real-time simulation --- power converters --- nonlinear control --- embedded systems --- high level programing --- SHIL --- DHIL --- 4T analog MOS control --- high frequency switching power supply --- water purification --- modulation index --- electromagnetic interference --- chaotic PWM --- DC-DC buck converter --- CMOS chaotic circuit --- triangular ramp generator --- spread-spectrum technique --- system in package --- electric vehicle --- wireless power transfer --- inductive coupling --- coupling factor --- phase-shift control --- series-series compensation --- PSpice --- fixed-frequency double integral sliding-mode (FFDISM) --- class-D amplifier --- Q-factor --- GaN cascode --- direct torque control (DTC) --- composite active vectors modulation (CVM) --- permanent magnet synchronous motor (PMSM) --- effect factors --- double layer capacitor (DLC) models --- energy storage modelling --- simulation models --- current control loops --- dual three-phase (DTP) permanent magnet synchronous motors (PMSMs) --- space vector pulse width modulation (SVPWM) --- vector control --- voltage source inverter --- active rectifiers --- single-switch --- analog phase control --- digital phase control --- wireless power transfer --- three-level boost converter (TLBC) --- DC-link cascade H-bridge (DCLCHB) inverter --- conducting angle determination (CAD) techniques --- total harmonic distortion (THD) --- three-phase bridgeless rectifier --- fault diagnosis --- fault tolerant control --- hardware in loop --- compensation topology --- electromagnetic field (EMF) --- electromagnetic field interference (EMI) --- misalignment --- resonator structure --- wireless power transfer (WPT) --- WPT standards --- EMI filter --- electromagnetic compatibility --- AC–DC power converters --- electromagnetic interference filter --- matrix converters --- current source --- power density --- battery energy storage systems --- battery chargers --- active receivers --- frequency locking --- reference phase calibration --- synchronization --- wireless power transfer --- lithium-ion batteries --- SOC estimator --- parameter identification --- particle swarm optimization --- improved extended Kalman filter --- battery management system --- PMSG --- DC-link voltage control --- variable control gain --- disturbance observer --- lithium-ion power battery pack --- composite equalizer --- active equalization --- passive equalization --- control strategy and algorithm --- n/a --- common-mode inductor --- high-frequency modeling --- electromagnetic interference --- filter --- fault diagnosis --- condition monitoring --- induction machines --- support vector machines --- expert systems --- neural networks --- DC-AC power converters --- frequency-domain analysis --- impedance-based model --- Nyquist stability analysis --- small signal stability analysis --- harmonic linearization --- line start --- permanent magnet --- synchronous motor --- efficiency motor --- rotor design --- harmonics --- hybrid power filter --- active power filter --- power quality --- total harmonic distortion --- equivalent inductance --- leakage inductance --- switching frequency modelling --- induction motor --- current switching ripple --- multilevel inverter --- cascaded topology --- voltage doubling --- switched capacitor --- nearest level modulation (NLM) --- total harmonic distortion (THD) --- dead-time compensation --- power converters --- harmonics --- n/a

Applications of Power Electronics

Authors: --- ---
ISBN: 9783039210206 / 9783039210213 Year: Volume: 2 Pages: 500 DOI: 10.3390/books978-3-03921-021-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering --- Technology (General)
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

Power electronics technology is still an emerging technology, and it has found its way into many applications, from renewable energy generation (i.e., wind power and solar power) to electrical vehicles (EVs), biomedical devices, and small appliances, such as laptop chargers. In the near future, electrical energy will be provided and handled by power electronics and consumed through power electronics; this not only will intensify the role of power electronics technology in power conversion processes, but also implies that power systems are undergoing a paradigm shift, from centralized distribution to distributed generation. Today, more than 1000 GW of renewable energy generation sources (photovoltaic (PV) and wind) have been installed, all of which are handled by power electronics technology. The main aim of this book is to highlight and address recent breakthroughs in the range of emerging applications in power electronics and in harmonic and electromagnetic interference (EMI) issues at device and system levels as discussed in ?robust and reliable power electronics technologies, including fault prognosis and diagnosis technique stability of grid-connected converters and ?smart control of power electronics in devices, microgrids, and at system levels.

Keywords

energy storage --- lithium-ion battery --- battery management system BMS --- battery modeling --- state of charge SoC --- grid-connected inverter --- power electronics --- multi-objective optimization --- switching frequency --- total demand distortion --- switching losses --- EMI filter --- power converter --- power density --- optimal design --- electrical drives --- axial flux machines --- magnetic equivalent circuit --- torque ripple --- back EMF --- permanent-magnet machines --- five-phase permanent magnet synchronous machine --- five-leg voltage source inverter --- multiphase space vector modulation --- sliding mode control --- extended Kalman filter --- voltage source inverters (VSI) --- voltage control --- current control --- digital control --- predictive controllers --- advanced controllers --- stability --- response time --- lithium-ion batteries --- electric vehicles --- battery management system --- electric power --- dynamic PV model --- grid-connected VSI --- HF-link MPPT converter --- nanocrystalline core --- SiC PV Supply --- DC–DC converters --- multi-level control --- renewable energy resources control --- electrical engineering communications --- microgrid control --- distributed control --- power system operation and control --- variable speed pumped storage system --- droop control --- vector control --- phasor model technique --- nine switch converter --- synchronous generator --- digital signal controller --- static compensator, distribution generation --- hybrid converter --- multi-level converter (MLC) --- series active filter --- power factor correction (PFC) --- field-programmable gate array --- particle swarm optimization --- selective harmonic elimination method --- voltage source converter --- plug-in hybrid electric vehicles --- power management system --- renewable energy sources --- fuzzy --- smart micro-grid --- five-phase machine --- fault-tolerant control --- induction motor --- one phase open circuit fault (1-Ph) --- adjacent two-phase open circuit fault (A2-Ph) --- volt-per-hertz control (scalar control) --- current-fed inverter --- LCL-S topology --- semi-active bridge --- soft switching --- voltage boost --- wireless power transfer --- DC–DC conversion --- zero-voltage switching (ZVS) --- transient control --- DC–DC conversion --- bidirectional converter --- power factor correction --- line frequency instability --- one cycle control --- non-linear phenomena --- bifurcation --- boost converter --- converter --- ice melting --- modular multilevel converter (MMC) --- optimization design --- transmission line --- static var generator (SVG) --- hardware-in-the-loop --- floating-point --- fixed-point --- real-time emulation --- field programmable gate array --- slim DC-link drive --- VPI active damping control --- total harmonic distortion --- cogging torque --- real-time simulation --- power converters --- nonlinear control --- embedded systems --- high level programing --- SHIL --- DHIL --- 4T analog MOS control --- high frequency switching power supply --- water purification --- modulation index --- electromagnetic interference --- chaotic PWM --- DC-DC buck converter --- CMOS chaotic circuit --- triangular ramp generator --- spread-spectrum technique --- system in package --- electric vehicle --- wireless power transfer --- inductive coupling --- coupling factor --- phase-shift control --- series-series compensation --- PSpice --- fixed-frequency double integral sliding-mode (FFDISM) --- class-D amplifier --- Q-factor --- GaN cascode --- direct torque control (DTC) --- composite active vectors modulation (CVM) --- permanent magnet synchronous motor (PMSM) --- effect factors --- double layer capacitor (DLC) models --- energy storage modelling --- simulation models --- current control loops --- dual three-phase (DTP) permanent magnet synchronous motors (PMSMs) --- space vector pulse width modulation (SVPWM) --- vector control --- voltage source inverter --- active rectifiers --- single-switch --- analog phase control --- digital phase control --- wireless power transfer --- three-level boost converter (TLBC) --- DC-link cascade H-bridge (DCLCHB) inverter --- conducting angle determination (CAD) techniques --- total harmonic distortion (THD) --- three-phase bridgeless rectifier --- fault diagnosis --- fault tolerant control --- hardware in loop --- compensation topology --- electromagnetic field (EMF) --- electromagnetic field interference (EMI) --- misalignment --- resonator structure --- wireless power transfer (WPT) --- WPT standards --- EMI filter --- electromagnetic compatibility --- AC–DC power converters --- electromagnetic interference filter --- matrix converters --- current source --- power density --- battery energy storage systems --- battery chargers --- active receivers --- frequency locking --- reference phase calibration --- synchronization --- wireless power transfer --- lithium-ion batteries --- SOC estimator --- parameter identification --- particle swarm optimization --- improved extended Kalman filter --- battery management system --- PMSG --- DC-link voltage control --- variable control gain --- disturbance observer --- lithium-ion power battery pack --- composite equalizer --- active equalization --- passive equalization --- control strategy and algorithm --- n/a --- common-mode inductor --- high-frequency modeling --- electromagnetic interference --- filter --- fault diagnosis --- condition monitoring --- induction machines --- support vector machines --- expert systems --- neural networks --- DC-AC power converters --- frequency-domain analysis --- impedance-based model --- Nyquist stability analysis --- small signal stability analysis --- harmonic linearization --- line start --- permanent magnet --- synchronous motor --- efficiency motor --- rotor design --- harmonics --- hybrid power filter --- active power filter --- power quality --- total harmonic distortion --- equivalent inductance --- leakage inductance --- switching frequency modelling --- induction motor --- current switching ripple --- multilevel inverter --- cascaded topology --- voltage doubling --- switched capacitor --- nearest level modulation (NLM) --- total harmonic distortion (THD) --- dead-time compensation --- power converters --- harmonics --- n/a

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