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This book covers several research items related to LLC resonant converters, which were published in a Special Issue of Energies on the subject area of "Advances in High-Efficiency LLC Resonant converter". It focuses on emerging power electronic topologies related to the LLC resonant converter, and its design methodology and control algorithms. Topics of interest include LLC resonant topologies, resonant tank design methodology for high efficiency, power loss analysis in LLC resonant converters, high-frequency magnetics for resonant converters, wide band-gap devices applied to LLC resonant converter, and advanced control algorithm for LLC resonant converter.
History of engineering & technology --- resonant converter --- bidirectional power conversion --- zero voltage switching --- asymmetric pulse width modulation --- LLC resonant converter --- integrated transformer --- adjustable leakage inductance --- LED driver --- aircraft power conversion --- LLC resonant converters --- high efficiency --- ZVS auxiliary circuit --- dual output converter --- pulse frequency modulation (PFM) --- asymmetric pulse width modulation (APWM) --- control --- current mode control --- voltage control --- transfer function --- power converter --- soft-switching converter --- battery charging --- PV micro-inverter --- LLC converter --- high switching frequency --- transformer loss --- center-tapped transformer --- flux walking --- flux-balance control loop --- magnetizing current estimation --- LLC Converter --- Duty Control --- Extended Describing Function --- Small Signal Modeling --- solid-state-transformer (SST) --- isolation dc-dc converter --- series-connected devices
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This book covers several research items related to LLC resonant converters, which were published in a Special Issue of Energies on the subject area of "Advances in High-Efficiency LLC Resonant converter". It focuses on emerging power electronic topologies related to the LLC resonant converter, and its design methodology and control algorithms. Topics of interest include LLC resonant topologies, resonant tank design methodology for high efficiency, power loss analysis in LLC resonant converters, high-frequency magnetics for resonant converters, wide band-gap devices applied to LLC resonant converter, and advanced control algorithm for LLC resonant converter.
resonant converter --- bidirectional power conversion --- zero voltage switching --- asymmetric pulse width modulation --- LLC resonant converter --- integrated transformer --- adjustable leakage inductance --- LED driver --- aircraft power conversion --- LLC resonant converters --- high efficiency --- ZVS auxiliary circuit --- dual output converter --- pulse frequency modulation (PFM) --- asymmetric pulse width modulation (APWM) --- control --- current mode control --- voltage control --- transfer function --- power converter --- soft-switching converter --- battery charging --- PV micro-inverter --- LLC converter --- high switching frequency --- transformer loss --- center-tapped transformer --- flux walking --- flux-balance control loop --- magnetizing current estimation --- LLC Converter --- Duty Control --- Extended Describing Function --- Small Signal Modeling --- solid-state-transformer (SST) --- isolation dc-dc converter --- series-connected devices
Choose an application
This book covers several research items related to LLC resonant converters, which were published in a Special Issue of Energies on the subject area of "Advances in High-Efficiency LLC Resonant converter". It focuses on emerging power electronic topologies related to the LLC resonant converter, and its design methodology and control algorithms. Topics of interest include LLC resonant topologies, resonant tank design methodology for high efficiency, power loss analysis in LLC resonant converters, high-frequency magnetics for resonant converters, wide band-gap devices applied to LLC resonant converter, and advanced control algorithm for LLC resonant converter.
History of engineering & technology --- resonant converter --- bidirectional power conversion --- zero voltage switching --- asymmetric pulse width modulation --- LLC resonant converter --- integrated transformer --- adjustable leakage inductance --- LED driver --- aircraft power conversion --- LLC resonant converters --- high efficiency --- ZVS auxiliary circuit --- dual output converter --- pulse frequency modulation (PFM) --- asymmetric pulse width modulation (APWM) --- control --- current mode control --- voltage control --- transfer function --- power converter --- soft-switching converter --- battery charging --- PV micro-inverter --- LLC converter --- high switching frequency --- transformer loss --- center-tapped transformer --- flux walking --- flux-balance control loop --- magnetizing current estimation --- LLC Converter --- Duty Control --- Extended Describing Function --- Small Signal Modeling --- solid-state-transformer (SST) --- isolation dc-dc converter --- series-connected devices
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Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measurement
n/a --- inosine --- immune checkpoint inhibitor --- geometric singular perturbation theory --- simulation --- BioModels Database --- ADAR --- calcium current --- bifurcation analysis --- bacterial biofilms --- nonlinear dynamics --- explanatory model --- turning point bifurcation --- oscillator --- workflow --- bioreactor integrated modeling --- modeling methods --- elementary flux modes visualization --- multiscale systems biology --- evolutionary algorithm --- metabolic model --- differential evolution --- reduced-order model --- computational model --- gut microbiota dysbiosis --- canard-induced EADs --- computational biology --- metabolic modelling --- methods --- SREBP-2 --- mechanistic model --- systems modeling --- biological networks --- macromolecular composition --- provenance --- flux balance analysis --- immunotherapy --- compartmental modeling --- immuno-oncology --- metabolic network visualization --- mechanism --- bistable switch --- Clostridium difficile infection --- bioreactor operation optimization --- microRNA targeting --- CFD simulation --- biomass reaction --- RNA editing --- ordinary differential equation --- metabolic modeling --- mass-action networks --- hybrid model --- multiple time scales --- quantitative systems pharmacology (QSP) --- mathematical modeling --- microRNA --- cancer --- parameter optimization --- Hopf bifurcation --- breast
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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.
Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning
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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.
metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning
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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.
Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning
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