Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is devoted to the latest advances in the area of electrothermal modelling of electronic components and networks. It contains eight sections by different teams of authors. These sections contain the results of: (a) electro-thermal simulations of SiC power MOSFETs using a SPICE-like simulation program; (b) modelling thermal properties of inductors taking into account the influence of the core volume on the efficiency of heat removal; (c) investigations into the problem of inserting a temperature sensor in the neighbourhood of a chip to monitor its junction temperature; (d) computations of the internal temperature of power LEDs situated in modules containing multiple-power LEDs, taking into account both self-heating in each power LED and mutual thermal couplings between each diode; (e) analyses of DC-DC converters using the electrothermal averaged model of the diode–transistor switch, including an IGBT and a rapid-switching diode; (f) electrothermal modelling of SiC power BJTs; (g) analysis of the efficiency of selected algorithms used for solving heat transfer problems at nanoscale; (h) analysis related to thermal simulation of the test structure dedicated to heat-diffusion investigation at the nanoscale.

Dual-Phase-Lag heat transfer model --- thermal simulation algorithm --- thermal measurements --- Finite Difference Method scheme --- Grünwald–Letnikov fractional derivative --- Krylov subspace-based model order reduction --- algorithm efficiency analysis --- relative error analysis --- algorithm convergence analysis --- computational complexity analysis --- finite difference method scheme --- BJT --- modelling --- self-heating --- silicon carbide --- SPICE --- IGBT --- DC–DC converter --- electrothermal model --- averaged model --- thermal phenomena --- diode–transistor switch --- power electronics --- multi-LED lighting modules --- device thermal coupling --- compact thermal models --- temperature sensors --- microprocessor --- throughput improvement --- inductors --- ferromagnetic cores --- thermal model --- transient thermal impedance --- thermal resistance --- electrothermal (ET) simulation --- finite-element method (FEM) --- model-order reduction (MOR) --- multicellular power MOSFET --- silicon carbide (SiC)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

time series --- anomaly detection --- unsupervised learning --- kernel density estimation --- missing data --- multivariate time series --- nonstationary --- spectral matrix --- local field potential --- electric power --- forecasting accuracy --- machine learning --- extended binomial distribution --- INAR --- thinning operator --- time series of counts --- unemployment rate --- SARIMA --- SETAR --- Holt–Winters --- ETS --- neural network autoregression --- Romania --- integer-valued time series --- bivariate Poisson INGARCH model --- outliers --- robust estimation --- minimum density power divergence estimator --- CUSUM control chart --- INAR-type time series --- statistical process monitoring --- random survival rate --- zero-inflation --- cointegration --- subspace algorithms --- VARMA models --- seasonality --- finance --- volatility fluctuation --- Student’s t-process --- entropy based particle filter --- relative entropy --- count data --- time series analysis --- Julia programming language --- ordinal patterns --- long-range dependence --- multivariate data analysis --- limit theorems --- integer-valued moving average model --- counting series --- dispersion test --- Bell distribution --- count time series --- estimation --- overdispersion --- multivariate count data --- INGACRCH --- state-space model --- bank failures --- transactions --- periodic autoregression --- integer-valued threshold models --- parameter estimation --- models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis.

Technology: general issues --- History of engineering & technology --- process monitoring --- dynamics --- variable time lag --- dynamic autoregressive latent variables model --- sintering process --- hammerstein output-error systems --- auxiliary model --- multi-innovation identification theory --- fractional-order calculus theory --- canonical variate analysis --- disturbance detection --- power transmission system --- k-nearest neighbor analysis --- statistical local analysis --- intelligent fault diagnosis --- stacked pruning sparse denoising autoencoder --- convolutional neural network --- anti-noise --- flywheel fault diagnosis --- belief rule base --- fuzzy fault tree analysis --- Bayesian network --- evidential reasoning --- aluminum reduction process --- alumina concentration --- subspace identification --- distributed predictive control --- spatiotemporal feature fusion --- gated recurrent unit --- attention mechanism --- fault diagnosis --- evidential reasoning rule --- system modelling --- information transformation --- parameter optimization --- event-triggered control --- interval type-2 Takagi–Sugeno fuzzy model --- nonlinear networked systems --- filter --- gearbox fault diagnosis --- convolution fusion --- state identification --- PSO --- wavelet mutation --- LSSVM --- data-driven --- operational optimization --- case-based reasoning --- local outlier factor --- abnormal case removal --- bearing fault detection --- deep residual network --- data augmentation --- canonical correlation analysis --- just-in-time learning --- fault detection --- high-speed trains --- autonomous underwater vehicle --- thruster fault diagnostics --- fault tolerant control --- robust optimization --- ocean currents --- n/a --- interval type-2 Takagi-Sugeno fuzzy model

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems.

landslide --- image classification --- spectrum similarity analysis --- extreme rainfall-induced landslide susceptibility model --- landslide ratio-based logistic regression --- landslide evolution --- Typhoon Morakot --- Taiwan --- vegetation community --- vegetation importance value --- root system --- soil erosion --- grey correlation analysis --- sediment yield --- RUSLE --- Lancang–Mekong River basin --- rainfall threshold --- landslide probability model --- debris flow --- Zechawa Gully --- mitigation countermeasures --- Jiuzhaigou Valley --- water erosion --- susceptibility --- Gaussian process --- climate change --- radial basis function kernel --- weighted subspace random forest --- extreme events --- extreme weather --- naive Bayes --- feature selection --- machine learning --- hydrologic model --- simulated annealing --- earth system science --- PSED Model --- loess --- ICU --- static liquefaction --- mechanical behavior --- pore structure --- alpine swamp meadow --- alpine meadow --- degradation of riparian vegetation --- root distribution --- tensile strength --- tensile crack --- soil management --- land cover changes --- Syria --- hillslopes --- gully erosion --- vegetation restoration --- soil erodibility --- land use --- bridge pier --- overfall --- scour --- landform change impact on pier --- shallow water equations --- wet-dry front --- outburst flood --- TVD-scheme --- MUSCL-Hancock method --- laboratory model test --- extreme rainfall --- rill erosion --- shallow landslides --- deep lip surface --- safety factor --- rainfall erosivity factor --- USLE R --- Deep Neural Network --- tree ring --- dendrogeomorphology --- landslide activity --- deciduous broadleaved tree --- Shirakami Mountains --- spatiotemporal cluster analysis --- landslide hotspots --- dam breach --- seepage --- overtopping --- seismic signal --- flume test --- breach model --- n/a --- Lancang-Mekong River basin

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Wind Power Plant (WPP) and Wind Turbine (WT) modeling are becoming of key importance due to the relevant wind-generation impact on power systems. Wind integration into power systems must be carefully analyzed to forecast the effects on grid stability and reliability. Different agents, such as Transmission System Operators (TSOs) and Distribution System Operators (DSOs), focus on transient analyses. Wind turbine manufacturers, power system software developers, and technical consultants are also involved. WPP and WT dynamic models are often divided into two types: detailed and simplified. Detailed models are used for Electro-Magnetic Transient (EMT) simulations, providing both electrical and mechanical responses with high accuracy during short time intervals. Simplified models, also known as standard or generic models, are designed to give reliable responses, avoiding high computational resources. Simplified models are commonly used by TSOs and DSOs to carry out different transient stability studies, including loss of generation, switching of power lines or balanced faults, etc., Assessment and validation of such dynamic models is also a major issue due to the importance and difficulty of collecting real data. Solutions facing all these challenges, including the development, validation and application of WT and WPP models are presented in this Issue.

bearing current --- common mode current --- doubly fed induction generators --- permanent magnet synchronous generators --- wind turbine generator --- doubly-fed generator --- converter control --- short-circuit current --- second harmonic component --- low-voltage ride-through (LVRT) field test data --- complex terrain --- terrain-induced turbulence --- turbulence intensity --- LES --- vortex shedding --- frequency control --- wind power integration --- power system stability --- turbulence --- statistical modelling --- Wind Turbine (WT) --- Doubly Fed Induction Generator (DFIG) --- unbalanced grid voltage --- DC-linked voltage control --- Proportional Resonant with Resonant Harmonic Compensator (PR+HC) controller --- Adaptive Proportional Integral (API) control --- power control --- wind turbine near wake --- wind turbine wakes --- wake aerodynamics --- computational fluid dynamics --- rotor aerodynamics --- wind turbine validation --- MEXICO experiment --- wind energy --- model validation --- wind turbine aerodynamics --- wind farms --- wind turbines interaction --- wind farm modeling --- kernel density estimation --- multiple wind farms --- joint probability density --- ordinal optimization --- reactive power capability --- wind power plant --- wind power collection system --- aggregated, modelling --- wind integration studies --- long term voltage stability --- fault-ride through capability --- IEC 61400-27-1 --- Spanish PO 12.3 --- Type 3 wind turbine --- inertia --- wind power --- droop --- primary control --- frequency containment process --- wind integration --- demand response --- ancillary services --- wind turbine nacelle --- lightning electromagnetic pulse (LEMP) --- magnetic field intensity --- shielding mesh --- wake steering --- yaw misalignment --- multi body simulation --- main bearing loads --- rain flow counts --- aeroelasticity --- multi-rotor system --- wind turbine --- computational fluid dynamics (CFD) --- horizontal-axis wind turbine (HAWT) --- permanent-magnet synchronous-generator (PMSG) --- linear quadratic regulator (LQR) --- PI control algorithm --- LQR-PI control --- wind turbine blade --- large-eddy simulation --- turbulence evaluation index --- fatigue damage evaluation index --- DIgSILENT-PowerFactory --- MATLAB --- transient stability --- type 3 wind turbine --- DFIG --- field testing --- full-scale converter --- generic model --- validation --- HAWT --- aerodynamic characteristics --- dynamic yawing process --- near wake --- start-stop yaw velocity --- load frequency control (LFC) --- equivalent input disturbance (EID) --- active disturbance rejection control (ADRC) --- wind --- linear matrix inequalities (LMI) --- dynamic modeling --- grey-box parameter identification --- subspace identification --- recursive least squares --- optimal identification