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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.

History of engineering & technology --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention

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Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting.

Technology: general issues --- Energy industries & utilities --- DFIG --- ES --- virtual inertia control --- capacity allocation --- fuzzy logic controller --- wind power generation --- multi-model predictive control --- fuzzy clustering --- virtual synchronous generator --- doubly fed induction generator --- sub-synchronous resonance --- impedance modeling --- renewable energy sources (RESs) --- regional RoCoF --- model-based operational planning --- linear sensitivity-based method (LSM) --- cumulant-based method (CBM) --- collaborative capacity planning --- distributed wind power (DWP) --- energy storage system (ESS) --- optimization --- variable-structure copula --- Reynolds-averaged Navier-Stokes method --- wind turbine wake model --- 3D aerodynamic model --- turbulence model --- correction modules --- hybrid prediction model --- wavelet decomposition --- long short-term memory --- scenario analysis --- weak grids --- full-converter wind --- active power output --- control parameters --- subsynchronous oscillation --- eigenvalue analysis --- doubly fed induction generator (DFIG) --- wind generation --- frequency control --- artificial neural network (ANN) --- error following forget gate-based long short-term memory --- ultra-short-term prediction --- wind power --- load frequency control (LFC) --- wind farm --- particle swarm optimization --- kinetic energy --- inertial response --- low inertia --- the center of inertia --- frequency response metrics --- wind integration --- PSS/E --- FORTRAN --- electromechanical dynamics --- FCWG dynamics --- strong interaction --- electromechanical loop correlation ratio (ELCR) --- FCWG dynamic correlation ratio (FDCR) --- quasi- electromechanical loop correlation ratio (QELCR) --- permanent magnet synchronous generator (PMSG) --- supercapacitor energy storage (SCES) --- rotor overspeed control --- low voltage ride through (LVRT) --- capacity configuration of SCES --- DFIG --- ES --- virtual inertia control --- capacity allocation --- fuzzy logic controller --- wind power generation --- multi-model predictive control --- fuzzy clustering --- virtual synchronous generator --- doubly fed induction generator --- sub-synchronous resonance --- impedance modeling --- renewable energy sources (RESs) --- regional RoCoF --- model-based operational planning --- linear sensitivity-based method (LSM) --- cumulant-based method (CBM) --- collaborative capacity planning --- distributed wind power (DWP) --- energy storage system (ESS) --- optimization --- variable-structure copula --- Reynolds-averaged Navier-Stokes method --- wind turbine wake model --- 3D aerodynamic model --- turbulence model --- correction modules --- hybrid prediction model --- wavelet decomposition --- long short-term memory --- scenario analysis --- weak grids --- full-converter wind --- active power output --- control parameters --- subsynchronous oscillation --- eigenvalue analysis --- doubly fed induction generator (DFIG) --- wind generation --- frequency control --- artificial neural network (ANN) --- error following forget gate-based long short-term memory --- ultra-short-term prediction --- wind power --- load frequency control (LFC) --- wind farm --- particle swarm optimization --- kinetic energy --- inertial response --- low inertia --- the center of inertia --- frequency response metrics --- wind integration --- PSS/E --- FORTRAN --- electromechanical dynamics --- FCWG dynamics --- strong interaction --- electromechanical loop correlation ratio (ELCR) --- FCWG dynamic correlation ratio (FDCR) --- quasi- electromechanical loop correlation ratio (QELCR) --- permanent magnet synchronous generator (PMSG) --- supercapacitor energy storage (SCES) --- rotor overspeed control --- low voltage ride through (LVRT) --- capacity configuration of SCES

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Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting.

DFIG --- ES --- virtual inertia control --- capacity allocation --- fuzzy logic controller --- wind power generation --- multi-model predictive control --- fuzzy clustering --- virtual synchronous generator --- doubly fed induction generator --- sub-synchronous resonance --- impedance modeling --- renewable energy sources (RESs) --- regional RoCoF --- model-based operational planning --- linear sensitivity-based method (LSM) --- cumulant-based method (CBM) --- collaborative capacity planning --- distributed wind power (DWP) --- energy storage system (ESS) --- optimization --- variable-structure copula --- Reynolds-averaged Navier–Stokes method --- wind turbine wake model --- 3D aerodynamic model --- turbulence model --- correction modules --- hybrid prediction model --- wavelet decomposition --- long short-term memory --- scenario analysis --- weak grids --- full-converter wind --- active power output --- control parameters --- subsynchronous oscillation --- eigenvalue analysis --- doubly fed induction generator (DFIG) --- wind generation --- frequency control --- artificial neural network (ANN) --- error following forget gate-based long short-term memory --- ultra-short-term prediction --- wind power --- load frequency control (LFC) --- wind farm --- particle swarm optimization --- kinetic energy --- inertial response --- low inertia --- the center of inertia --- frequency response metrics --- wind integration --- PSS/E --- FORTRAN --- electromechanical dynamics --- FCWG dynamics --- strong interaction --- electromechanical loop correlation ratio (ELCR) --- FCWG dynamic correlation ratio (FDCR) --- quasi- electromechanical loop correlation ratio (QELCR) --- permanent magnet synchronous generator (PMSG) --- supercapacitor energy storage (SCES) --- rotor overspeed control --- low voltage ride through (LVRT) --- capacity configuration of SCES --- n/a --- Reynolds-averaged Navier-Stokes method