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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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This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.
Research & information: general --- Physics --- Prophet model --- Holt–Winters model --- long-term forecasting --- peak load --- prophet model --- multiple seasonality --- time series --- demand --- load --- forecast --- DIMS --- irregular --- galvanizing --- short-term electrical load forecasting --- machine learning --- deep learning --- statistical analysis --- parameters tuning --- CNN --- LSTM --- short-term load forecast --- Artificial Neural Network --- deep neural network --- recurrent neural network --- attention --- encoder decoder --- online training --- bidirectional long short-term memory --- multi-layer stacked --- neural network --- short-term load forecasting --- power system
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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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Electronic engineering and design innovation are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation via electronic engineering includes electrical circuits and devices, computer science and engineering, communications and information processing, and electrical engineering communications. The Special Issue selected excellent papers presented at the International Conference on Knowledge Innovation and Invention 2018 (IEEE ICKII 2018) on the topic of electronics and their applications. This conference was held on Jeju Island, South Korea, 23–27 July 2018, and it provided a unified communication platform for researchers from all over the world. The main goal of this Special Issue titled “Selected papers from IEEE ICKII 2018” is to discover new scientific knowledge relevant to the topic of electronics and their applications.
n/a --- bandpass filter --- total harmonic distortion (THD) --- long short term memory (LSTM) --- integrated passive device --- intertwined spiral inductor --- global navigation satellite system (GNSS) --- hardware in the loop (HIL) --- interdigital capacitor --- inertial navigation system (INS) --- finite-time convergence control (FTCC) --- digital speckle correlation measurement method --- discrete grey prediction model (DGPM) --- interior permanent magnet synchronous motor --- fuzzy logic --- full pixel search algorithm --- maximum torque per voltage (MTPV) --- spiral capacitor --- gated recurrent unit (GRU) --- chattering --- microelectronics system (MEMS) --- field weakening --- maximum torque per ampere (MTPA) --- hardware implementation --- AC power supply
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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.
localization --- reactive power optimization --- model predictive control --- CNN --- long short term memory (LSTM) --- meter allocation --- particle update mode --- combined economic emission/environmental dispatch --- glass insulator --- emission dispatch --- genetic algorithm --- grid observability --- defect detection --- feature extraction --- parameter estimation --- incipient cable failure --- active distribution system --- boiler load constraints --- multivariate time series --- particle swarm optimization --- inertia weight --- VMD --- NOx emissions constraints --- spatial features --- penalty factor approach --- self-shattering --- differential evolution algorithm --- short term load forecasting (STLF) --- genetic algorithm (GA) --- economic load dispatch --- least square support vector machine --- Combustion efficiency --- electricity load forecasting
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Increasing energy efficiency; reducing energy demand, greenhouse gas emissions, and the use of waste; and integrating renewable and recycled heat from low-temperature sources are significant challenges today and are key parts of 4th Generation District Heating (4GDH) concept. On the other hand, currently about one billion people around the world are suffering from water scarcity, and another three billion are approaching this situation. Only 2.5% of all water on the planet is freshwater, of which around 70% is not available and only 0.4% constitutes the most valuable portion of freshwater. Adsorption cooling technology is one of the most effective ways of addressing both these issues. This technology cools and produces potable water from the renewable and wasted heat of the near ambient temperature, including from sewage water, solar heat, and underground resources. This Special Issue Reprint Book provides the detailed information concerning the above-mentioned issues.
Technology: general issues --- Chemical engineering --- adsorption chiller --- coefficient of performance --- desalination --- energy efficiency --- low-temperature heat --- silica gel --- specific cooling power --- waste heat recovery --- sorption processes --- deep learning --- neural networks --- Long Short-Term Memory (LSTM) --- additives --- sorption capacity --- sorption process time --- kinetics sorption --- adsorption --- exergy --- dead state --- adsorption cooling --- reheat cycle, mass recovery --- chiller --- adsorptive water harvesting from the atmosphere --- metal–organic frameworks --- MIL-160 --- water vapor adsorption --- specific water productivity --- specific energy consumption --- zeolite --- SAPO-34 --- mass recovery --- variable mode --- adsorption working pairs --- coated beds --- comparative analysis --- natural refrigerants --- preheating --- steam --- copper --- cycle time --- CFD --- metal organic silica --- nanocomposites --- sorption --- thermal diffusivity --- n/a --- metal-organic frameworks
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The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology.
History of engineering & technology --- HVAC energy conservation --- optimal control --- genetic algorithm --- FCU group control --- central pattern generator --- Theo Jansen Linkage --- non-collocated actuators --- fast Fourier transform --- FFT --- kernel --- MIMO --- OFDM --- multistandard --- electrical resistivity tomography --- boundary effect --- 3D effect --- error compensation --- free-form surface --- on-machine measurement --- mirror compensation method --- stereo matching --- cost aggregation --- image filtering --- binocular stereo vision --- deep learning for network security --- long short-term memory --- malicious traffic classification --- assembly design --- usability and operation complexity --- fuzzy theory --- product design --- layout strategy --- design method --- n/a
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The stratospheric ozone is important for the protection of the biosphere from the dangerous ultraviolet radiation of the sun, forms the temperature and dynamical structure of the stratosphere, and, therefore, has a direct influence on the general circulation and the surface climate. The tropospheric ozone can damage the biosphere, impact human health, and plays a role as a powerful greenhouse gas. That is why the understanding of the past and future evolution of the ozone in different atmospheric layers, as well as its influence on surface UV radiation doses, and human health is important. The problems of preventing further destruction of the ozone layer, the restoration of the ozone shield in the future, and air quality remain important for society. The interest in these problems was recently enhanced by the unexpected discovery of a negative ozone trend in the lower stratosphere and the appearance of a large ozone hole over the Arctic in spring 2020. This book includes papers describing several aspects of the ozone layer’s state and evolution based on the recent experimental, statistical, and modeling works. The book will be useful for readers, scientists, and students interested in environmental science.
Research & information: general --- ozone --- PM2.5 --- PM10 --- nitrogen dioxide --- respiratory disease --- decision tree model --- merra ozone data --- discontinuities in reanalysis time series --- trend analyses --- total ozone content --- cloudiness --- erythemal radiation --- trend --- chemical–climate model --- ERA-Interim reanalysis --- Northern Eurasia --- UV resources --- stratospheric ozone --- natural and anthropogenic factors --- numerical modeling --- satellite observations --- trend estimations --- tropospheric ozone --- stratospheric intrusion --- horizontal-trough --- ozone layer evolution --- modeling --- climate change --- solar forcing --- ozone precursors --- total column of ozone (TCO) --- trend estimates --- long short-term memory networks (LSTM) --- empirical wavelet transform (EWT) --- forecasting --- Mann-Kendall --- ozone exceedance --- urban site --- rural site --- human health --- ozone enhancement --- Irene --- ozone decline --- potential vorticity --- ozonesondes --- ultraviolet radiation --- forcing
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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 --- n/a --- Reynolds-averaged Navier-Stokes method
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The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition.
Technology: general issues --- History of engineering & technology --- human action recognition --- graph convolution --- high-order feature --- spatio-temporal feature --- feature fusion --- dynamic gesture recognition --- multi-modalities network --- class regularization --- 3D-CNN --- spatiotemporal activations --- class-specific features --- Dynamic Hand Gesture Recognition --- human-computer interaction --- hand shape features --- pose estimation --- stacked hourglass network --- deep learning --- convolutional receptive field --- hand gesture recognition --- human–machine interface --- artificial intelligence --- feedforward neural networks --- spatio-temporal image formation --- human activity recognition --- fusion strategies --- transfer learning --- activity recognition --- data augmentation --- multi-person pose estimation --- partitioned centerpose network --- partition pose representation --- continuous hand gesture recognition --- gesture spotting --- gesture classification --- multi-modal features --- 3D skeletal --- CNN --- spatiotemporal feature --- embedded system --- real-time --- action recognition --- Long Short-Term Memory --- spatio–temporal differential --- n/a --- human-machine interface --- spatio-temporal differential
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