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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.
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.
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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In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products.
sensory --- physicochemical measurements --- artificial neural networks --- near infra-red spectroscopy --- wine quality --- machine learning modeling --- weather --- consumer acceptance prediction --- data fusion --- emotion recognition --- facial expression recognition --- galvanic skin response --- machine learning --- neural networks --- sensory analysis --- avocado --- cultivars --- preference mapping --- sensory evaluation --- sensory descriptive analysis --- consumer science --- unifloral honeys --- botanical origin --- physicochemical parameters --- classification --- natural language processing --- deep learning --- sensory science --- flavor lexicon --- long short-term memory --- n/a
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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.
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 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.
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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This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains.
tourism big data --- text mining --- NLP --- deep learning --- clinical named entity recognition --- information extraction --- multitask model --- long short-term memory --- conditional random field --- relation extraction --- entity recognition --- long short-term memory network --- multi-turn chatbot --- dialogue context encoding --- WGAN-based response generation --- BERT word embedding --- text summary --- reinforce learning --- FAQ classification --- encoder-decoder neural network --- multi-level word embeddings --- BERT --- bidirectional RNN --- cloze test --- Korean dataset --- machine comprehension --- neural language model --- sentence completion --- primary healthcare --- chief complaint --- virtual medical assistant --- spoken natural language --- disease diagnosis --- medical specialist --- protein–protein interactions --- deep learning (DL) --- convolutional neural networks (CNN) --- bidirectional long short-term memory (bidirectional LSTM) --- dialogue management --- user simulation --- reward shaping --- conversation knowledge --- multi-agent reinforcement learning --- language modeling --- classification --- error probability --- error assessment --- logic error --- neural network --- LSTM --- attention mechanism --- programming education --- neural architecture search --- word ordering --- Korean syntax --- adversarial attack --- adversarial example --- sentiment classification --- dual pointer network --- context-to-entity attention --- text classification --- rule-based --- word embedding --- Doc2vec --- paraphrase identification --- encodings --- R-GCNs --- contextual features --- sentence retrieval --- TF−ISF --- BM25 --- partial match --- sequence similarity --- word to vector --- word embeddings --- antonymy detection --- polarity --- text normalization --- natural language processing --- deep neural networks --- causal encoder --- question classification --- multilingual --- convolutional neural networks --- Natural Language Processing (NLP) --- transfer learning --- open information extraction --- recurrent neural networks --- bilingual translation --- speech-to-text --- LaTeX decompilation --- word representation learning --- word2vec --- sememes --- structural information --- sentiment analysis --- zero-shot learning --- news analysis --- cross-lingual classification --- multilingual transformers --- knowledge base --- commonsense --- sememe prediction --- attention model --- ontologies --- fixing ontologies --- quick fix --- quality metrics --- online social networks --- rumor detection --- Cantonese --- XGA model --- delayed combination --- CNN dictionary --- named entity recognition --- deep learning NER --- bidirectional LSTM CRF --- CoNLL --- OntoNotes --- toxic comments --- neural networks --- n/a --- protein-protein interactions
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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 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.
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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