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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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Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.
data mining --- decision-making system --- rough set --- mixed integer linear programming --- assembly clearance --- diesel engine quality --- Internet of things --- Wireless nodes --- Hybrid clustering --- Multi-hop routing --- Network lifetime --- Artificial intelligence --- data envelopment analysis --- decision making --- artificial intelligence --- performance --- visual analytics --- system --- air quality --- spatiotemporal --- multivariate --- dimension reduction --- clustering --- regular patterns --- anomalies --- speech recognition --- Long Short Term Memory (LSTM) --- speech output correction --- most-matching --- empirical correlations --- rheological properties --- real-time --- water-based drill-in fluid --- artificial neural network --- elastic parameters --- Poisson’s ratio --- sandstone --- self-adaptive differential evolution --- total organic carbon --- barnett shale --- devonian shale --- fishbone multilateral wells --- predictive models --- well productivity --- international research --- knowledge map visualization --- policy documents quantification --- research hotspot --- policy keyword --- minimum miscibility pressure (MMP) --- CO2 flooding --- new models --- face recognition --- security --- spoofing --- histogram of oriented gradients --- smart cities --- deep learning --- LSTM --- neural networks --- location prediction --- trajectories --- smart tourism --- static Young’s modulus --- sandstone formations --- machine learning --- n/a --- Poisson's ratio --- static Young's modulus
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In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
HIGD2A --- cancer --- DNA methylation --- mRNA expression --- miRNA --- quercetin --- hypoxia --- eQTL --- CRISPR-Cas9 --- single-cell clone --- fine-mapping --- power --- RNA N6-methyladenosine site --- yeast genome --- methylation --- computational biology --- deep learning --- bioinformatics --- hepatocellular carcinoma --- transcriptomics --- proteomics --- bioinformatics analysis --- differentiation --- Gene Ontology --- Reactome Pathways --- gene-set enrichment --- meta-analysis --- transcription factor --- binding sites --- genomics --- chilling stress --- CBF --- DREB --- CAMTA1 --- pathway --- text mining --- infiltration tactics optimization algorithm --- classification --- clustering --- microarray --- ensembles --- machine learning --- infiltration --- computational intelligence --- gene co-expression network --- murine coronavirus --- viral infection --- immune response --- data mining --- systems biology --- obesity --- differential genes expression --- exercise --- high-fat diet --- pathways --- potential therapeutic targets --- DNA N6-methyladenine --- Chou’s 5-steps rule --- Convolution Neural Network (CNN) --- Long Short-Term Memory (LSTM) --- machine-learning --- chromatin interactions --- prediction --- genome architecture --- n/a --- Chou's 5-steps rule
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In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Research & information: general --- Biology, life sciences --- HIGD2A --- cancer --- DNA methylation --- mRNA expression --- miRNA --- quercetin --- hypoxia --- eQTL --- CRISPR-Cas9 --- single-cell clone --- fine-mapping --- power --- RNA N6-methyladenosine site --- yeast genome --- methylation --- computational biology --- deep learning --- bioinformatics --- hepatocellular carcinoma --- transcriptomics --- proteomics --- bioinformatics analysis --- differentiation --- Gene Ontology --- Reactome Pathways --- gene-set enrichment --- meta-analysis --- transcription factor --- binding sites --- genomics --- chilling stress --- CBF --- DREB --- CAMTA1 --- pathway --- text mining --- infiltration tactics optimization algorithm --- classification --- clustering --- microarray --- ensembles --- machine learning --- infiltration --- computational intelligence --- gene co-expression network --- murine coronavirus --- viral infection --- immune response --- data mining --- systems biology --- obesity --- differential genes expression --- exercise --- high-fat diet --- pathways --- potential therapeutic targets --- DNA N6-methyladenine --- Chou's 5-steps rule --- Convolution Neural Network (CNN) --- Long Short-Term Memory (LSTM) --- machine-learning --- chromatin interactions --- prediction --- genome architecture
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Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.
Information technology industries --- data mining --- decision-making system --- rough set --- mixed integer linear programming --- assembly clearance --- diesel engine quality --- Internet of things --- Wireless nodes --- Hybrid clustering --- Multi-hop routing --- Network lifetime --- Artificial intelligence --- data envelopment analysis --- decision making --- artificial intelligence --- performance --- visual analytics --- system --- air quality --- spatiotemporal --- multivariate --- dimension reduction --- clustering --- regular patterns --- anomalies --- speech recognition --- Long Short Term Memory (LSTM) --- speech output correction --- most-matching --- empirical correlations --- rheological properties --- real-time --- water-based drill-in fluid --- artificial neural network --- elastic parameters --- Poisson's ratio --- sandstone --- self-adaptive differential evolution --- total organic carbon --- barnett shale --- devonian shale --- fishbone multilateral wells --- predictive models --- well productivity --- international research --- knowledge map visualization --- policy documents quantification --- research hotspot --- policy keyword --- minimum miscibility pressure (MMP) --- CO2 flooding --- new models --- face recognition --- security --- spoofing --- histogram of oriented gradients --- smart cities --- deep learning --- LSTM --- neural networks --- location prediction --- trajectories --- smart tourism --- static Young's modulus --- sandstone formations --- machine learning
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Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions.
movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension
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Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions.
Medical equipment & techniques --- movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension
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