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This Special Issue “Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” was in session from 1 May 2019 to 31 May 2020. For this Special issue, we invited articles on current state-of-the-art technologies and solutions in G2V and V2G, including but not limited to the operation and control of gridable vehicles, energy storage and management systems, charging infrastructure and chargers, EV demand and load forecasting, V2G interfaces and applications, V2G and energy reliability and security, environmental impacts, and economic benefits as well as demonstration projects and case studies in the aforementioned areas. Articles that deal with the latest hot topics in V2G are of particular interest, such as V2G and demand-side response control technique, smart charging infrastructure and grid planning, advanced power electronics for V2G systems, adaptation of V2G systems in the smart grid, adaptation of smart cities for a large number of EVs, integration, and the optimization of V2G systems, utilities and transportation assets for advanced V2G systems, wireless power transfer systems for advanced V2G systems, fault detection, maintenance and diagnostics in V2G processes, communications protocols for V2G systems, energy management system (EMS) in V2G systems, IoT for V2G systems, distributed energy and storage systems for V2G, transportation networks and V2G, energy management for V2G, smart charging/discharging stations for efficient V2G, environmental and socio-economic benefits and challenges of V2G systems, and building integrated V2G systems (BIV2G). Five manuscripts are published in this Special Issue, including “An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads” by Agyeman et al., “Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid, An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter” by Shipman et al., “Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids” by Xia et al., and “A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies” by Savitti et al.
History of engineering & technology --- vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means --- vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means
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This Special Issue “Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” was in session from 1 May 2019 to 31 May 2020. For this Special issue, we invited articles on current state-of-the-art technologies and solutions in G2V and V2G, including but not limited to the operation and control of gridable vehicles, energy storage and management systems, charging infrastructure and chargers, EV demand and load forecasting, V2G interfaces and applications, V2G and energy reliability and security, environmental impacts, and economic benefits as well as demonstration projects and case studies in the aforementioned areas. Articles that deal with the latest hot topics in V2G are of particular interest, such as V2G and demand-side response control technique, smart charging infrastructure and grid planning, advanced power electronics for V2G systems, adaptation of V2G systems in the smart grid, adaptation of smart cities for a large number of EVs, integration, and the optimization of V2G systems, utilities and transportation assets for advanced V2G systems, wireless power transfer systems for advanced V2G systems, fault detection, maintenance and diagnostics in V2G processes, communications protocols for V2G systems, energy management system (EMS) in V2G systems, IoT for V2G systems, distributed energy and storage systems for V2G, transportation networks and V2G, energy management for V2G, smart charging/discharging stations for efficient V2G, environmental and socio-economic benefits and challenges of V2G systems, and building integrated V2G systems (BIV2G). Five manuscripts are published in this Special Issue, including “An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads” by Agyeman et al., “Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid, An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter” by Shipman et al., “Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids” by Xia et al., and “A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies” by Savitti et al.
History of engineering & technology --- vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means
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This Special Issue “Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” was in session from 1 May 2019 to 31 May 2020. For this Special issue, we invited articles on current state-of-the-art technologies and solutions in G2V and V2G, including but not limited to the operation and control of gridable vehicles, energy storage and management systems, charging infrastructure and chargers, EV demand and load forecasting, V2G interfaces and applications, V2G and energy reliability and security, environmental impacts, and economic benefits as well as demonstration projects and case studies in the aforementioned areas. Articles that deal with the latest hot topics in V2G are of particular interest, such as V2G and demand-side response control technique, smart charging infrastructure and grid planning, advanced power electronics for V2G systems, adaptation of V2G systems in the smart grid, adaptation of smart cities for a large number of EVs, integration, and the optimization of V2G systems, utilities and transportation assets for advanced V2G systems, wireless power transfer systems for advanced V2G systems, fault detection, maintenance and diagnostics in V2G processes, communications protocols for V2G systems, energy management system (EMS) in V2G systems, IoT for V2G systems, distributed energy and storage systems for V2G, transportation networks and V2G, energy management for V2G, smart charging/discharging stations for efficient V2G, environmental and socio-economic benefits and challenges of V2G systems, and building integrated V2G systems (BIV2G). Five manuscripts are published in this Special Issue, including “An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads” by Agyeman et al., “Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid, An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter” by Shipman et al., “Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids” by Xia et al., and “A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies” by Savitti et al.
vehicle-to-grid (V2G) --- vehicle-to-home (V2H) --- bi-directional charging topologies --- communication standards --- battery cycle --- smart grid --- optimization --- energy management --- electric vehicles --- distributed generation --- MPC --- AV2R --- V2G --- inverter --- vehicle-to-grid --- vehicle location prediction --- automated machine learning --- machine learning --- Bayesian --- deep neural network --- demand load forecast --- distributed load --- ensemble algorithm stochastic --- K-means
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This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs.
nuclei detection --- System-on-Chip --- FPGA --- K-Means --- hardware acceleration --- image analysis --- perceptual coding --- line buffer --- heterogeneous computing --- window filters --- processor architectures --- hardware accelerators --- stream processing --- embedded systems --- image processing pipeline --- image processing --- generalized Laplacian of Gaussian filter --- background estimation --- real-time systems --- FPGA implementation --- hardware architecture --- compression --- image borders --- memory --- zig-zag scan --- histopathology --- just-noticeable difference (JND) --- memory management --- downsampling --- image segmentation --- feature extraction --- design --- mean Shift clustering --- high-throughput --- segmentation --- streaming architecture --- power --- D-SWIM --- hardware/software co-design --- high-level synthesis --- contrast masking --- texture detection --- pipeline --- field programmable gate array (FPGA) --- JPEG-LS --- low-latency --- connected components analysis --- luminance masking --- field programmable gate arrays (FPGA)
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The renewable energy sector is one of the fastest growing branches of the economy in the world, including in Poland. Extensive investigation in research centers results in the increased efficiency of obtaining energy from renewable sources, as well as a decrease in the prices of renewable energy installations. The development of renewable energy motivates further research and the development of new technologies. Investments in renewable energy may also benefit the local community by increasing the attractiveness of the region to tourists, creating opportunities for professional activation (especially in areas with high unemployment), increasing the competitiveness of the local economy and its energy efficiency and obtaining raw materials from local producers, mainly farmers, which are an additional source of income for them. Another possible economic advantage is charging lease fees, for instance, for land under wind turbines or fees for ground easement, in order to ensure access to the construction of power lines, e.g., connecting turbines to the grid; lowering heat prices for residents of a given town; building investment plots in or near heat plants and biogas plants, with the provision of heat and electricity at competitive prices directly from these plants; investors covering the costs of modernizing local roads; and creating new transmission, power lines and supply points.
Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- waste management --- energy recovery --- model of energy recovery --- biogas --- fermentation --- combustion --- mini-grids --- energy access --- energy sustainability --- SDG 7 --- energy affordability --- green growth --- sustainable development --- environmental production --- relationships --- multicriteria taxonomy --- renewable energy sources --- household --- primary solid biofuels --- solar thermal system --- ambient pumps --- : CSR strategy --- financial performance --- energy sector --- : gross electricity production --- renewable sources --- energy transformation --- concentrationanalysis --- cluster analysis --- k-means --- European Union --- renewable energy sources (RES) --- the new EU member states --- Ward’s method: alternative energy sources --- photovoltaic systems --- wind systems --- hydropower systems --- biomass systems ---
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The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.
Technology: general issues --- History of engineering & technology --- supervised classification --- independent and non-identically distributed features --- analytical error probability --- empirical risk --- generalization error --- K-means clustering --- model compression --- population risk --- rate distortion theory --- vector quantization --- overfitting --- information criteria --- entropy --- model-based clustering --- merging mixture components --- component overlap --- interpretability --- time series prediction --- finite state machines --- hidden Markov models --- recurrent neural networks --- reservoir computers --- long short-term memory --- deep neural network --- information theory --- local information geometry --- feature extraction --- spiking neural network --- meta-learning --- information theoretic learning --- minimum error entropy --- artificial general intelligence --- closed-loop transcription --- linear discriminative representation --- rate reduction --- minimax game --- fairness --- HGR maximal correlation --- independence criterion --- separation criterion --- pattern dictionary --- atypicality --- Lempel–Ziv algorithm --- lossless compression --- anomaly detection --- information-theoretic bounds --- distribution and federated learning
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An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice.
Technology: general issues --- History of engineering & technology --- entrained flow coal gasification --- ash melting point --- operation temperature --- Markov process --- stochastic optimization model --- genetic algorithm --- gallium nitride --- magnetic-free converters --- module-level converters --- parallel architecture --- partial shading --- photovoltaic systems --- switched capacitor converters --- hybrid energy storage system --- supercapacitor --- lead–acid battery --- energy management system --- battery degradation --- depth of discharge --- techno-economic analysis --- hybrid power station --- green island --- energy storage --- remote community --- reserves --- k-means --- probabilistic dimensioning --- dynamic dimensioning --- balancing --- wave energy converters --- deep neural networks --- renewable energy sources --- spatial planning --- sentinel satellite imagery --- permanent magnet synchronous machines --- generators --- fault detection --- demagnetization --- artificial intelligence --- data mining --- machine learning --- advanced deep learning --- windspeed forecasting --- solar irradiation forecasting --- increased RES penetration --- smart grid --- scalability --- replicability --- FLEXITRANSTORE --- Angolan economy --- diversification --- strategic alternative --- biofuels
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Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges.
Medical equipment & techniques --- inertial measurement unit --- movement analysis --- long-track speed skating --- validity --- IMU --- principal component analysis --- wearable --- scoring --- carving --- balance assessment --- data augmentation --- gated recurrent unit --- human activity recognition --- one-dimensional convolutional neural network --- intermittent claudication --- vascular rehabilitation --- 6 min walking test --- functional walking --- TUG --- kinematics --- fall risk --- logistic regression --- elderly --- inertial sensor --- artificial intelligence --- supervised machine learning --- head rotation test --- neck pain --- cerebral palsy --- dystonia --- choreoathetosis --- machine learning --- home-based --- wearable device --- MLP --- gesture recognition --- flex sensor --- model search --- neural network --- inertial measurement unit—IMU --- movement complexity --- sample entropy --- trunk flexion --- low back pain --- lifting technique --- camera system --- ward clustering method --- K-means clustering method --- ensemble clustering method --- Bayesian neural network --- pain self-efficacy questionnaire --- n/a --- inertial measurement unit-IMU
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The management of natural resources can be approached using different data sources and techniques, from images registered by sensors of onboard satellites to UAV platforms, using remote sensing techniques and geographic information systems, among others. The variability of problems and projects to be analyzed, studied, and solved is very wide. This book presents a collection of different experiences, ranging from the location of areas of interest to the simulation of future scenarios of a territory at local and regional scales, considering spatial resolutions ranging from centimeters to hundreds of meters. The common objective of all the works compiled in this book is to support decision-making in environmental management.
Research & information: general --- secondary succession monitoring --- Natura 2000 threats --- tree detection --- archival photographs --- spectro-textural classification --- granulometric analysis --- GLCM --- alpine grassland --- fractional vegetation cover --- ground survey --- precision evaluation --- multi-scale LAI product validation --- PROSAIL model --- EBK --- crop growth period --- adaptive K-means algorithm --- heavy industry heat sources --- NPP-VIIRS --- active fire data --- night-time light data --- spatial autocorrelation --- spatial pattern --- spatial relationship --- natural wetlands changes --- associated influencing factors --- mainland China --- farmland abandonment mapping --- textural segmentation --- aerial imagery --- land use --- Poznań --- agent based modeling --- disaster management --- resource allocation --- high severity level --- first come first serve --- geographical information system --- bearing capacity --- analytic hierarchy process --- geographical survey of national conditions --- hotspot analysis --- topsis algorithm --- automatic identification system data --- 21st Century Maritime Silk Road region --- oil flow analysis --- maritime oil chokepoint --- Middle East Respiratory Syndrome --- seismic parameters --- GIS --- seismicity --- spatial analysis --- b-value --- earthquake catalog --- future scenarios --- prelude --- dynamic of land use --- Spatial Decision Support System, CORINE Land Cover --- remote sensing --- geographic information system
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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]
Medicine --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a
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