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This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services.

Information technology industries --- cyber-physical systems --- digital twin --- advanced manufacturing --- grinding process --- grinding wheel --- seismic event detection --- detection model --- seismology --- classification --- flexible unit systems --- degradation --- residual life distribution --- workload strategy --- upgradation --- predictive maintenance --- network sciences --- social network --- coauthorship networks --- methods for modeling digital twins --- system design of digital twins --- artificial intelligence methods --- quantitative trading --- cryptocurrencies --- blockchain --- n/a

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Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

self-healing grid --- machine-learning --- feature extraction --- event detection --- optimization techniques --- manta ray foraging optimization algorithm --- multi-objective function --- radial networks --- optimal power flow --- automatic P2P energy trading --- Markov decision process --- deep reinforcement learning --- deep Q-network --- long short-term delayed reward --- inter-area oscillations --- modal analysis --- reduced order modeling --- dynamic mode decomposition --- machine learning --- artificial neural networks --- steady-state security assessment --- situation awareness --- cellular computational networks --- load flow prediction --- contingency --- fuzzy system --- change detection --- data analytics --- data mining --- filtering --- optimization --- power quality --- signal processing --- total variation smoothing --- n/a

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The Environmental Noise Directive (END) requires that a five-year updating of noise maps is carried out to check and report on the changes that have occurred during the reference period. The updating process is usually achieved using a standardized approach consisting of collecting and processing information through acoustic models to produce the updated noise maps. This procedure is time consuming and costly, and has a significant impact on the financial statement of the authorities responsible for providing the maps. Furthermore, the END requires that easy-to-read noise maps are made available to the public to provide information on noise levels and the subsequent actions to be undertaken by local and central authorities to reduce noise impacts. In order to update the noise maps more easily and in a more effective way, it is convenient to design an integrated system incorporating real-time noise measurement and signal processing to identify and analyze the noise sources present in the mapping area (e.g., road traffic noise, leisure noise, etc.) as well as to automatically generate and present the corresponding noise maps. This wireless acoustic sensor network design requires transversal knowledge, from accurate hardware design for acoustic sensors to network structure design and management of the information with signal processing to identify the origin of the measured noise and graphical user interface application design to present the results to end users. This book is collection in which several views of methodology and technologies required for the development of an efficient wireless acoustic sensor network from the first stages of its design to the tests conducted during deployment, its final performance, and possible subsequent implications for authorities in terms of the definition of policies. Contributions include several LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks with a focus on the publication of good practices for the design and deployment of intelligent networks in other locations.

History of engineering & technology --- motor --- mechanical fault --- detection --- RMS --- sound --- drill --- safety --- pattern --- bearing --- fan --- shaft --- road traffic noise --- noise events --- intermittency ratio --- urban sites classification --- noise monitoring --- real-time noise mapping --- wireless sensor networks --- noise mapping --- noise mitigation --- DYNAMAP project --- outdoors noise --- sound level meter --- digital signal processing --- multirate filters --- dynamic noise maps --- anomalous noise events --- individual impact --- aggregate impact --- WASN --- sensor nodes --- urban and suburban environments --- noise control --- sensor concept --- road traffic noise model --- dynamic model --- acoustics --- smart cities --- deep learning --- long short-term memory --- temporal forecast --- p-u sensor --- p-p sensor --- noise --- Adrienne --- stabilization --- damping --- acoustic impedance --- road surfaces --- low-cost sensors --- networks --- noise sources --- regression analysis --- contribution analysis --- vehicle interior noise --- acoustic sensor design --- acoustic event detection --- map generation --- public information --- END --- CNOSSOS-EU --- motor --- mechanical fault --- detection --- RMS --- sound --- drill --- safety --- pattern --- bearing --- fan --- shaft --- road traffic noise --- noise events --- intermittency ratio --- urban sites classification --- noise monitoring --- real-time noise mapping --- wireless sensor networks --- noise mapping --- noise mitigation --- DYNAMAP project --- outdoors noise --- sound level meter --- digital signal processing --- multirate filters --- dynamic noise maps --- anomalous noise events --- individual impact --- aggregate impact --- WASN --- sensor nodes --- urban and suburban environments --- noise control --- sensor concept --- road traffic noise model --- dynamic model --- acoustics --- smart cities --- deep learning --- long short-term memory --- temporal forecast --- p-u sensor --- p-p sensor --- noise --- Adrienne --- stabilization --- damping --- acoustic impedance --- road surfaces --- low-cost sensors --- networks --- noise sources --- regression analysis --- contribution analysis --- vehicle interior noise --- acoustic sensor design --- acoustic event detection --- map generation --- public information --- END --- CNOSSOS-EU

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The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.

Technology: general issues --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group

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The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.

Technology: general issues --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group