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(Produktform)Electronic book text --- Augmentation --- Augmented Reality --- Augmented work --- Berufliche Bildung --- berufliche Rehabilitation --- Digitale Transformation --- Digitalisierung --- Duales System --- Fusion Skills --- gewerblich-technische Berufe --- Hybrid Intelligence --- Individualisierung --- Industrie 4.0 --- Industrieberufe --- IT-Berufe --- KI --- Künstliche Intelligenz --- Learning Analytics --- Lernortkooperation --- Mensch-Maschine Interaktion --- Pflegeberufe --- Problemfelder Künstliche Intelligenz --- Substitution von Arbeit --- Virtual Reality --- (VLB-WN)9570

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Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue.

Technology: general issues --- pancreas --- segmentation --- computed tomography --- deep learning --- data augmentation --- neoplasm metastasis --- ovarian neoplasms --- radiation exposure --- tomography --- x-ray computed --- prostate carcinoma --- microscopic --- convolutional neural network --- machine learning --- handcrafted --- oral carcinoma --- medical image segmentation --- colon cancer --- colon polyps --- OCT --- optical biopsy --- animal rat models --- CADx --- airway volume analysis --- artificial intelligence --- coronary artery disease --- SPECT MPI scans --- convolutional neural networks --- transfer learning --- classification models --- pancreas --- segmentation --- computed tomography --- deep learning --- data augmentation --- neoplasm metastasis --- ovarian neoplasms --- radiation exposure --- tomography --- x-ray computed --- prostate carcinoma --- microscopic --- convolutional neural network --- machine learning --- handcrafted --- oral carcinoma --- medical image segmentation --- colon cancer --- colon polyps --- OCT --- optical biopsy --- animal rat models --- CADx --- airway volume analysis --- artificial intelligence --- coronary artery disease --- SPECT MPI scans --- convolutional neural networks --- transfer learning --- classification models

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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

History of engineering & technology --- short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity

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The book contains the research contributions belonging to the Special Issue "Numerical Simulation of Wind Turbines", published in 2020-2021. They consist of 15 original research papers and 1 editorial. Different topics are discussed, from innovative design solutions for large and small wind turbine to control, from advanced simulation techniques to noise prediction. The variety of methods used in the research contributions testifies the need for a holistic approach to the design and simulation of modern wind turbines and will be able to stimulate the interest of the wind energy community.

large-scale wind turbine balde --- computational aeroacoustics --- sound source detection --- low Mach number turbulent flows --- NACA0012 airfoil --- fluid–structure interaction --- wind turbine --- atmospheric boundary layer --- composite materials --- gusts --- wind energy --- actuator line method --- wind turbine simulation --- regularization kernel --- small wind turbine (SWT) --- computational fluid dynamics (CFD) --- composites --- fluid–structure interaction (FSI) --- VAWT --- gurney flap --- CFD --- RBF --- power augmentation --- Darrieus --- turbulence --- experiments --- turbine wake --- turbine size --- large-eddy simulation --- actuator surface model --- wind turbine wake --- actuator disk model --- dynamic mode decomposition --- coherent structures --- wake meandering --- vertical axis wind turbine (VAWT) --- Savonius turbine --- deformable blades --- power coefficient --- blade load --- fluid-structure interaction (FSI) --- uncertainty quantification --- blade damage --- AEP --- winglet --- computational fluid dynamics (CFD), wind energy --- renewable energy --- rotor blade --- tip vortices --- aerodynamics --- ansys fluent --- savonius turbine --- icewind turbine --- static torque --- three-dimensional simulation --- Delayed DES --- H-Darrieus --- micro wind power generation --- wind turbine control --- load mitigation --- individual pitch control --- lifting line free vortex wake --- vortex methods --- pitch --- stall --- engineering codes --- n/a --- fluid-structure interaction

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This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes.

spatial-temporal data --- pasting process --- process image --- convolutional neural network --- Industry 4.0 --- auto machine learning --- failure mode effects analysis --- risk priority number --- rolling bearing --- condition monitoring --- classification --- OPTICS --- statistical process control --- control chart pattern --- disruptions --- disruption management --- fault diagnosis --- construction industry --- plaster production --- neural networks --- decision support systems --- expert systems --- failure mode and effects analysis (FMEA) --- discriminant analysis --- non-intrusive load monitoring --- load identification --- membrane --- data reconciliation --- real-time --- online --- monitoring --- Six Sigma --- multivariate data analysis --- latent variables models --- PCA --- PLS --- high-dimensional data --- statistical process monitoring --- artificial generation of variability --- data augmentation --- quality prediction --- continuous casting --- multiscale --- time series classification --- imbalanced data --- combustion --- optical sensors --- spectroscopy measurements --- signal detection --- digital processing --- principal component analysis --- curve resolution --- data mining --- semiconductor manufacturing --- quality control --- yield improvement --- fault detection --- process control --- multi-phase residual recursive model --- multi-mode model --- process monitoring --- n/a