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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

Technology: general issues --- History of engineering & technology --- fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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In present book, an analysis of the literature pertaining to parametric and non-parametric descriptions of surface topography in basics manufacturing processes (e.g., turning, milling, grinding) has been performed. The book focuses on the improvement of machining processes, with particular attention to the functional properties of surfaces, and, also, in the control of process parameters by a selected group of parameters. Here, the specific areas of interest are: surface topography analysis; advanced manufacturing metrology; surface metrology; measurement science; and measurement systems. The proposed approach of the description of surface for the functional properties of surfaces leads to the control of the whole manufacturing process, reduction of production cost by eliminating manufacturing defects and energy consumption, as well as the improvements of surface quality. The study presented in the book is a compendium of knowledge regarding surface metrology and emerging aim in a novel scientific approach.

Technology: general issues --- profile --- two-process surface --- correlation length --- austenitization --- cryogenic --- microstructure --- microhardness --- abrasive wear --- tempering --- thermoplastic polyurethane --- heat-welded V-belt --- IR thermography --- hardness --- surface roughness --- SEM morphology --- optical microscopy --- machining --- sintered aluminum --- 3D surface roughness parameters --- surface defects --- contact profilometry --- surface topography --- thermal disturbance --- thermal expansion --- thermal chamber --- micro turning --- material removal rate --- RSM --- Ti6Al4V alloy --- tool wear --- surface texture --- anisotropy --- multiscale --- roping --- ridging --- topography --- autocorrelation function --- roughness --- EDM --- craters --- multiscale analysis --- microgeometry --- bimodal distribution --- material ratio --- parameters --- fiber-reinforced polymers --- automated fiber placement --- path planning --- abrasive water jet machining --- cutting kerf --- soda–lime glass --- radius of the cutting head trajectory --- quality --- contact mechanics --- equivalent sum rough surface --- β-phase TNTZ alloy --- nano-finishing --- magnetic abrasive finishing --- material removal --- optimization --- parametric appraisal --- circulated coins --- surface condition --- optical methods --- measurements and analysis --- mechanical engineering --- roughness analysis --- high-efficiency video coding (HEVC) --- texture feature descriptors --- texture image classification --- support vector machine (SVM) --- electroplated grinding wheel --- grinding wheel wear --- grinding wheel surface texture --- n/a --- soda-lime glass

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In present book, an analysis of the literature pertaining to parametric and non-parametric descriptions of surface topography in basics manufacturing processes (e.g., turning, milling, grinding) has been performed. The book focuses on the improvement of machining processes, with particular attention to the functional properties of surfaces, and, also, in the control of process parameters by a selected group of parameters. Here, the specific areas of interest are: surface topography analysis; advanced manufacturing metrology; surface metrology; measurement science; and measurement systems. The proposed approach of the description of surface for the functional properties of surfaces leads to the control of the whole manufacturing process, reduction of production cost by eliminating manufacturing defects and energy consumption, as well as the improvements of surface quality. The study presented in the book is a compendium of knowledge regarding surface metrology and emerging aim in a novel scientific approach.

profile --- two-process surface --- correlation length --- austenitization --- cryogenic --- microstructure --- microhardness --- abrasive wear --- tempering --- thermoplastic polyurethane --- heat-welded V-belt --- IR thermography --- hardness --- surface roughness --- SEM morphology --- optical microscopy --- machining --- sintered aluminum --- 3D surface roughness parameters --- surface defects --- contact profilometry --- surface topography --- thermal disturbance --- thermal expansion --- thermal chamber --- micro turning --- material removal rate --- RSM --- Ti6Al4V alloy --- tool wear --- surface texture --- anisotropy --- multiscale --- roping --- ridging --- topography --- autocorrelation function --- roughness --- EDM --- craters --- multiscale analysis --- microgeometry --- bimodal distribution --- material ratio --- parameters --- fiber-reinforced polymers --- automated fiber placement --- path planning --- abrasive water jet machining --- cutting kerf --- soda–lime glass --- radius of the cutting head trajectory --- quality --- contact mechanics --- equivalent sum rough surface --- β-phase TNTZ alloy --- nano-finishing --- magnetic abrasive finishing --- material removal --- optimization --- parametric appraisal --- circulated coins --- surface condition --- optical methods --- measurements and analysis --- mechanical engineering --- roughness analysis --- high-efficiency video coding (HEVC) --- texture feature descriptors --- texture image classification --- support vector machine (SVM) --- electroplated grinding wheel --- grinding wheel wear --- grinding wheel surface texture --- n/a --- soda-lime glass

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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

metadata --- image classification --- sensitivity analysis --- ROI detection --- residual learning --- image alignment --- adaptive convolutional kernels --- Hough transform --- class imbalance --- land surface temperature --- inundation mapping --- multiscale representation --- object-based --- convolutional neural networks --- scene classification --- morphological profiles --- hyperedge weight estimation --- hyperparameter sparse representation --- semantic segmentation --- vehicle classification --- flood --- Landsat imagery --- target detection --- multi-sensor --- building damage detection --- optimized kernel minimum noise fraction (OKMNF) --- sea-land segmentation --- nonlinear classification --- land use --- SAR imagery --- anti-noise transfer network --- sub-pixel change detection --- Radon transform --- segmentation --- remote sensing image retrieval --- TensorFlow --- convolutional neural network --- particle swarm optimization --- optical sensors --- machine learning --- mixed pixel --- optical remotely sensed images --- object-based image analysis --- very high resolution images --- single stream optimization --- ship detection --- ice concentration --- online learning --- manifold ranking --- dictionary learning --- urban surface water extraction --- saliency detection --- spatial attraction model (SAM) --- quality assessment --- Fuzzy-GA decision making system --- land cover change --- multi-view canonical correlation analysis ensemble --- land cover --- semantic labeling --- sparse representation --- dimensionality expansion --- speckle filters --- hyperspectral imagery --- fully convolutional network --- infrared image --- Siamese neural network --- Random Forests (RF) --- feature matching --- color matching --- geostationary satellite remote sensing image --- change feature analysis --- road detection --- deep learning --- aerial images --- image segmentation --- aerial image --- multi-sensor image matching --- HJ-1A/B CCD --- endmember extraction --- high resolution --- multi-scale clustering --- heterogeneous domain adaptation --- hard classification --- regional land cover --- hypergraph learning --- automatic cluster number determination --- dilated convolution --- MSER --- semi-supervised learning --- gate --- Synthetic Aperture Radar (SAR) --- downscaling --- conditional random fields --- urban heat island --- hyperspectral image --- remote sensing image correction --- skip connection --- ISPRS --- spatial distribution --- geo-referencing --- Support Vector Machine (SVM) --- very high resolution (VHR) satellite image --- classification --- ensemble learning --- synthetic aperture radar --- conservation --- convolutional neural network (CNN) --- THEOS --- visible light and infrared integrated camera --- vehicle localization --- structured sparsity --- texture analysis --- DSFATN --- CNN --- image registration --- UAV --- unsupervised classification --- SVMs --- SAR image --- fuzzy neural network --- dimensionality reduction --- GeoEye-1 --- feature extraction --- sub-pixel --- energy distribution optimizing --- saliency analysis --- deep convolutional neural networks --- sparse and low-rank graph --- hyperspectral remote sensing --- tensor low-rank approximation --- optimal transport --- SELF --- spatiotemporal context learning --- Modest AdaBoost --- topic modelling --- multi-seasonal --- Segment-Tree Filtering --- locality information --- GF-4 PMS --- image fusion --- wavelet transform --- hashing --- machine learning techniques --- satellite images --- climate change --- road segmentation --- remote sensing --- tensor sparse decomposition --- Convolutional Neural Network (CNN) --- multi-task learning --- deep salient feature --- speckle --- canonical correlation weighted voting --- fully convolutional network (FCN) --- despeckling --- multispectral imagery --- ratio images --- linear spectral unmixing --- hyperspectral image classification --- multispectral images --- high resolution image --- multi-objective --- convolution neural network --- transfer learning --- 1-dimensional (1-D) --- threshold stability --- Landsat --- kernel method --- phase congruency --- subpixel mapping (SPM) --- tensor --- MODIS --- GSHHG database --- compressive sensing