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This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.

Computer science. --- Data structures (Computer science). --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Structures. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Optical pattern recognition. --- Data structures (Computer scienc. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data structures (Computer science) --- Cluster Analysis --- Dimensionality Reduction --- Swarm Intelligence --- Visualization --- Unsupervised Machine Learning --- Data Science --- Knowledge Discovery --- 3D Printing --- Self-Organization --- Emergence --- Game Theory --- Advanced Analytics --- High-Dimensional Data --- Multivariate Data --- Analysis of Structured Data

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This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation. .

Fossil fuel technologies --- Engineering thermodynamics --- Machine learning --- Thermodynamics & heat --- Machine Learning --- Combustion Simulations --- Combustion Modelling --- Big Data Analysis --- Dimensionality reduction --- Reduced-order modelling --- Neural Networks --- Turbulent Combustion --- Physics-based modelling --- Data-driven modelling --- Deep learning --- Thermoacoustics and its modelling --- Reactive molecular dynamics --- Simulations of reacting flows --- Cogeneration of electric power and heat. --- Fossil fuels. --- Thermodynamics. --- Heat engineering. --- Heat transfer. --- Mass transfer. --- Machine learning. --- Fossil Fuel. --- Engineering Thermodynamics, Heat and Mass Transfer. --- Machine Learning. --- Chemistry, Physical and theoretical --- Dynamics --- Mechanics --- Physics --- Heat --- Heat-engines --- Quantum theory --- Mass transport (Physics) --- Thermodynamics --- Transport theory --- Heat transfer --- Thermal transfer --- Transmission of heat --- Energy transfer --- Mechanical engineering --- Fossil energy --- Fuel --- Energy minerals --- Combined electric power and heat production --- Electric power and heat cogeneration --- Heat and electric power cogeneration --- Electric power production --- Learning, Machine --- Artificial intelligence --- Machine theory

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Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems.

microgrid --- distribution network operator --- double externalities --- subsidy --- PV system --- PI controller --- fuzzy control --- MPPT --- tracking speed --- error --- Micro Grid --- VSG --- power sharing --- inertia support --- energy support --- small signal stability --- day-ahead operational scheduling --- reconfigurable microgrid --- DRNN Bi-LSTM --- aggregated load forecasting --- bulk photovoltaic power generation forecasting --- solar potential assessment --- resource mapping --- geographic information systems (GIS) --- site selection --- Iran --- earthquake --- power distribution network --- resilience improvement planning --- water distribution network --- load disaggregation --- non-intrusive load monitoring (NILM) --- dimensionality reduction --- principal component analysis (PCA) --- smart home --- solar renewable --- thermal load --- stochastic operation --- energy storage --- sustainability --- desalination --- renewable energy --- water–energy-nexus --- photovoltaic grid-connected system --- power fluctuation --- DC bus voltage stabilization --- prescribed performance --- command-filtered adaptive backstepping control --- centralized control architecture --- DC microgrid --- distributed control architecture --- electricity price constraint --- hybrid control architecture --- power flow control strategy --- data pre-processing --- electricity theft --- imbalance data --- parameter tuning --- smart grid

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Data science, information theory, probability theory, statistical learning and other related disciplines greatly benefit from non-negative measures of dissimilarity between pairs of probability measures. These are known as divergence measures, and exploring their mathematical foundations and diverse applications is of significant interest. The present Special Issue, entitled “Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems”, includes eight original contributions, and it is focused on the study of the mathematical properties and applications of classical and generalized divergence measures from an information-theoretic perspective. It mainly deals with two key generalizations of the relative entropy: namely, the R_ényi divergence and the important class of f -divergences. It is our hope that the readers will find interest in this Special Issue, which will stimulate further research in the study of the mathematical foundations and applications of divergence measures.

Bregman divergence --- f-divergence --- Jensen–Bregman divergence --- Jensen diversity --- Jensen–Shannon divergence --- capacitory discrimination --- Jensen–Shannon centroid --- mixture family --- information geometry --- difference of convex (DC) programming --- conditional Rényi divergence --- horse betting --- Kelly gambling --- Rényi divergence --- Rényi mutual information --- relative entropy --- chi-squared divergence --- f-divergences --- method of types --- large deviations --- strong data–processing inequalities --- information contraction --- maximal correlation --- Markov chains --- information inequalities --- mutual information --- Rényi entropy --- Carlson–Levin inequality --- information measures --- hypothesis testing --- total variation --- skew-divergence --- convexity --- Pinsker’s inequality --- Bayes risk --- statistical divergences --- minimum divergence estimator --- maximum likelihood --- bootstrap --- conditional limit theorem --- Bahadur efficiency --- α-mutual information --- Augustin–Csiszár mutual information --- data transmission --- error exponents --- dimensionality reduction --- discriminant analysis --- statistical inference --- n/a --- Jensen-Bregman divergence --- Jensen-Shannon divergence --- Jensen-Shannon centroid --- conditional Rényi divergence --- Rényi divergence --- Rényi mutual information --- strong data-processing inequalities --- Rényi entropy --- Carlson-Levin inequality --- Pinsker's inequality --- Augustin-Csiszár mutual information

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Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

very high-resolution Pléiades imagery --- surface convergence --- data augmentation --- acquisition geometry --- SVM classification --- urban water mapping --- beaver dam analogue --- agriculture parcel segmentation --- morphological building index --- airborne hypespectral imagery --- sunglint correction --- water index --- over-segmentation index (OSI) --- High-resolution satellite imagery --- multi-resolution segmentation (MRS) --- GaoFen-2 (GF-2) --- benthic mapping --- scene classification --- greenhouse extraction --- edge constraint --- Deformable CNN --- built-up areas extraction --- ultra-dense connection --- seagrass --- beaver mimicry --- forested mountain --- natural hazards --- remote sensing --- dimensionality reduction techniques --- road extraction --- landslide monitoring --- Slumgullion landslide --- synthetic aperture radar --- building detection --- Worldview-2 --- saliency index --- under-segmentation index (USI) --- texture analysis --- fast marching method --- video satellite --- CNN --- capsule --- super-resolution --- feature distillation --- shadow detection --- PrimaryCaps --- semiautomatic --- compensation unit --- superpixels --- riparian --- QuickBird --- submesoscale --- linear unmixing --- accuracy assessment --- composite error index (CEI) --- cyanobacteria --- local feature points --- Faster R-CNN --- occluded object detection --- error index of total area (ETA) --- large displacements --- threshold stability --- remote sensing imagery --- water column correction --- canopy height model --- spiral eddy --- sub-pixel offset tracking --- consensus --- stream restoration --- western Baltic Sea --- Worldview --- very high-resolution image --- CapsNet --- atmospheric correction