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Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research, including propositional satisfiability, constraint satisfaction, routing, and scheduling. SLS algorithms have also become increasingly popular for solving challenging combinatorial problems in many application areas, such as e-commerce and bioinformatics.Hoos and Stützle offer the first systematic and unified treatment of SLS algorithms. In this groundbreaking new book, they examine the
Stochastic programming --- Algorithms --- Combinatorial analysis --- Programmation stochastique --- Algorithmes --- Analyse combinatoire --- 681.3*G13 --- Numerical linear algebra: conditioning; determinants; eigenvalues and eigenvectors; error analysis; linear systems; matrix inversion; pseudoinverses; singular value decomposition; sparse, structured, and very large systems (direct and iterative methods) --- Linear programming --- Combinatorics --- Algebra --- Mathematical analysis --- Algorism --- Arithmetic --- Foundations --- Algorithmes. --- Programmation stochastique. --- Analyse combinatoire. --- Algorithms. --- Combinatorial analysis. --- Stochastic programming.
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Heuristic algorithms. --- Computer algorithms. --- Heuristics (Computer algorithms) --- Computer algorithms --- Algorithms --- Heuristic algorithms --- 681.3*G13 --- Numerical linear algebra: conditioning; determinants; eigenvalues and eigenvectors; error analysis; linear systems; matrix inversion; pseudoinverses; singular value decomposition; sparse, structured, and very large systems (direct and iterative methods)
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The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers
numerical modelling --- unstructured meshes --- finite volume --- North Sea --- salinity --- deep learning --- martinez boundary salinity generator --- Sacramento–San Joaquin Delta --- residence time --- exposure time --- transport time scale --- hyper-tidal estuary --- singular value decomposition --- data assimilation --- ocean models --- observation strategies --- ocean forecasting systems --- ocean Double Gyre --- 4D-Var --- ROMS --- MEOF --- initial ensemble --- ensemble spread --- LETKF --- n/a --- Sacramento-San Joaquin Delta
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The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications.
Research & information: general --- Mathematics & science --- fuzzy Peterson’s syllogisms --- fuzzy intermediate quantifiers --- graded Peterson’s cube of opposition --- linguistic universals --- linguistic complexity --- evaluative expressions --- fuzzy grammar --- linguistic gradience --- linguistic constraints --- grammaticality --- sentiment analysis --- closeness --- closeness matrix --- closeness space --- function similarity --- fuzzy partition --- fuzzy transform --- preimage problem --- singular value decomposition --- evolving fuzzy neural network --- or-neuron --- auction fraud --- knowledge extraction --- n/a --- fuzzy Peterson's syllogisms --- graded Peterson's cube of opposition
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This Special Issue presents the latest state-of-the-art research on solid fuels technology with dedicated, focused research papers. There are a variety of topics to choose from among the seven published re-search works to bring you up to date with the current trends in academia and industry.
peak shaving --- battery storage --- peak demand pricing --- lithium-ion --- tariff structure --- receiving-end system --- multi-infeed HVDCs --- security assessment --- emergency control strategy --- electromagnetic transient (EMT)-transient stability (TS) hybrid simulation --- impedance determination --- lossy compression algorithms --- singular value decomposition --- wavelet transformation --- voltage control --- deep deterministic policy gradient --- deep reinforcement learning --- model uncertainties --- energy communities --- machine learning --- forecasting --- abnormal data --- wind power --- outliers --- electricity consumption representative profiles --- self-consumption
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Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields.
Mathematical statistics. --- Matrices. --- Multivariate analysis. --- Multivariate analysis -- Problems, exercises, etc. --- Singular value decomposition --- Matrix inversion --- Algebras, Linear --- Mathematics --- Physical Sciences & Mathematics --- Algebra --- Mathematical Statistics --- Decomposition method. --- Matrix inversion. --- Algebras, Linear. --- Linear algebra --- Inverse matrices --- Inverse of a matrix --- Inversion, Matrix --- Method, Decomposition --- Algebra, Matrix --- Cracovians (Mathematics) --- Matrix algebra --- Matrixes (Algebra) --- Statistics. --- Statistics, general. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Algebra, Universal --- Generalized spaces --- Mathematical analysis --- Calculus of operations --- Line geometry --- Topology --- Linear operators --- Matrices --- Operations research --- Programming (Mathematics) --- System analysis --- Algebra, Abstract --- Generalized inverses --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics .
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"A severe thunderstorm morphs into a tornado that cuts a swath of destruction through Oklahoma. How do we study the storm's mutation into a deadly twister? Avian flu cases are reported in China. How do we characterize the spread of the flu, potentially preventing an epidemic? The way to answer important questions like these is to analyze the spatial and temporal characteristics--origin, rates, and frequencies--of these phenomena. This comprehensive text introduces advanced undergraduate students, graduate students, and researchers to the statistical and algebraic methods used to analyze spatiotemporal data in a range of fields, including climate science, geophysics, ecology, astrophysics, and medicine. Gidon Eshel begins with a concise yet detailed primer on linear algebra, providing readers with the mathematical foundations needed for data analysis. He then fully explains the theory and methods for analyzing spatiotemporal data, guiding readers from the basics to the most advanced applications. This self-contained, practical guide to the analysis of multidimensional data sets features a wealth of real-world examples as well as sample homework exercises and suggested exams"--
Spatial analysis (Statistics) --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- EOF analysis. --- EOF. --- GramГchmidt orthogonalization. --- SVD analysis. --- SVD. --- astrophysics. --- autocorrelation functions. --- autocovariance. --- autoregressive model. --- climate science. --- column space. --- covariability matrix. --- data analysis. --- data matrices. --- degrees of freedom. --- deterministic science. --- ecology. --- eigen-decomposition. --- eigen-techniques. --- eigenanalysis. --- eigenvalues. --- empirical orthogonal functions. --- empirical science. --- empiricism. --- exercises. --- forward problem. --- geophysics. --- inverse problem. --- linear algebra. --- linear regression. --- matrices. --- matrix structure. --- matrix. --- medicine. --- multidimensional data sets. --- multidimensional data. --- nondeterministic phenomena. --- null space. --- phenomena. --- probability distribution. --- row space. --- singular value decomposition. --- spatiotemporal data. --- spectral representation. --- square matrices. --- statistics. --- stochastic processes. --- subjective decisions. --- theoretical science. --- time series. --- timescale. --- tornado. --- variables. --- vectors.
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As the most prominent and complicated terrain on the globe, the Tibetan Plateau (TP) is often called the “Roof of the World”, “Third Pole” or “Asian Water Tower”. The energy and water cycles in the Third Pole have great impacts on the atmospheric circulation, Asian monsoon system and global climate change. On the other hand, the TP and the surrounding higher elevation area are also experiencing evident and rapid environmental changes under the background of global warming. As the headwater area of major rivers in Asia, the TP’s environmental changes—such as glacial retreat, snow melting, lake expanding and permafrost degradation—pose potential long-term threats to water resources of the local and surrounding regions. To promote quantitative understanding of energy and water cycles of the TP, several field campaigns, including GAME/Tibet, CAMP/Tibet and TORP, have been carried out. A large amount of data have been collected to gain a better understanding of the atmospheric boundary layer structure, turbulent heat fluxes and their coupling with atmospheric circulation and hydrological processes. The focus of this reprint is to present recent advances in quantifying land–atmosphere interactions, the water cycle and its components, energy balance components, climate change and hydrological feedbacks by in situ measurements, remote sensing or numerical modelling approaches in the “Third Pole” region.
Tibetan Plateau --- climate warming --- lake surface temperature --- heat exchange --- lake ice phenology --- terrestrial evapotranspiration --- convection-permitting modeling --- monsoon season --- non-monsoon season --- Sichuan Basin --- water vapor budget --- summer precipitation --- water resource variation --- Indian Ocean --- East Asia climate --- vertical motion of air --- surface characteristic parameter --- radiation fluxes --- observation data --- land-atmosphere interaction --- WRF-Hydro model --- runoff --- precipitation --- three river source region --- surface fluxes --- HYSPLIT_v4 model --- water vapor transport --- singular value decomposition --- glacier modeling --- mass balance --- full-Stokes model --- ET --- Qinghai Province --- driving factors --- elevation-dependency --- i-PFASs --- China --- river --- lake --- the Tibetan Plateau --- n/a
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Ultrasound medical imaging stands out among the other diagnostic imaging modalities for its patient-friendliness, high temporal resolution, low cost, and absence of ionizing radiation. On the other hand, it may still suffer from limited detail level, low signal-to-noise ratio, and narrow field-of-view. In the last decade, new beamforming and image reconstruction techniques have emerged which aim at improving resolution, contrast, and clutter suppression, especially in difficult-to-image patients. Nevertheless, achieving a higher image quality is of the utmost importance in diagnostic ultrasound medical imaging, and further developments are still indispensable. From this point of view, a crucial role can be played by novel beamforming techniques as well as by non-conventional image formation techniques (e.g., advanced transmission strategies, and compounding, coded, and harmonic imaging). This Special Issue includes novel contributions on both ultrasound beamforming and image formation techniques, particularly addressed at improving B-mode image quality and related diagnostic content. This indeed represents a hot topic in the ultrasound imaging community, and further active research in this field is expected, where many challenges still persist.
n/a --- signal-to-noise ratio (SNR) --- multi-perspective ultrasound imaging --- dictionary learning --- common carotid artery --- spatial resolution --- contrast enhancement --- sparse representation --- PMUT linear array --- K-singular value decomposition --- time resolution --- cardiac imaging --- coded excitation --- plane wave --- beam pattern --- grating lobe suppression --- spatial coherence --- subcutaneous fat layer --- cylindrical scanning --- parallel beam forming --- microbubble --- MR-visible fiducial marker --- ultrasonic imaging --- speckle reduction --- multi-line transmission --- MRI --- adaptive beamforming --- super-resolution --- filtered-delay multiply and sum beamforming --- B-mode imaging --- medical ultrasound --- intima-media complex longitudinal motion --- synthetic aperture --- quantitative parametrization --- arterial wall motion --- pth root --- beam forming --- medical image processing --- crosstalk artifacts --- ultrasound imaging --- diverging wave --- 1-3 piezocomposite material --- dynamic focusing --- multi-line acquisition --- image reconstruction --- plane wave imaging --- ultrasound --- multi-line transmit --- reconstruction --- thyroid imaging --- beamforming
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This Special Issue is a collection of twelve papers on the design and application of biomedical circuits and systems. We hope you enjoy reading this Special Issue and become inspired to address technological challenges toward helping the medical industry and biologists to increase the quality of life for humans, which is the main objective. Several topics have been highlighted: muscle electrostimulation, analog front-end (AFE) circuits, waveform generators, real-time velocimetry estimators, interference suppression, bio-signal encryption, IoT electronic nose, ultrasound image processing, noise in medical imaging, elbow actuators, and aids for visually impaired people. We are conscious about the very wide scope of biomedical circuits and systems applications, and that our contribution represents only a grain of sand, though we expect to be useful in contributing to the progress of knowledge in the field.
elbow soft exoskeleton --- dual motor-tendon actuator --- PI control --- flexion/extension --- pronation/supination --- electronic travel aids --- radar --- electromagnetic --- visual impairment --- blind people --- microwaves --- mobility --- antennas --- infrared and thermal image analysis --- incremental low rank noise reduction --- incremental singular value decomposition --- segmentation --- monitoring of body temperature --- particle filter tracking --- phase aberration --- receive beamforming --- spatial resolution --- sound speed estimation --- Breath analysis --- electronic nose (E-nose) --- Internet-of-Things (IoT) --- RSA --- bio-signal --- TRNG --- data encryption --- IoT --- wireless communication --- interference suppression --- non-contact electrode --- impedance mismatch --- driven-right-leg --- electrocardiogram --- bioelectric acquisition --- doppler velocimetry --- doppler spectrum --- FPGA --- centroid estimation --- neuromodulation --- multi-channel --- stimulation protocol --- analog front-end (AFE) --- electrooculogram (EOG) --- electrooculography --- interference --- noise --- signal acquisition --- electrode array --- functional electrical stimulation --- rehabilitation --- DC offset current --- electrode stimulation --- biphasic signal --- platinum electrode matrix --- n/a
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