Search Results - (((((((ant OR wante) OR mantis) OR when) OR cantor) OR anne) OR share) OR hints) algorithms.

Hierarchical Protection for Smart Grids. by Ma, Jing

Table of Contents: “…2.3.4 Adaptive Overload Identification Method Based on the Complex Phasor Plane2.3.5 Novel Fault Phase Selection Scheme Utilizing Fault Phase Selection Factors; 2.4 Summary; References; Chapter 3 Local Area Protection for Renewable Energy; 3.1 Introduction; 3.2 Fault Transient Characteristics of Renewable Energy Sources; 3.2.1 Mathematical Model and LVRT Characteristics of the DFIG; 3.2.2 DFIG Fault Transient Characteristics When Crowbar Protection Is Not Put into Operation; 3.2.3 DFIG Fault Transient Characteristics When Crowbar Protection Is Put into Operation.…”
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Comprehensive Ruby Programming. by Hudgens, Jordan

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Multicriteria decision aid and artificial intelligence : links, theory and applications by Doumpos, Michael

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Regression Analysis with Python. by Massaron, Luca, Boschetti, Alberto

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Integration of demand response into the electricity chain : challenges, opportunities and smart grid solutions

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Modern aerodynamic methods for direct and inverse applications by Chin, Wilson C.

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Insight into Theoretical and Applied Informatics : Introduction to Information Technologies and Computer Science. by Yatsko, Andrzej

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Image processing and jump regression analysis by Qiu, Peihua, 1965-

Table of Contents: “…Cover -- Contents -- Preface -- 1 Introduction -- 1.1 Images and image representation -- 1.2 Regression curves and sugaces with jumps -- 1.3 Edge detection, image restoration, and jump regression analysis -- 1.4 Statistical process control and some other related topics -- 1.5 Organization of the book -- Problems -- 2 Basic Statistical Concepts and Conventional Smoothing Techniques -- 2.1 Introduction -- 2.2 Some basic statistical concepts and terminologies -- 2.2.1 Populations, samples, and distributions -- 2.2.2 Point estimation of population parameters -- 2.2.3 Confidence intervals and hypothesis testing -- 2.2.4 Maximum likelihood estimation and least squares estimation -- 2.3 Nadaraya- Watson and other kernel smoothing techniques -- 2.3.1 Univariate kernel estimators -- 2.3.2 Some statistical properties of kernel estimators -- 2.3.3 Multivariate kernel estimators -- 2.4 Local polynomial kernel smoothing techniques -- 2.4.1 Univariate local polynomial kernel estimators -- 2.4.2 Some statistical properties -- 2.4.3 Multivariate local polynomial kernel estimators -- 2.4.4 Bandwidth selection -- 2.5 Spline smoothing procedures -- 2.5.1 Univariate smoothing spline estimation -- 2.5.2 Selection of the smoothing parameter -- 2.5.3 Multivariate smoothing spline estimation -- 2.5.4 Regression spline estimation -- 2.6 Wavelet transformation methods -- 2.6.1 Function estimation based on Fourier transformation -- 2.6.2 Univariate wavelet transformations -- 2.6.3 Bivariate wavelet transformations -- Problems -- 3 Estimation of Jump Regression Curves -- 3.1 Introduction -- 3.2 Jump detection when the number of jumps is known -- 3.2.1 Difference kernel estimation procedures -- 3.2.2 Jump detection based on local linear kernel smoothing -- 3.2.3 Estimation of jump regression functions based on semiparametric modeling -- 3.2.4 Estimation of jump regression functions by spline smoothing -- 3.2.5 Jump and cusp detection by wavelet transformations -- 3.3 Jump estimation when the number of jumps is unknown -- 3.3.1 Jump detection by comparing three local estimators -- 3.3.2 Estimation of the number of jumps by a sequence of hypothesis tests -- 3.3.3 Jump detection by DAKE -- 3.3.4 Jump detection by local polynomial regression -- 3.4 Jump-preserving curve estimation -- 3.4.1 Jump curve estimation by split linear smoothing -- 3.4.2 Jump-preserving curve fitting based on local piecewise-linear kernel estimation -- 3.4.3 Jump-preserving smoothers based on robust estimation -- 3.5 Some discussions -- Problems -- 4 Estimation of Jump Location Curves of Regression Surfaces -- 4.1 Introduction -- 4.2 Jump detection when the number of jump location curves is known -- 4.2.1 Jump detection by RDKE -- 4.2.2 Minimax edge detection -- 4.2.3 Jump estimation based on a contrast statistic -- 4.2.4 Algorithms for tracking the JLCs -- 4.2.5 Estimation of JLCs by wavelet transformations -- 4.3 Detection of arbitrary jumps by local smoothing -- 4.3.1 Treat JLCs as a pointset in the design space -- 4.3.2 Jump detection by local linear estimation -- 4.3.3 Two modijication procedures -- 4.4 Jump detection in two or more given directions -- 4.4.1 Jump detection in two given directions -- 4.4.2 Measuring the p.…”
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Bayesian Analysis with Python. by Osvaldo Martin

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Automatic Modeling and Fault Diagnosis of Timed Concurrent Discrete Event Systems : Automatische Modellierung und Fehlerdiagnose Zeitlicher Nebenläufiger Ereignisdiskreter Systeme.... by Schneider, Stefan

Table of Contents: “…3.3.3 Precision and Completeness Properties3.3.4 Identification Parameters; 3.4 Timed Distributed Modeling; 3.5 Identification of Timed Distributed Models; 3.5.1 Timed Distributed Identification Approach; 3.5.2 Precision and Completeness Properties; 3.5.3 Discussion on Shared I/Os; 3.6 Identification of Timed Distributed BMS Models; 3.6.1 Data Collection; 3.6.2 Timed Distributed Identification; 4 Partitioning of DES Models; 4.1 Preliminaries; 4.2 Causal Partitioning; 4.2.1 Distance and Causality; 4.2.2 Causal Partitioning Algorithm; 4.3 Optimal Partitioning; 4.3.1 Optimization Approach.…”
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Building Machine Learning Systems with Python. by Richert, Willi

Table of Contents: “…Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python -- the dream team; What the book will teach you (and what it will not); What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, and Matplotlib; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling non-existing values; Comparing runtime behaviors; Learning SciPy; Our first (tiny) machine learning application.…”
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Combinatorial and Computational Mathematics : Present and Future.

Table of Contents: “…2 LIST OF APPLICABLE MATHEMATICS IN SOCIAL SCIENCE3 SOCIAL WELFARE FUNCTIONS (SWF); 4 PROSPECTS; 5 OPEN PROBLEMS; References; Twelve Views of Matroid Theory; INTRODUCTION; 1 LINEAR DEPENDENCE WITHOUT SCALARS; 2 BASIS EXCHANGE PROPERTIES; 3 GEOMETRIC LATTICES; 4 GRAPH THEORY WITHOUT VERTICES; 5 GRAPH THEORY AND LEAN LINEAR ALGEBRA; 6 VARIETIES OF FINITE MATROIDS; 7 SECRET-SHARING MATROIDS; 8 GREEDY ALGORITHMS, MATROID INTERSECTION, AND MATROID PARTITION; 9 MATRIX MULTIPLICATION AND THE CAUCHY-BINET IDENTITY; 10 BASIS GENERATING FUNCTIONS AND THE MATRIX-TREE THEOREM…”
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Language, vision, and music : selected papers from the 8th International Workshop on the Cognitive Science of Natural Language Processing, Galway, Ireland, 1999

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Applied Artificial Intelligence : Proceedings of the 7th International FLINS Conference. by Arena, Paolo, Fortuna, Luigi

Table of Contents: “…An extended branch-and-bound algorithm for fuzzy linear bilevel programming / G. …”
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Biological systematics : principles and applications by Brower, Andrew V. Z. (Andrew Van Zandt), 1962-, Schuh, Randall T.

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Automatic indexing and abstracting of document texts by Moens, Marie-Francine, 1957-

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Fast finance : does the financial world allow social loafing? by Williams, Gervais

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Google's PageRank and Beyond - the Science of Search Engine Rankings. by Langville, Amy N., Meyer, Carl D.

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Advanced wireless networks : technology and business models by Glisic, Savo G.

Table of Contents: “…16.1 Introduction 523 -- 16.2 Layering as Optimization Decomposition 524 -- 16.3 Cross-Layer Optimization 533 -- 16.4 Optimization Problem Decomposition Methods 543 -- References 554 -- 17 Network Information Theory 557 -- 17.1 Capacity of Ad Hoc Networks 557 -- 17.2 Information Theory and Network Architectures 569 -- 17.3 Cooperative Transmission in Wireless Multihop Ad Hoc Networks 577 -- References 584 -- 18 Stability of Advanced Network Architectures 585 -- 18.1 Stability of Cooperative Cognitive Wireless Networks 585 -- 18.2 System Model 586 -- 18.4 Optimal Control Policy 592 -- 18.5 Achievable Rates 594 -- 18.6 Stabilizing Transmission Policies 598 -- References 605 -- 19 Multi-Operator Spectrum Sharing 607 -- 19.1 Business Models for Spectrum Sharing 607 -- 19.2 Spectrum Sharing in Multi-hop Networks 638 -- References 656 -- 20 Large Scale Networks and Mean Field Theory 659 -- 20.1 MFT for Large Heterogeneous Cellular Networks 659 -- 20.2 Large Scale Network Model Compression 664 -- 20.3 Mean Field Theory Model of Large Scale DTN Networks 668 -- 20.4 Mean Field Modeling of Adaptive Infection Recovery in Multicast DTN Networks 674 -- 20.5 Mean Field Theory for Scale-Free Random Networks 701 -- 20.6 Spectrum Sharing and MFT 709 -- 20.7 Modeling Dynamics of Complex System 711 -- Appendix A.20 Iterative Algorithm to Solve Systems of Nonlinear ODEs (DiNSE-Algorithm) 721 -- Appendix B.20 Infection Rate of Destinations for DNCM 722 -- Appendix C.20 Infection Rate for Basic Epidemic Routing 722 -- References 722 -- 21 mmWave Networks 726 -- 21.1 mmWave Technology in Subcellular Architecture 726 -- 21.2 Microeconomics of Dynamic mmWave Networks 737 -- References 747 -- 22 Cloud Computing in Wireless Networks 750 -- 22.1 Technology Background 750 -- 22.2 System Model 752 -- 22.3 System Optimization 756 -- 22.4 Dynamic Control Algorithm 758 -- 22.5 Achievable Rates 761 -- 22.6 Stabilizing Control Policies 763 -- References 769 -- 23 Wireless Networks and Matching Theory 771.…”
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Politics of disinformation : the influence of fake news on the public sphere

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