Search Results - (((((((want OR wkant) OR santis) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Stochastic Models in Reliability Engineering. by Cui, Lirong

Table of Contents: “…5.3 Time-Varying Reliability Evaluation Based on MDD -- 5.3.1 The Construction of System MDD -- 5.3.2 System Reliability Evaluation Based on MDD -- 5.4 Numerical Studies -- 5.4.1 Illustrative Example -- 5.4.2 System MDD for the Illustrative Example -- 5.4.3 System Reliability Assessment for the Illustrative Example -- 5.5 Conclusions -- References -- Chapter 6 An Upside-Down Bathtub-Shaped Failure Rate Model Using a DUS Transformation of Lomax Distribution -- 6.1 Introduction -- 6.2 DUS-Lomax Distribution -- 6.3 Shapes -- 6.3.1 Shape of Probability Density Function…”
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Differential algebra and related topics - proceedings of the international workshop. by SIT, WILLIAM Y.

Table of Contents: “…6 Reduction Algorithms 7 Rosenfeld Properties of an Autoreduced Set ; 8 Coherence and Rosenfeld's Lemma ; 9 Ritt-Raudenbush Basis Theorem ; 10 Decomposition Problems ; 11 Component Theorems ; 12 The Low Power Theorem ; Appendix: Solutions and hints to selected exercises ; References…”
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Energy Psychology : Explorations at the Interface of Energy, Cognition, Behavior, and Health. by Gallo, Fred P.

Table of Contents: “…Manual Muscle Testing and KinesiologyApplied Kinesiology Offshoots; Empirical Research on Manual Muscle Testing; Muscle Testing Proficiency; Integrity and Muscle Testing; Therapy and Diagnosis; Self-Testing; Therapeutic Algorithms vs. Causal Diagnostics; Energy Psychology and Manual Muscle Testing; Abuses of Manual Muscle Testing and Algorithms; Manual Muscle Testing and Intuition; Unwarranted Uses; When We're Stumped; Systemic Manual Muscle Testing.…”
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Fast sequential Monte Carlo methods for counting and optimization by Rubinstein, Reuven Y.

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Applications of digital signal processing to audio and acoustics

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Prognostics and Health Management of Engineering Systems : an Introduction. by Kim, Nam-Ho

Table of Contents: “…2.2.2 When a Degradation Model Is Available (Physics-Based Approaches)2.2.2.1 Problem Definition; 2.2.2.2 Parameter Estimation and Degradation Prediction; 2.2.2.3 Effect of Noise in Data; 2.2.3 When a Degradation Model Is NOT Available (Data-Driven Approaches); 2.2.3.1 Function Evaluation; 2.2.3.2 Overfitting; 2.2.3.3 Prognosis with More Training Data; 2.3 RUL Prediction; 2.3.1 RUL; 2.3.2 Prognostics Metrics; 2.3.2.1 Prognostic Horizon (PH); 2.3.2.2 varvec{ alpha { -- } lambda} Accuracy; 2.3.2.3 (Cumulative) Relative Accuracy (RA, CRA); 2.3.2.4 Convergence; 2.3.2.5 Results with MATLAB Code.…”
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Multi-agent machine learning : a reinforcement approach

Table of Contents: “…2.12 Eligibility TracesReferences; Chapter 3: Learning in Two-Player Matrix Games; 3.1 Matrix Games; 3.2 Nash Equilibria in Two-Player Matrix Games; 3.3 Linear Programming in Two-Player Zero-Sum Matrix Games; 3.4 The Learning Algorithms; 3.5 Gradient Ascent Algorithm; 3.6 WoLF-IGA Algorithm; 3.7 Policy Hill Climbing (PHC); 3.8 WoLF-PHC Algorithm; 3.9 Decentralized Learning in Matrix Games; 3.10 Learning Automata; 3.11 Linear Reward-Inaction Algorithm; 3.12 Linear Reward-Penalty Algorithm; 3.13 The Lagging Anchor Algorithm; 3.14 L R-I Lagging Anchor Algorithm; References.…”
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Radon Transforms and Tomography. by Quinto, Eric Todd

Table of Contents: “…Expository Papers -- Local tomography and related problems -- Tomography problems arising in synthetic aperture radar -- Introduction to local tomography -- Algorithms in ultrasound tomography -- Radon transforms, differential equations, and microlocal analysis -- Supplementary bibliography to A bibliographic survey of the Pompeiu problem -- II. …”
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Pervasive Computing in Cognitive Communications Applications. by Ghinea, George

Table of Contents: “…Cover -- Guest editorial -- Single-loop sigma delta modulator design and verification for cognitive IoT applications -- Efficient task scheduling on virtual machine in cloud computing environment -- High gain switched beam Yagi-Uda antenna for millimeter wave communications -- PAPR reduction using OQAM for spectrum sensing -- Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm -- Soft computing based audio signal analysis for accident prediction -- Kuznets curve with parabola shaped ultra-wideband antenna with defected ground plane for communications…”
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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications by Lantz, Brett

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Smart intelligent computing and communication technology

Table of Contents: “…Intro -- Title Page -- Preface -- Committees -- Contents -- Evolution of Computing Intelligence -- A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images -- Performance Analysis of K-Nearest Neighbor Classification Algorithms for Bank Loan Sectors -- An Effective Comparative Analysis of Data Preprocessing Techniques in Network Intrusion Detection System Using Deep Neural Networks -- Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors -- Impact of Personality Traits on Interpersonal Dependency…”
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Reconstructing evolution : new mathematical and computational advances

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Smart buildings, smart communities and demand response

Table of Contents: “…Demand Response in Smart Zero Energy Buildings and Grids / Nikos Kampelis -- DR in Smart and Near-zero Energy Buildings: The Leaf Community / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- Performance of Industrial and Residential Near-zero Energy Buildings / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response / Nikos Kampelis, Nikolaos Sifakis, Denia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions / Nikos Kampelis, Elisavet Tsekeri, Denia Kolokotsa, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Conclusions and Recommendations.…”
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Rediscovering mathematics : you do the math by Simonson, Shai

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Visible Business : Uncover the Blindspots and Engage the World's Female Economy. by Brideson, RJ

Table of Contents: “…Blind Spots; Contents; Foreword; About the Author; Acknowledgements; To my community; To my village; Preface; Introduction; Chapter 1 From history to herstory; The birth of business marketing; Things have changed -- or have they?; Women want more; The wo-man algorithm; Become conscious of the bias; From reject to results; Chapter 2 Are you blind?…”
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Numerical methods for viscosity solutions and applications

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DSP with Python Programming. by Charbit, Maurice

Table of Contents: “…Gaussian linear case: Kalman algorithm; 3.5. Discrete finite Markov case; 4 Monte-Carlo Methods; 4.1. …”
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Artificial intelligence with Python : build real-world artificial intelligence applications with Python to intelligently interact with the world around you by Joshi, Prateek

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Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language by Fischetti, Tony

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MATLAB for Machine Learning. by Ciaburro, Giuseppe

Table of Contents: “…Multiple linear regression with categorical predictorPolynomial regression; Regression Learner App; Summary; Chapter 5: Pattern Recognition through Classification Algorithms; Predicting a response by decision trees; Probabilistic classification algorithms -- Naive Bayes; Basic concepts of probability; Classifying with Naive Bayes; Bayesian methodologies in MATLAB; Describing differences by discriminant analysis; Find similarities using nearest neighbor classifiers; Classification Learner app; Summary; Chapter 6: Identifying Groups of Data Using Clustering Methods; Introduction to clustering.…”
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