Search Results - (((((((ant OR wikant) OR santis) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Advanced Research in Materials and Engineering Applications

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Statistical analysis techniques in particle physics : fits, density estimation and supervised learning by Narsky, Ilya, Porter, Frank Clifford

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Limits, Limits Everywhere : the Tools of Mathematical Analysis by Applebaum, David, 1956-

Table of Contents: “…Continued Fractions; 9.1 Euclid's Algorithm; 9.2 Rational and Irrational Numbers as Continued Fractions; 10. …”
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Multiple Sclerosis : Clinician's Guide to Diagnosis and Treatment. by Birnbaum, Gary

Table of Contents: “…Long-Term Disease-Modifying Therapies; Issues with Long-Term Immune-Modulating Therapies; Choosing a Disease-Modifying Therapy; Ensuring Treatment Adherence and Compliance; When Do I Initiate Treatment With Disease-Modifying Therapies?…”
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Robust statistics : theory and methods (with R) by Maronna, Ricardo A.

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Microbiological sensors for the drinking water industry

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Decision Intelligence for Dummies by Baker, Pamela

Table of Contents: “…Any road will take you there -- The great rethink when it comes to making decisions at scale -- Applying the Upside-Down V: The Path to the Output and Back Again -- Evaluating Your Inverted V Revelations -- Having Your Inverted V Lightbulb Moment -- Recognizing Why Things Go Wrong -- Aiming for too broad an outcome -- Mimicking data outcomes -- Failing to consider other decision sciences -- Mistaking gut instincts for decision science -- Failing to change the culture -- Part 2 Reaching the Best Possible Decision -- Chapter 5 Shaping a Decision into a Query -- Defining Smart versus Intelligent -- Discovering That Business Intelligence Is Not Decision Intelligence -- Discovering the Value of Context and Nuance -- Defining the Action You Seek -- Setting Up the Decision -- Chapter 6 Mapping a Path Forward -- Putting Data Last -- Recognizing when you can (and should) skip the data entirely -- Leaning on CRISP-DM -- Using the result you seek to identify the data you need -- Digital decisioning and decision intelligence -- Don't store all your data - know when to throw it out -- Adding More Humans to the Equation -- The shift in thinking at the business line level -- How decision intelligence puts executives and ordinary humans back in charge -- Limiting Actions to What Your Company Will Actually Do -- Looking at budgets versus the company will -- Setting company culture against company resources -- Using long-term decisioning to craft short-term returns -- Chapter 7 Your DI Toolbox -- Decision Intelligence Is a Rethink, Not a Data Science Redo -- Taking Stock of What You Already Have -- The tool overview -- Working with BI apps -- Accessing cloud tools -- Taking inventory and finding the gaps -- Adding Other Tools to the Mix -- Decision modeling software -- Business rule management systems -- Machine learning and model stores -- Data platforms.…”
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A Statistical approach to genetic epidemiology : concepts and applications, with an e-Learning Platform. by Ziegler, Andreas, 1966-

Table of Contents: “…; 4.3 HOW SHOULD GENOTYPING ERRORS BE CHECKED IN POPULATION-BASED STUDIES USING THE HARDY-WEINBERG EQUILIBRIUM?; Algorithm 4.1.; Algorithm 4.2.; Algorithm 4.3.; 4.4 HOW CAN GENOTYPING ERRORS BE DETECTED IN HIGH-THROUGHPUT GENOTYPING STUDIES?…”
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Drones and other unmanned weapons systems under international law by Casey-Maslen, Stuart, Homayounnejad, Maziar, Stauffer, Hilary, Weizmann, Nathalie

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The Maudsley Prescribing Guidelines in Psychiatry by Taylor, David, 1963-, Paton, Carol, Kapur, Shitij

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Creating wicked students : designing courses for a complex world by Hanstedt, Paul, 1965-

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Applied mechanics and industrial technologies : selected, peer reviewed papers from the 2012 International Conference on Applied Mechanics and Manufacturing Technology (AMMT 2012),...

Table of Contents: “…An Assessment of Wet Compression Process in Gas Turbine Systems with an Analytical ModelingAssessment of Hole Quality and Thrust Force when Drilling CFRP/Al Stack Using Carbide Tools; Multi-Objective Optimization of Filament Wound Composite Pressure Vessels Based on Weight and Matrix Cracking/ Burst Pressure Using Imperialist Competitive Algorithm; In Situ Laser Build-Up Welding of Shipboard Crankshafts; Stress and Deformation Analysis for Caster Segment Based on ANSYS; Instable Crack Propagation Analysis with PDS-FEM; Reducing the Manufacturing Cost by Using Combination Die.…”
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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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Media and the affective life of slavery by Page, Allison, 1983-

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

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Measurement and control of granular materials : selected peer reviewed papers from the 9th International Conference on Measurement and Control of Granular Materials, MCGM 2011, (Gl...

Table of Contents: “…Process Tomographic Measurements of Granular Flow in a Pneumatic Conveying SystemImage Reconstruction Based on Compressed Sensing with Split Bregman Algorithm and Fuzzy Bases; Optimum Design of an Internal 8-Electrode Electrical Capacitance Tomography Sensor Array; Research on Human ADL Data Real-Time Transmission Optimization Method Based on Bayesian Network; Optimization Design of Capacitance Sensor with Helical Shaped Surface Plates; An Image Quality Assessment Algorithm for Palm-Dorsa Vein Based on Multi-Feature Fusion; Chapter 4:Powder Explosion and System Protection…”
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Reviews in computational chemistry.

Table of Contents: “…Small Molecule Docking and Scoring; Introduction; Algorithms for Molecular Docking; The Docking Problem; Placing Fragments and Rigid Molecules; Flexible Ligand Docking; Handling Protein Flexibility; Docking of Combinatorial Libraries; Scoring; Shape and Chemical Complementary Scores; Force Field Scoring; Empirical Scoring Functions; Knowledge-Based Scoring Functions; Comparing Scoring Functions in Docking Experiments: Consensus Scoring.…”
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Modern aerodynamic methods for direct and inverse applications by Chin, Wilson C.

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The finite element method for three-dimensional thermomechanical applications by Dhondt, Guido D. C.

Table of Contents: “…. -- General Equations. -- The Shape Functions. -- Numerical Integration. -- Extrapolation of Integration Point Values to the Nodes. -- Problematic Element Behavior. -- Linear Constraints. -- Transformations. -- Loading. -- Modal Analysis. -- Cyclic Symmetry. -- Dynamics: the alpha-method. -- 3. …”
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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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