Search Results - (((((((ant OR wanting) OR semantic) OR wind) OR cantor) OR anne) OR plane) OR hints) algorithms.

Informatics and technology in clinical care and public health

Table of Contents: “…Intro -- Title Page -- Preface -- ICIMTH 2021 Scientific Programme Committee and Reviewers -- Contents -- Using Artificial Intelligence to Develop a Lexicon-Based African American Tweet Detection Algorithm to Inform Culturally Sensitive Twitter-Based Social Support Interventions for African American Dementia Caregivers -- A Comparison of Word Embeddings to Study Complications in Neurosurgery -- Gulf Cooperation Council Clinical Trials in the Pursuit of Medications for COVID-19…”
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Regression Analysis with Python. by Massaron, Luca, Boschetti, Alberto

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Mathematical methods in computer aided geometric design II

Table of Contents: “…A New Characterization of PlaneElastica; 1. Introduction; 2. A Characterization of Elástica by their Curvature Function; 3. …”
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Aha! Solutions by Erickson, Martin

Table of Contents: “…-- Lines Dividing the Plane -- A Number that Counts -- A Broken Odometer -- How Many Matrices? …”
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Machine learning for protein subcellular localization prediction by Wan, Shibiao, Mak, M. W.

Table of Contents: “…5.2.2 Problem transformation methods -- 5.2.3 Multi-label classification in bioinformatics -- 5.3 mGOASVM: A predictor for both single- and multi-location proteins -- 5.3.1 Feature extraction -- 5.3.2 Multi-label multiclass SVM classification -- 5.4 AD-SVM: An adaptive decision multi-label predictor -- 5.4.1 Multi-label SVM scoring -- 5.4.2 Adaptive decision for SVM (AD-SVM) -- 5.4.3 Analysis of AD-SVM -- 5.5 mPLR-Loc: A multi-label predictor based on penalized logistic regression -- 5.5.1 Single-label penalized logistic regression -- 5.5.2 Multi-label penalized logistic regression -- 5.5.3 Adaptive decision for LR (mPLR-Loc) -- 5.6 Summary -- 6 Mining deeper on GO for protein subcellular localization -- 6.1 Related work -- 6.2 SS-Loc: Using semantic similarity over GO -- 6.2.1 Semantic similarity measures -- 6.2.2 SS vector construction -- 6.3 HybridGO-Loc: Hybridizing GO frequency and semantic similarity features -- 6.3.1 Hybridization of two GO features -- 6.3.2 Multi-label multiclass SVM classification -- 6.4 Summary -- 7 Ensemble random projection for large-scale predictions -- 7.1 Random projection -- 7.2 RP-SVM: A multi-label classifier with ensemble random projection -- 7.2.1 Ensemble multi-label classifier -- 7.2.2 Multi-label classification -- 7.3 R3P-Loc: A compact predictor based on ridge regression and ensemble random projection -- 7.3.1 Limitation of using current databases -- 7.3.2 Creating compact databases -- 7.3.3 Single-label ridge regression -- 7.3.4 Multi-label ridge regression -- 7.4 Summary -- 8 Experimental setup -- 8.1 Prediction of single-label proteins -- 8.1.1 Datasets construction -- 8.1.2 Performance metrics -- 8.2 Prediction of multi-label proteins -- 8.2.1 Dataset construction -- 8.2.2 Datasets analysis -- 8.2.3 Performance metrics -- 8.3 Statistical evaluation methods -- 8.4 Summary.…”
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Eat, cook, grow : mixing human-computer interactions with human-food interactions

Table of Contents: “…"You don't have to be a gardener to do urban agriculture": understanding opportunities for designing interactive technologies to support urban food production / William Odom -- Augmented agriculture, algorithms, aerospace, and alimentary architectures / Jordan Geiger -- The allure of provenance: tracing food through user-generated production information / Ann Light -- Beyond gardening: a new approach to HCI and urban agriculture / Tad Hirsch -- Hungry for data: metabolic interaction from farm to fork to phenotype / Marc Tuters and Denisa Kera -- Food futures: three provocations to challenge HCI interventions / Greg Hearn and David Lindsay Wright -- Bringing technology to the dining table / Charles Spence -- List of recipes.…”
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FRBR, before and after : a look at our bibliographic models by Coyle, Karen

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The Johns Hopkins guide to digital media

Table of Contents: “…Berry -- Cognitive implications of new media / Anne Mangen and Jean-Luc Velay -- Collaborative narrative / Scott Rettberg -- Collective intelligence / John Duda -- Combinatory and automatic text generation / Philippe Bootz and Christopher Funkhouser -- Computational linguistics / Inderjeet Mani -- Conceptual writing / Darren Wershler -- Copyright / Benjamin J. …”

Acoustic Microscopy and Ultrasonic Imaging : From Principles to Advanced Applications. by Maev, Roman Gr

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3D Trajectory Extraction from 2D Videos for Human Activity Analysis. by Boukhers, Zeyd

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The History of Mathematics : A Brief Course. by Cooke, Roger L.

Table of Contents: “…Of Part II -- Chapter 3: Overview of Mesopotamian Mathematics -- 3.1 A Sketch of Two Millennia of Mesopotamian History -- 3.2 Mathematical Cuneiform Tablets -- 3.3 Systems of Measuring and Counting -- 3.4 The Mesopotamian Numbering System -- Problems and Questions -- Chapter 4: Computations in Ancient Mesopotamia -- 4.1 Arithmetic -- 4.2 Algebra -- Problems and Questions -- Chapter 5: Geometry in Mesopotamia -- 5.1 The Pythagorean Theorem -- 5.2 Plane Figures -- 5.3 Volumes -- 5.4 Plimpton 322 -- Problems and Questions -- Chapter 6: Egyptian Numerals and Arithmetic -- 6.1 Sources…”
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Computer aided design and manufacturing by Bi, Zhuming, Wang, Xiaoqin (Writer on manufacturing)

Table of Contents: “…Evaluate Platform Solutions 159; 4.11 Computer Aided Tools (CAD) for Platform Technologies 160; 4.11.1 Modelling Techniques of Product Variants 163; 4.11.2 Design Toolboxes 163; 4.11.3 Custom Design Libraries 164; 4.12 Summary 165; 4.13 Design Projects 166; References 169; 5 Computer Aided Reverse Engineering 173; 5.1 Introduction 173; 5.2 RE as Design Methodology 175; 5.3 RE Procedure 178; 5.4 Digital Modelling 179; 5.4.1 Types of Digital Models 180; 5.4.2 Surface Reconstruction 181; 5.4.3 Algorithms for Surface Reconstruction 181; 5.4.4 Limitations of Existing Algorithms 182; 5.4.5 Data Flow in Surface Reconstruction 183; 5.4.6 Surface Reconstruction Algorithm 184; 5.4.7 Implementation Examples 186; 5.5 Hardware Systems for Data Acquisition 188; 5.5.1 Classification of Hardware Systems 191; 5.5.2 Positioning of Data Acquisition Devices 197; 5.5.3 Control of Scanning Processes 199; 5.5.4 Available Hardware Systems 200; 5.6 Software Systems for Data Processing 201; 5.6.1 Data Filtering 201; 5.6.2 Data Registration and Integration 204; 5.6.3 Feature Detection 205; 5.6.4 Surface Reconstruction 205; 5.6.5 Surface Simplification 205; 5.6.6 Segmentation 206; 5.6.7 Available Software Tools 206; 5.7 Typical Manufacturing Applications 206; 5.8 Computer Aided Reverse Engineering (CARE) 208; 5.8.1 Recap to Convert Sensed Data into Polygonal Models 209; 5.8.2 ScanTo3D for Generation of Parametric Models 211; 5.8.3 RE of Assembled Products 212; 5.9 RE -- Trend of Development 213; 5.10 Summary 213; 5.11 Design Project 214; References 215; 6 Computer Aided Machine Design 219; 6.1 Introduction 219; 6.2 General Functional Requirements (FRs) of Machines 222; 6.3 Fundamentals of Machine Design 223; 6.3.1 Link Types 223; 6.3.2 Joint Types and Degrees of Freedom (DoFs) 223; 6.3.3 Kinematic Chains 225; 6.3.4 Mobility of Mechanical Systems 226; 6.4 Kinematic Synthesis 230; 6.4.1 Type Synthesis 230; 6.4.2 Number Synthesis 230; 6.4.3 Dimensional Synthesis 232; 6.5 Kinematics 233; 6.5.1 Positions of Particles, Links, and Bodies in 2D and 3D Space 233; 6.5.2 Motions of Particles, Links, and Bodies 235; 6.5.3 Vector-Loop Method for Motion Analysis of a Plane Mechanism 240; 6.5.4 Kinematic Modelling Based on Denavit-Hartenberg (D-H) Parameters 246; 6.5.5 Jacobian Matrix for Velocity Relations 248; 6.6 Dynamic Modelling 259; 6.6.1 Inertia and Moments of Inertia 259; 6.6.2 Newton-Euler Formulation 261; 6.6.3 Lagrangian Method 266; 6.7 Kinematic and Dynamics Modelling in Virtual Design 269; 6.7.1 Motion Simulation 269; 6.7.2 Model Preparation 271; 6.7.3 Creation of a Simulation Model 271; 6.7.4 Define Motion Variables 274; 6.7.5 Setting Simulation Parameters 275; 6.7.6 Run Simulation and Visualize Motion 275; 6.7.7 Analyse Simulation Data 276; 6.7.8 Structural Simulation Using Motion Loads 277; 6.8 Summary 278; 6.9 Design Project 279; References 279; Part II Computer Aided Manufacturing (CAM) 281; 7 Group Technology and Cellular Manufacturing 283; 7.1 Introduction 283; 7.2 Manufacturing System and Components 2…”
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Analysis, Modeling, and Stability of Fractional Order Differential System : the Infinite State Approach. by Trigeassou, Jean-Claude

Table of Contents: “…Heat transfer and diffusive model of the plane wall; 4.2.1. Heat transfer; 4.2.2. Physical model of the diffusive interface; 4.2.3. …”
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Advanced composites for marine engineering : selected, peer reviewed papers from the 1st International Conference on Advanced Composites for Marine Engineering (ICACME 2013), Septe...

Table of Contents: “…Advanced Composites for Marine Engineering; Preface; Table of Contents; Chapter 1: Numerical Methods and Analysis, Optimization, Algorithm and Modelling for Materials and Structures; An Efficient Analytical Failure Analysis Approach for Multilayered Composite Offshore Production Risers; Composite Wind Turbine Blade Aerodynamic and Structural Integrated Design Optimization Based on RBF Meta-Model; Rapid Predicting the Impact Behaviors of Marine Composite Laminates; Study on the Factors of Residual Thermal Stress on Single Lap Joint.…”
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Rediscovering mathematics : you do the math by Simonson, Shai

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Debates in the digital humanities 2016

Table of Contents: “…Father Busa Female Punch Card Operatives / Melissa Terras and Julianne Nyhan -- On the Origin of "Hack" and "Yack" / Bethany Nowviskie -- Reflections on a Movement: #transformDH, Growing Up / Moya Bailey, Anne Cong-Huyen, Alexis Lothian, and Amanda Phillips.…”
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Advanced research on information science, automation and material system IV : selected, peer reviewed papers from the 2014 4th International Conference on Information Science, Auto...

Table of Contents: “…Applied Technology in Using HPLC for Determining Aflatoxin Contents in PeanutsChapter 2: Applied Mechanics, Mechanical Engineering, Electrical and Electronic Engineering; Simulation on the Behavior of Individual Vehicles with Influence Evaluation of Flow Rate and Speed Limits on Freeway; Effect of Jet Hole Size on Drag Reduction Performance of Bionic Jet Surface; Optimization of Horizontal Plate Fin Heat Sink in Natural Convection for Electronics Cooling by Simulated Annealing Algorithm; Error Analysis of a Non-Contact Parallel Plane Sensor Based on Monte Carlo Method…”
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Advances in applied mechanics and materials : selected, peer reviewed papers from the International Conference on Mechanical Engineering (ICOME 2013), September 19-21, 2013, Matara...

Table of Contents: “…Validation of AWTSim as Aerodynamic Analysis for Design Wind Turbine BladeNumerical Study on the Influence of the Corner Curvature of Circular Micropillar on Microdroplet Size via a Dewetting Process; Redesign ITS Central Library through Smart Building; Optimization of Maximum Lift to Drag Ratio on Airfoil Design Based on Artificial Neural Network Utilizing Genetic Algorithm; Carbon Dioxide Effects on the Flammability Characteristics of Biogas; Heat Transfer Effectiveness and Coefficient of Pressure Drop on the Shell Side of a Staggered Elliptical Tubes Bank.…”
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Manufacturing automation technology and system II

Table of Contents: “…On the Size Effects in Micro/Meso Upsetting of Brass H62 at Elevated TemperaturesResearch on the Hot Stamping of Invar Based on Flexible Mould; Acoustic Emission Signal Acquisition and Analysis on Tool Wear; Chapter II: Mechanical Engineering and Engineering Design; Tacho Plate's Technological Procedure Compilation and the Fixture's Digital Design; Analysis of Bearing Mechanics Characteristic of Wind Turbine; Torsional Vibration of Precise Cable Drive System; Research on Production Control Mode of Manufacturing System Based on Neuroendocrine Hormonal Regulation.…”
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Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. by Zaccone, Giancarlo

Table of Contents: “…; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs).…”
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