Note: Intro -- Writing Committee -- Preface -- Description -- Contents -- 1 Introduction -- References -- 2 Prerequisites for Green-Mine Selection -- 3 Green Mine Construction Rating Sheet -- 4 Mining Area Environment -- 1 Mining Area Appearance -- 2 Mine Afforestation -- 5 Resource Development Methods -- 1 Resource Development -- 2 Mineral Processing -- 3 Mine Environmental Restoration and Control and Land Reclamation -- 4 Environmental Management and Monitoring -- 6 Comprehensive Utilization of Resources -- 1 Comprehensive Utilization of Associated Resources -- 2 Solid Waste Disposal and Comprehensive Utilization -- 3 Wastewater Treatment and Comprehensive Utilization -- 4 Ore Resources' Comprehensive Utilization -- 5 Solid Waste Disposal and Comprehensive Utilization -- 6 Wastewater Treatment and Comprehensive Utilization -- 7 Energy Conservation and Emission Reduction -- 1 Energy Conservation -- 2 Exhaust Emissions -- 3 Wastewater Discharge -- 4 Solid Waste Discharge -- 5 Noise Emission -- 8 Scientific and Technological Innovation and Intelligent Mines -- 1 Scientific and Technological Innovation -- 2 Intelligent Mine -- 9 Enterprise Management and Enterprise Image -- 1 Green-Mine Management System -- 2 Enterprise Culture -- 3 Enterprise Management -- 4 Community Harmony -- 5 Enterprise Integrity -- 10 Related Knowledge -- 1 Reference Documents -- 2 Basic Knowledge.

Note: Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Pattern Recognition and Machine Learning -- Margin Constraint for Low-Shot Learning -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation -- 3.2 Cosine-Similarity Based Classifier -- 3.3 Incorporating the Margin Constraint -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions -- References -- Enhancing Open-Set Face Recognition by Closing It with Cluster-Inferred Gallery Augmentation -- 1 Introduction -- 1.1 Closing Open-Set Recognition -- 1.2 Use-Case: Individualized School Photo Albums -- 2 Related Work -- 3 Cluster-Inferred Gallery Augmentation -- 3.1 Clustering -- 3.2 Selecting References for Unknown Identities -- 3.3 Discarding Similar Gallery Items -- 4 Experiments and Results -- 4.1 DBSCAN Hyperparameters -- 4.2 CIGA in an Open-Set Face Recognition Pipeline -- 5 Conclusion -- References -- Action Recognition in Untrimmed Videos with Composite Self-attention Two-Stream Framework -- 1 Introduction -- 1.1 Action Recognition -- 1.2 Zero-Shot Learning -- 2 Related Work -- 3 Our Approach -- 3.1 Sub-branch of the Multi-channel Self-attention Model -- 3.2 Model of Composite Feature Branch -- 3.3 Model of Composite Feature Branch -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Evaluation -- 4.3 Implement Details -- 4.4 Result -- 5 Conclusion -- References -- Representation Learning for Style and Content Disentanglement with Autoencoders -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Style Disentanglement -- 3.2 Content Disentanglement -- 3.3 Overall Framework -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Residual Attention Encoding Neural Network for Terrain Texture Classification. , 1 Introduction -- 2 Residual Attention Encoding Neural Network -- 2.1 Network Structure -- 2.2 Residual Attention Block -- 2.3 Encoding Block -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Datasets -- 3.3 Experimental Results -- 4 Conclusion -- References -- The Shape of Patterns Tells More -- 1 Introduction -- 2 Analysing the Shape of Pattern in Hough Space -- 3 Basic Steps -- 3.1 Estimating the Radius of the Circle -- 3.2 Estimating the Centre of the Circle -- 4 Simulation Results and Discussion -- 5 Conclusion and Future Works -- References -- A Spatial Density and Phase Angle Based Correlation for Multi-type Family Photo Identification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Facial Key Points Detection -- 3.2 Spatial Density and Phase Angle Based Correlation Feature Extraction -- 4 Experimental Results -- 4.1 Experiments on Three Classes Classification -- 4.2 Experiments on Two Class Classification -- 5 Conclusion and Future Work -- References -- Does My Gait Look Nice? Human Perception-Based Gait Relative Attribute Estimation Using Dense Trajectory Analysis -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 3.1 Dense Trajectory Extraction from Gait Videos -- 3.2 Dense Trajectory-Based HOF Descriptor -- 3.3 Fisher Vector Encoding for Gait Motion -- 4 Learning Gait Relative Attributes -- 4.1 Gait Attribute Annotation -- 4.2 Evaluation Criteria -- 5 Experiments -- 5.1 Zero-Shot Learning Results -- 6 Conclusion -- References -- Scene-Adaptive Driving Area Prediction Based on Automatic Label Acquisition from Driving Information -- 1 Introduction -- 2 Automatic Label Assignment Using Driving Information -- 2.1 Assigning Labels: Pedestrian, Vehicle, Roads, etc. -- 2.2 Assigning Labels: Scene-Adaptive Driving Area -- 3 Scene-Adaptive Driving Area Prediction Model Using Semantic Segmentation and ConvLSTM. , 4 Experiment -- 4.1 Dataset -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Enhancing the Ensemble-Based Scene Character Recognition by Using Classification Likelihood -- 1 Introduction -- 2 Hierarchical Recognition Method in the Ensemble Scheme -- 2.1 Flow of the Hierarchical Recognition Method -- 2.2 Classification Likelihood of the Ensemble Voting Method -- 2.3 Pre-processing Method and Judgement of Final Answer -- 3 Performance Evaluation of Hierarchical Recognition Method -- 3.1 Experimental Environment and Dataset -- 3.2 Pre-processing Parameters in the Hierarchical Recognition Method -- 3.3 Evaluation with Simple Ensemble Scheme -- 3.4 Evaluation with Latest Ensemble Scheme -- 4 Conclusion -- References -- Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection Masks -- 1 Introduction -- 2 Related Work -- 2.1 EWC -- 2.2 Piggyback -- 3 Method -- 4 Experiments -- 4.1 Evaluation on Continual Learning of Image Translation Tasks -- 4.2 Analysis on Trained Binary Masks -- 5 Conclusions -- References -- A Real-Time Eye Tracking Method for Detecting Optokinetic Nystagmus -- 1 Introduction -- 2 Background -- 3 Experimental Methods -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Network Structure for Personalized Face-Pose Estimation Using Incrementally Updated Face-Shape Parameters -- 1 Introduction -- 2 Related Research -- 2.1 Appearance-Based Face-Pose Estimation Methods -- 2.2 Model-Based Face-Pose Estimation Methods -- 2.3 Person-Specific Estimation/Recognition -- 3 Proposed Approach -- 4 Implementation -- 4.1 Shape Parameter Representation -- 4.2 Shape-Estimation Network -- 4.3 Pose-Estimation Network -- 5 Experiment -- 5.1 Learning Process of Networks -- 5.2 Evaluation of Pose-Estimation Network -- 5.3 Parameter Personalization Using Shape-Estimation Network -- 5.4 Discussion. , 6 Conclusion -- References -- Optimal Rejection Function Meets Character Recognition Tasks -- 1 Introduction -- 2 Related Work -- 3 Learning with Rejection -- 4 Experimental Result -- 4.1 Experimental Setup -- 4.2 Classification of Similar Characters with Reject Option -- 4.3 Classification of Character and Non-character with Reject Option -- 5 Conclusion -- References -- Comparing the Recognition Accuracy of Humans and Deep Learning on a Simple Visual Inspection Task -- 1 Introduction -- 2 Design of the Simple Task -- 2.1 Overview -- 2.2 Generation of Stimulus Images -- 3 Inspection Accuracy of Human Participants -- 3.1 Setting -- 3.2 Experimental Procedures -- 3.3 Results -- 3.4 Analysis of Gaze Locations of the Participants -- 4 Inspection Accuracy of Deep Learning Techniques -- 4.1 Overview -- 4.2 Visual Inspection Using SSD -- 4.3 Visual Inspection Using U-Net -- 4.4 Comparison with Human Participants -- 5 Conclusions -- References -- Improved Gamma Corrected Layered Adaptive Background Model -- 1 Introduction -- 2 Related Work -- 3 IGLABM -- 3.1 ABM, Adaptive Background Model ch16ABM -- 3.2 GLABM, Gamma Corrected Layered Adaptive Background Model ch16GLABM -- 3.3 Proposed Method -- 4 Experimental Results -- 4.1 Validity of Background Image Synthesis -- 4.2 Comparison of Gamma Estimation Accuracy with Existing Methods -- 4.3 Performance Comparison of Background Estimation with Existing Methods -- 5 Conclusion -- References -- One-Shot Learning-Based Handwritten Word Recognition -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Siamese Network Model -- 4.1 Model Adaptation -- 5 Model Parameters -- 5.1 Loss Function -- 5.2 Optimization -- 5.3 Weights -- 5.4 Learning -- 6 Experimental Framework -- 6.1 Dataset -- 6.2 Evaluation -- 7 Results and Discussions -- 7.1 Results on George Washington Database. , 7.2 Results on Indian City Names Dataset -- 7.3 Comparison with Other One-Shot Recognition Frameworks -- 7.4 Comparison with Respect to a Pre-trained CNN -- 8 Conclusion and Future Work -- References -- First-Person View Hand Parameter Estimation Based on Fully Convolutional Neural Network -- 1 Introduction -- 2 Related Work -- 3 Hand Parameter Estimation via Two Stream Convolution -- 3.1 Encoder-Decoder -- 3.2 Posture Encoder and Detection -- 3.3 Two Stream Convolutional Layers -- 3.4 Loss Functions -- 3.5 Implementation Detail -- 3.6 NTU Synthetic Dataset -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Evaluation Metric -- 4.3 Results -- 5 Conclusion -- References -- Dual-Attention Graph Convolutional Network -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Overview of Dual-Attention GCN -- 3.2 Graph Construction -- 3.3 Connection-Attention -- 3.4 Hop-Attention -- 3.5 The Loss Function -- 4 Implementation Details -- 5 Experiments -- 5.1 Datasets -- 5.2 Results and Comparisons -- 5.3 Ablation Study -- 6 Conclusion -- References -- Chart-Type Classification Using Convolutional Neural Network for Scholarly Figures -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Chart Type Classification -- 4 Evaluation Experiments -- 4.1 Dataset -- 4.2 Outline of Experiment -- 4.3 Comparative Experiment -- 5 Results and Discussion -- 6 Conclusion and Future Works -- References -- Handwritten Digit String Recognition for Indian Scripts -- 1 Introduction -- 2 Properties Indian Scripts -- 3 Data Collection -- 4 Methodology -- 4.1 Dense Block -- 4.2 Transition Block -- 4.3 Residual Connections -- 4.4 Dimension Adjustment -- 4.5 CTC Output Layer -- 5 Result and Discussions -- 5.1 Global Recognition Results -- 5.2 Confusing Numeral Pair Computation -- 5.3 Erroneous Results -- 6 Conclusion -- References. , Spatial-Temporal Graph Attention Network for Video-Based Gait Recognition.

Note: Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Classification -- Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Class Hierarchy -- 3.2 Probabilistic Model -- 3.3 Inference -- 3.4 Training -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Overall Improvement-ImageNet -- 4.3 Speedup-CIFAR-100 -- 4.4 Fine-Grained Recognition-NABirds -- 4.5 Overview and Discussion -- 5 Conclusion -- References -- Label-Smooth Learning for Fine-Grained Visual Categorization -- 1 Introduction -- 2 Related Work -- 2.1 Fine-Grained Visual Categorization -- 2.2 Label-Smooth Leaning -- 3 Approach -- 3.1 Label-Smoothing Learning -- 3.2 Cross-Category Cross-Semantic Regularization -- 4 Experiments -- 4.1 Fine-Grained Visual Categorization (FGVC) Datasets -- 4.2 Implementation -- 4.3 Ablation Studies -- 4.4 Comparison with State-of-the-Art -- 5 Conclusion -- References -- ForestNet - Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees -- 1 Introduction -- 2 Related Work -- 3 Model to Build a Sparse Multilayer Pereceptron Network -- 3.1 Building the Ensemble of Randomized Trees -- 3.2 Defining the SMLP Network Architecture -- 3.3 Initialization of the Weights -- 4 Experiment Settings -- 4.1 Hyperparameter Settings for Tree Ensembles -- 4.2 Hyperparameter Settings for Networks -- 5 Results and Discussion -- 5.1 Classification Accuracy -- 5.2 Amount of Connections -- 5.3 Amount of Hidden Neurons -- 5.4 Training and Validation Losses -- 5.5 Discussion -- 6 Conclusion and Future Work -- References -- Clustering-Based Adaptive Dropout for CNN-Based Classification -- 1 Introduction -- 2 The Proposed Algorithm -- 2.1 Clustering-Based Dropout with Adaptive Probability. , 2.2 Clustering Algorithm and Network Configuration -- 2.3 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- Action and Video and Motion -- Real-Time Detection and Tracking Using Hybrid DNNs and Space-Aware Color Feature: From Algorithm to System -- 1 Introduction -- 2 Related Work -- 2.1 Multiple Object Tracking -- 2.2 Color Feature -- 3 Whole Framework -- 4 Detection -- 5 Matching and Tracking -- 5.1 Similarity Sorting-Based Matching -- 5.2 Space-Aware Color-Based Bounding Box Refinement -- 5.3 Retire-Recall Mechanism -- 5.4 Whole Matching and Tracking Algorithm -- 6 Experiment Evaluation -- 6.1 Overall Performance -- 6.2 Technique Effect -- 6.3 Platform-Specific Exploration -- 6.4 Discussion -- 7 Conclusion -- References -- Continuous Motion Numeral Recognition Using RNN Architecture in Air-Writing Environment -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Motion Tracking Method -- 3.2 Numeral Recognition -- 4 Results and Discussion -- 4.1 Data Collection -- 4.2 Experimental Setup -- 4.3 Result -- 4.4 Error Analysis -- 5 Conclusion -- References -- Using Motion Compensation and Matrix Completion Algorithm to Remove Rain Streaks and Snow for Video Sequences -- 1 Introduction -- 2 Challenges -- 3 The Proposed Method -- 3.1 Rain Streaks Detection -- 3.2 Rain Map Refinement -- 3.3 Rain Streaks Reconstruction -- 4 Experimental Results -- 4.1 Synthetic Video Sequences -- 4.2 Real Video Sequences -- 4.3 Challenge Tasks -- 5 Conclusion -- References -- Assessing the Impact of Video Compression on Background Subtraction -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Study Design -- 4.1 Background Subtraction -- 4.2 Dataset -- 4.3 Quality Metrics for Videos -- 4.4 Performance Metric for Background Subtraction Algorithms -- 5 Results. , 5.1 Relation Between Percentage Drop in Quality and Performance -- 5.2 Identify the Encoding Parameters to Employ in Surveillance Scenarios -- 5.3 Predicting Performance Based on Video Quality -- 6 Conclusion -- References -- Object Detection and Anomaly Detection -- Learning Motion Regularity for Temporal Video Segmentation and Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Anomaly Detection Method -- 3.1 Temporal Video Segmentation -- 3.2 Anomaly Detection -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results -- 4.4 Analysis of the Impact of Temporal Video Segmentation -- 5 Conclusion -- References -- A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Fourier Spectral Distributions -- 3.2 Spectral Density and Variance Based Features for Forged Handwriting Classification -- 4 Experimental Results -- 4.1 Experiments on Forged Handwriting Word Detection -- 4.2 Experiments on Forged Caption Text Detection in Video Images -- 4.3 Experiments on Forged IMEI Number Detection -- 5 Conclusion and Future Work -- References -- Robust Pedestrian Detection: Faster Deployments with Fusion of Models -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 State-of-the-Art Object Detectors -- 3.2 Non-maxima Suppression -- 3.3 Detectors Divergences -- 3.4 Missed Detections -- 3.5 Proposed Architecture for Fusion of Models -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Metrics -- 5 Conclusion -- References -- Perceptual Image Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Perceptual Image Anomaly Detection -- 3.1 Relative-perceptual-L1 Loss -- 3.2 Training Objective -- 3.3 Gradient-Normalizing Weight Policy -- 4 Experiments -- 4.1 Results -- 4.2 Ablation Study -- 5 Conclusion -- References. , Segmentation, Grouping and Shape -- Deep Similarity Fusion Networks for One-Shot Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 4 Method -- 4.1 Dataset and Metric -- 4.2 Deep Similarity Fusion Network -- 4.3 Similarity Feature Fusion Module -- 5 Experiments -- 5.1 Implement Details -- 5.2 Results -- 5.3 Ablation Study -- 6 Conclusion -- References -- Seeing Things in Random-Dot Videos -- 1 Introduction -- 2 Signal Modelling -- 3 Literature Review -- 4 The A Contrario Framework -- 5 A Contrario on Random-Dot Videos -- 5.1 A Merging Strategy for Temporal Integration -- 5.2 Designing the A Contrario Algorithm -- 6 A Priori Analysis of A Contrario Performance -- 7 Empirical Results of the A Contrario Algorithm -- 7.1 Static Edge Case -- 7.2 Dynamic Edge Case -- 8 Human Performance Versus the A Contrario Process -- 8.1 Evaluating the Visual Angle -- 8.2 Evaluating the Time Integration -- 9 Concluding Remarks -- References -- Boundary Extraction of Planar Segments from Clouds of Unorganised Points -- 1 Introduction -- 2 Hough Voting Analysis -- 2.1 Hough Space -- 2.2 Voting Analysis -- 3 Plane Boundary Extraction -- 3.1 Top and Bottom Boundary Detection -- 3.2 Left and Right Boundary Detection -- 4 Experimental Results -- 4.1 Test on Synthetic Point Clouds -- 4.2 Test on Real-World Point Clouds -- 5 Conclusions -- References -- Real-Time Multi-class Instance Segmentation with One-Time Deep Embedding Clustering -- 1 Introduction -- 2 Related Works -- 3 One-Time Clustering Method -- 3.1 Encoder-Decoder Network -- 3.2 Loss Function -- 3.3 One-Time Clustering Algorithm -- 4 Experiments -- 4.1 Cityscapes Dataset -- 4.2 Evaluation Metrics -- 4.3 Training Environment Setup -- 4.4 Result and Discussion -- 5 Conclusion -- References -- Face and Body and Biometrics. , Multi-person Pose Estimation with Mid-Points for Human Detection under Real-World Surveillance -- 1 Introduction -- 2 Methodology -- 2.1 Generating Body Region Points and Mid-Points -- 2.2 Generating Core-of-Pose -- 2.3 Generating Pose Vectors and Human Bounding Boxes -- 3 Evaluation -- 4 Discussion -- 4.1 Associating Parts Rather Than Detecting the Whole Target -- 4.2 Training Mid-Points -- 4.3 Triangles in Core-of-Pose -- 5 Conclusion and Future Work -- References -- Gaze from Head: Gaze Estimation Without Observing Eye -- 1 Introduction -- 2 Eye-Head Coordination -- 3 Methods -- 4 Datasets -- 4.1 Real Dataset -- 4.2 VR Dataset -- 5 Experiment -- 5.1 Qualitative Evaluation -- 5.2 Quantitative Evaluation -- 5.3 Compatibility of the Real and VR Datasets -- 6 Conclusion -- References -- Interaction Recognition Through Body Parts Relation Reasoning -- 1 Introduction -- 2 Related Work -- 2.1 Action Recognition -- 2.2 Human Interaction Recognition -- 2.3 Relational Network -- 3 Relational Network Overview -- 4 Interaction Relational Network -- 5 Experiments -- 5.1 Datasets -- 5.2 Implementation Details -- 5.3 Experimental Results -- 6 Conclusion -- References -- DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences -- 1 Introduction -- 2 Related Work -- 3 DeepHuMS: Our Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Comparison with State of the Art -- 4.5 Discussion -- 4.6 Limitations -- 5 Conclusion -- References -- Adversarial Learning and Networks -- Towards a Universal Appearance for Domain Generalization via Adversarial Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Problem Statement and Motivations -- 3.2 Network Architecture -- 3.3 Appearance Generalizer -- 3.4 Adversarial Learning -- 4 Experiment -- 4.1 Evaluation of Appearance Generalizer. , 4.2 Comparison with Existing Methods.

Note: Intro -- Contents -- Preface -- Acknowledgments -- About the Author -- Acronyms -- Chapter 1 -- Specification of Convective Heat Transfer in a Way of Process -- Abstract -- Nomenclature -- Special Characters -- Superscripts -- Subscripts -- 1. Why Specification of Convective Heat Transfer in a Way of Process Is Necessary -- 1.1. The Energy Conservation Equation Only Indicates the Final Result of Convective Heat Transfer -- 1.2. The Process Occurs on the Wall Surface Cannot be Determined -- 1.3. The Role of Velocity Gradient in Convective Heat Transfer is Not Specified -- 1.4. Why Different Nusselt Numbers are Obtained at Different Thermal Boundary Condition? -- 1.5. Why Different Nusselt Numbers are Obtained When the Same Mechanical Energy is Consumed by Fluid Flow? -- 1.6. Why Secondary Flow with Velocity in One Order Smaller Than That of Main Flow Can Enhance Convective Heat Transfer Greatly? -- 1.7. Why α is Called Thermal Diffusivity Instead of Temperature Diffusivity? -- 1.8. There is No Specification of Intensity Convective Heat Transfer in Flow Field -- 1.9. Do Analogies between Momentum, Heat and Mass Transports Exist? -- 2. General Governing Equation for the Transport of the Heat Flux -- 3. Physical Explanations the Convective Transport Equation of the Heat Flux -- 3.1. The Role of Heat Conduction in Convective Transport of the Heat Flux -- 3.2. The Right Position of α -- 3.3. The Role of Velocity in Convective Heat Transfer -- 3.4. The Role of Velocity Gradient in Convective Heat Transfer -- 3.5. What Kind Process Does Occur on the Wall Surface? -- 4. General Governing Equation for the Transport of the Mass Flux -- 5. Physical Explanations the Convective Transport Equation of the Mass Flux -- 5.1. The Role of Mass Diffusion in Convective Transport of the Mass Flux -- 5.2. The Right Position of DAB. , 5.3. The Role of Velocity in Convective Mass Transfer -- 5.4. The Role of Velocity Gradient in Convective Mass Transfer -- 5.5. What Kind Process Does Occur on the Wall Surface? -- Summary -- References -- Chapter 2 -- Convective Transport Equation for the Momentum Flux and Energy Dissipation in Fluid Flow -- Abstract -- Nomenclature -- Special Characters -- Superscripts -- Subscripts -- 1. General Governing Equation for the Transport of the Momentum Flux -- 2. Physical Explanations the Convective Transport Equation of the Momentum Flux -- 2.1. The Role of Diffusion in Convective Transport of the Momentum Flux -- 2.2. The Right Position of ν -- 2.3. The Role of Velocity in Convective Transport of the Momentum Flux -- 2.4. The Role of Velocity Gradient in Convective Transport of the Momentum Flux -- 2.5. The Vorticity Has a Special Contribution to the Transport of the Momentum Flux -- 2.6. What Kind Process Does Occur on the Wall Surface? -- 2.7. The Momentum Flux Transported in Fully Developed Two Dimensional Channel Flow is a Diffusion Process -- 3. The Procedures and Agents of Energy Dissipation in Different Directions in In-Compressible Fluid Flow -- 3.1. Energy Equations Related with Flow -- 3.2. Conflict of Energy Dissipation in Turbulence Flow -- 3.3. Agent in Overall Energy Dissipation Procedure -- 4. Physical Views on Analogies between the Momentum Flux, the Heat Flux and the Mass Flux Transports -- Summary -- Appendix: Process to Get the Convective Transport Equation of the Momentum Flux in Cartesian Coordinate -- References -- Chapter 3 -- Velocity and Its Gradient Contributions to the Transport of the Heat Flux in Laminar Convection of Circular Tube and Channel -- Abstract -- Nomenclature -- Special Characters -- Subscripts -- 1. Explicit Form of Convective Transport Equation of the Heat Flux in Cylinder Coordinate. , 2. Explicit Form of Convective Transport Equation of the Heat Flux in Cartesian Coordinate -- 3. In Developing Flow Region -- 3.1. Circular Tube -- 3.1.1. On the Wall Surface -- 3.1.2. In Flow Region -- 3.2. Channel -- 3.2.1. On the Wall Surface -- 3.2.2. In Flow Region -- 4. In Fully Developed Flow Region -- 4.1. Circular Tube -- 4.1.1. On the Wall Surface -- 4.1.2. In Flow Region -- 4.2. Channel -- 4.2.1. On the Wall Surface -- 4.2.2. Inside the Flow -- Summary -- Appendix: Numerical Method -- A1. Circular Tube -- A2. Channel Formed by Two Parallel Plain Planes -- References -- Chapter 4 -- Heat Transfer Enhancement Mechanism Uncovered by the Convective Transport of the Heat Flux in a Channel with Vortex Generators and in a Twisted Elliptic Tube -- Abstract -- Nomenclature -- Special Characters -- Subscripts -- 1. Heat Transfer Enhancement Characteristics Realized by Longitudinal Vortex Generators -- 1.1. Local Nusselt Number Nulocal Characteristics -- 1.2. The Span Average Nusselt Number -- 2. Heat Transfer Enhancement Characteristics Realized by Twisting of Elliptical Tube -- 3. Parameters Definitions -- 4. The Enhancement of the Transport of the Heat Flux in Channel with Vortex Generators -- 4.1. The Field Characteristics of the Contributions -- 4.1.1. The Vector Fields of Wc/ΔT, We/ΔT, (Wc+We)/ΔT under UWT -- 4.1.2. The Vector Fields of Wc/ΔT, We/ΔT, (Wc+We)/ΔT under UWHF -- 4.2. The Local Velocity and Velocity Gradient Contributions -- 4.3. The Span Average the Velocity and Velocity Gradient Contributions -- 4.4. The Convective Heat Transfer Enhancement Mechanisms Enforced by Longitudinal Vortices -- 5. The Enhancement Mechanism of the Transport of the Heat Flux in Twisted Elliptic Tube -- 5.1. The Field Characteristics of the Contributions -- 5.1.1. The Vector Fields of Wc/ΔT, We/ΔT, (Wc+We)/ΔT and q/ΔT under UWT. , 5.1.2. The Vector Fields of Wc/ΔT, We/ΔT, (Wc+We)/ΔT and q/ΔT under UWHF -- 5.2. The Local Velocity and Velocity Gradient Contributions -- 5.2.1. The Contributions to the Transport of q∇ξ on the Inspected Lines -- 5.2.2. The Contributions to the Transport of q∇η on the Inspected Lines -- 5.2.3. The Contributions to the Transport of q∇ζ on the Inspected Lines -- 5.3. The Direction Averaged Contribution Characteristics -- 5.3.1. The Contribution to the Transport of q∇ξ Averaged along the ξ Direction -- 5.3.2. The Contribution to Transport of q∇ξ Averaged along the η Direction -- 5.3.3. The Contribution to the Transport of q∇η Averaged along the η Direction -- 5.3.4. The Contribution to the Transport of q∇ζ Averaged along the η Direction -- 5.4. The Convective Heat Transfer Enhancement Mechanisms Enforced by Twisting of Elliptic Tube -- Summary -- 1. The Channel with Vortex Generators Mounted on the Bottom Surface -- 2. The Twisted Elliptic Tube -- 3. The Common Characteristics -- Appendix: Numerical Method -- A1. Problems Studied and Their Mathematics Formulations -- A2. Numerical Method and Its Validation -- References -- Chapter 5 -- The Role of Secondary Flow in Convective Heat Transfer Uncovered by Convective Transport Equation of the Heat Flux -- Abstract -- Nomenclature -- Special Characters -- Subscripts -- 1. Description of the Role of Secondary Flow in Convective Transport of the Heat Flux -- 2. The Role of Secondary Flow in Developing Fluid Flow and Convective Heat Transfer -- 2.1. Laminar Developing Fluid Flow and Heat Transfer in a Channel -- 2.1.1. The Contributions to the Transport of qx -- 2.1.2. The Contributions to the Transport of qy -- 2.1.3. Secondary Flow Enhances the Contributions of the Main Flow to the Transport of the Heat Fluxes -- 2.2. Laminar Developing Fluid Flow and Convective Heat Transfer in Square Duct. , 2.2.1. The Contribution Fields -- 2.2.2. The Contributions to the Transport of qx -- 2.2.3. The Contributions to the Transport of qy -- 2.2.4. Secondary Flow Enhances the Contributions of the Main Flow to the Transport of the Heat Fluxes -- 2.3. Laminar Developing Fluid Flow and Convective Heat Transfer in a Tube -- 2.3.1. The Contributions to the Transport of qr -- 2.3.2. The Contributions to the Transport of qz -- 2.3.3. Secondary Flow Enhances the Contributions of the Main Flow to the Transport of the Heat Fluxes -- 3. The Role Secondary Flow in Fluid Flow and Convective Heat Transfer in a Tube with Twisted Tape Insert -- 3.1. The Contribution Field Information -- 3.2. Local Characteristics along the Line Intersected by η = const and ζ = const -- 3.2.1. Contribution to the Transport of qξ -- 3.2.2. Contribution to the Transport of qη -- 3.2.3. Contribution to the Transport of qζ -- 3.3. Local Characteristics along the Lines Intersected by ξ = const and ζ = const -- 3.3.1. Contribution to the Transport of qξ -- 3.3.2. Contribution to the Transport of qη -- 3.3.3. Contribution to the Transport of qζ -- 3.4. Averaged Contributions along the Line Intersected by η = const and ζ = const -- Summary -- Appendix: Numerical Model and Method -- A1. The Geometrical Model -- A2. Physical Model -- A3. Numerical Method -- A4. Validation of Numerical Method -- References -- Chapter 6 -- The Role of Vorticity in Convective Heat Transfer Uncovered by Convective Transport Equation of the Heat Flux -- Abstract -- Nomenclature -- Special Characters -- Subscripts -- 1. Description of the Role of Vorticity in Convective Transport of the Heat/Mass Flux -- 2. The Contribution of Vorticity to the Transport of the Heat Flux in the Channel Mounted VGS on the Bottom Plane -- 2.1. The Characteristics of Vorticity Generated by Rectangle Vortex Generators. , 2.2. The Field Characteristics of the Contributions.

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