An Effective Discretization Method for Disposing HighDimensional Data

Feature discretization is an extremely important preprocessing task used for classification in data mining and machine learning as many classification methods require that each dimension of the training dataset contains only discrete values. Most of discretization methods mainly concentrate on discr
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Model Predictive Stabilization Control of Highspeed Autonomous Ground Vehicles
20190906This paper presents a model predictive control (MPC) scheme for the stabilization of highspeed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a singletrack dynamic model with roll dynamics is derived. Variable timesteps are utilized for vehicle model discretization, enabling collision avoidance in the longterm without compromising the prediction accuracy in the nearterm. Accordingly, safe driving constraints such as sideslip envelope, zeromomentpoint, and lateral safety corridor are developed for the handling stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameters uncertainties. Simulations carried out in Matlab/CarSim environment validated the capability and realtime ability of the proposed control scheme.
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Dynamic Modeling and Control of Highspeed V1.3.pdf
20190906Lane change maneuver of highspeed automated vehicles is complicated since it involves highly nonlinear vehicle dynamics, which is critical for the driving safety and handling stability. Addressing this challenge, we present the dynamic modeling and control of highspeed automated vehicles for lane change maneuver. A nonlinear singletrack vehicle dynamics model and a multisegment lane change process model are employed. Variable timesteps are utilized for the vehicle model discretization to ensure a long enough prediction horizon while maintaining model fidelity and computational feasibility. A nonlinear singletrack vehicle dynamics model and a multisegment lane change process model are employed. Variable timesteps are utilized for the vehicle model discretization to ensure a long enough prediction horizon while maintaining model fidelity and computational feasibility. Accordingly, the control of lane change maneuver is addressed in two successive stages. First, by considering the lane change maneuver as primarily a longitudinal control problem, velocity profiles are determined to ensure the longitudinal safety of this maneuver. Then, the associated lateral control is generated with a model predictive controller, taking the handling stability envelope, coupled tire forces and environmental constraints into account.
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论文研究组合与概率的连续特征权衡量化方法.pdf
20190908连续特征量化方法是数据挖掘方法中必要的预处理过程。呈现一种组合与概率的连续特征权衡量化方法。基于最小描述长度以及组合与概率理论，提出连续特征量化的权衡标准，能够在量化所导致的分类错误与量化区间信息之间得到合理的权衡；基于该权衡标准提出一种有效的动态规划量化算法，以找到最好的量化结果；量化后的数据采用naive贝叶斯分类器进行分类预测，与其他连续特征量化方法的对比实验结果表明，新方法得到了较高的平均学习精度。
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Huge An Optimal Parallel RobinRobin Iterative Method for the Mixed Finite Element Discretization of the Second Order Elliptic Problems
20200302求解混合有限元离散二阶椭圆问题的并行RobinRobin迭代方法，王锋， 曾玉平，RobinRobin迭代方法是一种非重叠区域分解方法, 该方法借助界面上的Robin边界条件来交换子区域之间的信息. 针对混合有限元离散二阶椭圆�
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Hybird AI System 13th International conference
20180612Hybrid Artificial Intelligent Systems: 13th International Conference, HAIS 2018, Oviedo, Spain, June 2022, 2018, Proceedings (Lecture Notes in Computer Science) This volume constitutes the refereed proceedings of the 13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018, held in Oviedo, Spain, in June 2018. The 62 full papers published in this volume were carefully reviewed and selected from 104 submissions. They are organized in the following topical sections: Neurocomputing, fuzzy systems, rough sets, evolutionary algorithms, Agents and Multiagent Systems, and alike. Table of Contents Chapter 1. A Deep LearningBased Recommendation System to Enable End User Access to Financial Linked Knowledge Chapter 2. On the Use of Random Discretization and Dimensionality Reduction in Ensembles for Big Data Chapter 3. Hybrid Deep Learning Based on GAN for Classifying BSR Noises from Invehicle Sensors Chapter 4. Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming Services Chapter 5. Learning Logical Definitions of nAry Relations in Graph Databases Chapter 6. GAparsimony: An R Package for Searching Parsimonious Models by Combining Hyperparameter Optimization and Feature Selection Chapter 7. Improving Adaptive Optics Reconstructions with a Deep Learning Approach Chapter 8. Complexity of Rule Sets in Mining Incomplete Data Using Characteristic Sets and Generalized Maximal Consistent Blocks Chapter 9. Optimization of the University Transportation by Contraction Hierarchies Method and Clustering Algorithms Chapter 10. Identification of Patterns in Blogosphere Considering Social Positions of Users and Reciprocity of Relations Chapter 11. SmartFD: A Real Big Data Application for Electrical Fraud Detection Chapter 12. Multiclass Imbalanced Data Oversampling for Vertebral Column Pathologies Classification Chapter 13. A Hybrid GeneticBootstrapping Approach to Link Resources in the Web of Data Chapter 14. Modelling and Forecasting of the 222Rn Radiation Level Time Series at the Canfranc Underground Laboratory Chapter 15. Sensor Fault Detection and Recovery Methodology for a Geothermal Heat Exchanger Chapter 16. Distinctive Features of Asymmetric Neural Networks with Gabor Filters Chapter 17. Tuning CNN Input Layout for IDS with Genetic Algorithms Chapter 18. Improving the Accuracy of Prediction Applications by Efficient Tuning of Gradient Descent Using Genetic Algorithms Chapter 19. Monomodal Medical Image Registration with Coral Reef Optimization Chapter 20. Evaluating Feature Selection Robustness on HighDimensional Data Chapter 21. Generalized Probability Distribution Mixture Model for Clustering Chapter 22. A Hybrid Approach to Mining Conditions Chapter 23. A First Attempt on Monotonic Training Set Selection Chapter 24. Dealing with Missing Data and Uncertainty in the Context of Data Mining Chapter 25. A Preliminary Study of Diversity in Extreme Learning Machines Ensembles Chapter 26. Orthogonal Learning Firefly Algorithm Chapter 27. Multilabel Learning by Hyperparameters Calibration for Treating Class Imbalance Chapter 28. Drifted Data Stream Clustering Based on ClusTree Algorithm Chapter 29. Featuring the Attributes in Supervised Machine Learning Chapter 30. Applying VorEAl for IoT Intrusion Detection Chapter 31. Evaluation of a WristBased Wearable Fall Detection Method Chapter 32. EnerVMAS: Virtual Agent Organizations to Optimize Energy Consumption Using Intelligent Temperature Calibration Chapter 33. Tool Wear Estimation and Visualization Using Image Sensors in Micro Milling Manufacturing Chapter 34. Compensating Atmospheric Turbulence with Convolutional Neural Networks for Defocused Pupil Image WaveFront Sensors Chapter 35. Using Nonlinear Quantile Regression for the Estimation of Software Cost Chapter 36. A Distributed DroneOriented Architecture for InFlight Object Detection Chapter 37. 3D Gabor Filters for Chest Segmentation in DCEMRI Chapter 38. Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis Chapter 39. Listen to This: Music Recommendation Based on OneClass Support Vector Machine Chapter 40. Improving Forecasting Using Information Fusion in Local Agricultural Markets Chapter 41. Chord Progressions Selection Based on Song Audio Features Chapter 42. LearnSec: A Framework for Full Text Analysis Chapter 43. A Mood Analysis on Youtube Comments and a Method for Improved Social Spam Detection Chapter 44. An Improved Comfort Biased Smart Home Load Manager for Grid Connected Homes Under Direct Load Control Chapter 45. Remifentanil Dose Prediction for Patients During General Anesthesia Chapter 46. Classification of Prostate Cancer Patients and Healthy Individuals by Means of a Hybrid Algorithm Combing SVM and Evolutionary Algorithms Chapter 47. A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments Chapter 48. TaxonomyBased Detection of User Emotions for Advanced Artificial Intelligent Applications Chapter 49. Prediction of the Energy Demand of a Hotel Using an Artificial IntelligenceBased Model Chapter 50. A Hybrid Algorithm for the Prediction of Computer Vision Syndrome in Health Personnel Based on Trees and Evolutionary Algorithms Chapter 51. An Algorithm Based on Satellite Observations to Quality Control Ground Solar Sensors: Analysis of Spanish Meteorological Networks Chapter 52. Predicting Global Irradiance Combining Forecasting Models Through Machine Learning Chapter 53. A Hybrid Algorithm for the Assessment of the Influence of Risk Factors in the Development of Upper Limb Musculoskeletal Disorders Chapter 54. Evolutionary Computation on Road Safety Chapter 55. Memetic Modified Cuckoo Search Algorithm with ASSRS for the SSCF Problem in SelfSimilar Fractal Image Reconstruction Chapter 56. A Redescription Based Developmental Approach to the Generation of Value Functions for Cognitive Robots Chapter 57. A Hybrid Iterated Local Search for Solving a Particular TwoStage FixedCharge Transportation Problem Chapter 58. Change Detection in Multidimensional Data Streams with Efficient Tensor Subspace Model Chapter 59. A View of the State of the Art of Dialogue Systems Chapter 60. An Adaptive Approach for Index Tuning with Learning Classifier Systems on Hybrid Storage Environments Chapter 61. Electrical Behavior Modeling of Solar Panels Using Extreme Learning Machines Chapter 62. A Hybrid Clustering Approach for Diagnosing Medical Diseases
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Vector field processing
20181205This course reviews the three main families of discretizations used to design computational tools for vector ﬁeld processing on triangle meshes: facebased, edgebased, and vertexbased representations. In the process of reviewing the computational tools offered by these representations, we go over a large body of recent developments in vector ﬁeld processing in the area of discrete differential geometry. We also discuss the theoretical and practical limitations of each type of discretization, and cover increasinglycommon extensions such as ndirection and nvector ﬁelds.
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Identification of Dynamic Systems
20110214An introduction of the book on Identification of Dynamic Systems.
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Mathematical Objects in C++
20190115Mathematical Objects in C++: Computational Tools in A Unified ObjectOriented Approach (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series) By 作者: Yair Shapira ISBN10 书号: 1439811474 ISBN13 书号: 9781439811474 Edition 版本: 1 出版日期: 20090619 pages 页数: (600) $210 Emphasizing the connection between mathematical objects and their practical C++ implementation, this book provides a comprehensive introduction to both the theory behind the objects and the C and C++ programming. Objectoriented implementation of threedimensional meshes facilitates understanding of their mathematical nature. Requiring no prerequisites, the text covers discrete mathematics, data structures, and computational physics, including highorder discretization of nonlinear equations. Exercises and solutions make the book suitable for classroom use and a supporting website supplies downloadable code. Chapter 1：Natural Numbers Chapter 2：Integer Numbers Chapter 3：Rational Numbers Chapter 4：Real Numbers Chapter 5：Complex Numbers Chapter 6：Euclidean Geometry Chapter 7：Analytic Geometry Chapter 8：Sets Chapter 9：Vectors and Matrices Chapter 10：Multilevel Objects Chapter 11：Graphs Chapter 12：Polynomials Chapter 13：Basics of Programming Chapter 14：Recursion Chapter 15：Objects Chapter 16：Vectors and Matrices Chapter 17：Dynamic Vectors and Lists Chapter 18：Trees Chapter 19：Graphs Chapter 20：Sparse Matrices Chapter 21：Meshes Chapter 22：Triangulation Chapter 23：Mesh of Tetrahedra Chapter 24：Polynomials Chapter 25：Sparse Polynomials Chapter 26：Stiffiness and Mass Matrices Chapter 27：Splines Chapter 28：Appendix:Solutions of Exercises
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Distributed Optimal Consensus Control for Multiagent Systems With Input Delay
20191104This paper addresses the problem of distributed optimal consensus control for a continuoustime heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuoustime inputdelayed system is converted into a discretetime delayfree system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the inputdelayed system can be cast into that of the delayfree system. Second, by virtue of the Hamilton–Jacobi–Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delayfree system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a criticaction neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the criticaction NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.
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Learning pandas  Second Edition  2017 pdf 2分
20180424Table of Contents Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Errata Piracy Questions 1. pandas and Data Analysis Introducing pandas Data manipulation, analysis, science, and pandas Data mani
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Mixed order discretization based twolevel Schwarz preconditioners for a tracer transport problem on the cubedsphere
20210209Mixed order discretization based twolevel Schwarz preconditioners for a tracer transport problem on the cubedsphere
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运行时模型相关资料
20180930基于运行时模型的物联网方案，离散控制和连续控制。Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle highvolatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete objectoriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency.
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论文研究A Discretization Algorithm Based on Information Distance Criterion and Ant Colony Optimization Algorithm.pdf
20190821基于信息距离与蚁群优化的离散化算法，贾立新，诸文智，离散化算法是数据挖掘中十分重要的部分。由于现有的离散化方法无法准确地反映工业数据库中类属性间的关联性，本文基于信息距离��
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Data Mining
20091203数据挖掘目录： Contents Foreword v Preface xxiii Updated and revised content xxvii Acknowledgments xxix Part I Machine learning tools and techniques 1 1 What’s it all about? 3 1.1 Data mining and machine learning 4 Describing structural patterns 6 Machine learning 7 Data mining 9 1.2 Simple examples: The weather problem and others 9 The weather problem 10 Contact lenses: An idealized problem 13 Irises: A classic numeric dataset 15 CPU performance: Introducing numeric prediction 16 Labor negotiations: A more realistic example 17 Soybean classification: A classic machine learning success 18 1.3 Fielded applications 22 Decisions involving judgment 22 Screening images 23 Load forecasting 24 Diagnosis 25 Marketing and sales 26 Other applications 28 v i i 1.4 Machine learning and statistics 29 1.5 Generalization as search 30 Enumerating the concept space 31 Bias 32 1.6 Data mining and ethics 35 1.7 Further reading 37 2 Input: Concepts, instances, and attributes 41 2.1 What’s a concept? 42 2.2 What’s in an example? 45 2.3 What’s in an attribute? 49 2.4 Preparing the input 52 Gathering the data together 52 ARFF format 53 Sparse data 55 Attribute types 56 Missing values 58 Inaccurate values 59 Getting to know your data 60 2.5 Further reading 60 3 Output: Knowledge representation 61 3.1 Decision tables 62 3.2 Decision trees 62 3.3 Classification rules 65 3.4 Association rules 69 3.5 Rules with exceptions 70 3.6 Rules involving relations 73 3.7 Trees for numeric prediction 76 3.8 Instancebased representation 76 3.9 Clusters 81 3.10 Further reading 82 v i i i CONTENTS 4 Algorithms: The basic methods 83 4.1 Inferring rudimentary rules 84 Missing values and numeric attributes 86 Discussion 88 4.2 Statistical modeling 88 Missing values and numeric attributes 92 Bayesian models for document classification 94 Discussion 96 4.3 Divideandconquer: Constructing decision trees 97 Calculating information 100 Highly branching attributes 102 Discussion 105 4.4 Covering algorithms: Constructing rules 105 Rules versus trees 107 A simple covering algorithm 107 Rules versus decision lists 111 4.5 Mining association rules 112 Item sets 113 Association rules 113 Generating rules efficiently 117 Discussion 118 4.6 Linear models 119 Numeric prediction: Linear regression 119 Linear classification: Logistic regression 121 Linear classification using the perceptron 124 Linear classification using Winnow 126 4.7 Instancebased learning 128 The distance function 128 Finding nearest neighbors efficiently 129 Discussion 135 4.8 Clustering 136 Iterative distancebased clustering 137 Faster distance calculations 138 Discussion 139 4.9 Further reading 139 CONTENTS i x 5 Credibility: Evaluating what’s been learned 143 5.1 Training and testing 144 5.2 Predicting performance 146 5.3 Crossvalidation 149 5.4 Other estimates 151 Leaveoneout 151 The bootstrap 152 5.5 Comparing data mining methods 153 5.6 Predicting probabilities 157 Quadratic loss function 158 Informational loss function 159 Discussion 160 5.7 Counting the cost 161 Costsensitive classification 164 Costsensitive learning 165 Lift charts 166 ROC curves 168 Recall–precision curves 171 Discussion 172 Cost curves 173 5.8 Evaluating numeric prediction 176 5.9 The minimum description length principle 179 5.10 Applying the MDL principle to clustering 183 5.11 Further reading 184 6 Implementations: Real machine learning schemes 187 6.1 Decision trees 189 Numeric attributes 189 Missing values 191 Pruning 192 Estimating error rates 193 Complexity of decision tree induction 196 From trees to rules 198 C4.5: Choices and options 198 Discussion 199 6.2 Classification rules 200 Criteria for choosing tests 200 Missing values, numeric attributes 201 x CONTENTS Generating good rules 202 Using global optimization 205 Obtaining rules from partial decision trees 207 Rules with exceptions 210 Discussion 213 6.3 Extending linear models 214 The maximum margin hyperplane 215 Nonlinear class boundaries 217 Support vector regression 219 The kernel perceptron 222 Multilayer perceptrons 223 Discussion 235 6.4 Instancebased learning 235 Reducing the number of exemplars 236 Pruning noisy exemplars 236 Weighting attributes 237 Generalizing exemplars 238 Distance functions for generalized exemplars 239 Generalized distance functions 241 Discussion 242 6.5 Numeric prediction 243 Model trees 244 Building the tree 245 Pruning the tree 245 Nominal attributes 246 Missing values 246 Pseudocode for model tree induction 247 Rules from model trees 250 Locally weighted linear regression 251 Discussion 253 6.6 Clustering 254 Choosing the number of clusters 254 Incremental clustering 255 Category utility 260 Probabilitybased clustering 262 The EM algorithm 265 Extending the mixture model 266 Bayesian clustering 268 Discussion 270 6.7 Bayesian networks 271 Making predictions 272 Learning Bayesian networks 276 CONTENTS x i Specific algorithms 278 Data structures for fast learning 280 Discussion 283 7 Transformations: Engineering the input and output 285 7.1 Attribute selection 288 Schemeindependent selection 290 Searching the attribute space 292 Schemespecific selection 294 7.2 Discretizing numeric attributes 296 Unsupervised discretization 297 Entropybased discretization 298 Other discretization methods 302 Entropybased versus errorbased discretization 302 Converting discrete to numeric attributes 304 7.3 Some useful transformations 305 Principal components analysis 306 Random projections 309 Text to attribute vectors 309 Time series 311 7.4 Automatic data cleansing 312 Improving decision trees 312 Robust regression 313 Detecting anomalies 314 7.5 Combining multiple models 315 Bagging 316 Bagging with costs 319 Randomization 320 Boosting 321 Additive regression 325 Additive logistic regression 327 Option trees 328 Logistic model trees 331 Stacking 332 Errorcorrecting output codes 334 7.6 Using unlabeled data 337 Clustering for classification 337 Cotraining 339 EM and cotraining 340 7.7 Further reading 341 x i i CONTENTS 8 Moving on: Extensions and applications 345 8.1 Learning from massive datasets 346 8.2 Incorporating domain knowledge 349 8.3 Text and Web mining 351 8.4 Adversarial situations 356 8.5 Ubiquitous data mining 358 8.6 Further reading 361 Part II The Weka machine learning workbench 363 9 Introduction to Weka 365 9.1 What’s in Weka? 366 9.2 How do you use it? 367 9.3 What else can you do? 368 9.4 How do you get it? 368 10 The Explorer 369 10.1 Getting started 369 Preparing the data 370 Loading the data into the Explorer 370 Building a decision tree 373 Examining the output 373 Doing it again 377 Working with models 377 When things go wrong 378 10.2 Exploring the Explorer 380 Loading and filtering files 380 Training and testing learning schemes 384 Do it yourself: The User Classifier 388 Using a metalearner 389 Clustering and association rules 391 Attribute selection 392 Visualization 393 10.3 Filtering algorithms 393 Unsupervised attribute filters 395 Unsupervised instance filters 400 Supervised filters 401 CONTENTS x i i i 10.4 Learning algorithms 403 Bayesian classifiers 403 Trees 406 Rules 408 Functions 409 Lazy classifiers 413 Miscellaneous classifiers 414 10.5 Metalearning algorithms 414 Bagging and randomization 414 Boosting 416 Combining classifiers 417 Costsensitive learning 417 Optimizing performance 417 Retargeting classifiers for different tasks 418 10.6 Clustering algorithms 418 10.7 Associationrule learners 419 10.8 Attribute selection 420 Attribute subset evaluators 422 Singleattribute evaluators 422 Search methods 423 11 The Knowledge Flow interface 427 11.1 Getting started 427 11.2 The Knowledge Flow components 430 11.3 Configuring and connecting the components 431 11.4 Incremental learning 433 12 The Experimenter 437 12.1 Getting started 438 Running an experiment 439 Analyzing the results 440 12.2 Simple setup 441 12.3 Advanced setup 442 12.4 The Analyze panel 443 12.5 Distributing processing over several machines 445 x i v CONTENTS 13 The commandline interface 449 13.1 Getting started 449 13.2 The structure of Weka 450 Classes, instances, and packages 450 The weka.core package 451 The weka.classifiers package 453 Other packages 455 Javadoc indices 456 13.3 Commandline options 456 Generic options 456 Schemespecific options 458 14 Embedded machine learning 461 14.1 A simple data mining application 461 14.2 Going through the code 462 main() 462 MessageClassifier() 462 updateData() 468 classifyMessage() 468 15 Writing new learning schemes 471 15.1 An example classifier 471 buildClassifier() 472 makeTree() 472 computeInfoGain() 480 classifyInstance() 480 main() 481 15.2 Conventions for implementing classifiers 483 References 485 Index 505
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Practical Finite ElementModeling in Earth Science Using Matlab
20180725Over the past few decades, mathematical models have become an increasingly important tool for Earth scientists to understand and make predictions about how our planet functions and evolves through time and space. These models often consist of partial differential equations (PDEs) that are discretized with a numerical method and solved on a computer.Themost commonly used discretization methods are the finite difference method (FDM), the finite volume method, the finite element method (FEM), the discrete element method, the boundary element method, and various spectral methods.
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Computational methods for fluiddynamics
20101102Included are advanced methods in computational fluid dynamics, like direct and largeeddy simulation of turbulence, multigrid methods, parallel computing, moving grids, structured, blockstructured and unstructured boundaryfitted grids, free surface flows. The 3rd edition contains a new section dealing with grid quality and an extended description of discretization methods. The book shows common roots and basic principles for many different methods. The book also contains a great deal of practical advice for code developers and users; it is designed to be equally useful to beginners and experts.The issues of numerical accuracy, estimation and reduction of numerical errors are dealt with in detail, with many examples.
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vb api v b api window api
20090713vb api vb api vb api vb api
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风能手册(英文版)内容多
20100325一本英语的风能手册，很详细 目录如下 Acknowledgements xv List of symbols xvii 1 Introduction 1 1.1 Historical Development 1 1.2 Modern Wind Turbines 6 1.3 Scope of the Book 7 References 8 Bibliography 9 2 The Wind Resource 11 2.1 The Nature of the Wind 11 2.2 Geographical Variation in theWind Resource 12 2.3 LongtermWindspeed Variations 13 2.4 Annual and Seasonal Variations 14 2.5 Synoptic and Diurnal Variations 16 2.6 Turbulence 17 2.6.1 The nature of turbulence 17 2.6.2 The boundary layer 18 2.6.3 Turbulence intensity 21 2.6.4 Turbulence spectra 22 2.6.5 Length scales and other parameters 23 2.6.6 Crossspectra and coherence functions 26 2.7 Gust Wind Speeds 29 2.8 Extreme Wind Speeds 30 2.8.1 Extreme winds in standards 31 2.9 Windspeed Prediction and Forecasting 33 2.9.1 Statistical methods 33 2.9.2 Meteorological methods 34 2.10 Turbulence in Wakes and Wind Farms 35 2.11 Turbulence in Complex Terrain 37 References 38 3 Aerodynamics of Horizontalaxis Wind Turbines 41 3.1 Introduction 41 3.2 The Actuator Disc Concept 42 3.2.1 Momentum theory 43 3.2.2 Power coefficient 44 3.2.3 The Betz limit 45 3.2.4 The thrust coefficient 46 3.3 Rotor Disc Theory 46 3.3.1 Wake rotation 47 3.3.2 Angular momentum theory 47 3.3.3 Maximum power 49 3.3.4 Wake structure 50 3.4 Vortex Cylinder Model of the Actuator Disc 51 3.4.1 Introduction 51 3.4.2 Vortex cylinder theory 52 3.4.3 Relationship between bound circulation and the induced velocity 53 3.4.4 Root vortex 54 3.4.5 Torque and power 55 3.4.6 Axial flow field 56 3.4.7 Tangential flow field 57 3.4.8 Radial flow field 58 3.4.9 Conclusions 59 3.5 Rotor Blade Theory 59 3.5.1 Introduction 59 3.5.2 Blade element theory 60 3.5.3 The blade element – momentum (BEM) theory 61 3.5.4 Determination of rotor torque and power 64 3.6 Breakdown of the Momentum Theory 65 3.6.1 Freestream/wake mixing 65 3.6.2 Modification of rotor thrust caused by flow separation 66 3.6.3 Empirical determination of thrust coefficient 67 3.7 Blade Geometry 68 3.7.1 Introduction 68 3.7.2 Optimal design for variablespeed operation 68 3.7.3 A practical blade design 73 3.7.4 Effects of drag on optimal blade design 75 3.7.5 Optimal blade design for constantspeed operation 77 3.8 The Effects of a Discrete Number of Blades 78 3.8.1 Introduction 78 3.8.2 Tip losses 78 3.8.3 Prandtl’s approximation for the tiploss factor 83 3.8.4 Blade root losses 86 3.8.5 Effect of tip loss on optimum blade design and power 87 3.8.6 Incorporation of tiploss for nonoptimal operation 91 3.9 Calculated Results for an Actual Turbine 93 3.10 The Aerodynamics of a Wind Turbine in Steady Yaw 96 3.10.1 Momentum theory for a turbine rotor in steady yaw 96 vi CONTENTS 3.10.2 Glauert’s momentum theory for the yawed rotor 99 3.10.3 Vortex cylinder model of the yawed actuator disc 103 3.10.4 Flow expansion 107 3.10.5 Related theories 112 3.10.6 Wake rotation for a turbine rotor in steady yaw 113 3.10.7 The blade element theory for a turbine rotor in steady yaw 115 3.10.8 The blade element–momentum theory for a rotor in steady yaw 116 3.10.9 Calculated values of induced velocity 120 3.10.10 Blade forces for a rotor in steady yaw 121 3.10.11 Yawing and tilting moments in steady yaw 122 3.11 The Method of Acceleration Potential 125 3.11.1 Introduction 125 3.11.2 The general pressure distribution theory 126 3.11.3 The axisymmetric pressure distributions 129 3.11.4 The antisymmetric pressure distributions 133 3.11.5 The Pitt and Peters model 136 3.11.6 The general acceleration potential method 137 3.11.7 Comparison of methods 137 3.12 Stall Delay 138 3.13 Unsteady Flow – Dynamic Inflow 141 3.13.1 Introduction 141 3.13.2 Adaptation of the acceleration potential method to unsteady flow 142 3.13.3 Unsteady yawing and tilting moments 146 3.13.4 Quasisteady aerofoil aerodynamics 148 3.13.5 Aerodynamic forces caused by aerofoil acceleration 149 3.13.6 The effect of the wake on aerofoil aerodynamics in unsteady flow 150 References 154 Bibliography 155 Appendix: Lift and Drag of Aerofoils 156 A3.1 Definition of Drag 156 A3.2 Drag Coefficient 159 A3.3 The Boundary Layer 160 A3.4 Boundarylayer Separation 160 A3.5 Laminar and Turbulent Boundary Layers 161 A3.6 Definition of Lift and its Relationship to Circulation 163 A3.7 The Stalled Aerofoil 166 A3.8 The Lift Coefficient 167 A3.9 Aerofoil Drag Characteristics 168 A3.10 Variation of Aerofoil Characteristics with Reynolds Number 169 A3.11 Cambered Aerofoils 170 4 Windturbine Performance 173 4.1 The Performance Curves 173 4.1.1 The CP º performance curve 173 4.1.2 The effect of solidity on performance 174 4.1.3 The CQ º curve 176 4.1.4 The CT º curve 176 CONTENTS vii 4.2 Constant Rotational Speed Operation 177 4.2.1 The KP 1=º curve 177 4.2.2 Stall regulation 178 4.2.3 Effect of rotational speed change 179 4.2.4 Effect of blade pitch angle change 180 4.2.5 Pitch regulation 180 4.2.6 Pitching to stall 181 4.2.7 Pitching to feather 181 4.3 Comparison of Measured with Theoretical Performance 182 4.4 Variablespeed Operation 184 4.5 Estimation of Energy Capture 185 4.6 Windturbine Field Testing 190 4.6.1 Introduction 190 4.6.2 Information sources for windturbine testing 190 4.7 Windturbine Performance Measurement 191 4.7.1 Field testing methodology 192 4.7.2 Windspeed measurement 193 4.7.3 Winddirection measurement 194 4.7.4 Air temperature and pressure measurement 194 4.7.5 Power measurement 196 4.7.6 Windturbine status 196 4.7.7 Data acquisition system 196 4.7.8 Data acquisition rate 197 4.8 Analysis of Test Data 197 4.9 Turbulence Effects 198 4.10 Aerodynamic Performance Assessment 200 4.11 Errors and Uncertainty 204 4.11.1 Evaluation of uncertainty 204 4.11.2 Sensitivity factors 205 4.11.3 Estimating uncertainties 206 4.11.4 Combining uncertainties 206 References 207 5 Design Loads for Horizontalaxis Wind Turbines 209 5.1 National and International Standards 209 5.1.1 Historical development 209 5.1.2 IEC 614001 209 5.1.3 Germanisher Lloyd rules for certification 210 5.1.4 Danish Standard DS 472 211 5.2 Basis for Design Loads 211 5.2.1 Sources of loading 211 5.2.2 Ultimate loads 211 5.2.3 Fatigue loads 212 5.2.4 Partial safety factors for loads 212 5.2.5 Functions of the control and safety systems 213 5.3 Turbulence and Wakes 213 viii CONTENTS 5.4 Extreme Loads 214 5.4.1 Nonoperational load cases – normal machine state 214 5.4.2 Nonoperational load cases – machine fault state 215 5.4.3 Operational load cases – normal machine state 215 5.4.4 Operational load cases – loss of load 217 5.4.5 Operational load cases – machine fault states 218 5.4.6 Startup and shutdown cases 218 5.4.7 Blade/tower clearance 218 5.5 Fatigue Loading 218 5.5.1 Synthesis of fatigue load spectrum 218 5.6 Stationary Blade Loading 219 5.6.1 Lift and drag coefficients 219 5.6.2 Critical configuration for different machine types 219 5.6.3 Dynamic response 220 5.7 Blade Loads During Operation 228 5.7.1 Deterministic and stochastic load components 228 5.7.2 Deterministic aerodynamic loads 228 5.7.3 Gravity loads 236 5.7.4 Deterministic inertia loads 236 5.7.5 Stochastic aerodynamic loads – analysis in the frequency domain 239 5.7.6 Stochastic aerodynamic loads – analysis in the time domain 249 5.7.7 Extreme loads 252 5.8 Blade Dynamic Response 255 5.8.1 Modal analysis 255 5.8.2 Mode shapes and frequencies 258 5.8.3 Centrifugal stiffening 259 5.8.4 Aerodynamic and structural damping 262 5.8.5 Response to deterministic loads—stepbystep dynamic analysis 264 5.8.6 Response to stochastic loads 268 5.8.7 Response to simulated loads 271 5.8.8 Teeter motion 271 5.8.9 Tower coupling 276 5.8.10 Wind turbine dynamic analysis codes 282 5.8.11 Aeroelastic stability 286 5.9 Blade Fatigue Stresses 287 5.9.1 Methodology for blade fatigue design 287 5.9.2 Combination of deterministic and stochastic components 288 5.9.3 Fatigue predictions in the frequency domain 290 5.9.4 Wind simulation 292 5.9.5 Fatigue cycle counting 293 5.10 Hub and Lowspeed Shaft Loading 293 5.10.1 Introduction 293 5.10.2 Deterministic aerodynamic loads 294 5.10.3 Stochastic aerodynamic loads 296 5.10.4 Gravity loading 297 5.11 Nacelle Loading 298 5.11.1 Loadings from rotor 298 CONTENTS ix 5.11.2 Cladding loads 299 5.12 Tower Loading 300 5.12.1 Extreme loads 300 5.12.2 Dynamic response to extreme loads 300 5.12.3 Operational loads due to steady wind (deterministic component) 304 5.12.4 Operational loads due to turbulence (stochastic component) 305 5.12.5 Dynamic response to operational loads 308 5.12.6 Fatigue loads and stresses 309 References 311 Appendix: Dynamic Response of Stationary Blade in Turbulent Wind 313 A5.1 Introduction 313 A5.2 Frequency Response Function 313 A5.2.1 Equation of motion 313 A5.2.2 Frequency response function 314 A5.3 Resonant Displacement Response Ignoring Wind Variations along the Blade 315 A5.3.1 Linearization of wind loading 315 A5.3.2 First mode displacement response 315 A5.3.3 Background and resonant response 315 A5.4 Effect of AcWind Turbulence Distribution on Resonant Displacement Response 317 A5.4.1 Formula for normalized cospectrum 319 A5.5 Resonant Root Bending Moment 320 A5.6 Root Bending Moment Background response 322 A5.7 Peak Response 324 A5.8 Bending Moments at Intermediate Blade Positions 326 A5.8.1 Background response 326 A5.8.2 Resonant response 327 References 327 6 Conceptual Design of Horizontal Axis Wind Turbines 329 6.1 Introduction 329 6.2 Rotor Diameter 329 6.2.1 Cost modelling 330 6.2.2 Simplified cost model for machine size optimization—an illustration 330 6.3 Machine Rating 333 6.3.1 Simplified cost model for optimizing machine rating in relation to diameter 333 6.3.2 Relationship between optimum rated wind speed and annual mean 336 6.3.3 Specific power of production machines 336 6.4 Rotational Speed 337 6.4.1 Ideal relationship between rotational speed and solidity 338 6.4.2 Influence of rotational speed on blade weight 339 6.4.3 Optimum rotational speed 339 6.4.4 Noise constraint on rotational speed 339 6.4.5 Visual considerations 340 x CONTENTS 6.5 Number of Blades 340 6.5.1 Overview 340 6.5.2 Ideal relationship between number of blades, rotational speed and solidity 340 6.5.3 Some performance and cost comparisons 341 6.5.4 Effect of number of blades on loads 345 6.5.5 Noise constraint on rotational speed 346 6.5.6 Visual appearance 346 6.5.7 Singlebladed turbines 346 6.6 Teetering 347 6.6.1 Load relief benefits 347 6.6.2 Limitation of large excursions 349 6.6.3 Pitch–teeter coupling 349 6.6.4 Teeter stability on stallregulated machines 349 6.7 Power Control 350 6.7.1 Passive stall control 350 6.7.2 Active pitch control 351 6.7.3 Passive pitch control 355 6.7.4 Active stall control 355 6.7.5 Yaw control 356 6.8 Braking Systems 357 6.8.1 Independent braking systems—requirements of standards 357 6.8.2 Aerodynamic brake options 358 6.8.3 Mechanical brake options 360 6.8.4 Parking versus idling 360 6.9 Fixedspeed, Twospeed or Variablespeed Operation 360 6.9.1 Twospeed operation 361 6.9.2 Variablespeed operation 362 6.9.3 Variableslip operation 363 6.9.4 Other approaches to variablespeed operation 363 6.10 Type of Generator 364 6.10.1 Historical attempts to use synchronous generators 365 6.10.2 Directdrive generators 366 6.11 Drivetrain Mounting Arrangement Options 366 6.11.1 Lowspeed shaft mounting 366 6.11.2 Highspeed shaft and generator mounting 369 6.12 Drivetrain Compliance 370 6.13 Rotor Position with Respect to Tower 373 6.13.1 Upwind configuration 373 6.13.2 Downwind configuration 373 6.14 Tower Stiffness 374 6.15 Personnel Safety and Access Issues 374 References 375 7 Component Design 377 7.1 Blades 377 7.1.1 Introduction 377 CONTENTS xi 7.1.2 Aerodynamic design 378 7.1.3 Practical modifications to optimum design 379 7.1.4 Form of blade structure 379 7.1.5 Blade materials and properties 380 7.1.6 Properties of glass/polyester and glass/epoxy composites 384 7.1.7 Properties of wood laminates 389 7.1.8 Governing load cases 392 7.1.9 Blade resonance 407 7.1.10 Design against buckling 413 7.1.11 Blade root fixings 417 7.2 Pitch Bearings 419 7.3 Rotor Hub 421 7.4 Gearbox 424 7.4.1 Introduction 424 7.4.2 Variable loads during operation 425 7.4.3 Drivetrain dynamics 427 7.4.4 Braking loads 427 7.4.5 Effect of variable loading on fatigue design of gear teeth 428 7.4.6 Effect of variable loading on fatigue design of bearings and shafts 432 7.4.7 Gear arrangements 433 7.4.8 Gearbox noise 435 7.4.9 Integrated gearboxes 437 7.4.10 Lubrication and cooling 437 7.4.11 Gearbox efficiency 438 7.5 Generator 438 7.5.1 Induction generators 438 7.5.2 Variablespeed generators 441 7.6 Mechanical Brake 442 7.6.1 Brake duty 442 7.6.2 Factors govnering brake design 443 7.6.3 Calculation of brake disc temperature rise 445 7.6.4 Highspeed shaft brake design 447 7.6.5 Two level braking 450 7.6.6 Lowspeed shaft brake design 450 7.7 Nacelle Bedplate 450 7.8 Yaw Drive 451 7.9 Tower 453 7.9.1 Introduction 453 7.9.2 Constraints on firstmode natural frequency 454 7.9.3 Steel tubular towers 455 7.9.4 Steel lattice towers 464 7.10 Foundations 464 7.10.1 Slab foundations 465 7.10.2 Multipile foundations 466 7.10.3 Concrete monopile foundations 467 7.10.4 Foundations for steel lattice towers 468 References 468 xii CONTENTS 8 The Controller 471 8.1 Functions of the Windturbine Controller 472 8.1.1 Supervisory control 472 8.1.2 Closedloop control 472 8.1.3 The safety system 473 8.2 Closedloop Control: Issues and Objectives 475 8.2.1 Pitch control 475 8.2.2 Stall control 476 8.2.3 Generator torque control 476 8.2.4 Yaw control 477 8.2.5 Influence of the controller on loads 478 8.2.6 Defining controller objectives 478 8.2.7 PI and PID controllers 479 8.3 Closedloop Control: General Techniques 480 8.3.1 Control of fixedspeed, pitchregulated turbines 480 8.3.2 Control of variablespeed pitchregulated turbines 481 8.3.3 Pitch control for variablespeed turbines 484 8.3.4 Switching between torque and pitch control 484 8.3.5 Control of tower vibration 486 8.3.6 Control of drive train torsional vibration 488 8.3.7 Variablespeed stall regulation 489 8.3.8 Control of variableslip turbines 490 8.3.9 Individual pitch control 492 8.4 Closedloop Control: Analytical Design Methods 493 8.4.1 Classical design methods 493 8.4.2 Gain scheduling for pitch controllers 498 8.4.3 Adding more terms to the controller 498 8.4.4 Other extensions to classical controllers 500 8.4.5 Optimal feedback methods 500 8.4.6 Other methods 504 8.5 Pitch Actuators 505 8.6 Control System Implementation 506 8.6.1 Discretization 507 8.6.2 Integrator desaturation 508 References 509 9 Windturbine Installations and Wind Farms 511 9.1 Project Development 511 9.1.1 Initial site selection 512 9.1.2 Project feasibility assessment 514 9.1.3 The measure–correlate–predict technique 514 9.1.4 Micrositing 515 9.1.5 Site investigations 517 9.1.6 Public consultation 517 9.1.7 Preparation and submission of the planning application 517 9.2 Visual and Landscape Assessment 519 9.2.1 Landscape character assessment 520 CONTENTS xiii 9.2.2 Design and mitigation 523 9.2.3 Assessment of impact 524 9.2.4 Shadow flicker 527 9.2.5 Sociological aspects 527 9.3 Noise 528 9.3.1 Terminology and basic concepts 528 9.3.2 Windturbine noise 531 9.3.3 Measurement, prediction and assessment of windfarm noise 534 9.4 Electromagnetic Interference 538 9.4.1 Modelling and prediction of EMI from wind turbines 541 9.5 Ecological Assessment 545 9.5.1 Impact on birds 546 9.6 Finance 549 9.6.1 Project appraisal 549 9.6.2 Project finance 553 9.6.3 Support mechanisms for wind energy 555 References 557 10 Electrical Systems 559 10.1 Powercollection Systems 559 10.2 Earthing (Grounding) of Wind Farms 562 10.3 Lightning Protection 565 10.4 Embedded (Dispersed) Wind Generation 568 10.4.1 The electric power system 568 10.4.2 Embedded generation 569 10.4.3 Electrical distribution networks 570 10.4.4 The perunit system 573 10.4.5 Power flows, slowvoltage variations and network losses 573 10.4.6 Connection of embedded wind generation 577 10.4.7 Power system studies 579 10.5 Power Quality 580 10.5.1 Voltage flicker 586 10.5.2 Harmonics 588 10.5.3 Measurement and assessment of power quality characteristics of gridconnected wind turbines 589 10.6 Electrical Protection 590 10.6.1 Windfarm and generator protection 592 10.6.2 Islanding and selfexcitation of induction generators 594 10.6.3 Interface protection 596 10.7 Economic Aspects of Embedded Wind Generation 598 10.7.1 Losses in distribution networks with embedded wind generation 599 10.7.2 Reactive power charges and voltage control 600 10.7.3 Connection charges ‘deep’ and ‘shallow’ 601 10.7.4 Useofsystem charges 602 10.7.5 Impact on the generation system 604 References 607 Index 609

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