use case 和 use case realization之间的区别？-CSDN社区

Rational Software Architect Workshop UML Diagrams What is a Model? • A model is a semantically closed abstraction of a subject system. – A model is defined in RUP as “a complete description of a system from a particular perspective.” • Examples of models: – UML model – Code – Data model Diagrams • Diagrams graphically depict a view of a part of your model. • Different diagrams represent different views of the system that you are developing. • A model element will appear on one or more diagrams. ...... Model Templates: Enterprise IT Design Model A design model describes the realization of use cases to design classes. Architectural Layers Separate business logic from data and user interface Use-Case Realization Shows how a use case is implemented in terms of collaborating objects

RUP方法论的设计文档模板。英文版的，RUP(Rational Unified Process,统一软件开发过程,统一软件过程。包含两个文档： Software Architecture Document，软件架构文档模板 Use-Case-Realization Specification，用户案例实现规范模板

Preface About the Editors Part 1—Fundamentals and Neuro-Fuzzy Signal Processing Chapter 1—Fuzzy Logic and Neuro-Fuzzy Systems in Medicine and Bio-Medical Engineering: A Historical Perspective 1. The First Period: The Infancy 2. Further Developments and Background 3. Neuro-Fuzzy Systems and their Applications in Medicine and Biology 4. Genetic Algorithms, Fuzzy Logic, and Neuro-Fuzzy Systems 5. Bibliographies 6. Conclusions and Predictions Chapter 2—The Brain as a Fuzzy Machine: A Modeling Problem 1. The Fuzzy Approach in Neurobiology: A Historical Perspective 2. The Generality of Young’s Hypothesis 2.1 Simple Stimuli 2.2 Neural Organization of Cryptic Events: From Tastes to Faces 2.2.1 The General Approach 2.2.2 Solutions Possible: Taste 2.2.3 Solutions Possible: Faces 2.3 Neural Codes 3. Fuzzy Models for Taste 3.1 Grades of Membership in Fuzzy Sets 3.2 A Fuzzy Model 3.2.1 The Model 3.2.2 The Synthesis of the Fuzzy Model 3.2.3 Simulating the Dynamics of Taste Neurons 4. Fuzzy Model For Brain Activity 4.1 A Neural Network Implementing a Fuzzy Machine? 4.2 An Artificial Neuron Implements a Fuzzy Membership Function 4.3 A Layer of Neurons Implements a Fuzzifier 4.4 A “Hidden” Neuron Implements a Fuzzy Rule 5. Applications of Fuzzy Logic to Neural Systems 5.1 Quantitative Aspects of the Fuzzy Neural Sets 5.1.1 Neural Mass 5.1.2 Sensitivity to Fine Gradations in Input 5.1.3 Intelligence 5.2 Defuzzification and Responses 5.3 Memory: Input and Retrieval 6. Conclusions Appendix 1. Abbreviations Appendix 2. Terminology References Chapter 3—Brain State Identification and Forecasting of Acute Pathology Using Unsupervised Fuzzy Clustering of EEG Temporal Patterns 1. Introduction 2. Background 2.1 The Electroencephalogram (EEG) Signal [1], [2] 2.2 Brain States and the EEG 2.3 Stimulus-Evoked EEG Patterns 2.4 Underlying Processes 2.5 Fuzzy Systems and the EEG 3. Tools 3.1 Data Acquisition 3.1.1 Spontaneous Ongoing Signal 3.1.2 Evoked Responses 3.2 Feature Extraction 3.2.1 Spectrum Estimation 3.2.2 Time-Frequency Analysis 3.2.2.1 Multiscale Decomposition By The Fast Wavelet Transform 3.2.2.2 Multichannel Model-Based Decomposition by Matching Pursuit 3.3 The Unsupervised Optimal Fuzzy Clustering (UOFC) Algorithm. 3.4 The Weighted Fuzzy K-Mean (WFKM) Algorithm 3.5 The Clustering Validity Criteria 4. Examples of Uses 4.1 Sleep-Stage Scoring 4.2 Forecasting Epilepsy 4.3 Classifying Evoked and Event-Related Potentials by Waveform 5. Concluding Remarks and Future Applications 5.1 Dynamic Version of State Identification by UOFC 5.2 Data Fusion Appendiex 1: The Fast Wavelet Transform Appendix 2: Multichannel Model-Based Decomposition by Matching Pursuit Appendix 3: Feature Extraction and Reduction by Principal Component Analysis List of Acronyms References Chapter 4—Contouring Blood Pool Myocardial Gated SPECT Images with a Sequence of Three Techniques Based on Wavelets, Neural Networks, and Fuzzy Logic 1. Introduction 2. Anatomy of the G-SPECT Images 3. Strategy of the Proposed Method 3.1. Overview of the Method 3.2. Wavelets-Based Image Pre-Processing 3.3. Neural Network Based Image Segmentation 3.4. Fuzzy Logic-Based Recognition of the Regions of Interest (Ventricles) 3.4.1. Definition of the Required Fuzzy Sentences 3.4.2. Combining Neuronal Approaches and Fuzzy Logic-Based Inference Systems 3.5. Training the Recognition System Using a Neuro-Fuzzy Technique 3.5.1. Automated Generation of Rules and Membership Functions (ALGORAM) 3.5.2. Adjustment of Membership Functions Using a Descent Method (FUNNY) 3.5.3. Combining the Automated Generation of Rules and Membership Functions and the Adjustment of their Parameters in a Parallel Implementation (FUNNY-ALGORAM) 4. In Vitro Experiments and Application to Medical Cases 4.1. Experiments with Phantoms 4.2. Clinical Test Cases 4.3. Implementation Issues 5. Conclusions References Chapter 5—Unsupervised Brain Tumor Segmentation Using Knowledge-Based Fuzzy Techniques 1. Introduction 2. Domain Background 2.1 Slices of Interest for the Study 2.2 Basic MR Contrast Principles 2.3 Knowledge-Based Systems 2.4 System Overview 3. Classification Stages 3.1 Stage Zero: Pathology Detection 3.2 Stage One: Building the Intra-Cranial Mask 3.3 Stage Two: Multi-spectral Histogram Thresholding 3.4 Stage Three: “Density Screening” in Feature Space 3.5 Stage Four: Region Analysis and Labeling 3.5.1 Removing Meningial Regions 3.5.2 Removing Non-Tumor Regions 3.6 Stage Five: Final T1 Threshold 4. Results 4.1 Knowledge-Based vs. Supervised Methods 4.2 Evaluation Over Repeat Scans 5. Discussion References Abbreviations Part 2—Neuro-Fuzzy Knowledge Processing Chapter 6—An Identification of Handling Uncertainties Within Medical Screening: A Case Study Within Screening for Breast Cancer 1. Introduction 2. Screening 2.1 Notations 2.2 The Screening Program 2.3 The Methods 3. The Select Function 3.1 The Decision Step 3.2 Disease-Specific Knowledge 3.3 The Refinement Step 4. A Breast Cancer Case Study 4.1 Minimizing A0 as Much as Possible in One Step 4.2 Finding the Screening Method 4.3 Defining Disease-Specific Knowledge 4.4 Performing the Refinement 4.5 The Integrated System 5. Conclusions and Further Work References Chapter 7—A Fuzzy System For Dental Developmental Age Evaluation 1. Introduction 2. Technical Consideration 2.1 Basic Conception of the Teeth Evaluation System 2.2 Rule Evaluation Module 3. System Optimization by Using Clinical Data 3.1 Material and Method 3.2 Dimensionality Analysis by Principal Component Analysis 3.3 System Optimization by Using Genetic Algorithm 3.4 System Evaluation and Results 4. Discussion and Conclusions Chapter 8—Fuzzy Expert System For Myocardial Ischemia Diagnosis 1. Introduction 2. Fuzzy Expert Systems 3. Difus - Hierarchical Diagnosis Fuzzy System 3.1 Characteristics 3.2 Knowledge Organization 3.3 Structure 3.4 Operation 4. Multimethod Myocardial Ischemia Diagnosis 5. Multimethod Myocardial Ischemia Diagnosis System 5.1 The Implementation of Fuzzy Score-Based Tests 5.1.1 Medical Patterns 5.1.2 Sequential Processing 5.1.3 Compact Representation of Fuzzy Score-Based Tests 5.2 MMIDS Structure and Operation 5.2.1 MMIDS Secondary Group 5.2.2 MMIDS Primary Groups 5.3 Experimental Results 6. Conclusions References Chapter 9—Design and Tuning of Fuzzy Rule-Based Systems for Medical Diagnosis 1. Introduction 2. Problem Statement and General Methodology 3. Design and Rough Tuning of Fuzzy Rules 3.1. Matrix of Knowledge 3.2. Fuzzy Model with Discrete Output 3.3. Fuzzy Model with Continuous Output 3.4. Rough Tuning of Fuzzy Rules 3.4.1. Rough Tuning of Membership Functions 3.4.2. Rough Tuning of Rules Weights 4. Fine Tuning of the Fuzzy Rules with Continuous Output 4.1. Tuning as a Problem of Optimization 4.2. Quality Evaluation of Fuzzy Inference 4.3. Computer Simulation 4.3.1. Experiment 1 4.3.2. Experiment 2 5. Fine Tuning of the Fuzzy Rules with Discrete Output 5.1. Tuning as a Problem of Optimization 5.2. Quality Evaluation of Fuzzy Inference 5.3. Computer Simulation 6. Application to Differential Diagnosis of Ischemia Heart Disease 6.1. Diagnosis Types and Parameters of Patient’s State 6.2. Fuzzy Rules 6.3. Fuzzy Logic Equation 6.4. Rough Membership Functions 6.5. Algorithm of Decision Making 6.6. Fine Tuning of The Fuzzy Rules in Medical Applications 7. Conclusions References Appendix 1—Comparison of real and inferred decisions for 65 patients Appendix 2—FUZZY EXPERT Shell and its Application Chapter 10—Integration of Medical Knowledge in an Expert System for Use in Intensive Care Medicine 1. Introduction 2. Software Design Principles 3. Medical Knowledge in Intensive Care Medicine 3.1. Structure of the Knowledge 3.2. Meaning of Colloquial Rules 3.3. Rule Processing and Result Calculation 3.4. Combining Different Rules 4. Transformation of Knowledge into FLORIDA Commands 4.1. Introduction 4.2. Comments 4.3. Modules 4.4. Linguistic Variables 4.5. The FLORIDA Calculator 4.6. Rules: The Knowledge Itself 4.7. Changing the Normal Value 5. Invocation of FLORIDA 6. Explaining More of FLORIDA’s Functionality — The Knowledge Base Inflammation 6.1. Structuring the Knowledge 6.2. Rules for Fever 6.3. Rules for Leukocytosis/Leukopenia 6.4. Rules for Tachycardia/Tachypnoe 6.5. Rules for Synthesis of Acute Phase Proteins 6.6. Rules for Consumption of Coagulation Components 6.7. Improvement of Explanation 7. Differentiation of Dysfunctions 8. Visualization of the Result 9. Discussion and Conclusions References Part 3—Neuro-Fuzzy Control and Hardware Chapter 11—Hemodynamic Management with Multiple Drugs using Fuzzy Logic 1. Introduction 1.1 Progress in Decision Making 1.2 Progress in Control 2. System Development 2.1 Decision-Making: Fuzzy Decision-Making Module (FDMM) 2.1.1 Purpose 2.1.2 Operation 2.2 Drug-Titration Control: Fuzzy Hemodynamic Control Module (FHCM) 2.2.1 Purpose 2.2.2 Operation 2.3 Supervisory Commands: Therapeutic Assessment Module (TAM) 2.4 System Evaluations 2.4.1 Example One 2.4.2 Example Two 3. Future Prospects 3.1 Design Possibilities 3.2 “Curse of Dimensions” 3.3 Machine Intelligence Additional Resources Appendix: Terminology References Chapter 12—Neuro-Fuzzy Hardware in Medical Applications 12 A.—System Requirements for Fuzzy and Neuro-Fuzzy Hardware in Medical Equipment 1. Introduction 2. Specific Requirements of Medical Applications 2.1 General System and Technological Requirements 2.2 Reliability Requirements 2.3 Precision and Sensitivity to Parameters 3. Analysis of Several Applications 3.1 Life-Support Applications 3.1.1 Artificial Heart Control 3.1.2 Assisted Ventilation 3.2 Anesthesia Related Equipment 3.3 Fuzzy and Neuro-Fuzzy-Based Equipment for Prosthetics 3.4 General Purpose Devices 3.5 Other Applications 4. General System Design Issues 4.1 Nonlinearity Implementation - Simulation Power 4.2 Dynamic Errors 5. Hardware Implementation Issues 5.1 Implementation Choice: Analog vs. Digital Fuzzy Processors 5.2 Hardware Minimization 5.3 Parallelism vs. Number of Rule Blocks 5.4 A Minimal System Design 6. Choosing the Right Design 7. Conclusions References Chapter 12 B—Neural Networks and Fuzzy-Based Integrated Circuit and System Solutions Applied to the Biomedical Field 1. Introduction 2. Required Properties for Embedded Medical Systems 2.1 Embedding medical systems 2.2 Autonomy 2.3 Reliability - safety 2.4 Precision of computation 2.5 Application-specific requirements 3. Architectures Applied to Neuro-Fuzzy IC Design 3.1 Artificial Neural Network Integrated Realization 3.2 Fuzzy-Based Integrated Realization 3.3 Hybrid Integrated Realization 3.4 An example of neuro-fuzzy realization 4. Concluding Remarks References Index of Terms

用例分析用例分析用於解释用例的目的分析以及它在生命周期中的执行位置识别执行事件用例流程的类将用例行为分配给这些类，确定类的责任开发用例模型实现，该模型对所确定的类的实例之间的协作进行建模用例分析包括用例模型(Use-case Model)、類(Analysis Classes)、分析模型(Analysis Model)、用例實現(Use-case Realization)、軟件架構文檔(Softw...

用例图主要用来描述“用户、需求、系统功能单元”之间的关系。它展示了一个外部用户能够观察到的系统功能模型图。 1.新建用例图 2.用例图工具： 3.一个简单用例：用例图所包含的元素如下： 1.Actor：参与者表示与您的应用程序或系统进行交互的用户、组织或外部系统。用一个小人表示。使用者是系统的一个用户；用户可以意味着人类的用户，一台...