Note: Förderkennzeichen BMBF 03EK3008 , Paralleltitel dem englischen Berichtsblatt entnommen , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Sprache der Zusammenfassung: Deutsch, Englisch

Note: Intro -- Preface -- Organization -- Table of Contents - Part I -- Author Index -- Erratum.

Note: Intro -- Preface -- Organization -- Table of Contents - Part II -- Author Index.

Note: Intro -- Preface -- ISNN 2006 Organization -- Table of Contents -- Author Index.

Note: Front Cover -- Nuclear Power Plant Design and Analysis Codes -- Copyright Page -- Contents -- List of contributors -- I. Map of code development -- 1 Road map -- 1.1 Overview -- 1.2 Fuel codes -- 1.2.1 FRAPCON/FRAPTRAN -- 1.2.2 TRANSURANUS -- 1.2.3 GERMINAL -- 1.3 System code -- 1.3.1 ATHLET -- 1.3.2 RELAP5 -- 1.3.3 RELAP5-3D -- 1.3.4 CATHARE -- 1.3.5 TRAC -- 1.3.6 MELCOR -- 1.3.7 Modular Accident Analysis Program -- 1.3.8 System Analysis Module -- 1.4 Subchannel analysis code -- 1.4.1 COBRA -- 1.4.2 HAMBO -- 1.4.3 FLICA -- 1.4.4 THINC -- 1.4.5 MATRA -- 1.4.6 CTF -- 1.4.7 VIPRE -- 1.5 Computational fluid dynamics code -- 1.5.1 CFX -- 1.5.2 Fluent -- 1.5.3 TransAT -- 1.5.4 STAR-CD -- 1.6 Coupled multiscale thermal-hydraulics codes -- 1.6.1 Multiphysics Object-Oriented Simulation Environment -- 1.6.2 Consortium for the Advanced Simulation of Light Water Reactors -- 1.6.3 Other coupled codes -- 1.7 Emerging methods for NPPs -- 1.7.1 Projection-based particle method -- 1.7.2 Lattice Boltzmann method -- 1.7.3 Cybersecurity system -- 1.7.4 Artificial intelligence and artificial neural network -- 1.7.5 Temporal data mining -- References -- 2 Guidance of nuclear power plant code development -- 2.1 Nuclear power plant code -- 2.1.1 Code classification -- 2.1.2 Code development process -- 2.1.3 Code development skills -- 2.2 Code development examples -- 2.2.1 Reactor -- 2.2.1.1 Model development -- 2.2.1.1.1 Reactor core -- 2.2.1.1.2 Lower and upper chamber -- 2.2.1.2 Numerical scheme -- 2.2.1.3 Verification -- 2.2.1.3.1 Steady-state results -- 2.2.1.3.2 Transient results -- 2.2.2 Pressurizer -- 2.2.2.1 Model development -- 2.2.2.1.1 Governing equations -- 2.2.2.1.2 Empirical correlations -- Bulk evaporation and condensation -- Condensation on spray -- 2.2.2.2 Numerical scheme -- 2.2.2.3 Verification -- 2.3 Source code. , 2.3.1 Fourth-order explicit Runge-Kutta method -- 2.3.2 Gauss-Jordan elimination with partial pivoting -- References -- 3 Nuclear engineering software quality assurance -- 3.1 Introduction -- 3.2 Software development life cycle -- 3.3 Software quality -- 3.4 Software quality assurance for nuclear engineering -- 3.4.1 Implementation framework -- 3.4.2 Verification and validation -- 3.5 Summary -- References -- 4 Multiphysics coupling plan -- 4.1 Introduction -- 4.2 Multiphysics coupling methods -- 4.2.1 Operator splitting methods -- 4.2.2 Jacobian-free Newton-Krylov methods -- 4.2.3 Approximate block Newton methods -- 4.3 Current status of research in multiphysics coupling -- 4.3.1 Neutronic and thermal-hydraulic code-to-code coupling -- 4.3.2 NURESAFE European project -- 4.3.3 Multiphysics Object-Oriented Simulation Environment -- 4.3.4 Consortium for the Advanced Simulation of Light Water Reactors -- 4.4 Conclusion -- References -- II. Physics and fuels codes -- 5 Nuclear physics deterministic code -- 5.1 Nuclear data processing codes -- 5.1.1 NJOY -- 5.2 Cross section generation codes -- 5.2.1 Winfrith improved multigroup scheme -- 5.2.2 CASMO-4 -- 5.2.3 HELIOS -- 5.2.4 APOLLO2 -- 5.2.5 Standard Reactor Analysis Code -- 5.2.6 DRAGON4 -- 5.2.7 AEGIS -- 5.2.8 SCALE -- 5.2.9 PARAGON -- 5.2.10 Bamboo-Lattice -- 5.2.11 MC2-3 -- 5.2.12 ECCO -- 5.3 Whole-core computational codes -- 5.3.1 SIMULATE-3 -- 5.3.2 ANC9 -- 5.3.3 PANTHER -- 5.3.4 Purdue Advanced Reactor Core Simulator -- 5.3.5 DONJON4 -- 5.3.6 DYN3D -- 5.3.7 Bamboo-Core -- 5.3.8 SCOPE2 -- 5.3.9 DIF3D/VARIANT/REBUS -- 5.3.10 European Reactor Analysis Optimized calculation System -- References -- 6 Nuclear physics probability code: OpenMC -- 6.1 Introduction to Monte Carlo method -- 6.2 Recently developed Monte Carlo codes -- 6.3 Typical methodologies in OpenMC -- 6.3.1 Random number generator. , 6.3.2 Computational geometry -- 6.3.3 Random walk -- 6.3.4 Tallies and statistics -- 6.3.5 Eigenvalue calculation -- 6.3.6 Fixed-source calculation -- 6.4 Usage of OpenMC -- 6.4.1 Preparation -- 6.4.2 Data library -- 6.4.3 Geometry definition -- 6.4.4 Material definition -- 6.4.5 Tally definition -- 6.4.6 Settings definition -- 6.4.7 Plots definition -- 6.4.8 Simulation in serial and parallel -- 6.4.9 Output description -- 6.4.10 Python API -- 6.5 Verification and validation -- 6.6 Summary -- References -- III. Fuels and sub-channel codes -- 7 FRAPCON and FRAPTRAN codes: Fuel rod performance analysis codes under normal and accident conditions -- 7.1 Objectives, relations, and limitations -- 7.1.1 Objectives of the two codes -- 7.1.2 Relations -- 7.1.3 Limitations -- 7.2 Code structures and physical models -- 7.2.1 Thermohydraulic models -- 7.2.1.1 Coolant conditions -- 7.2.1.2 Cladding temperature and heat generation -- 7.2.1.3 Gap heat conductance -- 7.2.1.4 Pellets temperature distributions -- 7.2.1.5 Plenum gas temperature model and energy storage model -- 7.2.2 Mechanical models -- 7.2.3 Internal gas response and fission gas release -- 7.3 Assessments -- 7.3.1 FRAPCON -- 7.3.2 FRAPTRAN -- 7.3.2.1 RIA condition -- 7.3.3 Loss of Coolant Accident condition -- 7.4 Summary -- References -- 8 The TRANSURANUS fuel performance code -- 8.1 Introduction: General overview of the TRANSURANUS code -- 8.2 TRANSURANUS code structure -- 8.2.1 Thermal analysis -- 8.2.2 Mechanical analysis -- 8.2.3 Fission gas behavior modeling -- 8.2.4 The TRANSURANUS burn-up module -- 8.2.5 Material conservation equations -- 8.3 Application to water reactor conditions -- 8.3.1 Assessment against the pressurized water reactor Super-Ramp irradiation experiment -- 8.3.2 Application to the safety analysis of the Atucha-II Nuclear Power Plant. , 8.3.2.1 Overview of the Atucha-II Nuclear Power Plant -- 8.3.2.2 Atucha-II large break - loss-of-coolant accident transient -- 8.3.3 Application to water-water energetic reactor conditions -- 8.4 Preliminary assessment against fast reactor conditions -- 8.4.1 Development of TRANSURANUS for fast reactor conditions -- 8.4.1.1 Plutonium redistribution model -- 8.4.1.2 Formation and closure of the fuel central void -- 8.4.2 Assessment against the HEDL P-19 irradiation experiment -- 8.4.3 Application to the safety assessment of the ALFRED reactor -- 8.5 Conclusions and future code developments -- Nomenclature -- References -- 9 Two fuel performance codes of the PLEIADES platform: ALCYONE and GERMINAL -- 9.1 General overview of the PLEIADES fuel software environment -- 9.1.1 Architecture and generic tools for fuel performance codes -- 9.1.2 Multiphysics computational scheme for fuel rod type geometries -- 9.1.2.1 Algorithm -- 9.1.2.2 Global scale -- 9.1.2.3 Local scale -- 9.1.2.4 Software implementation -- 9.1.3 Verification process and quality control for the fuel performance codes of the PLEIADES platform -- 9.1.3.1 Software quality control -- 9.1.3.2 Unit nonregression tests -- 9.1.3.3 Integral nonregression tests -- 9.2 ALCYONE fuel performance code for GEN II and III -- 9.2.1 General presentation -- 9.2.2 Physical models -- 9.2.2.1 Isotopic vector evolution and nuclear reactions products -- 9.2.2.2 Fission gas behavior and helium release -- 9.2.2.3 Thermochemical analysis and corrosive fission products release -- 9.2.2.4 Crack extension modeling in fuel -- 9.2.2.4.1 Macroscopic scale -- 9.2.2.4.2 Microscopic scale -- 9.2.2.5 Heterogeneous mechanical behavior -- 9.2.2.6 Multidimensional and multiscale analysis -- 9.2.2.6.1 Thermal model -- 9.2.2.6.2 Mechanical model -- 9.2.2.6.3 Pellet-to-cladding gap model -- 9.2.2.6.4 Multiscale analysis. , 9.2.3 3D simulation results and integral validation of the ALCYONE code -- 9.2.3.1 Base irradiation -- 9.2.3.2 Power ramp test -- 9.2.4 International benchmarks -- 9.3 GERMINAL fuel performance code for GEN IV -- 9.3.1 General presentation -- 9.3.2 Physical models -- 9.3.2.1 Fuel restructuring -- 9.3.2.2 Gap closure and relocation model -- 9.3.2.3 Joint Oxyde Gaine formation and interaction with thermomechanical behavior -- 9.3.3 Validation and application for fuel design -- 9.3.3.1 Validation -- 9.3.3.2 Fuel design for the ASTRID project -- 9.3.4 International benchmarks -- 9.3.4.1 NEA expert group on innovative fuels -- 9.3.4.2 ESFR-SMART -- 9.3.4.3 CEA-JAEA collaboration -- 9.3.4.4 INSPYRE European project -- 9.4 Conclusion and prospects -- Acknowledgment -- References -- 10 Subchannel codes: CTF and VIPRE-01 -- 10.1 Introduction and CTF code overview -- 10.2 CTF assessment: Motivation and work scope -- 10.3 VIPRE-01 code overview -- 10.4 CTF assessment: Flow mixing -- 10.4.1 Two-channel single-phase flow split problem -- 10.4.2 General Electric 3×3 benchmark -- 10.4.2.1 Single-phase flow benchmark -- 10.4.2.2 Two-phase flow benchmark -- 10.5 CTF assessment: Pressure drop -- 10.5.1 Boiling-water reactor full-size fine-mesh bundle tests pressure drop benchmark -- 10.5.1.1 Single-phase flow benchmark -- 10.5.1.2 Two-phase flow benchmark -- 10.5.2 Risø round tube benchmark -- 10.6 CTF assessment: Void fraction -- 10.6.1 Boiling-water reactor full-size fine-mesh bundle tests void benchmark -- 10.6.2 Pressurized-water reactor subchannel and bundle tests void benchmark -- 10.6.2.1 Single subchannel benchmark -- 10.6.2.2 Rod bundle benchmark -- 10.7 Summary -- References -- IV. Thermal-hydraulic codes -- 11 System-level code TRACE -- 11.1 Features of TRACE -- 11.2 Constitutive equations of TRACE -- 11.2.1 Conservation equations. , 11.2.2 Closure equations.

Note: Front Cover -- Remote Sensing of Aerosols, Clouds, and Precipitation -- Copyright -- Dedication -- Contents -- Contributors -- Author Biographies -- Preface -- Remote Sensing of Aerosols, Clouds, and Precipitation -- Chapter 1: Passive Remote Sensing of Aerosol Height -- 1. Introduction -- 2. Aerosol Vertical Distribution -- 2.1. What Controls the Vertical Distribution of Aerosol? -- 2.2. Representing Aerosol Profile and Height -- 3. Passive Remote Sensing Techniques for Retrieval of Aerosol Layer Height -- 3.1. Stereo Photogrammetry -- 3.2. Polarization at the Near-UV Spectrum -- 3.3. Oxygen (O2) Absorption Spectroscopy -- 3.4. IR Technique -- 4. Conclusions and Outlook -- Acknowledgments -- References -- Further Reading -- Chapter 2: Vertical Profiling of Aerosol Optical Properties From LIDAR Remote Sensing, Surface Visibility, and Columnar E ... -- 1. Introduction -- 1.1. Aerosol Sources and Vertical Distribution -- 1.2. Remote Sensing of Aerosol Vertical Distribution -- 2. Characterization of Vertical Variables With Active Remote-Sensing Techniques -- 2.1. Remote Sensing From the Ground -- 2.2. Remote Sensing From Space -- 3. Aerosol Vertical Profile Derived From Passive Remote Sensing -- 3.1. Remote Sensing From the Ground Based Sunphotometry -- 3.2. Remote Sensing From Space -- 4. Applications to the Radiative Impacts of Aerosols -- 5. Conclusion -- Acknowledgments -- References -- Chapter 3: Remote Sensing of Aerosols From Space: Retrieval of Properties and Applications -- 1. Introduction -- 2. Aerosols: Heterogeneity and Climatic Implications -- 3. Satellite Observations for Aerosol Monitoring: Developments -- 4. Satellite Retrieval of Aerosol Properties -- 4.1. Aerosol Retrieval Algorithms -- 4.2. Aerosol Optical Properties -- 5. Satellite Aerosol Database -- 5.1. Retrieval of Aerosol Properties From MODIS. , 5.2. Aerosol Properties From MISR -- 5.3. Aerosol Properties From AATSR -- 5.4. Aerosol Properties From CALIOP -- 5.5. Aerosol Properties From POLDER -- 5.6. Aerosol Properties From OMI -- 5.7. Aerosol Properties From VIIRS -- 5.8. Aerosol Properties From OCM -- 6. Aerosol Remote Sensing Over the Indo-Gangetic Plain, South Asia -- 7. Conclusions and Future Prospects -- Acknowledgments -- References -- Further Reading -- Chapter 4: Remote Sensing of Heavy Aerosol Pollution Episodes: Smoke and Dust -- 1. Introduction -- 2. Detection of Aerosol Episodes -- 2.1. Dust Storms -- 2.2. Biomass Burning Plumes -- 3. Aerosol Retrieval Framework -- 3.1. Aerosol Remote Sensing Algorithm -- 3.2. Aerosol Model -- 4. Retrieval of Biomass Burning Episodes -- 5. Miscellaneous-Polarization Remote Sensing -- 6. Concluding Remarks -- References -- Chapter 5: Aerosol and Cloud Bottom Altitude Covariations From Multisensor Spaceborne Measurements -- 1. Introduction -- 2. Data and Methods -- 2.1. Aerosol and Cloud Record: CCI Datasets -- 2.2. Cloud Bottom Height Record -- 3. Results -- 4. Summary and Conclusions -- Acknowledgments -- References -- Chapter 6: Cloud-Aerosol-Precipitation Interactions Based of Satellite Retrieved Vertical Profiles of Cloud Microstructure -- 1. Introduction -- 2. Physical Considerations in Retrieving Vertical Microphysical Profiles -- 3. The Microstructure of Vertical Profiles of Adiabatic Convective Clouds -- 4. The Observed Microstructure of Convective Clouds Vertical Profiles -- 5. Impacts of CCN and Updrafts on Vertical Microphysical Profiles of Convective Clouds -- 6. Application of re(T) to Observe Anthropogenic Aerosols Suppressing Precipitation -- 7. Application of re(T) to Observe Large Hygroscopic Aerosols Restoring Precipitation -- 8. Impacts of Aerosols on Clouds' Glaciation Temperature and Mixed Phase Precipitation. , 9. Vertical Microphysical Profiles of Severe Convective Storms -- 10. Applications of Vertical Profiles of re(T) to Retrieve Ndb and CCN(S) -- 11. Conclusions -- References -- Further Reading -- Chapter 7: Polarimetric Technique for Satellite Remote Sensing of Superthin Clouds -- 1. Introduction -- 2. Polarization Signature of Light Backscattered by Clouds -- 3. Method for Retrieving Superthin Cloud Optical Depth -- 4. Summary and Conclusion -- Acknowledgments -- References -- Chapter 8: Cloud Screening and Property Retrieval for Hyper-Spectral Thermal Infrared Sounders -- 1. Introduction -- 2. Sensor Characteristics -- 3. Typical Cloud Detection Techniques -- 3.1. Basic Threshold Method -- 3.2. CO2 Slicing Method -- 3.3. Split Window Method -- 4. Cloud Detection Strategy for the Sounders -- 4.1. AIRS -- 4.1.1. Cloud Screening -- 4.1.2. Cloud Parameter Retrieval -- 4.2. TES -- 4.2.1. Cloud Screening -- 4.2.2. Cloud Parameter Retrieval -- 4.3. IASI -- 4.3.1. Cloud Screening -- NWP Test -- AVHRR Test -- ANN Test -- 4.3.2. Cloud Parameter Retrieval -- 4.4. TANSO-FTS -- 4.4.1. Cloud Screening -- TIR Threshold Technique -- CO2 Slicing Technique -- 4.5. CrIS -- 4.5.1. Cloudy Scene Classification -- 5. Conclusion -- References -- Further Reading -- Chapter 9: Surface Remote Sensing of Liquid Water Cloud Properties -- 1. Introduction -- 2. Principle Method and Basic Assumptions -- 2.1. Droplet Size Distribution -- 2.2. The Vertical Cloud Models -- 2.2.1. The Vertically Uniform (VU) Cloud Model -- 2.2.2. The Scaled Adiabatic Stratified (SAS) Cloud Model -- 2.2.3. The Radar Reflectivity-Homogeneous Mixing (HM) Cloud Model -- 3. Case Study: Using Radiation Measurements for Validation -- 3.1. Data and Methods -- 3.2. Liquid Water Cloud Case Study -- 3.2.1. Observations -- 3.2.2. Cloud Property Retrievals -- 3.3. Comparison of Simulated and Observed SW Flux. , 4. Conclusions -- References -- Further Reading -- Chapter 10: Measuring Precipitation From Space -- 1. Introduction -- 2. Infrared and Visible Frequencies Methods -- 3. Microwave-Based Methods -- 3.1. Passive Microwave Methods -- 3.2. Active Microwave Methods -- 4. Methods Based on IR+MW Fusion -- 5. The Tropical Rainfall Measurement Mission (TRMM) -- 6. The Global Precipitation Measuring (GPM) Mission -- 7. The Future of Satellite Estimates of Precipitation -- Acknowledgments -- References -- Further Reading -- Chapter 11: Measurement of Precipitation from Satellite Radiometers (Visible, Infrared, and Microwave): Physical Basis, M ... -- 1. Background -- 2. Satellite Rainfall Estimation Methods -- 2.1. Visible (Vis) and Thermal Infrared (TIR) Methods -- 2.2. Passive Microwave Methods -- 2.2.1. Goddard Profiling Algorithm -- 2.2.2. Rain Retrieval Using Humidity Sounder Channels Near 183.31GHz -- 2.2.3. Multichannel Merged Rainfall Algorithm -- 2.3. Problems Associated With Passive Microwave Measurement of Precipitation -- 2.4. Validation of Precipitation -- 3. Conclusion -- References -- Chapter 12: Development of a Rain/No-Rain Classification Method Over Land for the Microwave Sounder Algorithm -- 1. Introduction -- 2. Data -- 2.1. Spatial and Temporal Matchup of AMSU Data Against PR Data -- 2.2. MSPPS Algorithm -- 2.3. The GSMaP_MWS Algorithm -- 3. Comparison of Rain/No-Rain Classification (RNC) Over Land Using the 89-GHz Channel With PR and MSPPS Rain Estimates -- 4. Proposed RNC Method -- 4.1. Comparison of Scattering Signatures Between the 89- and 150-GHz Channels -- 4.2. Proposed Method Using Scattering Index (SI) -- 4.3. Results -- 5. Conclusion -- Acknowledgments -- References -- Chapter 13: Remote Sensing of Precipitation from Airborne and Spaceborne Radar -- 1. Introduction -- 2. Radar Precipitation Measurement Fundamentals. , 3. The Particle Size Distribution -- 4. Single-Frequency Methods -- 5. Multifrequency Methods -- 6. Effects of Nonuniform Beam Filling -- 7. Multiple Scattering -- 8. Radar-Radiometer Methods -- 9. Summary -- Acknowledgments -- References -- Further Reading -- Chapter 14: Status of High-Resolution Multisatellite Precipitation Products Across India -- 1. Introduction -- 2. A Review of Recent Evaluations of High-Resolution MSPPs Across India -- 3. Evaluation of Five High-Resolution MSPPs for the Indian Monsoon 2014 -- 4. Operational Merged Satellite-Gauge Rainfall Product in India -- 5. Summary and Conclusion -- Acknowledgments -- References -- Chapter 15: Real-Time Wind Velocity Retrieval in the Precipitation System Using High-Resolution Operational Multi-radar Net... -- 1. Introduction -- 2. Multiple-Doppler Methodology for Wind Retrieval -- 2.1. Radar Background -- 2.2. Principle of Multi-Doppler Retrieval -- 2.3. Error Analysis -- 3. Real-Time Multiple-Doppler Retrieval System for CASA Radar Networks -- 3.1. CASA Radar Networks -- 3.1.1. CASA IP-1 Testbed -- 3.1.2. Operational CASA DFW Urban Network -- 3.1.3. Network Design Consideration for Multiple-Doppler Retrieval -- 3.2. CASA Multiple-Doppler Retrieval System -- 3.2.1. Data Transmission and Aggregation -- 3.2.2. Radar Data Resampling -- 3.2.3. Doppler Synthesis and Retrieval -- 3.2.4. Advection -- 4. Observations, Results, and Validation -- 4.1. EF4 Tornado in CASA IP1 Testbed (May 24, 2011) -- 4.2. EF0 Tornado in the CASA DFW Network (May 8, 2014) -- 4.3. High-Winds in the CASA DFW Network (October 2, 2014) -- 4.4. Downbursts Over DFW International Airport (August 12, 2016) -- 4.5. Validation -- 5. Summary -- Acknowledgments -- References -- Index -- Back Cover.

Note: Intro -- Title Page -- Preface -- Organization -- Table of Contents -- Bioinformatics and Biomedical Applications -- DRFE: Dynamic Recursive Feature Elimination for Gene Identification Based on Random Forest -- Introduction -- Untitled -- Random Forest -- Proposed Approach -- Dynamic Recursive Feature Elimination Algorithm -- Experiments -- Datasets -- Experimental Environments -- Experimental Results and Analysis -- Conclusions -- References -- Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares -- Introduction -- Review -- T-Test Method -- Kernel Partial Least Squares (KPLS) -- Gene Extraction Using T-Test and KPLS -- Experiments -- Setup -- Results -- Conclusions -- References -- An Empirical Analysis of Under-Sampling Techniques to Balance a Protein Structural Class Dataset -- Introduction -- Material and Methods -- Under-Sampling Techniques -- Datasets -- Evaluation -- Experiments -- Results -- Final Remarks -- References -- Prediction of Protein Interaction with Neural Network-Based Feature Association Rule Mining -- Introduction -- Feature Dimension Reduction and Protein Cluster Learning -- Rule Extraction from Trained Neural Network -- Experimental Results -- Conclusions -- References -- Prediction of Protein Secondary Structure Using Nonlinear Method -- Introduction -- The Prediction of Secondary Structure -- Using Blast Profiles in the Prediction -- Dimensional Reduction -- Principal Components Analysis (PCA) -- Cascaded Nonlinear Components Analysis (C-NLPCA) -- Classification -- Tests and Results -- Conclusions -- References -- Clustering Analysis for Bacillus Genus Using FourierTransform and Self-Organizing Map -- Introduction -- Fourier Transform for DNA Sequences -- Self-Organizing Map and Data Visualization -- Results -- Discussions -- Conclusions -- References. , Recurrence Quantification Analysis of EEG Predicts Responses to Incision During Anesthesia -- Introduction -- Materials and Data Acquisition -- Method -- Recurrence Plot -- Recurrence Quantification Analysis -- Artificial Neural Networks -- Results -- Discussion -- References -- Wavelet Spectral Entropy for Indication of Epileptic Seizure in Extracranial EEG -- Introduction -- Methods -- Results and Discussions -- References -- The Study of Classification of Motor Imaginaries Based on Kurtosis of EEG -- Introduction -- The Online Estimation of Kurtosis -- The Online Estimation of Kurtosis Based on Sliding Analytic Window -- The Online Estimation of Kurtosis with Increasing Window Length -- The Kurtosis Analysis and Classification of EEG During Imagination of Left and Right Hand Movement -- EEG Data Description -- The Kurtosis of EEG During Imagination of Left and Right Hand Movement -- Discussion -- References -- Automatic Detection of Critical Epochs in coma-EEG Using Independent Component Analysis and Higher Order Statistics -- Introduction -- PCA and ICA for Dominant Components Extraction -- Feature Extraction -- Kurtosis -- Renyi's Entropy -- Results -- EEG Data Description -- Dominant Components Extraction -- Feature Extraction -- Conclusions -- References -- Sparse Bump Sonification: A New Tool for Multichannel EEG Diagnosis of Mental Disorders -- Application to the Detection of the Early Stage of Alzheimer's Disease -- Introduction -- BUS: A Sparse Time-Frequency Sonification Method -- Application of BUS to Multichannel EEG Signals Investigation -- General Method of Multi Channel Sonification -- Application to Early Detection of Alzheimer's Disease -- Discussion -- References -- Effect of Diffusion Weighting and Number of Sensitizing Directions on Fiber Tracking in DTI -- Introduction -- Method -- Synthetic Data -- Fiber Tracking. , Fiber Similarity -- Results -- Conclusion -- References -- 3-D Reconstruction of Blood Vessels Skeleton Based on Neural Network -- Introduction -- Description of Problem -- Reconstruction Based on Neural Network -- B-spline Interpolation -- Experiment -- Conclusion -- References -- Design of a Fuzzy Takagi-Sugeno Controller to Vary the Joint Knee Angle of Paraplegic Patients -- Introduction -- Takagi-Sugeno Fuzzy Control -- Takagi-Sugeno Fuzzy Model -- Dynamic Model Used in the Control of the Joint Knee Angle of Paraplegic Patients -- Modelling and Controller Designs Using the Exact Model Fuzzy Takagi-Sugeno -- Fuzzy Controller Design -- Results of Simulations -- Conclusion -- References -- Characterization of Breast Abnormality Patterns in Digital Mammograms Using Auto-associator Neural Network -- Introduction -- Research Methodology -- Acquisition of Digital Mammograms -- Feature Extraction -- Characterization (Neural-Association) of Breast Abnormality Class Patterns -- Classification of Characterized Breast Abnormality Class Patterns -- Experimental Results -- Analysis of Experimental Results -- Conclusions -- References -- Evolving Hierarchical RBF Neural Networks for Breast Cancer Detection -- Introduction -- TheRBFNetwork -- The Hierarchical RBF Network -- Encode and Calculation -- Tree Structure Optimization by ECGP -- Parameter Optimization with DE Algorithm -- Procedure of the General Learning Algorithm -- Variable Selection Using HRBF Network Paradigms -- Simulations -- Conclusion -- References -- Ovarian Cancer Prognosis by Hemostasis and Complementary Learning -- Introduction -- Materials and Methods -- Dataset -- Hierarchical Complementary Learning -- Experimental Setup -- Experiments and Results -- Conclusion -- References -- Multi-class Cancer Classification with OVR-Support Vector Machines Selected by Naïve Bayes Classifier. , Introduction -- Background -- Cancer Classification Based on Gene Expression -- Multi-class Classification Using SVMs -- A Hybrid Classifier for Multi-class Cancer Classification -- Experimental Results -- Dataset -- Results and Analysis -- Conclusion -- References -- Breast Cancer Diagnosis Using Neural-Based Linear Fusion Strategies -- Introduction -- Optimal MLP Architecture Selection Based on Regularization -- Linear Fusion Strategies for Combining Neural Classifiers -- Experimental Results -- Data Description -- Results of Optimal MLP Architecture Selection -- Results of Breast Lesion Classification Via Linear Fusion Strategies -- Discussion -- Conclusion -- References -- A Quantitative Diagnostic Method Based on Bayesian Networks in Traditional Chinese Medicine -- Introduction -- United System of Syndrome Differentiation -- Diagnosis Using Bayesian Networks -- Symptom Selection -- Experiment Results -- Sample Database -- Evaluation of MISS Algorithm and the Syndrome Differentiation System -- Conclusion -- References -- Information Security -- High-Order Markov Kernels for Network Intrusion Detection -- Introduction -- Anomaly Detection with SVMs -- Markov Chain Model and Markov Kernel -- Experiments -- Conclusion -- References -- Improved Realtime Intrusion Detection System -- Introduction -- On-Line Feature Extraction -- Eigenspace Updating Criterion -- On-Line KPCA -- ProposedSystem -- Experiment -- Description of Dataset -- Experimental Condition -- Evaluate Feature Extraction and Classification Performance -- Suitable for Realtime IDS -- Comparison with Batch Way LS-SVM -- Conclusion and Remarks -- References -- A Distributed Neural Network Learning Algorithm for Network Intrusion Detection System -- Introduction -- DNNL -- Competitive Learning Algorithm Based on Kernel Function -- Post-prune Algorithm -- Learning Algorithm of DNNL. , Experiments -- Performance Measures -- Experiment Results -- Conclusion -- References -- A DGC-Based Data Classification Method Used for Abnormal Network Intrusion Detection -- Introduction -- Data Gravitation and Data Gravitation Field [17] -- Data Classification Based on Gravitation -- Principle of Classification -- Principle to Create Data Particle -- Data Classification in Intrusion Detection -- Feature Selection -- Classification Algorithm and Experiment Results -- A Prototype Platform Based on Network Processor -- Conclusion and Future Works -- Referencess -- Intrusion Alert Analysis Based on PCA and the LVQ Neural Network -- Introduction -- Intrusion Dataset and Principal Component Analysis -- LVQ Clustering Algorithm for Intrusion Alert Analysis -- Experiment Results and Analysis -- Conclusions -- References -- A Novel Color Image Watermarking Method Based on Genetic Algorithm and Neural Networks -- Introduction -- The Embedding Algorithm -- The Host Image Analyzing -- Intensity Optimizing Using GA -- Watermark Embedding -- Watermark Extracting -- Experimental Results -- Watermark Recovering -- Neural Network Training -- Watermark Recovering Based on the Trained Neural Networks -- Conclusion -- References -- Color Image Watermarking Algorithm Using BPN Neural Networks -- Introduction -- Related Theories -- Discrete Wavelet Transform (DWT) -- The Error Back-Propagation Neural Network (BPN) -- The BPN Neural Network Training Procedures -- Watermark Embedding and Extracting -- Watermark Embedding -- Watermark Extracting -- Experimental Results -- Conclusions -- References -- A Novel Blind Digital Watermark Algorithm Based on Neural Network and Chaotic Map -- Introduction -- The Chaotic Watermarks Generating -- Cellular Neural Network (CNN) -- Chebyschev Map -- Chaotic Sequence Generated -- Neural Network Training. , Decision of Watermarking Strength.

Note: Intro -- Preface -- ISNN 2006 Organization -- Table of Contents -- Author Index.

Note: Intro -- Preface -- ISNN 2006 Organization -- Table of Contents -- Author Index.

Note: Title Page -- Preface -- WSC 14 Organization -- List of Contributors -- Contents -- Part I Soft Computing for Modeling, Control, and Optimization -- Optimization of Co-rotating Twin-Screw Extruders Using Pareto Local Search -- Introduction -- Twin-Screw Extruders -- Multi-objective Optimization -- Multi-objective Evolutionary Algorithms -- Pareto Local Search Algorithm -- Results and Discussion -- Case Study -- Comparative Results -- Conclusions -- References -- Efficient Predictive Control Algorithm Based on Fuzzy Hammerstein Models: A Case Study -- Introduction -- Predictive Control Algorithms -- Motivating Example -- DMC Algorithm with Fuzzy Hammerstein Model -- Constraint Satisfaction Mechanisms -- Simulation Experiments -- Summary -- References -- Selection of Points Inside Cutoff Radius by Scanning All Points Sorted in Memory -- Introduction -- Method -- Results and Discussions -- Conclusion -- References -- Supervised Fuzzy Clustering Based Initialization of Fuzzy Partitions for Decision Tree Induction -- Introduction -- Fuzzy Decision Tree Classifiers -- Supervised Clustering Based Discretization of Attributes -- Experimental Study -- Conclusion -- References -- Fault Detection of the Tennessee Eastman Process Using Improved PCA and Neural Classifier -- Introduction -- Tennessee Eastman Process -- Principal Component Analysis -- Genetic Algorithm -- Neural Classifier -- Result and Discussion -- Conclusions -- References -- Part II New Evolutionary Methodologies -- Bare Bones Particle Swarm Applied to Parameter Estimation of MixedWeibull Distribution -- Introduction -- Problem Formulation -- Bare Bones Particle Swarm Optimization -- Results -- Conclusion -- References -- Structural Optimization Using Harmony Search Algorithm -- Introduction -- Background: Harmony Search Algorithm -- Implementation of Algorithm. , Constraint Violation - Penalty Approach [24] -- Numerical Example -- General Discussion -- Conclusion -- References -- Nash Dominance with Applications to Equilibrium Problems with Equilibrium Constraints -- Introduction -- Nash Equilibrium -- Computation of Nash Equilibrium -- Deterministic Approaches -- Evolutionary Methods -- Nash Domination -- Numerical Examples -- Conclusions -- References -- A New Evolutionary Based Approach for Solving the Uncapacitated Multiple Allocation p-Hub Median Problem -- Introduction -- Previous Work -- Mathematical Representation -- Proposed Evolutionary Algorithm -- Computational Results -- Conclusions -- References -- Part III Soft Computing for Bioinformatics, Knowledge Management, and Data Mining -- Prediction of the hERG Potassium Channel Inhibition Potential with Use of the Artificial Neural Networks -- Introduction -- hERG Channel Inhibition Prediction Model -- Computational Methods -- Algorithm -- Software -- Data Set and Data Preprocessing -- Test Sets -- Inputs -- Outputs -- Results and Discussion -- Conclusions -- References -- Michigan Style Fuzzy Classification for Gene Expression Analysis -- Introduction -- Fuzzy Rule-Based Classification -- Hybrid Fuzzy Classification -- Genetic Operations -- Algorithm Summary -- Increasing the Classification Performance -- Experimental Results -- Conclusions -- References -- Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach -- Introduction -- Bankruptcy Prediction -- Multi-objective Optimization -- Results and Discussion -- Case Studies -- Influence of Classification Methods Parameters -- Conclusions -- References -- Dynamic TimeWarping of Segmented Time Series -- Introduction -- Notation -- Related Work -- Slope-Based Warping of PLA -- Combining Slope and Mean-Based Measures -- Validation Results -- Conclusion -- References. , Feature Selection Using Combine of Genetic Algorithm and Ant Colony Optimization -- Introduction -- Ant Colony Optimization for Feature Selection (ACOFS) -- Combining Ant Colony Optimization and Genetic Algorithm for Feature Selection (GAACOFS) -- Experiments -- Conclusion -- References -- Part IV Soft Computing for Image and Signal Processing, and Pattern Recognition -- Fully Automatic Boundary Extraction of Coronary Plaque in IVUS Image by Anisotropic Diffusion and T-S Type Fuzzy Inference -- Introduction -- Intravascular Ultrasound (IVUS) Image and Plaque Boudary Extraction -- Proposed Method -- Experimental Results and Discussion -- Conclusions -- References -- Cost Effective 3D Localization for Low-Power WSN Based on Modified Sammon's Stress Function with Incomplete Distance Information -- Introduction -- MDS-MAP -- Sammon's Non-linear Mapping -- Simulations -- Experiments -- Results and Observations -- Conclusion -- References -- Classification of Pressure Drop Devices of Proto Type Fast Breeder Reactor through Seven Layered Feed Forward Neural Network -- Introduction -- Review on Cavitation Phenomenon -- Experimental Setup and Instrumentation Employed for Data Acquisition -- ANN Modeling Module -- Network Architecture -- Resilient Back Propagation Algorithm -- Performance Analysis -- Conclusion and Future Work -- References -- Music Visualization by Means of Comb Filter and Relaxation Time According to Human Perception -- Introduction -- Beat Detection (Rhythm Tracking) -- Tempo Extraction -- Beat Strength -- Phase Detection -- Covering Discontinuity -- Results -- Conclusion and Future Works -- References -- GA/SVM for Diagnosis Sleep Stages Using Non-linear and Spectral Features -- Introduction -- Data Collection -- Sleep Staging -- Data -- Feature Extraction -- Spectrogram -- Mixed Frequency Detection -- Approximate Entropy. , Fractal Dimension -- Gene Selection and Classification -- The Genetic Algorithm General Schema -- The SVM Classifier -- Parameter Setting -- Results -- Conclusion -- References -- Part V Special Session Soft Computing in Computer Graphics, Imaging, and Vision -- Prostate Boundary Detection in Ultrasound Images Based on Type-II Fuzzy Sets and Modified Fuzzy C-Means -- Introduction -- Background -- Fuzzy Image Processing -- Type-II Fuzzy Sets -- Prostate Boundary Detection Scheme -- Type-II Fuzzy Set Contrast Enhancement -- Modified Fuzzy C-Means Clustering -- Experimental Results -- Conclusions -- References -- Distributed Multimedia Computing Using Intelligent Agents -- Introduction -- Parallel Multimedia Processing -- Distributed Multiagent Blackboard Architecture -- Distributed Intelligent Agents for Multimedia Processing -- Blackboard Partitioning -- Blackboard Data and Communication -- Agent Implementation -- Distributor Agent -- Worker Agent -- Accumulator Agent -- Conclusions -- References -- Object Recognition Using Simulated Evolution on Fourier Descriptors -- Introduction -- Getting Bitmap Image and Boundary -- Fourier Theory and Similarity Measure -- Results and Analysis -- Proposed Technique Using Simulated Evolution (SE) -- Optimization of the Feature Vector Using Simulated Evolution -- Test Results Using Simulated Evolution -- Conclusion and Future Work -- References -- Part VI Special Session Soft Computing for Intelligent Control -- Embedding a KM Type Reducer for High Speed Fuzzy Controller into an FPGA -- Introduction -- General Concepts of Type-2 FIS -- Hardware Implementation of a Type-2 FIS -- Fuzzification Stage -- Inference Engine Stage -- Type Reducer Stage -- Defuzzification Stage -- Testing of the Type 2 FIS and Results -- Comparison of Results -- Conclusions -- References. , An Application of Fuzzy Lyapunov Synthesis in the Design of Type-2 Fuzzy Logic Controllers -- Introduction -- Dynamic Model -- Problem Statement -- Type-2 Fuzzy Sets and Systems -- Design of the Type-2 Fuzzy Logic Controller -- Simulation Results -- Conclusion -- References -- Access Control and Resource Allocation for Peer-to-Peer Video Streaming Based on Fuzzy Logic -- Introduction -- Access Control and Resource Allocation -- Fuzzy Representation and Enforcement -- Fuzzy Model of Uncertainty -- Fuzzy Representation of Uncertainty -- Fuzzy Enforcement -- System Architecture and Implementation -- A Case Study and Experiments -- Conclusion -- References -- Bio-inspired Optimization Methods of Fuzzy Logic Controllers Applied to Linear Plants -- Introduction -- Theoretical Basis and Problem Statement -- Fuzzy Logic Control Design -- Simulations Results -- Genetic Algorithm Results -- PSO Results -- Conclusions -- References -- Part VII Special Session Traffic and Transportation Systems -- An Adaptive Neuro-Fuzzy Inference System for Simulation of Pedestrians Behaviour at Unsignalized Roadway Crossings -- Introduction -- Statement of the Problem - Gap Acceptance Theory -- Proposed Model - Fuzzy Logic Approach -- FIS Model A -- FIS Model B -- Model Calibration - ANFIS -- Case Study, Model Specification and Application -- Model Results -- Model Validation and Result Analysis -- Conclusion and Further Research -- References -- A Cellular Genetic Algorithm for Solving the Vehicle Routing Problem with Time Windows -- Introduction -- Mathematical Formulations for VRPTW -- Cellular Genetic Algorithms for VRPTW -- Experimental Results and Comparisons -- Conclusion -- References -- Fuzzy C-Means Based Stop Line and Crosswalk Placement at Right Turn Vehicle Exclusive Lane -- Background of Research -- Research Objective -- Fuzzy c-Means Based Problem Formulation. , Methodology Description.