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[null,"1.","Record Nr.",null,"UNINA9910457227303321"]
[null,null,"Autore",null,"Mueller Thomas J"]
[null,null,"Titolo",null,"Fixed and Flapping Wing Aerodynamics for Micro Air Vehicle Applications [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"Reston, : American Institute of Aeronautics and Astronautics, 2000"]
[null,null,"ISBN",null,"1-60086-665-4"]
[{"$nulls":4},"1-60086-446-5"]
[null,null,"Descrizione fisica",null,"1 online resource (595 p.)"]
[null,null,"Collana",null,"Progress in Astronautics and Aeronautics ; ; v.195"]
[null,null,"Disciplina",null,"629.1 s 629.133/36"]
[null,null,"Soggetti",null,"Aerodynamics"]
[{"$nulls":4},"Drone aircraft"]
[{"$nulls":4},"Micro air vehicles"]
[{"$nulls":4},"Ornithopters"]
[{"$nulls":4},"Mechanical Engineering"]
[{"$nulls":4},"Engineering & Applied Sciences"]
[{"$nulls":4},"Aeronautics Engineering & Astronautics"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"\"\"Cover\"\"; \"\"Title\"\"; \"\"Copyright\"\"; \"\"Table of Contents\"\"; \"\"Preface\"\"; \"\"Chapter 1 An Overview of Micro Air Vehicle Aerodynamics\"\"; \"\"I. Introduction\"\"; \"\"II. Fixed Wing Vehicles\"\"; \"\"III. Flapping Wing Vehicles\"\"; \"\"IV. Concluding Remarks\"\"; \"\"References\"\"; \"\"Part I. Fixed Wing Aerodynamics\"\"; \"\"Chapter 2 Higher-Order Boundary Layer Formulation and Application to Low Reynolds Number Flows\"\"; \"\"I. Introduction\"\"; \"\"II. Curvilinear Coordinates and Equations\"\"; \"\"III. Equivalent Inviscid Flow\"\"; \"\"IV. Entrainment Equation and Viscous/Inviscid Coupling\"\""]
[{"$nulls":4},"\"\"V Integral Momentum and Kinetic Energy Equations\"\"\"\"VI. Turbulent Transport Equation\"\"; \"\"VII. Real Viscous Flow Profiles\"\"; \"\"VIII. Profile Families\"\"; \"\"IX. Higher-Order Corrections\"\"; \"\"X. High-Order Panel Method\"\"; \"\"XI. Viscous/Inviscid System Formulation\"\"; \"\"XII. Results\"\"; \"\"XIII. Conclusions\"\"; \"\"References\"\"; \"\"Chapter 3 Analysis and Design of Airfoils for Use at Ultra-Low Reynolds Numbers\"\"; \"\"I. Introduction\"\"; \"\"II. Computational Analysis Methods\"\"; \"\"III. Flowfield Assumptions\"\"; \"\"IV. Grid Topology\"\"; \"\"V. Comparison with Experiment\"\""]
[{"$nulls":4},"\"\"VI. Effects of Reynolds Number and Geometry Variations on Airfoil Performance\"\"\"\"VII. Airfoil Optimization\"\"; \"\"VIII. Conclusions\"\"; "]
[{"$nulls":4},"\"\"References\"\"; \"\"Chapter 4 Adaptive, Unstructured Meshes for Solving the Navier-Stokes Equations for Low-Chord-Reynolds-Number Flows\"\"; \"\"I. Introduction\"\"; \"\"II. Approach\"\"; \"\"III. The Finite Element Approximation\"\"; \"\"IV. Fluid Solver\"\"; \"\"V. Grid Generation and Adaptive Refinement\"\"; \"\"VI. Results\"\"; \"\"VII. Database Validation\"\"; \"\"VIII. Ongoing Work\"\"; \"\"IX. Conclusions\"\"; \"\"Acknowledgment\"\"; \"\"References\"\""]
[{"$nulls":4},"\"\"Chapter 5 Wind Tunnel Tests of Wings and Rings at Low Reynolds Numbers\"\"\"\"I. Introduction\"\"; \"\"II. Effect of Aspect Ratio and Planform on the Aerodynamic Lift and Drag\"\"; \"\"III. Effect of Low Reynolds Numbers on the Lift and Drag of Ring Airfoils\"\"; \"\"References\"\"; \"\"Chapter 6 Effects of Acoustic Disturbances on Low Re Aerofoil Flows\"\"; \"\"I. Introduction\"\"; \"\"II. Experimental Arrangements\"\"; \"\"III. Results\"\"; \"\"IV. Discussion\"\"; \"\"V. Potential Use of Sound to Improve Performance\"\"; \"\"VI. Conclusions\"\"; \"\"Acknowledgments\"\"; \"\"References\"\""]
[{"$nulls":4},"\"\"Chapter 7 Aerodynamic Characteristics of Low Aspect Ratio Wings at Low Reynolds Numbers\"\"\"\"I. Introduction\"\"; \"\"II. Apparatus\"\"; \"\"III. Procedures\"\"; \"\"IV. Uncertainty\"\"; \"\"V. Flow Visualization\"\"; \"\"VI. Discussion of Results\"\"; \"\"VII. Vortex-Lattice Method\"\"; \"\"VIII. Conclusions\"\"; \"\"Acknowledgments\"\"; \"\"References\"\"; \"\"Chapter 8 Systematic Airfoil Design Studies at Low Reynolds Numbers\"\"; \"\"I. Introduction\"\"; \"\"II. Design Process\"\"; \"\"III. Parametric Studies in Airfoil Design\"\"; \"\"IV. Summary and Conclusions\"\"; \"\"Acknowledgments\"\"; \"\"References\"\""]
[{"$nulls":4},"\"\"Chapter 9 Numerical Optimization and Wind-Tunnel Testing of Low Reynolds Number Airfoils\"\""]
[null,"2.","Record Nr.",null,"UNINA9910767543703321"]
[null,null,"Titolo",null,"Advanced Data Mining and Applications : 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008, Proceedings / / edited by Changjie Tang, Charles X. Ling, Xiaofang Zhou, Nick Cercone, Xue Li"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2008"]
[null,null,"ISBN",null,"3-540-88192-1"]
[null,null,"Edizione",null,"[1st ed. 2008.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 759 p.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 5139"]
[null,null,"Disciplina",null,"006.312"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Medical sciences"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Information technology - Management"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Health Sciences"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Software Engineering"]
[{"$nulls":4},"Computer Application in Administrative Data Processing"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Keynotes -- An Introduction to Transfer Learning -- Autonomy-Oriented Computing (AOC), Self-organized Computability, and Complex Data Mining -- Regular Papers -- Improving Angle Based Mappings -- Mining Natural Language Programming Directives with Class-Oriented Bayesian Networks -- Boosting over Groups and Its Application to Acronym-Expansion Extraction -- A Genetic-Based Feature Construction Method for Data Summarisation -- Suicidal Risk Evaluation Using a Similarity-Based Classifier -- Gene Selection for Cancer Classification Using DCA -- FARS: A Multi-relational Feature and Relation Selection Approach for Efficient Classification -- Enhancing Text Categorization Using Sentence Semantics -- Mining "]
[{"$nulls":4},"Evolving Web Sessions and Clustering Dynamic Web Documents for Similarity-Aware Web Content Management -- Data Quality in Privacy Preservation for Associative Classification -- Timeline Analysis of Web News Events -- Analysis of Alarm Sequences in a Chemical Plant -- Speed Up SVM Algorithm for Massive Classification Tasks -- Mining Supplemental Frequent Patterns -- A Distributed Privacy-Preserving Association Rules Mining Scheme Using Frequent-Pattern Tree -- Dichotomy Method toward Interactive Testing-Based Fault Localization -- Maintaining the Maximum Normalized Mean and Applications in Data Stream Mining -- Identification of Interface Residues Involved in Protein-Protein Interactions Using Naïve Bayes Classifier -- Negative Generator Border for Effective Pattern Maintenance -- CommTracker: A Core-Based Algorithm of Tracking Community Evolution -- Face Recognition Using Clustering Based Optimal Linear Discriminant Analysis -- A Novel Immune Based Approach for Detection of Windows PE Virus -- Using Genetic Algorithms for Parameter Optimization in Building Predictive Data Mining Models -- Using DataMining Methods to Predict Personally Identifiable Information in Emails -- Iterative Reinforcement Cross-Domain Text Classification -- Extracting Decision Rules from Sigmoid Kernel -- DMGrid: A Data Mining System Based on Grid Computing -- S-SimRank: Combining Content and Link Information to Cluster Papers Effectively and Efficiently -- Open Domain Recommendation: Social Networks and Collaborative Filtering -- An Effective Approach for Identifying Evolving Three-Dimensional Structural Motifs in Protein Folding Data -- Texture Image Retrieval Based on Contourlet Transform and Active Perceptual Similarity Learning -- A Temporal Dominant Relationship Analysis Method -- Leakage-Aware Energy Efficient Scheduling for Fixed-Priority Tasks with Preemption Thresholds -- Short Papers -- Learning and Inferences of the Bayesian Network with Maximum Likelihood Parameters -- TARtool: A Temporal Dataset Generator for Market Basket Analysis -- Dimensionality Reduction for Classification -- Trajectories Mining for Traffic Condition Renewing -- Mining Bug Classifier and Debug Strategy Association Rules for Web-Based Applications -- Test the Overall Significance of p-values by Using Joint Tail Probability of Ordered p-values as Test Statistic -- Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model -- Text Learning and Hierarchical Feature Selection in Webpage Classification -- The RSO Algorithm for Reducing Number of Set Operations in Association Rule Mining -- Predictive Performance of Clustered Feature-Weighting Case-Based Reasoning -- Selecting the Right Features for Bipartite-Based Text Clustering -- Image Emotional Classification Based on Color Semantic Description -- A Semi-supervised Clustering Algorithm Based on Must-Link Set -- T-rotation: Multiple Publications of Privacy PreservingData Sequence -- The Integrated Methodology of KPCA and Wavelet Support Vector Machine for Predicting Financial Distress -- Outlier Detection Based on Voronoi Diagram -- AWSum – Data Mining for Insight -- Integrative Neural Network Approach for Protein Interaction Prediction from Heterogeneous Data -- Rules Extraction Based on Data Summarisation Approach Using DARA -- A Rough-Apriori Technique in Mining Linguistic Association Rules -- Mining Causal Knowledge from Diagnostic Knowledge -- Modified Particle Swarm Optimizer with Adaptive Dynamic Weights for Cancer Combinational Chemotherapy -- MPSQAR: Mining Quantitative Association Rules Preserving Semantics -- Using Support Vector Regression for Classification -- Dynamic Growing Self-organizing Neural Network for Clustering -- A Design of Reward Function Based on Knowledge in Multi-agent Learning -- A Learning Method of "]
[{"$nulls":4},"Detecting Anomalous Pedestrian -- Moment+: Mining Closed Frequent Itemsets over Data Stream -- CDPM: Finding and Evaluating Community Structure in Social Networks -- Using Matrix Model to Find Association Rule Core for Diverse Compound Critiques -- Link-Contexts for Ranking -- DC-Tree: An Algorithm for Skyline Query on Data Streams -- Sequential Pattern Mining for Protein Function Prediction -- Improving Web Search by Categorization, Clustering, and Personalization -- JSNVA: A Java Straight-Line Drawing Framework for Network Visual Analysis -- Recognition of Data Records in Semi-structured Web-Pages Using Ontology and ? 2 Statistical Distribution -- Organizing Structured Deep Web by Clustering Query Interfaces Link Graph -- CBP: A New Efficient Method for Mining Multilevel and Generalized Frequent Itemsets -- Supporting Customer Retention through Real-Time Monitoring of Individual Web Usage -- A Comparative Study of CorrelationMeasurements for Searching Similar Tags -- Structure of Query Modification Process: Branchings -- Mining Top-n Local Outliers in Constrained Spatial Networks -- Mining Concept-Drifting Data Streams with Multiple Semi-Random Decision Trees -- Automatic Web Tagging and Person Tagging Using Language Models -- Real-Time Person Tracking Based on Data Field."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008. The 35 revised full papers and 43 revised short papers presented together with the abstract of 2 keynote lectures were carefully reviewed and selected from 304 submissions. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining and feature original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining."]