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UID:

Format: 1 Online-Ressource (X, 83 p. 40 illus., 30 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-3-031-25333-1

Series Statement: SpringerBriefs in Service Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25332-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25334-8

Language: English

DOI: 10.1007/978-3-031-25333-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (X, 83 p. 40 illus., 30 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-3-031-25333-1

Series Statement: SpringerBriefs in Service Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25332-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25334-8

Language: English

DOI: 10.1007/978-3-031-25333-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (X, 83 p. 40 illus., 30 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-3-031-25333-1

Series Statement: SpringerBriefs in Service Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25332-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25334-8

Language: English

DOI: 10.1007/978-3-031-25333-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (X, 83 p. 40 illus., 30 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-3-031-25333-1

Series Statement: SpringerBriefs in Service Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25332-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25334-8

Language: English

DOI: 10.1007/978-3-031-25333-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (X, 83 p. 40 illus., 30 illus. in color)

Edition: 1st ed. 2023

ISBN: 9783031253331

Series Statement: SpringerBriefs in Service Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25332-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-25334-8

Language: English

DOI: 10.1007/978-3-031-25333-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XII, 151 p. 47 illus. , online resource.

ISBN: 9783642398445

Series Statement: Lecture Notes in Computer Science, 8040

Content: This book constitutes the refereed proceedings of the International Conference for Smart Health, ICSH 2013, held in Beijing, China, in August 2013. The 15 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on Information Sharing, Integrating and Extraction; Mining Clinical and Medical Data; Smart Health Applications.

Note: Information Sharing, Integrating and Extraction -- Mining Clinical and Medical Data -- Smart Health Applications.

In: Springer eBooks

Additional Edition: Printed edition: ISBN 9783642398438

Language: English

Subjects: Computer Science

Keywords: Konferenzschrift ; Konferenzschrift ; Konferenzschrift

DOI: 10.1007/978-3-642-39844-5

URL: http://dx.doi.org/10.1007/978-3-642-39844-5

URL: Volltext

URL: Inhaltsverzeichnis

URL: Abstract

URL: Volltext  (lizenzpflichtig)

URL: Volltext  (lizenzpflichtig)

URL: Cover

UID:

Format: 1 online resource (278 pages)

Edition: 1st ed.

ISBN: 9780443216183

Note: Front Cover -- Computational Knowledge Vision -- Copyright -- Contents -- Biography -- Preface -- References -- Acknowledgments -- 1 Start from here -- 1.1 Motivation for writing this book -- 1.2 Organization of the book -- References -- 1 Computational Knowledge Vision framework -- 2 Reviewing the past enables us to learn -- 2.1 "Information is not knowledge" -- 2.2 "Knowledge is power" -- 2.3 Toward Computational Knowledge Vision -- 2.3.1 When humanoids inspire computer vision -- 2.3.1.1 Neural network model -- 2.3.1.2 Meta-learning model -- 2.3.2 When combinatorial mathematics meets computer vision -- 2.3.3 When natural language supplements computer vision -- 2.3.4 "Standing on the shoulders of giants" -- References -- 3 Computational Knowledge Vision -- 3.1 What is the goal of the Computational Knowledge Vision? -- 3.2 Why is Computational Knowledge Vision appropriate? -- 3.3 What is the logic of Computational Knowledge Vision by which it can be carried out? -- 3.3.1 Structurized knowledge -- 3.3.2 Knowledge projection and conditioned feedback -- 3.3.3 Reasoning, understanding, and representation -- References -- 2 Computational Knowledge Vision solution -- 4 Low vision: Computational Knowledge Vision for edge detection -- 4.1 Introduction -- 4.2 Proposed approach -- 4.2.1 Differential evolution algorithm -- 4.2.2 Differential-evolutionary-based generative adversarial networks -- 4.2.3 The process and structure of DEGAN -- 4.3 Experiments and results -- 4.3.1 Ablation study -- 4.3.2 Comparison with state-of-the-art methods -- 4.3.2.1 BSDS500 dataset -- 4.3.2.2 NYUD dataset -- 4.4 Conclusions -- References -- 5 Middle vision: Computational Knowledge Vision for visual translation -- 5.1 Introduction -- 5.2 Just-noticeable-difference model -- 5.2.1 Luminance adaptation -- 5.2.2 Contrast masking -- 5.3 Proposed approach. , 5.3.1 Just-noticeable-difference model of our approach -- 5.3.2 Network formulation -- 5.3.3 Network architecture -- 5.4 Experiments and results -- 5.4.1 Ablation study -- 5.4.2 Comparison with state-of-the-art methods -- 5.4.2.1 Qualitative evaluation -- 5.4.2.2 Quantitative evaluation -- 5.4.2.3 Domain adaptation -- 5.5 Conclusions -- References -- 6 Middle vision: Computational Knowledge Vision for jointly face recognition -- 6.1 Introduction -- 6.2 Proposed approach -- 6.2.1 Problem definition -- 6.2.2 Knowledge graph construction and representation -- 6.2.3 Network-based representation learning -- 6.2.4 Knowledge-based representation learning -- 6.2.5 Meta-learning model -- 6.2.6 Meta-continual learning model -- 6.3 Experiments and results -- 6.3.1 Experimental setup -- 6.3.2 Comparison with the state-of-the-art methods -- Sketch face recognition -- Caricature face recognition -- Cartoon face recognition -- 6.3.3 Discussion on the generalization ability -- 6.4 Conclusions -- References -- 7 High vision: Computational Knowledge Vision for visual reasoning -- 7.1 Introduction -- 7.2 Proposed approach -- 7.2.1 Building knowledge bases -- Multimodal Attributes Encoding -- Multimodal Attributes Recovery -- 7.2.2 Mutual modulation model -- Visual Modulation -- Language Modulation -- Cascaded Modulation -- 7.2.3 Knowledge-based key-value memory network -- Key Hashing -- Key Addressing and Value Reading -- Hop Iterations -- 7.2.4 Knowledge-based representation learning -- 7.3 Experiments and results -- 7.3.1 Dataset description -- 7.3.2 Experiment setup -- 7.3.3 Comparison with state-of-the-art methods -- 7.3.4 Ablation study -- 7.3.5 Discussion about key hashing in key-value memory networks -- 7.4 Conclusions -- References -- 3 Computational Knowledge Vision application -- 8 Affective computing: Computational Knowledge Vision for depression detection. , 8.1 Introduction -- 8.2 Proposed approach -- 8.2.1 Basic notation -- 8.2.2 Multimodal attention mechanisms -- 8.2.3 Temporal convolution networks -- 8.2.4 Knowledge-based representation learning -- 8.2.5 Objective function -- 8.3 Experiments and results -- 8.3.1 Dataset description -- DAIC-WOZ dataset -- SMD dataset -- 8.3.2 Experiment setup -- Evaluation measures -- Implementation details -- 8.3.3 Comparison with state-of-the-art methods -- Comparison on the DAIC-WOZ dataset -- Comparison on the SMD dataset -- 8.3.4 Ablation study -- 8.4 Conclusions -- References -- 9 Medical computing: Computational Knowledge Vision for COVID-19 detection -- 9.1 Introduction -- 9.2 Multimodal dataset construction -- 9.2.1 Dataset creation and structure -- 9.2.2 Dataset comparison -- 9.3 Proposed approach -- 9.3.1 Problem definition -- 9.3.2 Network representation learning -- 9.3.3 Data augmentation -- 9.3.4 Self-knowledge distillation -- 9.3.5 Training methods -- 9.4 Experiments and results -- 9.4.1 Experimental settings -- 9.4.2 The results of our model -- 9.4.3 Comparison with state-of-the-art methods -- COVID-19 diagnosis from pneumonia cases on our dataset -- COVID-19 cases diagnosis on COVID v2.0 dataset -- 9.5 Conclusions -- References -- 10 Medical computing: Computational Knowledge Vision for medical visual reasoning -- 10.1 Introduction -- 10.2 Proposed approach -- 10.2.1 Problem setup -- 10.2.2 Knowledge graph construction and representation -- 10.2.3 Feature fusion network -- 10.2.4 Knowledge-based representation learning -- 10.2.5 Meta-learning model -- 10.3 Experiments and results -- 10.3.1 Dataset description -- 10.3.2 Experiment setup -- 10.3.3 Comparison with state-of-the-art methods -- 10.3.4 Discussion about other similar methods -- Summary about comparison and discussion experiments -- 10.3.5 Error analysis -- 10.4 Conclusions -- References. , Index -- Back Cover.

Additional Edition: Print version: Zheng, Wenbo Computational Knowledge Vision San Diego : Elsevier Science & Technology,c2024 ISBN 9780443216190

Language: English

UID:

Format: 1 online resource (269 p.)

Edition: 1st ed.

ISBN: 1-280-58158-1 , 9786613611369 , 0-12-397323-6

Series Statement: Intelligent systems series

Content: The Intelligent Systems Series comprises titles that present state-of-the-art knowledge and the latest advances in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. Flexible manipulators play a critical role in applications in a diverse range of fields, such as construction automation, environmental applications, and space engineering. Due to the complexity of the link deformation and dynamics, the research effort on accurate modeling and high performance control of flexible manipulators has increased dramati

Note: Description based upon print version of record. , Front Cover; Intelligent Systems Series; Flexible Manipulators: Modeling, Analysis, and Optimum Design; Copyright; Contents; Preface; Chapter 1 - Introduction; 1.1Background and Problem Statement; 1.2Motivations; 1.3Organization of the Book; References; Chapter 2 - Past and Recent Works; 2.1Earlier Research on Flexible Manipulators; 2.2Recent Work on Flexible Manipulators; References; Chapter 3 - Modeling of Flexible Manipulators; 3.1Introduction; 3.2Problem Description and Energy Calculations; 3.3Derivation of Equations of Motion; 3.4Linearization of the Beam Dynamic Models , 3.5Finite-Dimensional Modeling of Flexible ManipulatorsReferences; Chapter 4 - Analysis of Flexible Manipulators; 4.1Introduction; 4.2 Dynamic Analysis of Vibrations of Flexible Manipulators ConsideringEffects of Rotary Inertia, Shear Deformation, and Tip Load; 4.3Passivity, Control, and Stability Analysis; References; Chapter 5 - Optimization of Flexible Manipulators; 5.1Optimum Design of Flexible Beams with a New Iteration Approach; 5.2Geometrically Constrained and Composite Material Designs; 5.3Optimum Shape Design of Flexible Manipulators with Tip Loads , 6.7Concurrent Design of a Single-Link Flexible Manipulator Based on PID ControllerReferences; Chapter 7 - Conclusions and Future Research; Index , English

Additional Edition: ISBN 0-12-397036-9

Language: English

UID:

Format: 1 online resource (129 p.)

Edition: 1st ed.

ISBN: 1-280-58126-3 , 9786613611048 , 0-12-397324-4

Series Statement: Intelligent systems series

Content: The Intelligent Systems Series comprises titles that present state of the art knowledge and the latest advances in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. Traditionally, Intelligence and Security Informatics (ISI) research and applications have focused on information sharing and data mining, social network analysis, infrastructure protection and emergency responses for security informatics. With the continuous advance of IT technologies and the increasing sophistication of national and international

Note: Description based upon print version of record. , Front cover; Half title page; Zhejiang University Press Intelligent Systems Series; Chief Editor; Editorial Board Members; Advances in Intelligence and Security Informatics; Copyright page; Table of Contents; Preface; Acknowledgements; Chapter 1 Intelligence and Security Informatics; 1.1 Research Methodology and Frameworks for ISI; 1.2 The ACP Approach; 1.2.1 Modeling with Artificial Societies; 1.2.2 Analysis with Computational Experiments; 1.2.3 Control Through Parallel Execution; 1.2.4 Foundations in Philosophy and Physics; 1.3 Outline of Chapters; References , Chapter 2 Agent Modeling of Terrorist Organization Behavior2.1 Modeling Organizational Behavior; 2.2 Action Extraction from the Web; 2.2.1 Action Data Collection; 2.2.2 Raw Action Extraction; 2.2.3 Action Elimination; 2.2.4 Action Refinement; 2.3 Extracting Causal Knowledge from the Web; 2.4 Construction of Action Hierarchy; 2.5 Designing Causal Scenarios; 2.6 Case Study on Terrorist Organization; 2.7 Conclusion; References; Chapter 3 Security Story Generation for Computational Experiments; 3.1 Story Generation Systems; 3.2 System Workflow and Narrative Structure , 3.3 Story Extraction Approach3.3.1 Text Processing with Domain Knowledge; 3.3.2 Event Detection and Event Element Extraction; 3.3.3 Design and Organization of Patterns; 3.3.4 Event Element Standardization; Number Conversion; Time Standardization; Location Standardization; 3.3.5 Evaluation of Event Relations; 3.4 Experiment; 3.5 Conclusion; References; Chapter 4 Forecasting Group Behavior via Probabilistic Plan Inference; 4.1 Review of Plan-Based Inference; 4.2 Probabilistic Plan Representation; 4.3 Probabilistic Reasoning Approach; 4.3.1 Notation; 4.3.2 Computation; Probability of States , Probability of Action ExecutionOutcome Probability and Expected Utility of Actions; Outcome Probability of Plans and Expected Plan Utility; 4.4 Case Study in Security Informatics; 4.4.1 Construction of Plan Library; 4.4.2 The Test Set; 4.4.3 Experimental Results; 4.5 Conclusion; References; Chapter 5 Forecasting Complex Group Behavior via Multiple Plan Recognition; 5.1 Multiple Plan Recognition for Behavior Prediction; 5.2 The MPR Problem Definition; 5.3 The Proposed MPR Approach; 5.3.1 Constructing the Explanation Graph; Observed Actions; Observed States , 5.3.2 Computing Probability of an Explanation5.3.3 Finding the Best Explanation; 5.3.4 Algorithm and Complexity Analysis; Algorithm (Plan Library PL, Observation Set O); Complexity; 5.3.5 Discussion; 5.4 Case Study in Security Informatics; 5.4.1 Experimental Design; 5.4.2 Results; 5.5 Conclusion; References; Chapter 6 Social Computing in ISI; 6.1 Social Computing; 6.1.1 Theoretical and Infrastructure Underpinnings; 6.1.2 Major Application Areas; 6.2 A Social Computing-Based ISI Research Framework; 6.2.1 Modeling with Artificial Societies; 6.2.2 Analysis with Computational Experiments , 6.2.3 Control and Management Through Parallel Execution , English

Additional Edition: ISBN 0-12-397200-0

Language: English

UID:

Format: 1 Online-Ressource

ISBN: 0387444076 , 9780387444079 , 9780387444093

Language: English

DOI: 10.1007/978-0-387-44409-3

URL: Volltext