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Block Backstepping Design of Nonlinear State Feedback Control Law for Underactuated Mechanical Systems / / by Shubhobrata Rudra, Ranjit Kumar Barai, Madhubanti Maitra
| Block Backstepping Design of Nonlinear State Feedback Control Law for Underactuated Mechanical Systems / / by Shubhobrata Rudra, Ranjit Kumar Barai, Madhubanti Maitra |
| Autore | Rudra Shubhobrata |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (183 p.) |
| Disciplina | 620 |
| Soggetto topico |
Vibration
Dynamical systems Dynamics Control engineering Electrical engineering Vibration, Dynamical Systems, Control Control and Systems Theory Electrical Engineering |
| ISBN | 981-10-1956-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Theoretical Preliminaries -- Block Backstepping Control of the Underactuated Mechanical Systems -- Applications on the 2-DOF Underactuated Mechanical Systems: Some Case Studies -- Applications on the Underactuated Mechanical Systems with Higher Degrees of Freedom: Some Case Studies -- Scope of the Future Research. |
| Record Nr. | UNINA-9910254351003321 |
Rudra Shubhobrata
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent Computing in Carcinogenic Disease Detection
| Intelligent Computing in Carcinogenic Disease Detection |
| Autore | Das Sharma Kaushik |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (189 pages) |
| Altri autori (Persone) |
KarSubhajit
MaitraMadhubanti |
| Collana | Computational Intelligence Methods and Applications Series |
| ISBN | 981-9724-24-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- List of Abbreviations -- 1 Introduction -- 1.1 Introduction -- 1.2 Historical Background of Carcinogenic Disease Detection -- 1.3 Intelligent Computing Tools in Carcinogenic Disease Detection and Classification -- 1.3.1 Image Processing Tools -- 1.3.2 Machine Learning Tools -- 1.3.2.1 Supervised Learning and Unsupervised Learning -- 1.3.2.2 Cross-Validation and Blind Test -- 1.3.2.3 Filter and Wrapper Methods -- 1.3.3 Modern Optimization Tools -- 1.4 Organization of the Book -- 1.5 Summary -- References -- 2 Biological Background of Benchmark Carcinogenic Data Sets -- 2.1 Introduction -- 2.2 Benchmark Carcinogenic Data Set I -- 2.2.1 Mathematical Representation of Microarray Gene Expression Data -- 2.2.2 Benchmark Microarray Gene Expression Data Sets -- 2.3 Benchmark Carcinogenic Data Set II -- 2.3.1 Microscopic Blood Smear Images -- 2.4 Benchmark Carcinogenic Data Set III -- 2.4.1 CT Images -- 2.5 Summary -- References -- 3 Intelligent Computing Approaches for Carcinogenic Disease Detection: A Review -- 3.1 Introduction -- 3.2 Intelligent Computing Approaches for Benchmark Carcinogenic Data Set I -- 3.3 Intelligent Computing Approaches for Benchmark Carcinogenic Data Set II -- 3.4 Intelligent Computing Approaches for Benchmark Carcinogenic Data Set III -- 3.5 Summary -- References -- 4 Classical Approaches in Gene Evaluation for Carcinogenic Disease Detection -- 4.1 Introduction -- 4.2 Filter Approaches in Gene Evaluation -- 4.2.1 T-Test Technique -- 4.2.2 Chi-Square Technique -- 4.2.3 Signal-to-Noise Ratio Technique -- 4.3 Wrapper Approaches in Gene Evaluation -- 4.3.1 Particle Swarm Optimization (PSO) -- 4.3.2 PSO-Based Wrapper Method for Gene Selection -- 4.4 Classifiers -- 4.4.1 Support Vector Machine (SVM) Classifier -- 4.4.2 k-Nearest Neighbor Classifier -- 4.5 Experimental Study.
4.5.1 Experimental Study on Filter Approaches -- 4.5.2 Experimental Study on Wrapper Approaches -- 4.6 Summary -- References -- 5 Intelligent Computing Approach in Gene Evaluation for Carcinogenic Disease Detection -- 5.1 Introduction -- 5.2 Adaptive k-Nearest Neighborhood Technique -- 5.2.1 PSO-Based Adaptive k-Nearest Neighborhood Technique for Gene Evaluation -- 5.2.2 Fitness Function -- 5.3 Experimental Study -- 5.4 Analysis of Experimental Results -- 5.4.1 Analysis of SRBCT Data Set -- 5.4.2 Analysis of ALL_AML Data Set -- 5.4.3 Analysis of MLL Data Set -- 5.5 Summary -- References -- 6 Intelligent Computing Approach for Leukocyte Identification -- 6.1 Introduction -- 6.2 Preprocessing -- 6.2.1 Identification of Leukocytes -- 6.2.2 Separation of Grouped Leukocytes -- 6.2.3 Image Cleaning -- 6.2.4 Separation of Whole Leukocyte, Nucleus, and Cytoplasm -- 6.3 Feature Extraction -- 6.4 Weighted Aggregation-Based Transposition PSO for Feature Evaluation -- 6.4.1 WATPSO-Based Feature Selection Technique -- 6.5 Experimental Study -- 6.5.1 Database -- 6.5.2 Performance Measure -- 6.5.3 Performance Evaluation -- 6.5.4 Comparative Study with Other Related Works Utilizing ALL Image Data -- 6.6 Summary -- References -- 7 Intelligent Computing Approach for Lung Nodule Detection -- 7.1 Introduction -- 7.2 Preprocessing -- 7.2.1 Lung Segmentation -- 7.2.2 Volumetric Shape Index -- 7.2.3 Multi-scale Dot Enhancement Filter -- 7.3 Lung Nodule Detection and Classification Methodology -- 7.3.1 Harmony Search Algorithm -- 7.3.2 Fitness Function -- 7.3.3 Adaptive Weight Selection Strategy -- 7.3.4 GrIHS-Based Feature Selection Strategy -- 7.4 Experimental Study -- 7.4.1 Result Analysis -- 7.5 Summary -- References -- 8 Conclusion -- 8.1 Future Research Directions -- Index. |
| Record Nr. | UNINA-9910861098403321 |
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Das Sharma Kaushik
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| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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