Artificial intelligence for future generation robotics:
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier
[2021]
|
| Schlagworte: | |
| Online-Zugang: | FHD01 FWS01 FWS02 |
| Beschreibung: | 1 Online-Ressource (xv, 162 Seiten) |
| ISBN: | 9780323857994 |
Internformat
MARC
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| 264 | 1 | |a Amsterdam |b Elsevier |c [2021] | |
| 300 | |a 1 Online-Ressource (xv, 162 Seiten) | ||
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| 505 | 8 | |a Front Cover -- Artificial Intelligence for Future Generation Robotics -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- 1. Robotic process automation with increasing productivity and improving product quality using artificial intelligence and... -- 1.1 Introduction -- 1.2 Related work -- 1.3 Proposed work -- 1.4 Proposed model -- 1.4.1 System component -- 1.4.2 Effective collaboration -- 1.5 Manufacturing systems -- 1.6 Results analysis -- 1.7 Conclusions and future work -- References | |
| 505 | 8 | |a 2. Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques -- 2.1 Introduction -- 2.2 Literature review -- 2.3 Modeling and design -- 2.3.1 Fitness function -- 2.3.2 Particle swarm optimization -- 2.3.3 Firefly algorithm -- 2.3.4 Proposed algorithm -- 2.4 Results and discussions -- 2.5 Conclusions and future work -- References -- 3. Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network -- 3.1 Introduction -- 3.2 2D CNN-a brief introduction | |
| 505 | 8 | |a 3.3 1D convolutional neural network -- 3.4 Statistical parameters for feature extraction -- 3.5 Dataset used -- 3.6 Results -- 3.7 Conclusion -- References -- 4. Single shot detection for detecting real-time flying objects for unmanned aerial vehicle -- 4.1 Introduction -- 4.2 Related work -- 4.2.1 Appearance-based methods -- 4.2.2 Motion-based methods -- 4.2.3 Hybrid methods -- 4.2.4 Single-step detectors -- 4.2.5 Two-step detectors/region-based detectors -- 4.3 Methodology -- 4.3.1 Model training -- 4.3.2 Evaluation metric -- 4.4 Results and discussions | |
| 505 | 8 | |a 4.4.1 For real-time flying objects from video -- 4.5 Conclusion -- References -- 5. Depression detection for elderly people using AI robotic systems leveraging the Nelder-Mead Method -- 5.1 Introduction -- 5.2 Background -- 5.3 Related work -- 5.4 Elderly people detect depression signs and symptoms -- 5.4.1 Causes of depression in older adults -- 5.4.2 Medical conditions that can cause elderly depression -- 5.4.3 Elderly depression as side effect of medication -- 5.4.4 Self-help for elderly depression -- 5.5 Proposed methodology -- 5.5.1 Proposed algorithm | |
| 505 | 8 | |a 5.5.2 Persistent monitoring for depression detection -- 5.5.3 Emergency monitoring -- 5.5.4 Personalized monitoring -- 5.5.5 Feature extraction -- 5.6 Result analysis -- References -- 6. Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder-Mead method -- 6.1 Introduction -- 6.1.1 Related work -- 6.1.2 Contributions -- 6.2 Data heterogeneity mitigation -- 6.2.1 Data preprocessing -- 6.2.2 Nelder-Mead method for mitigating data heterogeneity -- 6.3 LSTM-based classification of data -- 6.4 Experiments and results | |
| 650 | 4 | |a Robotics | |
| 650 | 4 | |a Artificial intelligence | |
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| 700 | 1 | |a Shaw, Rabindra Nath |4 edt | |
| 700 | 1 | |a Ghosh, Ankush |4 edt | |
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| 700 | 1 | |a Bianchini, Monica |4 edt | |
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| author2 | Shaw, Rabindra Nath Ghosh, Ankush Balas, Valentina E. 1956- Bianchini, Monica |
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| author_facet | Shaw, Rabindra Nath Ghosh, Ankush Balas, Valentina E. 1956- Bianchini, Monica |
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| contents | Front Cover -- Artificial Intelligence for Future Generation Robotics -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- 1. Robotic process automation with increasing productivity and improving product quality using artificial intelligence and... -- 1.1 Introduction -- 1.2 Related work -- 1.3 Proposed work -- 1.4 Proposed model -- 1.4.1 System component -- 1.4.2 Effective collaboration -- 1.5 Manufacturing systems -- 1.6 Results analysis -- 1.7 Conclusions and future work -- References 2. Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques -- 2.1 Introduction -- 2.2 Literature review -- 2.3 Modeling and design -- 2.3.1 Fitness function -- 2.3.2 Particle swarm optimization -- 2.3.3 Firefly algorithm -- 2.3.4 Proposed algorithm -- 2.4 Results and discussions -- 2.5 Conclusions and future work -- References -- 3. Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network -- 3.1 Introduction -- 3.2 2D CNN-a brief introduction 3.3 1D convolutional neural network -- 3.4 Statistical parameters for feature extraction -- 3.5 Dataset used -- 3.6 Results -- 3.7 Conclusion -- References -- 4. Single shot detection for detecting real-time flying objects for unmanned aerial vehicle -- 4.1 Introduction -- 4.2 Related work -- 4.2.1 Appearance-based methods -- 4.2.2 Motion-based methods -- 4.2.3 Hybrid methods -- 4.2.4 Single-step detectors -- 4.2.5 Two-step detectors/region-based detectors -- 4.3 Methodology -- 4.3.1 Model training -- 4.3.2 Evaluation metric -- 4.4 Results and discussions 4.4.1 For real-time flying objects from video -- 4.5 Conclusion -- References -- 5. Depression detection for elderly people using AI robotic systems leveraging the Nelder-Mead Method -- 5.1 Introduction -- 5.2 Background -- 5.3 Related work -- 5.4 Elderly people detect depression signs and symptoms -- 5.4.1 Causes of depression in older adults -- 5.4.2 Medical conditions that can cause elderly depression -- 5.4.3 Elderly depression as side effect of medication -- 5.4.4 Self-help for elderly depression -- 5.5 Proposed methodology -- 5.5.1 Proposed algorithm 5.5.2 Persistent monitoring for depression detection -- 5.5.3 Emergency monitoring -- 5.5.4 Personalized monitoring -- 5.5.5 Feature extraction -- 5.6 Result analysis -- References -- 6. Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder-Mead method -- 6.1 Introduction -- 6.1.1 Related work -- 6.1.2 Contributions -- 6.2 Data heterogeneity mitigation -- 6.2.1 Data preprocessing -- 6.2.2 Nelder-Mead method for mitigating data heterogeneity -- 6.3 LSTM-based classification of data -- 6.4 Experiments and results |
| ctrlnum | (OCoLC)1269385892 (DE-599)BVBBV047470218 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:08:49Z |
| indexdate | 2024-08-01T16:15:14Z |
| institution | BVB |
| isbn | 9780323857994 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032871892 |
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| spellingShingle | Artificial intelligence for future generation robotics Front Cover -- Artificial Intelligence for Future Generation Robotics -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- 1. Robotic process automation with increasing productivity and improving product quality using artificial intelligence and... -- 1.1 Introduction -- 1.2 Related work -- 1.3 Proposed work -- 1.4 Proposed model -- 1.4.1 System component -- 1.4.2 Effective collaboration -- 1.5 Manufacturing systems -- 1.6 Results analysis -- 1.7 Conclusions and future work -- References 2. Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques -- 2.1 Introduction -- 2.2 Literature review -- 2.3 Modeling and design -- 2.3.1 Fitness function -- 2.3.2 Particle swarm optimization -- 2.3.3 Firefly algorithm -- 2.3.4 Proposed algorithm -- 2.4 Results and discussions -- 2.5 Conclusions and future work -- References -- 3. Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network -- 3.1 Introduction -- 3.2 2D CNN-a brief introduction 3.3 1D convolutional neural network -- 3.4 Statistical parameters for feature extraction -- 3.5 Dataset used -- 3.6 Results -- 3.7 Conclusion -- References -- 4. Single shot detection for detecting real-time flying objects for unmanned aerial vehicle -- 4.1 Introduction -- 4.2 Related work -- 4.2.1 Appearance-based methods -- 4.2.2 Motion-based methods -- 4.2.3 Hybrid methods -- 4.2.4 Single-step detectors -- 4.2.5 Two-step detectors/region-based detectors -- 4.3 Methodology -- 4.3.1 Model training -- 4.3.2 Evaluation metric -- 4.4 Results and discussions 4.4.1 For real-time flying objects from video -- 4.5 Conclusion -- References -- 5. Depression detection for elderly people using AI robotic systems leveraging the Nelder-Mead Method -- 5.1 Introduction -- 5.2 Background -- 5.3 Related work -- 5.4 Elderly people detect depression signs and symptoms -- 5.4.1 Causes of depression in older adults -- 5.4.2 Medical conditions that can cause elderly depression -- 5.4.3 Elderly depression as side effect of medication -- 5.4.4 Self-help for elderly depression -- 5.5 Proposed methodology -- 5.5.1 Proposed algorithm 5.5.2 Persistent monitoring for depression detection -- 5.5.3 Emergency monitoring -- 5.5.4 Personalized monitoring -- 5.5.5 Feature extraction -- 5.6 Result analysis -- References -- 6. Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder-Mead method -- 6.1 Introduction -- 6.1.1 Related work -- 6.1.2 Contributions -- 6.2 Data heterogeneity mitigation -- 6.2.1 Data preprocessing -- 6.2.2 Nelder-Mead method for mitigating data heterogeneity -- 6.3 LSTM-based classification of data -- 6.4 Experiments and results Robotics Artificial intelligence Artificial intelligence fast Robotics fast Künstliche Intelligenz (DE-588)4033447-8 gnd Roboter (DE-588)4050208-9 gnd Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4050208-9 (DE-588)4125909-9 |
| title | Artificial intelligence for future generation robotics |
| title_auth | Artificial intelligence for future generation robotics |
| title_exact_search | Artificial intelligence for future generation robotics |
| title_exact_search_txtP | Artificial intelligence for future generation robotics |
| title_full | Artificial intelligence for future generation robotics edited by Rabindra Nath Shaw, Ankush Ghosh, Valentina E. Balas, Monica Bianchini |
| title_fullStr | Artificial intelligence for future generation robotics edited by Rabindra Nath Shaw, Ankush Ghosh, Valentina E. Balas, Monica Bianchini |
| title_full_unstemmed | Artificial intelligence for future generation robotics edited by Rabindra Nath Shaw, Ankush Ghosh, Valentina E. Balas, Monica Bianchini |
| title_short | Artificial intelligence for future generation robotics |
| title_sort | artificial intelligence for future generation robotics |
| topic | Robotics Artificial intelligence Artificial intelligence fast Robotics fast Künstliche Intelligenz (DE-588)4033447-8 gnd Roboter (DE-588)4050208-9 gnd Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd |
| topic_facet | Robotics Artificial intelligence Künstliche Intelligenz Roboter Mensch-Maschine-Kommunikation |
| work_keys_str_mv | AT shawrabindranath artificialintelligenceforfuturegenerationrobotics AT ghoshankush artificialintelligenceforfuturegenerationrobotics AT balasvalentinae artificialintelligenceforfuturegenerationrobotics AT bianchinimonica artificialintelligenceforfuturegenerationrobotics |