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Artificial intelligence for future generation robotics /
Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Diego :
Elsevier,
2021.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Artificial intelligence for future generation robotics / |c edited by Rabindra Nath Shaw [and more]. |
| 260 | |a San Diego : |b Elsevier, |c 2021. | ||
| 300 | |a 1 online resource (180 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |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 | |
| 500 | |a 6.4.1 Data heterogeneity mitigation using Nelder-Mead method. | ||
| 520 | |a Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application into ten chapters, this book offers a practical tool for researchers and engineers looking for new avenues and use-cases that combine AI with smart robotics. As we witness exponential growth in automation and the rapid advancement of underpinning technologies, such as ubiquitous computing, sensing, intelligent data processing, mobile computing and context aware applications, this book is an ideal resource for future innovation. Brings AI and smart robotics into imaginative, technically-informed dialogue Integrates fundamentals with real-world applications Presents potential applications for AI in smart robotics by use-case Gives detailed theory and mathematical calculations for each application Stimulates new thinking and research in applying AI to robotics. | ||
| 650 | 0 | |a Robotics. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Robotics |0 (DNLM)D012371 | |
| 650 | 2 | |a Artificial Intelligence |0 (DNLM)D001185 | |
| 650 | 6 | |a Robotique. |0 (CaQQLa)201-0110752 | |
| 650 | 6 | |a Intelligence artificielle. |0 (CaQQLa)201-0008626 | |
| 650 | 7 | |a artificial intelligence. |2 aat |0 (CStmoGRI)aat300251574 | |
| 650 | 7 | |a Artificial intelligence |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Robotics |2 fast |0 (OCoLC)fst01098997 | |
| 700 | 1 | |a Shaw, Rabindra Nath. | |
| 700 | 1 | |a Ghosh, Ankush. | |
| 700 | 1 | |a Balas, Valentina Emilia. | |
| 700 | 1 | |a Bianchini, Monica. | |
| 776 | 0 | 8 | |i Print version: |a Shaw, Rabindra Nath. |t Artificial Intelligence for Future Generation Robotics. |d San Diego : Elsevier, �2021 |z 9780323854986 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780323854986 |z Texto completo |