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| Robust Disturbance Rejection Based Control with Extended-state Resonant Observer for Sway Reduction in Uncertain Tower-cranes 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 812-827 作者: Horacio Coral-Enriquez; Santiago Pulido-Guerrero; John Cortés-Romero Adobe PDF(2214Kb)  |  收藏  |  浏览/下载:177/47  |  提交时间:2021/02/22 Active disturbance rejection control (ADRC) extended state observer (ESO) tower-crane control resonant observer disturbance observer linear matrix inequality. |
| A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 800-811 作者: Snehasis Banerjee; Tanushyam Chattopadhyay; Utpal Garain Adobe PDF(891Kb)  |  收藏  |  浏览/下载:191/58  |  提交时间:2021/02/22 Feature engineering sensor data analysis Internet of things (IoT) analytics interpretable learning automation. |
| An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 737-747 作者: Siuly Siuly; Varun Bajaj; Abdulkadir Sengur; Yanchun Zhang Adobe PDF(4171Kb)  |  收藏  |  浏览/下载:149/49  |  提交时间:2021/02/22 Electroencephalogram (EEG) alcoholism optimum allocation technique feature extraction decision table. |
| Phenomenological Based Soft Sensor for Online Estimation of Slurry Rheological Properties 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 696-706 作者: Jenny L. Diaz C.; Diego A. Muñoz; Hernan Alvarez Adobe PDF(2306Kb)  |  收藏  |  浏览/下载:156/43  |  提交时间:2021/02/22 Soft sensor phenomenological based semi-physical model non-Newtonian fluids unknown input observer slurry flow. |
| Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 683-695 作者: Olfa Yahya; Zeineb Lassoued; Kamel Abderrahim Adobe PDF(2179Kb)  |  收藏  |  浏览/下载:84/50  |  提交时间:2021/02/22 Nonlinear control hybrid systems mixed logical dynamic (MLD) model predictive control fuzzy supervisor. |
| Development of STEP AP224 Extractor for Interfacing Feature Based CAPP to STEP-NC (AP238) 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 655-670 作者: Deepanshu Srivastava; Venkateswara Rao Komma Adobe PDF(3913Kb)  |  收藏  |  浏览/下载:115/38  |  提交时间:2021/02/22 Reduced-order observer discrete-time systems Lipschitz systems H∞ ARDUINO MEGA 2560 device. |
| New LMI Conditions for Reduced-order Observer of Lipschitz Discrete-time Systems: Numerical and Experimental Results 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 644-654 作者: Noussaiba Gasmi; Assem Thabet; Mohamed Aoun Adobe PDF(1366Kb)  |  收藏  |  浏览/下载:92/27  |  提交时间:2021/02/22 Reduced-order observer discrete-time systems Lipschitz systems H∞ ARDUINO MEGA 2560 device. |
| A Fault Tolerant Control Scheme Using the Feasible Constrained Control Allocation Strategy 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 628-643 作者: Mehdi Naderi; Tor Arne Johansen; Ali Khaki Sedigh Adobe PDF(1916Kb)  |  收藏  |  浏览/下载:98/43  |  提交时间:2021/02/22 Control allocation feasibility fault tolerant control model predictive control domain of attraction. |
| Auto-tuning of FOPI Controllers for TITO Processes with Experimental Validation 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 589-603 作者: Gargi Baruah; Somanath Majhi; Chitralekha Mahanta Adobe PDF(5332Kb)  |  收藏  |  浏览/下载:167/57  |  提交时间:2021/02/22 Auto-tuning fractional order proportional and integral (FOPI) two-input two-output (TITO) robust relay with hysteresis model reduction coupled tank system (CTS). |
| Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics 期刊论文 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 563-574 作者: Ao-Xue Li; Ke-Xin Zhang; Li-Wei Wang Adobe PDF(1089Kb)  |  收藏  |  浏览/下载:183/55  |  提交时间:2021/02/22 Fine-grained image classification zero-shot learning deep feature learning domain adaptation semantic graph. |