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Time-optimal and Smooth Trajectory Planning for Robot Manipulators
Tie Zhang,Meihui Zhang,Yan-biao Zou 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.1
This paper presents a practical time-optimal and smooth trajectory planning algorithm and then appliesit to robot manipulators. The proposed algorithm uses the time-optimal theory based on the dynamics model toplan the robot’s motion trajectory, constructs the trajectory optimization model under the constraints of the geometric path and joint torque, and dynamically selects the optimal trajectory parameters during the solving process toprominently improve the robot’s motion speed. Moreover, the proposed algorithm utilizes the input shaping algorithm instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The inputshaping of trajectory parameters during postprocessing not only suppresses the residual vibration of the robot butalso takes the signal delay caused by traditional input shaping into account. The combination of these algorithmsmakes the proposed time-optimal and smooth trajectory planning algorithm ensure absolute time optimality andachieve a smooth trajectory. The results of an experiment on a six-degree-of-freedom industrial robot indicate thevalidity of the proposed algorithm.
A General Distributed Deep Learning Platform: A Review of Apache SINGA
Lee, Chonho,Wang, Wei,Zhang, Meihui,Ooi, Beng Chin Korean Institute of Information Scientists and Eng 2016 정보과학회지 Vol.34 No.3
This article reviews Apache SINGA, a general distributed deep learning (DL) platform. The system components and its architecture are presented, as well as how to configure and run SINGA for different types of distributed training using model/data partitioning. Besides, several features and performance are compared with other popular DL tools.
A General Distributed Deep Learning Platform
Chonho Lee,Wei Wang,Meihui Zhang,Beng Chin Ooi 한국정보과학회 2016 정보과학회지 Vol.34 No.3
This article reviews Apache SINGA, a general distributed deep learning (DL) platform. The system components and its architecture are presented, as well as how to configure and run SINGA for different types of distributed training using model/data partitioning. Besides, several features and performance are compared with other popular DL tools.
SEM-based study on the impact of safety culture on unsafe behaviors in Chinese nuclear power plants
Dai Licao,Ma Li,Zhang Meihui,Liang Ziyi 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.10
This paper uses 135 Licensed Operator Event Reports (LOER) from Chinese nuclear plants to analyze how safety culture affects unsafe behaviors in nuclear power plants. On the basis of a modified human factors analysis and classification system (HFACS) framework, structural equation model (SEM) is used to explore the relationship between latent variables at various levels. Correlation tests such as chi-square test are used to analyze the path from safety culture to unsafe behaviors. The role of latent error is clarified. The results show that the ratio of latent errors to active errors is 3.4:1. The key path linking safety culture weaknesses to unsafe behaviors is Organizational Processes/Inadequate Supervision/ Physical/Technical Environment/Skill-based Errors. The most influential factors on the latent variables at each level in the HFACS framework are Organizational Processes, Inadequate Supervision, Physical Environment, and Skill-based Errors.