http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Chengjin Qin (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Chengjin Qin,Gang Shi,Jianfeng Tao,Honggan Yu,Yanrui Jin,Dengyu Xiao,Chengliang Liu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.2
During tunneling process, it is of critical importance to dynamically adjust operation parameters of shield machine due to changes of geological conditions. Cutterhead torque is one of the key load parameters, and its accurate prediction could adjust operational parameters including cutterhead rotational speed and tunneling speed in advance and avoid potential cutterhead jamming. Based on operation and state data collected by the monitoring system, we propose a residual-convolutional-LSTM neural network (RCLSTMNet) for forecasting cutter head torque in shield machine. On the basis of correlation analysis, parameters closely related to cutter head torque are selected as inputs by employing cosine similarity, which significantly reduces input dimension. Convolutional-LSTM neural network is fused and constructed for extracting deep useful features, while residual network module is utilized to avoid gradient disappearing and improve regression performance. Comparisons with recent data-driven cutterhead torque prediction methods are made on the actual engineering datasets, which demonstrate the presented RCLSTMNet outperforms the other data driven models in most cases. Moreover, the predicted curves of cutterhead torque using the proposed RCLSTMNet coincide with the actual curves much better than predicted curves using the other models. Meanwhile, the highest and average accuracy of RCLSTMNnet reach 98.1% and 95.6%, respectively.
-
Optimization and Control of Cable Tensions for Hyper-redundant Snake-arm Robots
Jianfeng Tao,Chengjin Qin,Zhilin Xiong,Xiang Gao,Chengliang Liu 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.11
Based on the feedback linearization of joints motion and the tension optimization of cables, a hyperredundant snake-arm robot control strategy is presented to solve the problems caused by the joint motion coupling and the cable drive redundancy. First, a hierarchical control system architecture of snake-arm robot is developed. Subsequently, the computed torque control method is utilized to decouple the motion in the joint space, and the tension distribution satisfying the constraint condition is obtained in the cable space by quadratic programming. Since it is hard and also expensive to feedback joint motion and cable tension by sensors, the cable tensions are obtained by the system dynamics equation and the position and speed of joints motion is calculated approximately from the driving electric motors’ position. Finally, the control performance under three typical conditions is studied by numerical simulation. The results demonstrate that the presented method can effectively limit the cable tensions within the range of the minimum preload tension and the maximum allowable tension.
-
Precise Cutterhead Clogging Detection for Shield Tunneling Machine Based on Deep Residual Networks
Ruihong Wu,Chengjin Qin,Guoqiang Huang,Jianfeng Tao,Chengliang Liu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3
During the construction process of tunnels, the cutterhead of shield tunneling machines may get cloggeddue to clay adhesion, which may seriously affect the efficiency of the project. Therefore, finding an intelligentdiagnosis method to detect the clogging status is of great importance. In this study, a deep residual network-basedmethod for diagnosing cutterhead clogging on shield tunneling machines is proposed. First, working state data ofthe shield tunneling machine is screened out, and parameters reflecting the clogging state are selected for furtheranalysis. After eliminating extreme outliers, an empirical formula is proposed to label the data. At the same time,several time-domain features of the selected excavation parameters within every five minutes are extracted. Thesefeatures are then fed into the proposed model as the input data to realize clogging detection. Because the originaldataset is imbalanced, the combination of f1-score and accuracy is used to evaluate the performance of the proposedmodel. The results show that the accuracy of the proposed algorithm reaches 95.71%, which is 1.21%, 2.84%,9.84%, 6.04%, and 0.86% higher than the support vector machine-based, random forest-based, AdaBoost-based,extreme gradient boosting-based and deep neural network-based methods. The f1 score of the proposed modelis 0.923, which is also 0.038, 0.042, 0.269, 0.169 and 0.02 higher than those compared methods. Therefore, theproposed deep residual network-based method can accurately detect cutterhead clogging conditions.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
