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检索条件"机构=Key Laboratory for Control Theory and Applications in Complicated Systems"

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Application of Improved PSO-ELM in Auto Insurance Customer Risk Level Prediction 17

Application of Improved PSO-ELM in Auto Insurance Customer R...

引用

17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Chen, Xi Xu, Liang Wang, Yalong Zhai, Xiang Guo, Xiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory Applications in Complicated Systems School of Electrical and Electronic Engineering Tianjin300384 China Picc Property and Casualty Limited Operation Sharing Department Beijing100022 China

ISBN: (纸本)9781728164151

In order to improve the prediction accuracy of automobile insurance customer's risk level, a prediction model of automobile insurance customer's risk level based on GSPSO - ELM is proposed. The premium, insurance amount, number of risks, gender, driving age and other indicators were used as the main criteria to predict the risk level. The simulation results show that the proposed GSPSO - ELM prediction model can not only overcome the local optimal, and has higher precision of prediction, forecasting results of the mean square error (mse), decision coefficient and mean absolute percentage error and dynamic inertia weight PSO algorithm to optimize extreme learning machine, linear decreasing inertia weight PSO algorithm to optimize extreme learning machine model are improved. © 2020 IEEE.

关键词： Forecasting

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A Multi-Feature Fusion Based Pedestrian Detection Method

A Multi-Feature Fusion Based Pedestrian Detection Method

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IEEE International Conference on Mechatronics and Automation

作者： Enzeng Dong Cunlei Jing Zufeng Zhang Tianjin Key Laboratory For Control Theory & Applications in Complicated Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

This paper proposes an improved pedestrian detection algorithm, the Haar-like feature and Adaboost cascade classifier Histogram of Oriented Gradient (HOG) feature are utilizing to construct hybrid features library, then the weak classifiers in Adaboost are trained by using the combined hybrid features. And the strong classifiers are built based on the weak classifiers. In the proposed algorithm, For improving the detection speed, the HOG feature is rapidly calculated by integral histogram instead of gradient histogram features, and the experimental results showed that the method proposed improved the detection performance and detection accuracy of pedestrians.

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Study on control Strategy of the Vascular Interventional Surgical Robot Based on LADRC

Study on Control Strategy of the Vascular Interventional Sur...

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IEEE International Conference on Mechatronics and Automation

作者： Wenqiang Zhang Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5Zhongguancun South Street Beijing China

As a medical device used in surgery, the vascular interventional surgical robot itself needs to have good control accuracy to ensure the safety of the surgery process. Given this security problem, this paper selects the Line Active Disturbance Rejection control (LADRC) as the control strategy of the vascular interventional robot, aiming at improving the master-slave tracking accuracy of the vascular interventional robot. This article compares the LADRC and PID(Proportional-Integral-Derivative) control strategies through simulation and experiments. The experimental results show that LADRC has good control performance for master-slave tracking and can be applied to the vascular interventional surgical robots.

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Deep fuzzy infrared handprint target extraction algorithm based on wave-particle duality mechanism

Deep fuzzy infrared handprint target extraction algorithm ba...

引用

Chinese Automation Congress (CAC)

作者： Xuejuan Bao Dahua Li Qiang Gao Xiao Yu Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In criminal cases, handprint information on-site is one of the most critical evidences in the detection of the case. When shooting with an infrared camera, the heat trace images collected are blurred and incomplete due to heat loss, which makes it difficult to detect. The wave-particle duality shows the particle property and volatility interaction between macroscopic objects and microscopic particles, a handprint target extraction algorithm based on the wave-particle duality mechanism is proposed. Through the wave-particle duality relation between adjacent pixels, the target area of the hand is obtained in the image of the deep- blurred infrared handprints through the matching of the hand skeleton template. The gray value is adjusted by the influence of the volatility between the pixels in the target area on the gray, at the same time according to the particle property relationship expands the pixels on the edge of the skeleton to obtain a clear target area in the deep blurred image. Finally, the K-means clustering segmentation algorithm is used to segment the handprint and the background in the blurred image, and to extract the handprint target. Experimental results show that the target positive extraction rate of the algorithm is 97%. Compared with other traditional algorithms, the algorithm can more effectively and accurately extract the handprint target in the deep blurred infrared image.

关键词： Image segmentation Infrared heating Image edge detection Microscopy Clustering algorithms Skeleton Data mining

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Prediction Model of User Purchase Behavior Based on Machine Learning 17

Prediction Model of User Purchase Behavior Based on Machine ...

引用

17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Zhai, Xiang Shi, Peng Xu, Liang Wang, Yalong Chen, Xi Tianjin University of Technology Binshui Xidao 391 Tianjin Key Laboratory for Control Theory Applications in Complicated Systems School of Electrical and Electronic Engineering Tianjin China Luoyang Institute of Electro-Optical Equipment Avic Luoyang471000 China

ISBN: (纸本)9781728164151

In recent years, with the continuous popularity of the Internet, the number of online shopping users in my country has reached 639 million, which contains huge commercial value. In order to maintain the prosperity, diversity and order of merchants, and fully meet consumers' one-stop shopping needs, it is necessary to analyze and predict user purchase behaviors more accurately. This article uses the interaction between users and products within a certain period of time provided by *** to predict whether users will place orders in designated categories and shops in the next week. First of all, we aim at the problem that the original features are not strongly related, and use feature fusion to construct strong features. For high-dimensional systems that are slow to calculate and prone to overfitting, we use random forests to filter features, reducing the complexity of the model. Finally, a fusion algorithm based on XGBoost and LightGBM is proposed to predict the purchase behavior of users, and the prediction effects of random forest and GBDT algorithm are compared at the same time. The results show that the fusion model has the best prediction effect, and can help merchants more accurately conduct marketing activities to potential customers based on the prediction results. © 2020 IEEE.

关键词： Forecasting

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Study on 6D Pose Estimation System of Occlusion Targets for the Spherical Amphibious Robot based on Neural Network

Study on 6D Pose Estimation System of Occlusion Targets for ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Chaofeng Du Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications in Complicated systems and Intelligent Robot Laboratory Tianjin University of Technology BinshuiXidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

The amphibious robot needs to accurately estimate the 6D pose of the target in tasks such as target tracking, docking with the recovery module, and target grasping. The current research on target 6D pose estimation is mainly applied to unoccluded targets, but when the target is occluded, the target’s pose cannot be accurately identified. Compared with other algorithms, the PVNet algorithm shows better robustness when target is occluded, but the accuracy is still low. To improve the accuracy of the PVNet algorithm, this paper adds the confidence score prediction of the prediction vector at the last layer of the PVNet network, and designs a vector confidence score loss function to train the network. Before generating the hypothetical keypoints, the pixels whose confidence score is lower than the set threshold are screened out, so that the generated hypothetical 2D keypoints are closer to the true 2D keypoints. Finally, the method in this paper is compared with the Tekin, PoseCNN, Oberweger and Pvnet algorithm, and demonstrate the superiority of the proposed method.

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A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment

A DDPG-Based Method of Autonomous Catheter Navigation in Vir...

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IEEE International Conference on Mechatronics and Automation

作者： Wei Tian Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5Zhongguancun South Street Beijing China

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. Automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. The experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process.

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Design of control System for Lower Limb Rehabilitation Robot on the Healthy Side sEMG Signal

Design of Control System for Lower Limb Rehabilitation Robot...

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IEEE International Conference on Mechatronics and Automation

作者： Shiqin Yu Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

With the number of stroke patients increasing year by year, rehabilitation exoskeleton robot has been paid more and more attention. For the rehabilitation exoskeleton robot, human-computer interaction ability is an important index, which affects the effect of rehabilitation therapy to a great extent. Surface Electromyography (sEMG) signals are the combined effect of sEMG signals and electrical activities on nerve stem on the skin surface, which can reflect neuromuscular activities in advance and can be used to predict movement intention by sEMG signals. Therefore, this article proposes to use sEMG signals to monitor the motion information of the healthy leg in real-time, extract the characteristics of the electromyography signals, use the sEMG of the healthy leg as the control signal, reflect the motion patterns reflected by the sEMG collected from the healthy side, and then use the motion intention recognition method of Long Short Term Memory(LSTM) neural network to identify the motion intention of the prosthetic limb by identifying the motion patterns of the swing phase of the healthy side. The results indicate that the predicted maximum Root Mean Squared Error(RMSE) is 5.3729, which proves the feasibility of using LSTM model for motion intention recognition and contributes to the real-time and accuracy of lower limb exoskeleton rehabilitation.

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Study on Autonomous Surgical control System of The Vascular Interventional Surgical Robot Based on Catheter Tip Location Tracking

Study on Autonomous Surgical Control System of The Vascular ...

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IEEE International Conference on Mechatronics and Automation

作者： Zhimin Feng Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Applications In Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5Zhongguancun South Street Beijing China

The control system of vascular interventional surgical robot is a complex nonlinear system. When the catheter is inserted into human blood vessels, due to the irregular and curved shape of human blood vessels, the catheter will undergo small deformation, which will affect the displacement of the catheter. In order to reduce the displacement tracking error, a BP neural network PID controller was designed to compensate the displacement error of the autonomous vascular interventional surgical robot. Firstly, the dynamic analysis is carried out according to the axial displacement motion of the vascular interventional surgical robot, and the catheter displacement tracking controller was designed. According to the displacement error, BP neural network PID control is used to output three parameters of PID. Finally, in the experiment and PID control simulation comparison, the results show that the BP neural network PID control has a small displacement tracking error and superior tracking performance, which can meet the requirements.

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Study on anti-interference performance of three-phase voltage mode PWM rectifier

Study on anti-interference performance of three-phase voltag...

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2021 China Automation Congress, CAC 2021

作者： Li, Guohong Yang, Weiyue Wei, Pengfei Gao, Ran Tianjin Key Laboratory for Control Theory and Applications in Complex Systems Tianjin University of Technology Binshui Xidao Extension 391 Tianjin300384 China School of Electrical and Electronic Engineering Tianjin University of Technology Binshui Xidao Extension 391 Tianjin300384 China

ISBN: (纸本)9781665426473

The operation of three-phase voltage type PWM rectifier often contains harmonics, voltage fluctuations and other problems. In this paper, based on the traditional sliding mode controller, the first-order active disturbance rejection control (ADRC) is introduced to control the outer voltage loop, and the state variables observed by the linear extended state observer (LESO) are fed back to the sliding mode controller for feedforward compensation. The controller has resonance suppression ability and strong disturbance immunity, and does not depend on the accurate mathematical model. The effectiveness of the control strategy is verified by simulation and experiment. The results show that the control strategy has the advantages of good dynamic performance, strong robustness and strong anti-interference ability. © 2021 IEEE

关键词： Sliding mode control

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