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检索条件"机构=Tianjin Key Laboratory for Control Theory& Applications in Complicated System"

共 820 条记录，以下是81-90 订阅

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A Discrete-Time Sliding Mode control Method for Multi-Cylinder Hydraulic Press 17

A Discrete-Time Sliding Mode Control Method for Multi-Cylind...

引用

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

作者： Jia, Chao Wei, Jiaqi Dong, Enzeng Gao, Xuewei He, Haocheng Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

In this paper, a discrete-time sliding mode control method for multi-cylinder hydraulic presses is proposed, which can be applied exactly for computer real-time discrete control in practical engineering. Proposed method bases on discretetime multi-cylinder hydraulic press model and adopts the design method of the distributed controller. First, the expected virtual force is designed according to the overall output demand, and the discrete-time sliding mode controller is designed to obtain the virtual force. Then the virtual force is separated by control allocation method to obtain the desired output force of each cylinder, finally, the second discrete-time sliding mode controller is designed to control the actual output force of each cylinder. The simulation results show that the proposed method has a good effect on output and leveling control of multi-cylinder hydraulic press and solves its problems of non-linear control and redundancy in discrete-time model. © 2020 IEEE.

关键词： Sliding mode control

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Research on Abnormal Power Consumption Detection Technology Based on Decision Tree and Improved SVM 17

Research on Abnormal Power Consumption Detection Technology ...

引用

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

作者： Wang, Hongjun Li, Zhiqi Zhao, Hui Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

The main purpose of abnormal power consumption detection for power users is to maintain the legitimate rights and interests of normal users, improve the economic benefits of power grid companies, and thereby reduce non-technical losses (NTL). In order to realize the rational use of user-side data and improve the efficiency of power audit, this article proposes an improved SVM power theft detection model based on decision tree. First of all, to address the problem of less electricity stealing data, this article combines convolutional neural networks (CNN) and generative adversarial networks (GAN) for data generation, and uses the powerful feature extraction function of CNN to extract the features of different stealing methods to guide GAN. Then the improved support vector machine (SVM) model based on decision tree (DT) detects user data. When classifying, it combines SVM and KNN for classification, which solves the point of SVM near the decision plane. For the problem of low classification accuracy, simulation experiments finally verified the effectiveness of the proposed model. © 2020 IEEE.

关键词： Decision trees

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Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method

Path Planning of Mobile Robot Based on Improved Artificial P...

引用

Chinese control and Decision Conference, CCDC

作者： Jianyun Ni Helei Du Tie Wang Hao Li Chenyang Xue Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin China School of Electronic Engineering and Automation Tianjin University of Technology Tianjin China Tianjin Climate Center Tianjin China Tianjin University of Technology Tianjin China

An improved artificial potential field method is proposed to address the problems of target unreachability and falling into local minima in the path planning process of the traditional artificial potential field method. Firstly, the distance factor between the robot and the target point is introduced to solve the target unreachability problem; secondly, the robot is guided out of the local minima point by setting the virtual target point through the double-circle strategy; finally, in order to satisfy the continuity of robot velocity and acceleration, the resulting path is smoothed by using three uniform B-sample curves. The experimental results show that the improved algorithm can effectively solve the problems of target unreachability and falling into local minima in the traditional algorithm, and the smoothness of the path after the spline treatment is better compared with the original path.

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Grade quantification of Parkinson's disease based on principal component analysis

Grade quantification of Parkinson's disease based on princip...

引用

2021 China Automation Congress, CAC 2021

作者： Zhou, Long Tong, Jigang Dong, Enzeng Zhu, Lin Zhang, Jiachen Tianjin Key Laboratory For Control Theory and Applications in Complicated Systems Tianjin University of Technology Tianjin300384 China China North Industries Group 210 Reseasrch Institute Beijing China

ISBN: (纸本)9781665426473

Parkinson's disease is a common neurodegenerative disease, which seriously affects the life of patients. Whether there is an objective and reliable method to classify and identify the disease grade is of great significance in the clinical diagnosis of Parkinson's disease. In this paper, principal component analysis was used to comprehensively evaluate the degree of Parkinson's disease of 28 randomly selected subjects, establish a comprehensive scoring model of Parkinson's disease degree, and fit it according to HY grade, so as to realize the quantification of Parkinson's disease grade. Based on the data of vertical ground reaction force, 12 gait features with strong correlation are calculated as the main features to establish the scoring model. The validity of the model is verified by predicting the HY grade of another 12 subjects in the randomly selected data set. The results show that the prediction accuracy is 83.3%, which can effectively predict the Parkinson's disease level of subjects. © 2021 IEEE

关键词： Principal component analysis

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Study on Freshwater Fish Image Recognition Integrating SPP and DenseNet Network 17

Study on Freshwater Fish Image Recognition Integrating SPP a...

引用

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

作者： Wang, Hongjun Shi, Yangyang Yue, Youjun Zhao, Hui Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

With the development of image processing and machine vision, there are emerging fish species classification algorithms that use machine vision technology to identify specific scenes. In order to solve the problem of poor working environment and low efficiency of traditional artificial identification of fish species, this paper proposes a deep learning method combining SPP (spatial pyramid pooling) and DenseNet (dense neural network). The model is connects the spatial pyramid pooling layer in front of the full connection layer of DenseNet network. On the basis of maintaining good recognition accuracy of DenseNet, the integrated new network can increase scale invariance and reduce overfitting. Through the identification test of 6 kinds of freshwater fish, the classification accuracy rate reached 97%. Experimental results show that the new network SPP-Densenet can effectively identify species of freshwater fish images. © 2020 IEEE.

关键词： Image recognition

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A Robust Data Reconciliation Method Based on Shape Parameters 17

A Robust Data Reconciliation Method Based on Shape Parameter...

引用

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

作者： Ni, Jianyun Qi, Yong Wang, Hainan Zhao, Mingyang Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164151

Data reconciliation plays an important role in coordinating and meeting the law of conservation in industrial processes. Generally, the actual measured industrial data is easily contaminated by different overall errors. Therefore, it is important to build a robust data reconciliation method that minimizes the impact of severe errors on the data and improves its usability. This paper presents a robust estimator based on a new robust estimation function to improve the robustness of the data reconciliation method. First, for the newly proposed robust estimator, its main robustness and its objective and impact functions are analyzed. Then, the validity of the new robust estimator in data reconciliation is demonstrated in an example. © 2020 IEEE.

关键词： Data reconciliation Industrial data Robust estimation

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UAV aerial image mosaic algorithm based on FAST-Tomasi feature and Delaunay triangulation 17

UAV aerial image mosaic algorithm based on FAST-Tomasi featu...

引用

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

作者： Zhao, Hui Du, YaXin Wang, Hongjun Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System 300384 Tianjin China

ISBN: (纸本)9781728164151

In view of the current inability to meet the requirements of fast speed and strong stability in feature extraction, and the slow speed in feature point elimination, this paper proposes an UAV remote sensing image mosaic algorithm based on FAST-Tomasi features and Delaunay triangulation. Firstly, FAST feature detection algorithm is used to accelerate Shi-Tomasi algorithm to obtain fast and accurate feature points, the feature points are described by BRIEF operator, and the phase is conducted by hamming distance. Then, Delaunay triangulation network is constructed according to the initial matching points, and the similarity between the triangles in the two images to be stitched is calculated. The triangle pair with large similarity is the matching triangle pair, so the corresponding accurate matching points are obtained. Then, the improved SPHP algorithm is used for registration. Finally, the weighted algorithm is used to fuse the image. Experimental results indicate that the algorithm retains more correct matching points, the accuracy of eliminating false matching points is higher, and the matching speed is significantly improved. © 2020 IEEE.

关键词： Hamming distance

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Intelligent Management and control system of Polluted Site Remediation Based on Internet of Things

Intelligent Management and Control System of Polluted Site R...

引用

2020 IEEE International Conference on Advances in Electrical Engineering and Computer applications, AEECA 2020

作者： Sun, Peiye Zhang, Baofeng Tian, Rui Zhu, Junchao Tianjin University of Technology Tianjin Key Laboratory for Control Theory Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728165202

For the lack of effective security guarantee and risk assessment and forewarning in the process of remediation of contaminated sites, as well as the potential safety problem of secondary pollution, an intelligent management and control system of polluted site remediation based on internet of things is proposed. The safety state of the working environment can be evaluated and forewarned by real-time monitoring the toxic gas state and the physiological state of the workers. Intelligent video monitoring, GPS and GIS technology are used to realize whole-process and all-round monitoring, so as to prevent secondary pollution and ensure the safe and orderly construction of pollution remediation. The Emergency command plan can be activated quickly with one key when dangerous abnormal state occurs. The application experiment of the system shows that the system is stable and robust, and can meet the technical requirements of the remediation of contaminated sites. © 2020 IEEE.

关键词： Big data

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Short-term Photovoltaic Power Prediction based on DE-GWO-LSTM 17

Short-term Photovoltaic Power Prediction based on DE-GWO-LST...

引用

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

作者： Zhao, Hui Zhao, Zhili Wang, Hongjun Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

In order to solve the uncertainty and randomness of photovoltaic output prediction caused by weather factors, and improve the accuracy of short-term photovoltaic power prediction, this study uses long short-term memory (LSTM) to establish a prediction model, and trains historical time series data to predict short-term photovoltaic power. First, a layer-by-layer training algorithm is used to construct a multi-hidden-layer network structure with excellent performance in regression prediction analysis. By analyzing the characteristics of the LSTM network function, a photovoltaic output prediction model is established. Then, the grey wolf optimization (GWO) algorithm of differential evolution algorithm (DE) is proposed to optimize the connection weight of LSTM to prevent the local minimum caused by random initialization, overcome the shortcomings of back-propagation algorithm used in traditional LSTM network, and improve the prediction accuracy of LSTM network. Finally, the simulation experiment of the improved LSTM model is carried out to test the prediction effect of the model under three kinds of weather conditions to prove the effectiveness of the model. © 2020 IEEE.

关键词： Long short-term memory

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Motor Imagery and Mental Arithmetic Classification Based on Transformer Deep Learning Network

Motor Imagery and Mental Arithmetic Classification Based on ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yuanyuan Ye Jigang Tong Sen Yang Yinghui Chang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Non-invasive electroencephalography (EEG) signals find widespread application in brain-computer interfaces (BCI), with paradigms such as motor imagery (MI), mental arithmetic (MA) and emotion recognition being particularly common. This study aims to explore feature extraction and classification methods for MI and MA tasks. We employed convolutional neural networks (CNN) combined with Transformer networks and Fasternet Block to extract features. Through our reaearch, we obtained features for MI and MA tasks, and used softmax classification for binary classification of these tasks. We conducted on a publicly available dataset consisting of data from 29 subjects. The experimental results demonstrate that our method achieved high classification accuracy in MI and MA tasks. The final accuracy rates for MI and MA tasks were 88.67% and 91.23%, respectively.

关键词： Accuracy Mechatronics Feature extraction Brain modeling Transformers Motors Electroencephalography

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