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

作者： Yue, Youjun Li, Lixin Wang, Hongjun Zhao, Hui Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9781728164151

In view of the damage of transmission line short-circuit faults and low fault identification rate, A novel fault identification method based on variational mode decomposition-fuzzy entropy and improved grey wolf algorithm - kernel extreme learning machine is proposed. First, the VMD is used to decompose the three-phase current after the fault to obtain a series of three-phase stable modal components;secondly, the fuzzy entropy value of each group of components is calculated separately as the fault characteristics of the transmission line;Finally, The fault characteristics of the transmission line are input to the kernel extreme learning machine for training to obtain the diagnosis model. The experimental results show that the method is not affected by the distance of the fault, the initial phase angle of the fault and the transition resistance, and can accurately identify the fault type. © 2020 IEEE.

关键词： Variational mode decomposition

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Chaos Suppression of Power system Based on Finite-time Adaptive Sliding Mode control

Chaos Suppression of Power System Based on Finite-time Adapt...

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IEEE International Conference on Mechatronics and Automation

作者： Hongjun Wang Shuyi Li Hui Zhao Youjun Yue Key Laboratory of Control Theory and Application for Complex Systems in Tianjin Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China College of Engineering and Technology Tianjin Agricultural University 22 Jinjing Road Tianjin China

Under certain conditions, the power system will produce chaotic oscillation, which may impact the safety and stability of the power grid. Under the action of inductive load, the power system will generate a certain amount of reactive power. Excessive reactive power will lead to the deviation of the voltage phase angle of the power system. Usually, reactive compensation equipment is used to absorb the reactive power in the power system and adjust the voltage phase angle. In this article, the dynamics analysis of chaotic oscillation of the second-order power system model (SOPSM) is firstly carried out, and a global adaptive sliding mode controller based on finite time is designed by comprehensively using backstepping, fixed-time controller and adaptive sliding mode control methods (ASM), which not only realizes the suppression of chaotic oscillation of the power system, but also reduces the computational burden and improves the convergence speed of the system, Finally, MATLAB/Simulink numerical simulation shows that the designed controller can quickly restore the power system from chaotic state to equilibrium state, thus verifying the effectiveness and efficiency of the controller.

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application of Hybrid Energy Storage Device in High Power Direct-current Power Supply 17

Application of Hybrid Energy Storage Device in High Power Di...

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

作者： Liu, Yue Wang, Chunjie Chen, Peng Yin, Jinliang School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin Complex System Control Theory and Application Key Laboratory Tianjin China

ISBN: (纸本)9781728164151

The drop-away current of 1500V low-voltage direct-current circuit breaker used in metro DC traction power supply system needs to be checked regularly. The instantaneous power is very high when the detection device is working, and the field power supply is often difficult to meet the requirements. Therefore, an energy storage unit is needed to supply power to the detector. In this paper, a design scheme of high-power DC power supply with supercapacitor and battery as energy storage is proposed, and the energy management and discharge control strategies are studied in theory and simulation, which verify the effectiveness and feasibility of the scheme. © 2020 IEEE.

关键词： Drops

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Design of a Capsule Robot system for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 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 Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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Research on the control system of six joint industrial robot based on CPAC 17

Research on the control system of six joint industrial robot...

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

作者： Dong, Chao Xie, Bang Shao, Xin Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

The defects of the existing commercial robot control system are analyzed and the feasibility of the open control system is studied. The robot control system was designed based on the CPAC development platform with the six-joint robot as the research object. Firstly, a kinematic model of the robot was established by the d-h linkage coordinate system method, and its motion control was described in detail from two aspects of forward and inverse kinematics. Secondly, the inverse kinematics of the robot was solved by using the RPY description method and PIPER criterion, and the correctness of the robot kinematics model was verified. Third, the robot control system software design. Finally, the function modules of the control system are verified through experiments. The experiments show that the robot runs with a small error of pose and posture, which can meet the requirements of the control system and meet the expected design objectives. © 2020 IEEE.

关键词： control systems

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The junction temperature measurement of insulated gate bipolar transistor based on multi-layer feed -forward neural network is presented 17

The junction temperature measurement of insulated gate bipol...

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

作者： Dong, Chao Mao, Peiye Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

For large power flow type insulated gate bipolar transistor (IGBT) saturation voltage drop and non-linear relation between the collector current and junction temperature of junction temperature prediction problem, set up a big electric saturated pressure drop down IGBT test system, access to settle moderate collector current and saturation nonlinear relation between the pressure drop curve, analyses the relation curve change rule the corresponding physical mechanism. The error back propagation (BP) neural network model was established by using Matlab software to predict the junction temperature. The comparison with the prediction results of the polynomial mathematical model shows that the relative error and prediction error of the neural network model are smaller than that of the polynomial mathematical model, and the prediction accuracy of the junction temperature is higher. It shows the feasibility of neural network for junction temperature prediction, so BP neural network model can be given priority when selecting junction temperature prediction model. © 2020 IEEE.

关键词： Junction temperature

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application of Difficult Sample Mining based on Cosine Loss in Face Recognition 17

Application of Difficult Sample Mining based on Cosine Loss ...

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

作者： Dong, Enzeng Qiao, Yifan Zhang, Zufeng Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

Due to the development of deep convolutional neural networks, face recognition has made great progress, and its main goal is how to improve feature recognition capabilities. In this regard, several loss functions based on angular boundaries have been proposed to increase the feature margin between different classes. Although very good results have been achieved in this direction, there are still some problems. These loss functions only expand the feature margin from the perspective of real classification in training, and do not provide distinguishability for misclassified samples. In order to solve this problem, this paper improves on the original cosine loss function, and implements feature learning in the direction of difficult samples based on misclassified feature vectors. © 2020 IEEE.

关键词： Face recognition

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NewsGPT: ChatGPT Integration for Robot-Reporter

arXiv

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arXiv 2023年

作者： Hireche, Abdelhadi Belkacem, Abdelkader Nasreddine Jamil, Sadia Chen, Chao College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates School of International Communications University of Nottingham Ningbo China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China

The integration of large language models (LLMs) with social robots has emerged as a promising avenue for enhancing human–robot interactions at a time when news reports generated by artificial intelligence (AI) are gaining in credibility. This integration is expected to intensify and become a more productive resource for journalism, media, communication, and education. In this paper a novel system is proposed that integrates AI’s generative pretrained transformer (GPT) model with the Pepper robot, with the aim of improving the robot’s natural language understanding and response generation capabilities for enhanced social interactions. By leveraging GPT’s powerful language processing capabilities, this system offers a comprehensive pipeline that incorporates voice input recording, speech-to-text transcription, context analysis, and text-to-speech synthesis action generation. The Pepper robot is enabled to comprehend user queries, generate informative responses with general knowledge, maintain contextually relevant conversations, and act as a more domain-oriented news reporter. It is also linked with a news resource and powered with a Google search capability. To evaluate the performance of the framework, experiments were conducted involving a set of diverse questions. The robot’s responses were assessed on the basis of eight criteria, including relevance, context, and fluency. Despite some identified limitations, this system contributes to the field of journalism and human–robot interaction by showcasing the potential of integrating LLMs with social robots. The proposed framework opens up opportunities for improving the conversational capabilities of robots, enabling interactions that are smoother, more engaging, and more context aware. © 2023, CC0.

关键词： Human robot interaction

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UAV Remote Sensing Image Mosaic Technology Combined with Improved SPHP Algorithm 17

UAV Remote Sensing Image Mosaic Technology Combined with Imp...

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

作者： Leng, Jianwei Wang, Sining Tianjin University of Technology Tianjin Key Laboratory of Complex Control Theory Application Binshui Xidao 391 Tianjin300384 China

ISBN: (纸本)9781728164151

The paper proposes an improved algorithm, for the problem that SPHP algorithm is not suitable for the UAV Remote sensing image mosaic algorithm because it is susceptible to geomorphic factors, resulting in deformation or ghosting. The algorithm firstly extracts feature points by combining SURF algorithm and Harris algorithm. The obtained feature points are coarsely matched by KNN algorithm, then matched by PROSAC algorithm. Finally, the weight coefficient is introduced to calculate the spatial transformation model of the image overlap region. This model replaces the original spatial model of SPHP algorithm. The ghosting of the image overlap area is reduced, and the stitched image is caused to have a smaller deformation. The final result shows that this improved SPHP algorithm can effectively remove the ghost image of the stitched image and generate better stitching results. © 2020 IEEE.

关键词： Image enhancement

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Image Segmentation Method of Crop Diseases Based on Improved Segnet Neural Network

Image Segmentation Method of Crop Diseases Based on Improved...

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IEEE International Conference on Mechatronics and Automation

作者： Youjun Yue Xuesong Li Hui Zhao Hongjun Wang Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters; secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training; in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it; then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network; finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s; compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper.

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