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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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NewsGPT: ChatGPT Integration for Robot-Reporter

arXiv

引用

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...

引用

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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Fault Type identification in Transmission Lines based on VMD-FE and IGWO-KELM

Fault Type identification in Transmission Lines based on VMD...

引用

IEEE International Conference on Mechatronics and Automation

作者： Youjun Yue Lixin Li Hongjun Wang Hui Zhao 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 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.

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

引用

arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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

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

引用

IEEE International Conference on Mechatronics and Automation

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

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

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.

关键词： Low voltage Power supplies Circuit breakers Supercapacitors Hybrid power systems Mathematical model Integrated circuit modeling

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A Multi-Objective Optimization Algorithm: Power system Hybrid Static Economic Dispatch with Wind Energy

SSRN

引用

SSRN 2022年

作者： Qu, Linan Li, Lingling Tseng, Ming-Lang Chen, Chao Lim, Ming K. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Nanjing210003 China Institute of Innovation and Circular Economy Asia University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Adam Smith Business School University of Glasgow United Kingdom

This study proposes a novel multi-objective optimization based on improved pathfinder algorithm (IPFA) algorithm and contributes to build a hybrid static dynamic economic dispatch with wind energy. The novel IPFA optimizes the built hybrid static economic dispatch model, while considering inequality and equality constraints during the model solving process. Especially, new energy system reduces the fuel consumption and operation cost of the system;however, the randomness of renewable energy output destroys the stability of the system. The upper limit of wind power grid connection is limited by the permeability constraint, and the constraints such as forbidden operation area constraint and slope rate constraint are considered. The solution speed and stability of the IPFA algorithm are improved through introducing Kent map initialization, nonlinear adaptation factor, and following correction strategy. The validity of the built model and the optimization performance of the proposed method is verified by two cases. The test results reveal that the operation cost obtained by the IPFA considering wind energy is 9.3% and 7.5% lower than that without considering wind energy. The novel method contributes in ensuring the economy and stability of the power system and improving the utilization rate of renewable energy. © 2022, The Authors. All rights reserved.

关键词： Electric load dispatching

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

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

引用

IEEE International Conference on Mechatronics and Automation

作者： Enzeng Dong Yifan Qiao Zufeng Zhang Complex System Control Theory and Application Key Laboratory Tianjin University of Technology(TJUT) 391Binshui Xidao Xiqing DistrictTianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

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.

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

The junction temperature measurement of insulated gate bipol...

引用

IEEE International Conference on Mechatronics and Automation

作者： Chao Dong Peiye Mao Complex System Control Theory and Application Key Laboratory Tianjin University of Technology(TJUT) 391 Binshui Xidao Xiqing District Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

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.

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