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IEEE International Conference on Mechatronics and Automation

作者： Shuxiang Guo Fang Huang Jian Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Application in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Binshui Xidao Extention 391 Tianjin China Kagawa University Takamatsu Kagawa Japan

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a novel biopsy robot is proposed. In order to solve the problem of the single function of the commercial capsule endoscope, this paper proposes a novel biopsy capsule robot driven by an external magnetic field. The module includes two radial magnetized magnetic rings and a biopsy mechanism. Without considering the addition energy consumption, using the coupling effect of the three-axis Helmholtz coil and the robot's built-in magnet, the crank slider mechanism converts the torque acting on the magnet by a uniform rotating magnetic field into the cutting force during biopsy. The magnetic switch used to control the switching between the motion function of the anchor biopsy robot and the anchor biopsy function. Finally, the functional modules in the pipeline of anchored biopsy robot is verified by experiments. The results show that the biopsy robot can realize the robot's anchoring in the intestine while performing the biopsy function. The realization of the biopsy capsule robot has important medical diagnostic significance.

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application of Improved Canny Algorithm in Image of Surgical Instruments

Application of Improved Canny Algorithm in Image of Surgical...

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IEEE International Conference on Mechatronics and Automation

作者： Hongli Liu Kuan Lu Lei Shao Ji Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology Binshui Xidao Xiqing District Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In order to improve the inventory efficiency of surgical instruments, a vision system needs to be installed. The edge detection of the image of surgical instruments is an important step to inventory surgical instruments. However, due to the wide variety of surgical instruments and the different sizes, it is necessary to improve the existing edge detection algorithm to satisfy the actual needs. Canny algorithm has been successfully applied in many computer vision systems, but it needs to be set manually when selecting high-low thresholds, it has poor adaptability and relies on human experience, so it cannot be directly applied to edge detection of surgical instruments. In this paper, based on the study of the traditional Canny algorithm, an improved Canny operator edge detection algorithm is proposed which shows the ability to autonomously select the threshold. Experimental results show that the improved Canny algorithm can effectively retain the true edge information, make the edges more continuous, and effectively achieve the edge detection of surgical instruments compared with the traditional Canny algorithm and the multi-scale morphology algorithm.

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Remaining Useful Life Prediction of Lithium-Ion Batteries Using Multi-model Gaussian Process

Remaining Useful Life Prediction of Lithium-Ion Batteries Us...

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Li, Meng Sadoughi, Mohammadkazem Shen, Sheng Hu, Chao School of Electrical and Electronic Engineering and Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology 391 Binshui Xidao Xiqing District Tianjin China

ISBN: (纸本)9781538683576

This paper presents a multi-model Gaussian process (MMGP) approach for predicting the RUL of lithium-ion batteries. The proposed MMGP approach incorporates multiple candidate capacity fade models, as the trend functions of a Gaussian process (GP) model, to capture the multi-stage capacity fade trend of the batteries. First, the hypothetical capacities at a predefined number of future cycles are predicted at the current cycle based on the offline data using similarity-based extrapolation. Then, the active fade model is selected by comparing the hypothetical capacities with the GP model-projected capacities using each candidate fade model and the active model is employed as the trend function of the GP model. Finally, the distribution of the RUL is estimated by determining when the projected capacity curves using the GP model down-cross a pre-defined capacity threshold. The MMGP approach was used for the RUL prediction of eight lithiumion battery cells that show multi-stage capacity fade behavior when cycled with a daily current rate (i.e., C/24). The capacities of these cells initially degrade rapidly, followed by a reduced fade rate and then a faster linear fade rate. The RUL prediction results suggest that the proposed MMGP approach can adaptively select proper trend functions for the GP model at different capacity fade stages throughout the lifetime, as well as adapting the models to accommodate variations in the capacity fade performance among different cells.

关键词： Lithium-ion battery Capacity Remaining useful life Prognostics Multiple fade models

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Evaluation Performance of the Magnetic Rotational Field for Magnetically Actuated Microrobot System

Evaluation Performance of the Magnetic Rotational Field for ...

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IEEE International Conference on Mechatronics and Automation

作者： Qiang Fu Zhuocong Cai Jian Guo Shuxiang Guo Tianjin Key Laboratory for Control Theory & Application in Complicated Systems and Biomedical Robot Laboratory Tianjin University of Technology Binshui Xidao391 Tianjin China Faculty of Engineering and Design Kagawa University Takamatsu Kagawa Japan

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

At present, the capsule robot for clinical examination has been widely used, which can complete the tasks that ordinary cable gastrointestinal endoscopy cannot achieve, especially the magnetic driven capsule robot has become a research trend because of its active control. However, the active control accuracy of the magnetic driven capsule robot is not high, so it is not enough to reach the level of clinical examination. In this study, in order to deal with the difficult operation of the medical magnetic driven capsule robot, a method to drive the micro robot more accurately by controlling the magnetic field is proposed. The electromagnetic simulation is consistent with the parameters of the actual platform. Moreover, its rationality can be verified by mathematical model. The magnetic field platform for medical examination can be improved through this method to achieve a more accurate control level of the capsule robot.

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Fire Detection Method Based on Deep Residual Network and Multi-Scale Feature Fusion

Fire Detection Method Based on Deep Residual Network and Mul...

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Chinese Automation Congress (CAC)

作者： Zehao Xiao Enzeng Dong Shengzhi Du Tianjin Key Laboratory For Control Theory & Applications in Complicated Systems Tianjin University of Technology Tianjin China Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Fire can cause serious damage to the natural environment and social economy, if it is not intervened early. Therefore, an effective fire detection method is significant and helpful. In this paper, a fire detection method based on deep learning is proposed to detect fire and smoke. Firstly, the residual network structure is applied to extract depth feature of the image. The problem of shallow features easily disappearing is solved with improved ResNet-50. A network composed of multiple BiFPN modules is established for multi-scale feature fusion and enhancement. Intersection over Union and cross entropy are applied to predict the scope and category of boundary boxes. Finally, the prediction results are obtained by comparing the confidence of the bounding box. Experimental results show that this method performs better in running time and accuracy than the existing detection networks. The feasibility of this method is verified in the field of fire detection.

关键词： Feature extraction Real-time systems Training Convolution Semantics Object detection Residual neural networks

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Optimal Design of Laser SLAM Algorithm Based on RBPF improved resampling technology

Optimal Design of Laser SLAM Algorithm Based on RBPF improve...

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IEEE International Conference on Mechatronics and Automation

作者： Hainan Wang Jianyun Ni Yong Qi Mingyang Zhao Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Simultaneous Localization and Mapping (SLAM) has always been a fundamental and central issue for mobile robots. At present, Rao-Blackwellized particle filter (RBPF) is a practical method to solve the Simultaneous Localization and Mapping of robots. In order to improve the problem of lack of particles and lack of diversity in the traditional RBPF algorithm, an optimized laser SLAM algorithm is proposed. Minimum sampling-variance(MSV) resampling is used to improve the original resampling method, and improve the proposal distribution to make it closer to the target distribution. In this paper, simulation experiments are carried out on Matlab and ROS platforms, and the experimental results prove that the improved algorithm can effectively improve the positioning effect and the quality of mapping.

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control Strategy of Grid-connected Wind Power Inverter Based on Linear Active Disturbance Rejection control

Control Strategy of Grid-connected Wind Power Inverter Based...

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IEEE International Conference on Mechatronics and Automation

作者： Yulong Chen Xuesong Zhou Youjie Ma Key Research Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology 391 Binshui Xidao Xiqing District Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Aiming at the problem of insufficient anti-disturbance performance and control accuracy of the traditional double closed-loop control strategy in the grid-connected inverter of permanent magnet direct drive wind power system, this paper proposes a control strategy that applies linear active disturbance rejection control technology to the voltage outer loop controller. To improve the control performance of the DC bus voltage. By introducing the sum disturbance signal in the expanded state observer (ESO) to observe its change trend, an effective early correction signal is generated, thereby improving the control system dynamic disturbance observation ability. The analysis of frequency response characteristics and simulation results under various working conditions show that, compared with traditional control methods, this method not only has a faster response speed, but also has a better anti-interference ability.

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Research and Implementation of Online Learning System Based on Electroencephalogram Emotion Computing

Research and Implementation of Online Learning System Based ...

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International Conference on Mechanical, control and Computer Engineering (ICMCCE)

作者： Xiaolin Song Yu Song Engineering Training Center Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University oTechnology Tianjin China

The lack of emotion is the bottleneck to improve the efficiency of online learning. In this paper, an online learning system based on EEG emotion computing is developed by using Python and PHP mixed programming method. The original EEG data are collected by Emotive EPOC, PSD is extracted as EEG features, and SVM classifier is used to classify emotional states. The system can analyze the emotional state of online learners in real time and accurately. It has the characteristics of humanized interface and modular algorithm. It can enhance the emotional interaction in online learning environment, and has reference significance for the development of educational application based on BCI.

关键词： Learning systems Support vector machines Ethics Emotion recognition Data privacy Feature extraction Electroencephalography

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Fault Location Based on Energy Relative Entropy

Fault Location Based on Energy Relative Entropy

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IEEE International Conference on Mechatronics and Automation

作者： Hui Zhao Lei Wang Hongjun Wang Youjun Yue Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology Tianjin China College of Engineering and Technology Tianjin Agricultural University Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

When wavelet transform and empirical mode decomposition(EMD) transform decompose the transient signal of line fault, the problem of missing some fault information will occur, which will affect the accuracy of fault location of distribution line. A new fault location method based on the relative entropy of variational mode decomposition (VMD) transformation energy is ***, the fault zero sequence current signal collected by the detection device is decomposed by VMD transformation, and the transient energy of different components of the signal is calculated. Then, according to the characteristics of energy relative entropy difference between the sections, the fault section of the line is determined. 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 it can accurately locate the fault section of the line.

关键词： Wavelet transforms Resistance Mechatronics Conferences Fault location Entropy Transient analysis

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Evolution of cooperation in spatial public goods games driven by reinforcement learning and environmental feedback

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Chaos, Solitons & Fractals 2025年 199卷

作者： Yujin Yang Dawei Zhao Juan Wang Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin 300384 PR China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 PR China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 PR China

The human decision-making process is highly complex, involving individual emotion, personality, preference and even social background or environment. In order to mimic intergenerational interest conflicts within the carbon generalized system of preferences, we propose a hybrid strategy update mechanism that integrates reinforcement learning (Q-learning) and Fermi-like imitation dynamics, aiming to explore the evolutionary patterns of cooperative behaviors in social dilemmas. By constructing a spatial evolutionary game model that simulates individual interactions in regular lattices, we analyze the impact of dynamic environmental state changes on strategic selection. The numerical results reveal that reinforcement learning significantly promotes the emergence of altruistic cooperators and maintains superior environmental conditions by balancing short-term gains with long-term environmental benefits. The hybrid update mechanism effectively mitigates the exploitation of cooperators by defectors, achieving the dynamic co-existence of three different strategies at the stationary state. Furthermore, the environmental payoff coefficient and the threshold range of the environmental health index emerge as critical parameters to resolve cooperation dilemmas, while the network scale does not show a significant impact on the extension and generalizability of the model. These findings provide some theoretical support for us to optimize carbon emission reduction policies, highlighting the crucial role of heterogeneous individual decision-making and long-term environmental feedback in low-carbon transitions.

关键词： Environmental feedback Evolutionary game theory Fermi-like imitation Public goods game Reinforcement learning

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