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42nd Chinese control Conference, CCC 2023

作者： Zhu, Zilong Tan, Chao Chen, Yixuan Dong, Feng School of Electrical and Information Engineering Tianjin Key Laboratory of Process Measurement and Control Tianjin University Tianjin300072 China

ISBN: (纸本)9789887581543

Due to the nonradioactive, noninvasive, low cost and portable features, magnetic induction tomography (MIT) is a promising technology in medical imaging. The conventional MIT sensor array generates divergent excitation magnetic field, resulting in poor sensitivity intensity and uniformity. To improve the intensity and uniformity of the sensitivity in MIT for hemorrhage detection, a local MIT method with Helmholtz coil is proposed. The Helmholtz coil provides a uniform background magnetic field in the measured region. To obtain sufficient independent measurements for hemorrhage reconstruction, the open planar MIT sensor array with gradiometers is applied in combination with the Helmholtz coil. The structure and excitation-detection strategy of the MIT sensor array are designed. The distribution of the magnetic field, phase sensitivity and the phase response of hemorrhages under the designed MIT sensor array are analyzed. The results indicate that the open planar MIT sensor array with Helmholtz coil can effectively improve the intensity and uniformity of the phase sensitivity. In addition, it can also enhance the intensity of the hemorrhage signals for different depths and volumes of the hemorrhages. The proposed method provides a solution to improve the detection accuracy and the detection depth of the hemorrhage by MIT. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Magnetic fields

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A Multi-Gas Detection system Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Air quality

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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43rd Chinese control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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Development of a Bio-inspired Tailless FWMAV with High-Frequency Flapping Wings Trajectory Tracking control

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Journal of Bionic Engineering 2024年第5期21卷 2145-2166页

作者： Qingcheng Guo Chaofeng Wu Yichen Zhang Feng Cui Wu Liu Xiaosheng Wu Junguo Lu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghai200240China Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai200240China Department of Automation Shanghai Jiao Tong UniversityShanghai200240China The Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China

The development of a tailless Flapping Wing Micro Aerial Vehicle(FWMAV)inspired by the hummingbird is presented in this *** implementing mechanical simplifications,it is possible to use planar machining technology for manufacturing of the FWMAV’s body,greatly reducing assembly ***,studies on flapping wing aircraft are limited to open-loop wing kinematics *** this work,an instantaneous closed-loop wing trajectory tracking control system is introduced to minimize wings’trajectory tracking *** control system is based on Field-Oriented control(FOC)with a loop shaping compensation technique near the flapping *** frequency analysis,the loop shaping compensator ensures the satisfactory bandwidth and performance for the closed-loop flapping *** implement the proposed controller,a compact autopilot board integrated with FOC hardware is designed,weighing only 2.5 *** utilizing precise wing trajectory tracking control,the hummingbird-inspired FWMAV demonstrates superior ability to resist external disturbances and exhibits reduced attitude tracking errors during hovering flight compared to the open-loop wing motion.

关键词： Flapping Wing Micro Aerial Vehicle(FWMAV) Bio-inspired robotics Trajectory control Loop shaping compensator Field-Oriented control(FOC)

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Learning to traverse challenging terrain using BGRU-Attention

Learning to traverse challenging terrain using BGRU-Attentio...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Liu, Huazheng Xu, Yanbin Dong, Jiajun Dong, Feng Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China

ISBN: (纸本)9798350325706

In this paper, we present a model-free deep reinforcement learning (DRL) algorithm to control the locomotion of a quadruped robot adaptively at challenging terrain. This approach is used to improve the robot's ability to traverse unstructured ground. Combining Bidirectional Gate Recurrent Unit (BGRU) and attention mechanism, we train the quadruped robot to climb up and down stairs, traverse various field terrain and bricks. We train the controller in simulation using knowledge distillation, and deploy on real robots without any adjustment. In the simulation, the performance of our Student Module even exceeds that of Teacher Module. The experimental results demonstrate that our proposed approach can make the quadruped robot traverse obstacles swiftly. © 2023 IEEE.

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A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and Security Center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

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Monocular 3D Object Detection Based on Pseudo-LiDAR Point Cloud for Autonomous Vehicles 41

Monocular 3D Object Detection Based on Pseudo-LiDAR Point Cl...

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第41届中国控制会议

作者： Yijing Wang Sheng Xu Zhiqiang Zuo Zheng Li Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Pseudo-LiDAR point clouds are generated from monocular *** with the point cloud from LiDAR,it can provide denser *** to the inaccuracy of depth estimation,there is still a performance gap between the pseudo-LiDAR point cloud and the LiDAR *** this paper,we propose an approach to eliminate the performance degradation caused by deviation of depth estimation and realize 3 D object detection based on pseudo-LiDAR point *** results on KITTI 3 D benchmark illustrate that in contrast to other methods,our scheme can achieve more reliable performance on both object localization and shape estimation.

关键词： 3D object detection pseudo-LiDAR point cloud image morphing depth estimation

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Broadband Chaos Generation and Synchronization Induced by Integrated Tri-section Laser

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IEEE Access 2024年 13卷 1-1页

作者： Guo, Yuanyuan Wei, Wuyin Gao, Hua Zhang, Yuanye Zhao, Tong Jia, Zhiwei Chang, Pengfa Wang, Longsheng Wang, Anbang Wang, Yuncai Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan China College of Information Engineering Shanxi Vocational University of Engineering Science and Technology Taiyuan China School of Information Engineering Guangdong University of Technology Guangzhou China

Broadband chaos generation is numerically demonstrated using an integrated tri-section laser which is composed of mutually coupled distributed feedback (DFB) lasers with a phase section, and the broadband chaos synchronization of two DFB lasers is achieved using the integrated laser as a driver. The simulation results indicate that the integrated tri-section laser with optical feedback can generate broadband chaos with a bandwidth up to 34 GHz, based on dual-mode beating effect. The influence of feedback strength and frequency interval on the chaos bandwidth are studied in detail. As a common driving signal, the broadband chaos signal can induce the response DFB lasers to generate chaos signals with a bandwidth of no less than 25 GHz and meanwhile achieve chaos synchronization with a correlation coefficient beyond 0.98. The low correlation coefficient between the drive signal and the response signal ensures the security of the chaos carrier from the public channel. Moreover, the dynamic properties of the integrated laser and the influence of the external parameters on chaotic synchronization are also investigated. Authors

关键词： Distributed feedback lasers

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Generic Software Design for Radar Basic Data Inspection

Generic Software Design for Radar Basic Data Inspection

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2024 Advanced Fiber Laser Conference, AFL 2024

作者： Chen, HongLi Fu, JianChuan Liu, RenJie Ding, Chao Wang, Mingyang Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology No. 51 North Muxidi Xicheng District Beijing100038 China

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

A general data inspection software based on the basic data of multi-source radar was designed, which was the central part of the data pre-processing. In this paper, software requirements analysis, overall architecture design, workflow design and detailed function design were carried out. Detailed functions included radar basic data loading, data analysis and recombination, data inspection requirement analysis, preprocessing model loading and data inspection. Data checks included normalization, completeness, correctness, consistency, current status, and satisfaction. Eventually, the software can present the results of the data inspection in the form of visualizations or reports. This software provided more general radar data review and filtering services. © 2025 SPIE.

关键词： Inspection Data modeling Radar Design Databases Data processing Statistical analysis Data acquisition Data storage Reliability

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