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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Yibo Wu, Wentao Xie, Tao Wu, Di Hu, Zhihuan Zhang, Weidong Shanghai Jiao Tong University Shanghai Department of Automation China Shanghai Jiao Tong University Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts Department of Automation Shanghai China

ISBN: (纸本)9789887581581

This paper presents a secure output-feedback control method for coordinated maneuvering of nonlinear systems subject to immeasurable states, uncertain nonlinearities, timevarying disturbances, and denial of service (DoS) attacks. The attacks considered herein occur in the links rather than on the nodes. The proposed method includes a full-order state observer that observes states information, and a neural network that approximates the nonlinear uncertainties. An observer-based neural network adaptive control law is developed using a dynamic surface control approach to achieve path maneuvering. Besides a resilient distributed path update law is developed to enable distributed secure coordination under DoS attacks. The stability of the closed-loop system is established using an input-to-state cascade system analysis, and the secure coordination of path updates is analyzed. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Feedback control

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Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness 21

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Ambreen, Hira Imran, Muhammad Diyin, Tang Zhang, Chongsheng Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Henan Univerity Department of EE Kaifeng475001 China School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics Department of Detection and Automation Engineering China School of Computer & Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9798331516680

Fruit ripeness evaluation is essential for harvest optimization and post-harvest loss prevention. Traditional visual inspection is inefficient. This study employs sensor fusion and AI algorithms to evaluate fruit maturity in real-time using gas sensors (i.e., MQ-3, MQ-6, MQ-8, MQ-135) and Near-Infrared spectral sensors (i.e., GY-AS7263). Primarily, data pro-cessing-sensor fusion, data preparation, and AI model application-is crucial to the proposed framework. Subsequently, sensor fusion approaches improve data accuracy by combining signals from various sensors. Secondly, data pretreatment techniques including noise reduction, normalization, and feature extraction prepare data for analysis. Thirdly, we use powerful machine learning algorithms such as Random Forest to judge maturity more accurately and reliably than conventional techniques. Lastly, integrating NIR spectroscopy with gas sensor data resolves cost and model transferability issues, enhancing accuracy and acces-sibility for agricultural use. Static image processing techniques provide challenges for real-time applications, including expensive computer costs and lower reliability. The proposed framework based on multi-sensor fusion system enhances agricultural applications' versatility, scalability, and reactivity, surpassing existing methodologies. © 2024 IEEE.

关键词： Fruits

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Enhancing IC substrate manufacturing through differential geometry and lightweight networks for etching defect detection

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Journal of Manufacturing Systems 2025年 80卷 902-915页

作者： Yu, Yongxing Huang, Dan Hu, Yueming School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Engineering Research Center of Precision Electronic Manufacturing Equipment Ministry of Education and Guangdong Provincial Engineering Laboratory for Advanced Chip Intelligent Packaging Equipment South China University of Technology Guangzhou510641 China Department of Automation North China Electric Power University Baoding071003 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System North China Electric Power University Baoding071003 China

With the advancement of high-density interconnect technology in semiconductor manufacturing, the precision and complexity of integrated circuit (IC) substrates have significantly increased, placing higher demands on quality control. Efficient and accurate detection of complex etching defects, which often occur during manufacturing, has become critical to preventing potential product defects. A defect detection method is proposed that combines a lightweight network with differential geometry tools to address the issue of etching defects in IC substrates. First, an improved deformable model is used to rapidly extract regular circuit trace contours from complex metallographic images, and morphological processing is applied to enhance the details, achieving precise image segmentation. For under-etching defects between circuit traces, differential processing of the original and segmented images is performed to locate abnormal regions. Subsequently, an optimized lightweight network based on MobileNet, termed OMNet, is designed to achieve the rapid identification of under-etching defects in these regions. For etching defects occurring on circuit traces, the DGEtch method employs a high-precision discrete curvature calculation based on the Frenet frame to evaluate angular discontinuities in contours, enabling accurate detection of etching defects. Experimental results demonstrate that the proposed method achieves an average recall rate of over 95% and maintains a precision above 90%. It exhibits high accuracy and stability in detecting etching defects and consistently outperforms existing models, particularly in handling complex mixed defects. This study provides an effective solution for detecting complicated defects in high-density IC substrate manufacturing. © 2025 The Society of Manufacturing Engineers

关键词： Inspection

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A nuclide identification method ofγspectrum and model building based on the transformer

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Nuclear Science and Techniques 2025年第1期36卷 80-93页

作者： Fei Li Chu-Yang Luo Ying-Zi Wen Sheng Lv Feng Cheng Guo-Qiang Zeng Jian-Feng Jiang Bing-Hai Li College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610000China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu 610000China Department of Physics Universitat Politècnica de CatalunyaBarcelona 08028Spain Airborne Survey and Remote Sensing Center of Nuclear Industry Shijiazhuang 050000China Hebei Key Laboratory of Airborne Detection and Remote Sensing Technology Shijiazhuang 050000China

In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on γ spectrum analysis because of their inherent mathematical *** is difficult to make progress in terms of training data requirements and prediction *** contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent ***,in this paper,a transformer-based neural network (TBNN) model is proposed to achieve nuclide identification for the first ***,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and *** on the encoder–decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32×32 energy spectrum sequence as the model *** experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7%.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long shortterm memory neural networks,with overall recognition rates of 92.8%,95.3%,96.3%,and 96.6%,*** indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitativ

关键词： Nuclide identification Neural network Transformer

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Secure Output-Feedback Coordinated Maneuvering of Nonlinear Systems via Full-Order Neural Observer and Resilient Distributed Paths Update

Secure Output-Feedback Coordinated Maneuvering of Nonlinear ...

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第43届中国控制会议

作者： Yibo Zhang Wentao Wu Tao Xie Di Wu Zhihuan Hu Weidong Zhang Department of Automation Shanghai Jiao Tong University Department of Automation Shanghai Jiao Tong University Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper presents a secure output-feedback control method for coordinated maneuvering of nonlinear systems subject to immeasurable states,uncertain nonlinearities,timevarying disturbances,and denial of service(DoS) *** attacks considered herein occur in the links rather than on the *** proposed method includes a full-order state observer that observes states information,and a neural network that approximates the nonlinear *** observer-based neural network adaptive control law is developed using a dynamic surface control approach to achieve path ***,a resilient distributed path update law is developed to enable distributed secure coordination under DoS *** stability of the closed-loop system is established using an input-to-state cascade system analysis,and the secure coordination of path updates is ***,a simulation example is presented to demonstrate the effectiveness of the proposed method.

关键词： Output feedback Coordinated Maneuvering Dynamic surface control Neural observer DoS attacks

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Underwater arbitrary ranging based on chirped pulse interferometry with a mode-locked laser

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Journal of Lightwave Technology 2025年第11期43卷 5235-5243页

作者： Shi, Haonan Zhao, Haihan Xu, Xinyang Zhu, Zhiwei Zhai, Jingsheng Li, Xiaobo Tianjin University School of Marine Science and Technology Tianjin300072 China Research Institute of China Electronics Technology Group Corporation Science and Technology on Electro-Optical Information Security Control Laboratory No. 53 Tianjin300308 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong999077 Hong Kong Nanjing University of Aeronautics and Astronautics Key Laboratory of Space Photoelectric Detection and Perception Ministry of Industry and Information Technology Nanjing211106 China

Underwater distance measurement and length sensing, as fundamental functions of geoscience, are integrated into underwater robots and vessels in various forms of sensor payloads and play a crucial role in a wide range of remote sensing applications. Advanced laser-based detection systems and algorithms are essential for underwater tasks, such as detecting damage in underwater pipelines and conducting detailed structural health assessments. In this paper, we experimentally demonstrated an arbitrary underwater ranging system by the chirped pulse interferometry with a mode-locked laser. Water's inherent characteristic introduces dispersion, enabling unique distance discrimination. Adjusting the repetition frequency allows us to acquire the interference spectrum. A long fiber delay line, stabilized by the servo-controlled system, eliminates the ranging dead zone. The widest fringe of the interference spectrum, corresponding to an instantaneous repetition frequency, indicates the arbitrary measured distance. Leveraging a charge-coupled device detection module, we achieved an update rate of 13.5 Hz for the interference spectrum. Experimental results show a precise distance difference within 59.8 μm at the 5 m range underwater without the dead zone. © 2025 IEEE. All rights reserved.

关键词： Laser mode locking

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Multi-center solitary solid pulmonary nodule classification algorithm based on mutual information of graph federation 5

Multi-center solitary solid pulmonary nodule classification ...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Shi, Jiangfeng Feng, Bao Liu, Yu Chen, Xiangmeng Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin541004 China Guilin University of Aerospace Technology Laboratory of Intelligent Detection and Information Processing Guangxi Province Guilin City541004 China Jiangmen Central Hospital Department of Radiology Jiangmen529000 China

ISBN: (纸本)9798350309034

With the continuous growth of medical image data and the imbalance in distribution across multiple medical centers, accurate and reliable classification of Solitary solid pulmonary nodules has become increasingly important. Firstly, to address the issue of data silos, a federated learning framework is employed, which ensures data security while improving the diagnostic performance of the model. Secondly, to tackle the non-independent and identically distributed (non-IID) nature of multi-center data, a graph federated personalized strategy based on mutual information is proposed. Graph convolutional algorithms and mutual information are utilized to explore the topological structure and correlation information among the data for personalized modeling. Additionally, considering the variations in shape and size of pulmonary nodules in different CT images, this paper combines a Diverse Branch Block (DBB) convolutional structure with ResNet18, serving as the framework for local and global models, to enhance the model's capability of extracting multi-scale features. The results demonstrate promising classification performance of the proposed algorithm across five pulmonary nodule data centers, with AUC values of 0.7572, 0.8378, 0.7616, 0.7813, and 0.9130 on the respective test sets. © 2023 IEEE.

关键词： Diverse Branch Block Graph Convolution Mutual Information Personalized Federated Learning Solitary solid pulmonary Nodule Classification

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Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Hira Ambreen Muhammad Imran Tang Diyin Chongsheng Zhang Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Department of EE Military College of Signals National University of Sciences & Technology (NUST) Henan Univerity Kaifeng China Department of Detection and Automation Engineering School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China School of Computer & Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

关键词： Productivity Accuracy Machine learning algorithms Noise reduction Sensor fusion Feature extraction Real-time systems Reliability Monitoring Gas detectors

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Multi-Channel Based on Attention Network for Infrared Small Target detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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An Automatic Ridge Estimation Method for Tacholess Order Tracking of Wind Turbine Gearbox Under Varying-Speed Operation

SSRN

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SSRN 2023年

作者： Liu, Changliang Liu, Shaokang Wu, Yingjie Liu, Weiliang Liu, Shuai Control and Computer Engineering Department University of North China Electric Power Beijing102200 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Automation Engineering Department University of Northeast Electric Power Jinlin132000 China

The instantaneous shaft speed estimation method of wind turbine gearbox based on time-frequency ridge estimation is the most widely used method, but it requires parameter adjustment and expert knowledge to ensure the accuracy of ridge estimation, which significantly reduces the effectiveness of such methods in practice. In this paper, an end-to-end automated ridge estimating (ARE) method is proposed. ARE uses the inherent harmonic relationship in the vibration signal of wind turbine (WT) gearbox to construct a selection index for automatically selecting the target ridge with clear physical meaning. Then, a sub-region division strategy is proposed to avoid the interference of other components and improve computational efficiency. Finally, a parameter-free objective function considering both the amplitude and smoothness of the ridge is proposed to achieve target ridge estimation. The vibration signals of different WT gearboxes collected on-site were applied to verify the performance of ARE. The result shows that ARE realizes the automatic estimation of a ridge. It outperforms other methods in terms of accuracy and computational efficiency. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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