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Channel pruning on frequency response

Science China(Information Sciences) 2025年第1期68卷 159-170页

作者： Hang LIN Yifan PENG Lin BIE Chenggang YAN Xibin ZHAO Yue GAO School of Software Tsinghua University School of Automation Hangzhou Dianzi University

Network pruning has a significant role in reducing network parameters and accelerating the inference time of the network. Some existing methods prune the network based on the frequency of the data, and finally obtain a sub-network with high accuracy. However, according to our experimental analysis, different frequencies of information in the data contribute differently to the accuracy of the model, and using this information directly for pruning without making a selection will lead to incorrect results. We believe that pruning should retain the convolutional kernels in the network that process important information, while those kernels that process unimportant information should be removed. In this paper, we first investigate the meaning of each frequency band information in the spectrum and their contribution to the prediction accuracy of the network,and according to these results, we propose a new pruning method based on frequency response(PFR). Our PFR finds and removes the convolutional kernels in the network that specialize in processing unimportant information, resulting in a compact neural network model. PFR obtains significant experimental results on different datasets, for example, a 56.0% raduction of float points operations(FLOPs) on Res Net-50 and only 0.37% of Top-1 accuracy degradation on the Image Net dataset.

关键词： deep learning model compression filter pruning channel pruning frequency response

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Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection

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Chinese Journal of Electronics 2025年第1期34卷 186-203页

作者： Qingsheng Feng Shuai Xiao Wangyang Liu Hong Li School of Automation and Electrical Engineering Dalian Jiaotong University School of Software Dalian Jiaotong University

Turnouts and switch machines play a crucial role in facilitating train line operations and establishing routes, making them vital for ensuring the safety and efficiency of railway transportation. Through the gap detection system of switch machines, the real-time working status of turnouts and switch machines on railway sites can be quickly known. However, due to the challenging working environment and demanding conversion tasks of switch machines, the current gap detection system has often experienced the issues of fault detection. To address this, this study proposes an automatic gap detection method for railway switch machines based on object detection and combination clustering. Firstly, a lightweight object detection network, specifically the MobileNetV3-YOLOv5s model, is used to accurately locate and extract the focal area. Subsequently, the extracted image undergoes preprocessing and is then fed into a combination clustering algorithm to achieve precise segmentation of the gap area and background,the algorithm consists of simple linear iterative clustering, Canopy and kernel fuzzy c-means clustering. Finally, the Fisher optimal segmentation criterion is utilized to divide the data sequence of pixel values, determine the classification nodes and calculate the gap size. The experimental results obtained from switch machine gap images captured in various scenes demonstrate that the proposed method is capable of accurately locating focal areas, efficiently completing gap image segmentation with a segmentation accuracy of 93.55%, and swiftly calculating the gap size with a correct rate of 98.57%. Notably, the method achieves precise detection of gap sizes even after slight deflection of the acquisition camera, aligning it more closely with the actual conditions encountered on railway sites.

关键词： Image segmentation Accuracy Clustering algorithms Transportation Switches Object detection Cameras Rail transportation Classification algorithms Safety

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A nonlinear hysteresis compensation control of hydro-viscous drive in air transport operations

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Chinese Journal of Aeronautics 2025年第2期38卷 161-174页

作者： Liang WANG Jianjian LIANG Lin ZHANG Shoukun WANG Junzheng WANG School of Automation and Software Shanxi UniversityTaiyuan 030031China Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China

The Hydro-Viscous Drive(HVD)speed regulating system finds extensive application in air transport transmission systems to regulate the stepless speed or conduct overload ***,its intrinsic hysteretic behaviors,such as the asymmetric hysteretic and dead zone,could introduce inaccuracy and delay in control applications,posing challenges to system *** paper investigates a Nonlinear Hysteresis Compensation Control(NHCC)that consists of two parts to control the HVD output speed by operating the valve under different engine operating *** the first part,the Inverse Hysteresis Compensator(IHC)based on major loop data is introduced for the asymmetric hysteresis characterization and compensation of the HVD speed control system of the power generation and distribution,which aims to reduce the hysteresis and dead zone effect and expand the effective input *** the second part,the Active Disturbance Rejection Controller(ADRC)is employed to mitigate the hysteresis effects of the compensated system and remove the steady-state error,which allows real-time compensation of the estimated perturbations as state feedback to achieve the required *** experimental laboratory station has been fabricated to evaluate the proposed *** test results show that the NHCC method can regulate the fan speed to the desired value(45 r/min at steady state)and broaden the effective input range to the full range under different engine ***,the proposed control method can reduce the non-linearity of the input and output curves(from 18%to 4%)and compensate for the asymmetric hysteresis(from 38%to 5%).

关键词： Hydro-viscous drive Nonlinear hysteresis compensation Inverse hysteresis compensation Speed regulating system ADRC

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A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2346-2348页

作者： Yi Liu Xiang Wu Yuming Bo Jiacun Wang Lifeng Ma the School of Automation Nanjing University of Science and Technology the Department of Computer Science and Software Engineering Monmouth University

Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1... 详细信息

关键词： Deep agent Framework

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Research on the Relationship Between Color Features and Painting Styles 2

Research on the Relationship Between Color Features and Pain...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Zhao, Qiang Zhang, Rui Bai, Xin School of Automation and Software Shanxi University Taiyuan China

ISBN: (纸本)9798350360240

In digital preservation of cultural heritage, collecting and classifying paintings and artworks is essential. There are notable differences in techniques and color schemes across painting styles. This study delves into image classification methods, focusing on color features. We extensively reviewed literature on color feature extraction methods in paintings, aiming to classify them by style. By categorizing and summarizing these methods, we explored the impact of various factors on classification results, including the choice of color space, the relationship between color and position, and the combination of color and non-color features. Comparative analysis revealed that methods accurately representing the color-position relationship significantly improve classification accuracy, while additional non-color features or more complex color features do not necessarily enhance it. We recommend using feature extraction methods that focus on both position and color characteristics for style classification in digital artwork collections. This emphasizes the importance of exploring color correlations and positional relationships in color image classification, offering new insights into the application of color features in artwork classification. © 2024 IEEE.

关键词： Color characteristics Color position Color similarity Minimum Volume Enclosing Ellipsoid Painting style classification

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OCRBench: on the hidden mystery of OCR in large multimodal models

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Science China(Information Sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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A Formal Model for Analyzing Fair Exchange Protocols Based on Event Logic

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2641-2663页

作者： Ke Yang Meihua Xiao Zehuan Li School of Electrical Engineering and Automation East China Jiaotong UniversityNanchang330013China School of Software East China Jiaotong UniversityNanchang330013China

Fair exchange protocols play a critical role in enabling two distrustful entities to conduct electronic data exchanges in a fair and secure *** protocols are widely used in electronic payment systems and electronic contract signing,ensuring the reliability and security of network *** order to address the limitations of current research methods and enhance the analytical capabilities for fair exchange protocols,this paper proposes a formal model for analyzing such *** proposed model begins with a thorough analysis of fair exchange protocols,followed by the formal definition of *** definition accurately captures the inherent requirements of fair exchange *** upon event logic,the model incorporates the time factor into predicates and introduces knowledge set *** enhancement empowers the improved logic to effectively describe the state and knowledge of protocol participants at different time points,facilitating reasoning about their acquired *** maximize the intruder’s capabilities,channel errors are translated into the behaviors of the *** participants are further categorized into honest participants and malicious participants,enabling a comprehensive evaluation of the intruder’s potential *** employing a typical fair exchange protocol as an illustrative example,this paper demonstrates the detailed steps of utilizing the proposed model for protocol *** entire process of protocol execution under attack scenarios is presented,shedding light on the underlying reasons for the attacks and proposing corresponding *** developedmodel enhances the ability to reason about and evaluate the security properties of fair exchange protocols,thereby contributing to the advancement of secure network transactions.

关键词： Fair exchange protocols fairness formal analysis logic reasoning

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A Hybrid Air Quality Prediction Model Based on Signal Decomposition and Multi-Objective Optimization Algorithm

A Hybrid Air Quality Prediction Model Based on Signal Decomp...

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2023 China automation Congress, CAC 2023

作者： Qiao, Yinwei Wang, Jianrong Jia, Xinchun Xie, Yunfei School of Automation and Software Engineering Shanxi University Taiyuan China School of Automation and Software Engineering Shanxi University China

ISBN: (纸本)9798350303759

Accurate and stable prediction of the Air Qual-ity Index (AQI) is a hot topic among many researchers. In response to the nonlinear oscillation of AQI data itself and the tendency for many studies to focus only on accuracy while neglecting stability, a hybrid prediction model based on intel-ligent optimization algorithms has been developed. The model utilizes Sequential Variational Mode Decomposition (SVMD) to reduce data complexity and optimizes Elman Neural Net-work (ENN) using a newly proposed multi-objective algorithm called Merged Differential Evolution and Whale Optimization Algorithm (MODEWOA). The algorithm employs two fitness functions that consider both accuracy and stability. Results show that the proposed MODEWOA algorithm outperforms MOPSO and NSGA-II, while the hybrid model surpasses other comparative models in terms of prediction accuracy and stability. © 2023 IEEE.

关键词： Stability

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A New Robust Stabilization/Tracking Control Algorithm for Stochastic Nonlinear Systems

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Journal of Systems Science & Complexity 2025年

作者： LIU Zhenguo LIU Enxin XUE Lingrong SUN Zongyao School of Automation and Software Engineering Shanxi University School of Information Shanxi University of Finance and Economics Institute of Automation Qufu Normal University

Numerous complex nonlinear systems in the real-world constantly suffer from random noises, which contribute to the enormous challenge of system control. Additionally, unknown powers in nonlinear systems always lead to the inapplicability of many reported control methods. This article investigates the control issue of stochastic systems which contain complicated nonlinearities and unknown system powers. With the newly constructed Lyapunov function, as well as the control algorithm presented in this work, the authors successfully obtain a controller so that the closed-loop system is semiglobally finite-time stable in probability(SGFSP). Besides, the system output can trackthe reference signal fast. The presented method significantly enlarges the range of application for nonlinear systems. The presented strategy is successfully applied to the liquid-level system.

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YOLO-based lightweight traffic sign detection algorithm and mobile deployment

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Optoelectronics Letters 2025年第4期21卷 249-256页

作者： WU Yaqin ZHANG Tao NIU Jianjun CHANG Yan LIU Ganjun Software College Shanxi Agricultural UniversityTaigu 030800China School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the ***,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone ***,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging ***,we accomplish the design and deployment for the mobile *** results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).

关键词： c f layer simple attention module simam reduce complexity traffic sign detection prioritize key features backbone networkemploying classification backbone networknextthe

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