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Chinese control Conference (CCC)

作者： Meijia Shang Xiaoping Wang Mian Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology and also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The traditional Pavlov associative memory circuit realizes the law of learning and forgetting in classical conditioned reflex. In addition, the law of generalization and differentiation also belongs to classical conditioned reflex, so the process of associative memory can be more effectively simulated by adding generalization and differentiation theory on the basis of traditional associative memory. In this paper, a memristor-based circuit is designed to implement generalization and differentiation based on Pavlov associative memory. The circuit can be applied to simple classification recognition. Based on the features of objects as input, the output of the circuit is used as the classification result to achieve the function of classification and recognition. Finally, the accuracy of classification recognition on the generalization and differentiation circuit proposed in this paper can be verified by the simulation results in PSPICE.

关键词： Neurons Threshold voltage Associative memory Dogs Memristors Logic gates Circuit synthesis

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Closer to Pre-trained Network Transfer Better

Closer to Pre-trained Network Transfer Better

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IEEE Joint International Information Technology and Artificial intelligence Conference (ITAIC)

作者： Siyu Chen Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People’s Republic of China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan People’s Republic of China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowadays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and inspiration for the future work.

关键词： Knowledge engineering Correlation Transfer learning Neural networks Task analysis Information technology Knowledge transfer

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A survey on negative transfer

arXiv

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arXiv 2020年

作者： Zhang, Wen Deng, Lingfei Zhang, Lei Wu, Dongrui the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China the School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

—Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces the learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to handle it. However, there does not exist a systematic survey on the formulation of NT, the factors leading to NT, and the algorithms that mitigate NT. This paper fills this gap, by first introducing the definition of NT and its factors, then reviewing about fifty representative approaches for overcoming NT, according to four categories: secure transfer, domain similarity estimation, distant transfer, and NT mitigation. NT in related fields, e.g., multi-task learning, lifelong learning, and adversarial attacks, are also discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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A visual kinematics calibration method for manipulator based on nonlinear optimization

arXiv

引用

arXiv 2020年

作者： Peng, Gang Wang, Zhihao Yang, Jin Li, Xinde Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China IEEE senior member School of Automation South East University Nanjing China

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera's parameters affects the calibration accuracy of the kinematics of the robot arm. Therefore, this paper proposes a method for calibrating the geometric parameters of a kinematic model of a manipulator based on monocular vision. Firstly, the classic Denavit-Hartenberg (D-H) modeling method is used to establish the kinematic parameters of the manipulator. Secondly, nonlinear optimization and parameter compensation are performed. The three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

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Reliability on Deep Learning Models: A Comprehensive Observation

Reliability on Deep Learning Models: A Comprehensive Observa...

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International Symposium on System and Software Reliability (ISSSR)

作者： Yuhong Zhang Chunjing Xiao School of Artificial Intelligence and Big Data Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (数字)9780738104973

ISBN: (纸本)9781665423236

This paper provides a comprehensive observation to examine the reliability of deep learning (DL) models. First, we will briefly introduce the essential background and kernel techniques in deep learning, such as downsampling and nonlinear discontinuity. Each of them may have some relation to the reliability of DL models. Then we discuss the inherent structural flaws of deep learning and the risk of unreliability that can result from it. Subsequently, we discuss various ways of generating adversarial samples that affect the DL model's reliability and corresponding preventive measures. Finally, we complete this observation by identifying current challenges and future trends for research.

关键词： Reliability Deep learning Biological neural networks Perturbation methods Neural networks Feature extraction Data models

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Observer-Based Robust Containment control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

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第三十九届中国控制会议

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunicationsand Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Containment control multi-agent system input saturation input additive disturbance

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Parameter training methods for convolutional neural networks with adaptive adjustment method based on Caputo fractional-order differences

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Chaos, Solitons & Fractals 2025年 199卷

作者： Haiming Zhao Honggang Yang Jiejie Chen Ping Jiang Zhigang Zeng School of Computer and Information Engineering Hubei Normal University Huangshi 435000 China School of Automation Wuhan University of Technology Wuhan 430000 China School of Computer Hubei Polytechnic University Huangshi 435000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan 430000 China

As deep learning technologies continue to permeate various sectors, optimization algorithms have become increasingly crucial in neural network training. This paper introduces two adaptive momentum algorithms based on Grünwald–Letnikov and Caputo fractional-order differences—Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam)—to update parameters more flexibly by adjusting momentum information. Commencing from the definitions of fractional derivatives, we propose integrating fractional-order differences with gradient algorithms in convolutional neural networks (CNNs). These adaptive momentum algorithms, leveraging Grünwald–Letnikov and Caputo fractional-order differences, offer enhanced flexibility, thereby accelerating convergence. Our nonlinear parameter tuning method for CNNs demonstrates superior performance compared to traditional integer-order momentum algorithms and the standard Adam algorithm. Experimental results on the BraTS2021 dataset and CIFAR-100 dataset reveal that the proposed fractional-order optimization algorithms significantly outperform their integer-order counterparts in model optimization. They not only expedite convergence but also improve the accuracy of image recognition and segmentation.

关键词： Adaptive momentum algorithms Convolutional neural networks Deep learning Fractional-order differences

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DWMFusion: A lightweight medical image fusion network based on discrete wavelet convolution and selective state space models

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Expert Systems with Applications 2025年 286卷

作者： Fan, Chao Yun, Mengyang Chen, Li Zhao, Huijun Peng, Bingcheng School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Ministry of Education Key Laboratory of Grain Information Processing and Control Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Multimodal medical image fusion improves the accuracy of clinical diagnosis by integrating medical imaging information from different sensors into unified and complementary multi-scale fused information images. However, existing fusion methods still face challenges in capturing modality-specific features, balancing local and global contextual features, and preserving complementary information from different representations. To overcome these shortcomings, this study proposes a lightweight feature complementary fusion network(DWMFusion) to achieve effective fusion of functional and structural medical images under lightweight premise. Specifically, a cross-channel feature extraction unit based on discrete wavelet transform (DWT) and convolutional coupling structure is designed to enhance the modal intrinsic frequency domain features. Meanwhile, a visual state space block (VSSB) and a contextual modeling structure with multiple layers of Mamba are introduced to balance local details with global semantics and reduce computational redundancy. In the image fusion stage, a multilevel cross-fusion unit (CFFM) that introduces position coding is designed to realize deep cross-modal spatio-temporal feature interaction. A joint loss function incorporating wavelet decomposition properties is proposed to effectively adaptively constrain the frequency domain consistency and maintain the structural information. Validation on the Harvard Medical School database shows that DWMFusion is capable of generating visually satisfactory fusion images. And compared with other methods, it improves the SD metrics by an average of 17.59%, the SF metrics by an average of 17.64%, and the AG metrics by an average of 19.96% on the three datasets. These results confirm the robustness and adaptability of the model. © 2025 Elsevier Ltd

关键词： Wavelet decomposition

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Adaptive Fuzzy Tracking control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

arXiv

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arXiv 2022年

作者： Mao, Bing Wu, Xiaoqun Liu, Hui Xu, Yuhua Lü, Jinhu The School of Mathematics and Statistics Wuhan University Hubei430072 China Hubei Key Laboratory of Computational Science Wuhan University Hubei430072 China Research Center of Complex Network Wuhan University Hubei430072 China The School of Automation Huazhong University of Science and Technology Hubei430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei430074 China The School of Statistics and Data Science Nanjing Audit University Jiangsu211815 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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The Design of Logic Gate Based on Triplex Structures 14th

The Design of Logic Gate Based on Triplex Structures

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Hu, Yingxin Wang, Zhiyu Chen, Zhekun Liao, Sulin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Information Science and Technology Shijiazhuang Tiedao University ShijiazhuangHebei050043 China School of Computer Science and Technology Huazhong University of Science and Technology WuhanHubei430074 China Department of Pathology Renmin Hospital of Wuhan University WuhanHubei430060 China

ISBN: (纸本)9789811534140

Molecular carch hotspots in the area of DNA nanotechnology due to its inherent large storage capacity, high parallelism, and energy efficiency. The assembled structures in the existing computing units mainly adopt a double-helix structure. However, besides duplex, DNA is known to adopt other helical forms such as triplex form. The triplex structure has introduced a novel paradigm into the DNA engineer owing to their unique characteristic sequence, structural and assembly requirements. Based on the assembly and disassembly of the triplex structure, a YES logic gate was constructed via strand displacement reaction. The experiment results showed that GC content and stem length would influence the displacement reaction. Then AND logic gate was also established through cooperative effect by two DNA strands. The construction of the cascading circuit indicated triplex-based logic gate could be integrated into larger circuits and enrich the toolbox of the computing devices. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Logic gates

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