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IEEE International Symposium on Biomedical Imaging

作者： Juntao Jiang Yali Bi Chunlin Zhou Yong Liu Jiangning Zhang College of Control Science and Engineering Zhejiang University College of Computer and Information Science Southwest University State Key Laboratory of Industrial Control Technology Zhejiang University Youtu Lab Tencent

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Image segmentation plays a critical role in computer-aided diagnosis and treatment planning for cervical cancer. Obtaining a large number of labeled images for supervised cervical segmentation is often labor-intensive and time-consuming. In this paper, we propose a multi-stage semi-supervised learning framework (Semi-CervixSeg) to address the cervical seg-mentation task in ultrasound images for Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation in ISBI 2025. Specifically, in the initial stage, we utilize unlabeled data through a multi-view random augmentation strategy, using consistency constraints and a contrastive learning method. Subsequently, a progressive multi-stage training strategy is adopted to generate and optimize pseudo-labels, further improving segmentation results. Experimental re-sults demonstrate that the proposed method significantly im-proves segmentation performance compared with supervised methods. As a technical report for the challenge, this paper elaborates on our methodology and experimental findings in detail. The code can be accessed at h t t p s: / / g i t hub. com/juntaoJianggavin/Semi-CervixSeg.

关键词： Training Image segmentation Ultrasonic imaging Codes Contrastive learning Semisupervised learning Writing Data models Planning computer aided diagnosis

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Efficient Wideband Adaptive Beamforming With Null Broadening Using MHSA-CNN

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Huang, Hai Liu, Ruxin Yang, Jinwei Suo, Luyao Du, Ruiyan Northeastern University at Qinhuangdao Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

In the case of interference perturbation, the wideband adaptive beamforming (WAB) weight vector may be mismatched, which leads to the decrease of interference suppression ability. To improve communication quality under the interference position perturbation, this paper presents a multi-head self-attention conventional neural network (MHSA-CNN)-based WAB algorithm with null broadening. In the presented approach, a MHSA-CNN structure is proposed to improve the prediction accuracy of beamforming weight vector in the case of interference perturbation. Specifically, by processing multiple attention heads in parallel to obtain the information of different signal subspaces, MHSA mechanism enables the network to dynamically adjust the attention distribution of signal features and effectively extract the global features of the covariance matrix. Then, based on focused reconstruction and null broadening, an effective neural network training label is used to enhance the ability of suppressing interferences. Finally, the well-trained MHSA-CNN can accurately output the weight vector suitable for WAB with null broadening in real time. Simulation results demonstrate that the proposed algorithm can suppress interferences accurately within the interference perturbation range and enhance the output signal-to-interference-plus-noise ratio while ensuring real-time communication performance. © 2025 IEEE.

关键词： Signal to noise ratio

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Collision avoidance for a mobile robot based on radial basis function hybrid force control technique

引用

Chinese Physics B 2009年第10期18卷 4222-4228页

作者：温淑焕 Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function （RBF） neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.

关键词： mobile robot collision avoidance hybrid force/position control path planning RBF neural network

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Fuzzy adaptive robust control for space robot considering the effect of the gravity

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Chinese Journal of Aeronautics 2014年第6期27卷 1562-1570页

作者： Qin Li Liu Fucai Liang Lihuan Gao Jingfang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control（FARC） strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative（PD） controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.

关键词： Fuzzy adaptive Microgravity Robustness Space robot Trajectory tracking control

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A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis

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Advanced engineering Informatics 2025年 66卷

作者： Yu, Yue Karimi, Hamid Reza Gelman, Len Liu, Xin Department of Mechanical Engineering Politecnico di Milano via La Masa 1 Milan20156 Italy School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao06600 China

Vibration-based fault diagnosis methods are widely used in industrial applications due to their high accuracy and reliability. However, their implementation is often hindered by challenges such as confined spaces, harsh environmental conditions, and cost limitations. Moreover, traditional deep learning-based approaches frequently overlook the critical relationship between virtual and physical signals, which can limit their diagnostic performance. To overcome these challenges, this study introduces a novel digital twin-driven feature imputation framework (NIFD-Net) for non-contact intelligent fault diagnosis. The NIFD-Net framework consists of three key stages: (1) codebook generation, where features from simulated vibration signals (SVS) and non-contact signals (NCS) are compressed and regularized using diagnosis-oriented codebooks;(2) mapping construction, where a Transformer is employed to establish the relationship between SVS and NCS in a compressed feature space;and (3) diagnostic enhancement, where imputed SVS features are integrated with original NCS features to improve diagnostic performance. First, digital twin (DT)-enabled models, developed using structural parameters and fault characteristics, dynamically simulate the operating conditions of rolling bearings to generate high-quality SVS samples. The relationship between the two feature spaces of SVS and NCS is then learned by a transformer to impute the SVS features. Finally, the imputed SVS features are integrated with the original NCS features to enhance fault diagnostic performance. Extensive experiments on three case studies demonstrate that NIFD-Net outperforms state-of-the-art methods, achieving an average accuracy of 98.34% across diverse tasks. Notably, the framework exhibits strong noise robustness, maintaining high diagnostic performance even under adverse conditions (e.g., SNR = -10 dB). The results highlight the effectiveness of integrating simulated and real signals for non-contact fault diag

关键词： Vibrations (mechanical)

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Robust Iterative Learning control of Single-phase Grid-connected Inverter

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International Journal of Automation and computing 2014年第4期11卷 404-411页

作者： Zhong-Qiang Wu Chun-Hua Xu Yang Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

For the single phase inductance-capacitance-inductance(LCL) grid-connected inverter in micro-grid, a kind of robust iterative learning controller is designed. Based on the output power droop characteristics of inverter, the current sharing among the inverters is achieved. Iterative learning strategy is suitable for repeated tracking control and inhibiting periodic disturbance, and is designed using robust performance index, so that it has the ability to overcome the uncertainty of system parameters. Compared with the repetitive control, the robust iterative learning control can get high precision output waveform, and enhance the tracking ability for waveform, and the distortion problem of the output signal can be solved effectively.

关键词： Inductance-capacitance-inductance (LCL) filter iterative learning robust control power droop-characteristics repetitive control

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Fuzzy PID control of Space Manipulator for Both Ground Alignment and Space Applications

引用

International Journal of Automation and computing 2014年第4期11卷 353-360页

作者： Fu-Cai Liu Li-Huan Liang Juan-Juan Gao Key Lab of Industrial Computer Control of Hebei Province Yanshan University

Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential(PID)control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments. The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller. Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem. Compared with the results of conventional PID control,the control performance of the fuzzy PID is more effective for manipulator trajectory control.

关键词： Space manipulator trajectory tracking ground alignment space applications fuzzy proportional-integral-differential (PID) gravity environment

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Multi-Objective Optimization for Tandem Cold Rolling Schedule

引用

Journal of Iron and Steel Research International 2010年第11期17卷 34-39页

作者： YANG Jing-ming ZHANG Qing CHE Hai-jun HAN Xin-yan Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei China

Considering the multivariable, strong-coupled, and nonlinear features of tandem cold rolling mill, a mathematical model of multi-objective optimization was built to facilitate the design of new systems aiming at equating the relative load, preventing slip, and obtaining best shape. BP （back propagation） neural network based on Levenberg- Marquardt algorithm was used for predicting the rolling force. The multi-objective fuzzy theory and method were introduced during the optimization. With an example of 1 370 mm tandem cold rolling mill, the rolling schedule of the common rolling, the single-objective optimization design, and the multi-objective fuzzy optimization design were compared with each other. The results generated from the case study showed that the proposed approach could significantly decreased the values of three objective functions simultaneously and the performance of the optimal rolling schedule was satisfactory and promising. Moreover, the capability and usefulness of fuzzy application in tandem cold rolling schedule were clearly demonstrated.

关键词： rolling schedule tandem cold rolling neural network multi-objective fuzzy optimization

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A novel multi-modal tactile sensor design using thermochromic material

引用

Science China(Information Sciences) 2019年第11期62卷 189-191页

作者： Fuchun SUN Bin FANG Hongxiang XUE Huaping LIU Haiming HUANG Department of Computer Science and Technology Tsinghua University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University College of Information Engineering Shenzhen University

Tactile sensors are important for ensuring robotic perception [1], and their performance directly affects a robot’s capability for performing dexterous operations. These sensors mimic the tactile sensing capability of human hands. This is because the skin of human hands is involved in considerably meticulous works and can provide multi-

关键词： A novel multi-modal tactile sensor design using thermochromic material

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