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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

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

ISBN: (纸本)9798331520526

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. © 2025 IEEE.

关键词： Semi-supervised learning

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Optimal Resource Allocation Scheme for RIS-Assisted Vehicular Networks

Optimal Resource Allocation Scheme for RIS-Assisted Vehicula...

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第43届中国控制会议

作者： Wenxuan Chen Jiawei Su Zhixin Liu the School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Deploying reconfigurable intelligent surfaces(RIS)in vehicular networks can effectively improve the quality of wireless channel and increase network throughout,which is realized by optimizing the phase ***,the dynamic network environment and complex multi-user interference seriously affect network *** transmission powers blindly cannot solve the interference problem but also reduces the transmission efficiency of the RIS-based vehicular *** address the above challenges,this paper proposed a joint optimization scheme of transmission beamforming and phase *** on the block coordinate descent(BCD) theory,this paper decomposed the scheme into two sub-problems:transmission beamforming optimization and RIS phase shift optimization,and the joint optimization algorithm was *** results have demonstrated that the transmission rate has been improved with the assistance of RIS,with the multi-user interference effect being well-depressed.

关键词： Vehicular networks RIS Interference management Phase shift optimization

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Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems

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Mathematical Biosciences and engineering 2024年第2期21卷 2608-2625页

作者： Xu, Wanying Yang, Xixin Guan, Yuanlin Cheng, Xiaoqing Wang, Yu College of Computer Science & Technology Qingdao University Qingdao266071 China School of Automation Qingdao University Qingdao266071 China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of Education Qingdao University of Technology Qingdao266520 China School of Mechanical & Automotive Engineering Qingdao University of Technology Qingdao266520 China

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI’s overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://***/licenses/by/4.0)

关键词： Learning systems

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Optimal Time and Power Allocation in Full-Duplex Wireless-Powered Cognitive Radio Networks 13

Optimal Time and Power Allocation in Full-Duplex Wireless-Po...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Feng, Zhenyu Su, Ming Zhou, Meihua Liu, Zhixin School of Electrical Engineering Yanshan University Qinhuangdao066004 China Training Center of Liren College Yanshan University China Key Lab of Industrial Computer Control Engineering of Hebei Province Hebei Province Qinhuangdao066004 China

ISBN: (纸本)9798350315196

In this paper, we integrate wireless powered communication networks (WPCNs) with cognitive radio networks (CRNs) in order to develop wireless powered CRNs (WP-CRNs). The hybrid access point (HAP) is used to broadcast radio-frequency signals to the devices at the downlink stage, and to receive information from these devices. The HAP has two antennas to achieve full-duplex: one is for downlink wireless power transfer and the other is for receiving uplink information transmission from the users simultaneously in the uplink. In the underlay paradigms. The SUs can harvest energy to transmit data when the interference to primary users (PUs) does not exceed a tolerable threshold. To tackle the above network issues, an optimal resource allocation method is proposed to maximize the secondary system uplink throughput. The optimization problem with respect to time and power allocation is formulated including the constraint of energy causality and interference level. The non-convex problem is transformed into an equivalent convex optimization problem, then the joint optimal power and time allocation (JOPTA) algorithm is presented to determine the optimal solutions. The effectiveness of the proposed algorithm is verified in the simulations. © 2023 IEEE.

关键词： Cognitive radio

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Fault Tolerant control Based on GA-DRE for a Manipulator with Actuator Fault

Fault Tolerant Control Based on GA-DRE for a Manipulator wit...

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第43届中国控制会议

作者： Jiongzhi Qiu Yiming Fang Donliang Zhao Le Liu Nuan Shao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Department of Environmental Engineering Hebei University of Environmental Engineering

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To solve the trajectory tracking control problem of a rigid manipulator system in the case of unexpected actuator failure,considering the uncertainty of system parameters and bounded disturbance,an adaptive fault-tolerant tracking control strategy is proposed in this ***,an adaptive strategy is designed to estimate and compensate the effects of actuator failure and system uncertainty,and the dynamic regression matrix is extended and mixed to improve the accuracy of parameter estimation without persistency of excitation(PE),which will enhance the trajectory tracking ***,in the framework of sliding mode fault-tolerant control,combined with standard Gradient Adaptive and Dynamic Regressor Extension(GA-DRE) parameter estimation technology,a composite adaptive fault-tolerant controller is ***,theoretical analysis and numerical simulation results show that the proposed method can perform accurate parameter estimation in a finite time under the premise of matching the new conditions based on DRE additional terms,and the trajectory tracking errors of the expected positions and velocities of each joint of the manipulator can asymptotically converge to zero.

关键词： Fault-tolerant control Gradient Adaptive and Dynamic Regressor Extension(GA-DRE) Manipulator Parameter estimation Persistence of excitation(PE)

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Predictive Cruise control for Connected Autonomous Vehicles Considering Communication Delay in Mixed Traffic

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IFAC-PapersOnLine 2024年第29期58卷 172-177页

作者： Xinyu Li Jin Zhang Jiaqi Xue Lin Qi Wenbin Tang Xiaohong Jiao Institute of Electrical Engineering Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei Province China

The stochastic behaviors of human-driven vehicles (HDVs) and the communication delay of Vehicle-to-Everything (V2X) technique present challenges to the advancement of connected autonomous vehicles (CAVs) in mixed traffic comprising CAVs, uncertain numbers of HDVs and connected HDVs (CHDVs). Consequently, a predictive cruise control strategy based on V2X communication is proposed for CAVs in a mixed platoon. Firstly, a long-short-term memory (LSTM) network combined with a conditional linear Gaussian (CLG) model is adopted for speed prediction of the preceding vehicle considering the communication delay. Subsequently, the CAV's speed planning is conducted for passing continuous signalized intersections without stopping. Then, the planned speed is tracked in energy-efficient, safe, and comfortable manners under a stochastic model predictive control (SMPC) framework. The simulation results validate the effectiveness and advantages of the proposed strategy.

关键词： Connected autonomous vehicles Mixed traffic Communication delay Predictive cruise control

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Improved Lightweight YOLOv5 Based on GhostNet and Its Application in Target Board Detection for Ladle Nozzle Positioning 43

Improved Lightweight YOLOv5 Based on GhostNet and Its Applic...

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43rd Chinese control Conference, CCC 2024

作者： Si, Fengfei Fang, Yiming Ju, Hao Zhao, Dongliang Xin, Jingliang Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Eng. Res. Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Qinhuangdao066004 China Tangshan Branch Hbis Company Limited Tangshan063016 China

ISBN: (纸本)9789887581581

The detection of the target board of ladle nozzle positioning is the basis of automatic loading and unloading of ladle nozzle by ladle robot in continuous casting. Due to the complex situation of high temperature, dust, backlight and so on, the target board of ladle nozzle positioning is prone to false detection or low detection accuracy, and it is difficult to take into account the contradiction between rapidity and detection accuracy. Therefore, this paper proposes an improved lightweight algorithm GVP-YOLOv5 (YOLOv5 with GhostNet, VoVGSCSP and Pruning), which takes into account both detection accuracy and rapidity, to detect the target board for ladle nozzle positioning. Firstly, Ghost module is used to improve the backbone network of YOLOv 5 as the backbone feature extraction network to reduce the number of parameters and calculation of the backbone. Then in the neck, the GSConv module is used to design a thin neck structure, which can ensure the multi-scale feature fusion capability and further reduce the complexity of the network. Finally, the model pruning technology is used to prune the whole model, and the channels with small scaling factor are removed to compress the model to the greatest extent, so as to meet the requirements of industrial field application deployment. The experimental results show that the lightweight GVP-YOLOv5 algorithm proposed in this paper can complete the real-time detection of the target board in complex industrial scenarios. Compared with the original YOLOv5s algorithm, the number of parameters in the model is reduced by 82.6 %, and the size of the model is significantly reduced to 19.4 %. At the same time, the detection accuracy is maintained at more than 95 % on mAP @ 50: 95 and the detection speed is effectively improved. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Continuous casting

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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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Science China(Information Sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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Hunger games search optimization with deep learning model for sustainable supply chain management

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Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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Configuration Synthesis of Weakly Coupled Parallel Hip Exoskeletons With Large Workspace

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Journal of Mechanisms and Robotics 2025年第7期17卷 074502页

作者： Xu, Jilong Liu, Fucai Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao 066004 China Key Lab of Industrial Computer Control of Heibei Province Yanshan University Qinhuangdao 066004 China

To address the issues of strong motion coupling, high control difficulty, and insufficient degrees-of-freedom (DoFs) in existing bio-syncretic parallel hip exoskeletons, a design scheme characterized by weak coupling is proposed. A configuration synthesis method for weakly coupled parallel hip exoskeletons is introduced by deriving the conditions that the transmission wrench screws (TWSs) in each limb must satisfy. Furthermore, to tackle the problem of limited workspace in parallel hip exoskeletons, a design method aimed at increasing the operational workspace is presented. By applying the proposed methods to the configuration synthesis of the hip exoskeleton, several weakly coupled configurations with expanded workspaces are generated. Finally, the correctness and effectiveness of the proposed methods are validated through case analyses.

关键词： weakly coupled hip exoskeleton configuration synthesis parallel robots Screws mechanism synthesis and analysis wearable devices parallel mechanism Exoskeleton devices workspace mechanisms and robots

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