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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Wenpeng Wei Zhaoyu Qiu Xiaoyuan Zhu Guodong Yin Tianyi He School of Mechanical Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan UT USA

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

This paper presents an Integrated Physics-DataBased (IPDB) modeling and control scheme of the combined longitudinal-lateral vehicle dynamics. A nonlinear bicycle vehicle model is used to derive the linear parameter-varying (LPV) system representation, where four vehicle motion variables are considered as scheduling parameters. Taking advantage of kernels from LPV representation, the combined longitudinal-lateral dynamics are further expressed by the data snapshots of states, inputs, and scheduling parameters, which formulate the IPDB model. After that, the IPDB model is used to design a state-feedback gain-scheduling tracking controller to follow a reference trajectory. For validation purposes, the proposed modeling and control method is implemented on a QCar experimental platform. First, the IPDB model of coupled longitudinal-lateral dynamics is derived from experimental data and is further validated with excellent model accuracy under various driving conditions. Furthermore, an IPDB model-based gain-scheduling controller is synthesized and compared with the baseline Stanley controller in the experiment to track a given trajectory. The experimental results demonstrate that the IPDB model-based controller renders better tracking control performance.

关键词： Accuracy Mechatronics Tracking Optical variables measurement Dynamic scheduling Data models Trajectory

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A Trajectory Tracking Algorithm Based on Interior Point Method for A Class of Mobile Manipulators

A Trajectory Tracking Algorithm Based on Interior Point Meth...

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Chinese Automation Congress (CAC)

作者： Xuechen Rao Yahui Gan Xiaoteng Wang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education NanJing China

At present, due to the high redundancy and strong coupling characteristics of mobile manipulator systems, there are few and in-depth studies on coordinated motion planning. Aiming at the above problems, this paper proposes a coordinated control algorithm that can track the desired trajectory of the end-effector based on a class of 5-DOF mobile manipulators. The algorithm takes the power consumption and trajectory stability of the entire system as the optimization objectives, and sets the function of the selected optimization objective on the desired trajectory, and performs optimization based on the interior point method of convex optimization to converge to the optimal solution faster. Finally, the system is simulated to verify the correctness and effectiveness of the control algorithm.

关键词： Smoothing methods Trajectory tracking Simulation Robot kinematics Redundancy Mathematical models End effectors

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital Communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data Blockchain Shar ding Associated transactions Transaction assi gnment

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Missing Small Bolt Detection on High-speed Train Using Improved Yolov5

Missing Small Bolt Detection on High-speed Train Using Impro...

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International Conference on systems and Informatics (ICSAI)

作者： Yiming Wei Xiaobo Lu School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665474498

Tiny bolts are widely used on high-speed trains, playing an important role in fixing train components. However, because of the complex running environment of trains, missing bolts occur from time to time and may cause traffic accidents, resulting in property damage and, in serious cases, endangering the lives of the occupants. Therefore, it is essential to detect missing bolts on high-speed trains. The bolts discussed in this paper are generally located on the underside of high-speed trains and their small size makes detection more difficult. In this paper, we first expand the dataset, and then add the Attention module and Transformer based on YOLOv5, and change the FPN of YOLOv5 to BiFPN, fuse the features of different layers using different weights, and crop the high-resolution original image during training and testing, and finally return to the original image. Our method eventually achieves 95.3% AP, effectively improving the detection accuracy.

关键词： Training Fuses Training data Object detection Fasteners Transformers Data models

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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Computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Optimization of optical convolution kernel of optoelectronic hybrid convolution neural network

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Optoelectronics Letters 2022年第3期18卷 181-186页

作者： XU Xiaofeng ZHU Lianqing ZHUANG Wei ZHANG Dongliang LU Lidan YUAN Pei School of Electro-Optical Engineering Changchun University of Science and TechnologyJilin 130022China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology UniversityBeijing 100192China Beijing Laboratory of Optical Fiber Sensing and System Beijing Information Science and Technology UniversityBeijing 100016China

To enhance the optical computation’s utilization efficiency, we develop an optimization method for optical convolution kernel in the optoelectronic hybrid convolution neural network(OHCNN). To comply with the actual calculation process, the convolution kernel is expanded from single-channel to two-channel, containing positive and negative weights. The Fashion-MNIST dataset is used to test the network architecture’s accuracy, and the accuracy is improved by 7.5% with the optimized optical convolution kernel. The energy efficiency ratio(EER) of two-channel network is 46.7% higher than that of the single-channel network, and it is 2.53 times of that of traditional electronic products.

关键词： convolution kernel weights

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Maximum power point tracking of photovoltaic array in partial shading condition based on improved Gray Wolf optimization algorithm

Maximum power point tracking of photovoltaic array in partia...

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Chinese control and Decision Conference, CCDC

作者： Chuang Kou Chenxi Li Kanjian Zhang Jinxia Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665478977

The partial shading condition (PSC) makes it challenging for the PV system to find the maximum power point. In this paper, an improved gray wolf algorithm (GWO) is proposed by introducing elimination mechanism, greedy mechanism and adjusting the convergence factor which overcomes the contradiction between global exploration ability and convergence time. Under fast varying irradiance and PSC, IGWO has been compared with GWO, particle swarm optimization (PSO), and adaptive particle swarm optimization (APSO), the results shows the superiority of IGWO in the MPPTs of the PV system.

关键词： Photovoltaic systems Maximum power point trackers Adaptive systems Heuristic algorithms Particle swarm optimization Oscillators Optimization

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Online Estimation of State of Healh for Lithium-ion Batteries Based on Attention-LSTM Netwrok Using Partial Charging Features

Online Estimation of State of Healh for Lithium-ion Batterie...

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Energy, Electrical and Power engineering (CEEPE), International Conference on

作者： Yue Yao Ying Wang Jiangfeng Zhang Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China State Grid Zhejiang Electric Power Research Institute Zhejiang China

Accurate estimation for state-of-health (SOH) of lithium-ion batteries is critical to ensure the security and durability of battery operation. Accordingly, this paper proposes a SOH estimation method based on Attention-LSTM neural network using the partial charging features. First, features are extracted from partial constant-current charging curves. Second, Pearson coefficient analysis is employed to analysis the mapping relationship between extracted features and SOH. Third, an Attention-LSTM network is utilized to realize the SOH estimation. The attention mechanism can adaptively assign weights to the input features and time steps, thereby improving the estimation accuracy. Validation experiments are conducted on public dataset from NASA Ames Prognostics Center of Excellence. The experimental results highlight that the proposed method has higher accuracy and robustness compared with other approaches.

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