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Finite-Time Sliding Mode Control of Vehicle Formation with Specified Performance

engineering Letters 2025年第3期33卷 620-629页

作者： Guo, Dongyang Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses on the problem of urban intelligent transportation planning, and proposes a new control method for vehicle formation regulation tracking performance control with bounded disturbances and model uncertainty, combining the multiple advantages of sliding mode control and finite time control and utilizing the universal approximation of neural network. This method can ensure that the vehicle formation can achieve the specified tracking performance and be stable in finite time instead of asymptotically stable. First, the third-order dynamics model and spacing strategy of the vehicle are given. The tracking performance of the vehicle formation is specified. Then, the control objective of the system is transformed by using the error transformation to obtain a new system. An improved sliding mode surface is designed for the transformed system. Furthermore, the universal approximation property of the neural network is utilised to overcome the parameter uncertainty in the system. The reconstruction error of neural network is handled by a robust term. In addition, the jittery vibration phenomenon of sliding mode control has been overcome. Finally, the finite-time stability of the system is analysed by constructing the Lyapunov function, simulations were performed to validate and compare with conventional control methods, simulation results show that the novel control method proposed in this paper is significantly faster than the conventional control method, validated the effectiveness of the method proposed in this paper. © 2025, International Association of Engineers. All rights reserved.

关键词： Lyapunov functions

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Surface Defect Detection Algorithm of Hot-Rolled Strip Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2024年第4期51卷 345-354页

作者： Shen, Lijia Cui, Wenhua Tao, Ye Shi, Tianwei Liao, Jinzhen School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

To enhance the capability of classifying and localizing defects on the surface of hot-rolled strips, this paper proposed an algorithm based on YOLOv7 to improve defect detection. The BI-SPPFCSPC structure was incorporated into the feature pyramid in this algorithm, enabling enhanced extraction of features from small objects and improved accuracy in network model positioning. Additionally, a small object detection layer was introduced to enhance shallow feature capture. Then the CARAFE sampling operator was used for up-sampling to reduce the feature loss problem of small objects. Finally, the WIoU served as the loss function for network model training to expedite convergence. The NEU-DET dataset was utilized for ablation and comparison tests. The findings indicated that the enhanced YOLOv7 model's mAP value had increased to 80.7%. The detection impact was much enhanced in comparison to other traditional models, and the frequency of false and missing detections was also decreased. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Surface defects

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Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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A Lightweight Object Detection Algorithm for Remote Sensing Images

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engineering Letters 2025年第3期33卷 704-711页

作者： Hou, Donghao Zhang, Yujun Ren, Jia School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou

关键词： Satellite imagery

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Secure cooperative output regulation for linear parameter-varying systems under DoS attacks: a resilient observer approach

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science China(information sciences) 2023年第9期66卷 35-49页

作者： Chengcheng FU Hao ZHANG Chao HUANG Zhuping WANG Huaicheng YAN School of Electronic and Information Engineering Tongji University School of Information Science and Engineering East China University of Science and Technology

This paper investigates the cooperative output regulation problem of heterogeneous linear parameter-varying multi-agent systems under denial-of-service(DoS) attacks. The matrix and state of the exosystem are taken to be unknown to the followers. Moreover, with the assumption that only a directed spanning tree exists in the communication topology, a resilient observer is proposed to exponentially estimate the global information, which can also be effective under DoS attacks with a certain intensity. Afterward,to avoid using the exact value of the exosystem matrix, a distributed online algorithm is proposed, through which the linear parameter-varying output regulation equation is asymptotically solved. Based on the resilient observer and the solution of the output regulation equation, a distributed regulator is proposed to achieve asymptotic cooperative output regulation for all agents. Some numerical simulations are conducted to verify the effectiveness of the proposed observer, algorithm, and controller.

关键词： cooperative output regulation heterogeneous multi-agent systems DoS attacks linear parameter-varying systems

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

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Computer Modeling in engineering & sciences 2023年第4期135卷 169-185页

作者： Yifei Xiao Shijie Zhou School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously ***,the data update performance is often bottlenecked by the constrained cross-rack *** techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch *** techniques were largely proposed individually previously,and in this work,we seek to use them *** mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch ***,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based *** proposed DXR-DU is evaluated via trace-driven local testbed *** experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

关键词： Data availability health data data update cloud storage IoT

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A lightweight false alarm suppression method in heterogeneous change detection

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Journal of Systems engineering and electronics 2024年第4期35卷 899-905页

作者： XU Cong HE Zishu LIU Haicheng School of Electronic and Information Engineering Heilongjiang Institute of EngineeringHarbin 150026China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection *** paper proposes a method to handle false alarms in heterogeneous change detection.A lightweight network of two channels is bulit based on the combination of convolutional neural network(CNN)and graph convolutional network(GCN).CNNs learn feature difference maps of multitemporal images,and attention modules adaptively fuse CNN-based and graph-based features for different *** with a new kernel filter adaptively distinguish between nodes with the same and those with different labels,generating change *** evaluation on two datasets validates the efficacy of the pro-posed method in addressing false alarms.

关键词： convolutional neural network(CNN) graph convolu-tional network(GCN) heterogeneous change detection lightweight false alarm suppression

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FedCLCC:A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing

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Defence technology(防务技术) 2025年第1期43卷 80-93页

作者： Kangning Yin Xinhui Ji Yan Wang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Federated learning(FL)is a distributed machine learning paradigm for edge cloud *** can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge ***,the diversity of clients in edge cloud computing presents significant challenges for *** federated learning(pFL)received considerable attention in recent *** example of pFL involves exploiting the global and local information in the local *** pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized *** achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional *** core of FedCLCC is the use of contrastive learning and conditional *** learning determines the feature representation similarity to adjust the local *** computing separates the global and local information and feeds it to their corresponding heads for global and local *** comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.

关键词： Federated learning Statistical heterogeneity Personalized model Conditional computing Contrastive learning

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Extreme Learning Machine Soft-sensing Model of SMB Chromatographic Separation Process Based on Multi-strategy Fusion Coati Optimization Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第2期52卷 447-463页

作者： Xing, Cheng Yang, Qing-Da Wang, Jie-Sheng ShuangGuan, Yi-Peng Sun, Yong-Cheng School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114044 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

Simulated Moving Bed (SMB) chromatographic separation technology is an innovative method that combines traditional fixed-bed adsorption operation with real moving bed (TMB) chromatographic separation technology. In order to accurately evaluate the purity and yield of extracted and residual in the SMB chromatography separation process, a extreme learning machine soft-sensing model based on multi-strategy fusion coati optimization algorithm (COA) was proposed. This model selects auxiliary variables based on the analysis of the SMB chromatographic separation process. The proposed multi-strategy fusion COA includes logistic mapping, prey position vector and meme grouping to enhance the randomness and diversity of COA. Simulation results demonstrate that the optimized ELM can proficiently predict the key pecuniary and technical index of the SMB chromatography separation process. © (2025), (International Association of Engineers). All rights reserved.

关键词： Machine learning

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