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Event-based sliding mode control under denial-of-service attacks

Science China(Information Sciences) 2022年第6期65卷 125-141页

作者： Yingxin TIAN Xiang LI Bo DONG Yabin GAO Ligang WU Department of Control Science and Engineering Harbin Institute of Technology School of Software Harbin Institute of Technology

This paper deals with the problem of estimator-based sliding mode control against denial-of-service(DoS) attacks and discrete events via a time-delay approach. A networked system is considered an uncertain dynamical system with matched and mismatched perturbations and exogenous disturbance in the network environment. A network-resource-aware event-triggering mechanism is designed with aperiodically releasing system measurements. Furthermore, to describe the DoS attack duration and inter-event time, a time-delay modeling approach considers the DOS attack duration and inter-event time as a “time delay” of the measurements between the sensor and controller over the network is proposed. Consequently, an intervaltime-delay system with uncertainties is formulated. A state-observer-based sliding mode controller, by which the ideal sliding mode can be achieved, is proposed against the DoS attacks. The resulting sliding motion is proved to be robust and stable with an Lgain performance. Finally, the effectiveness and applicability of the present sliding mode control are validated in a simulated pendulum system.

关键词： sliding mode control time delay uncertain system state observer event trigger

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Auto-3D-house Design from Structured User Requirements

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Machine Intelligence Research 2025年第2期22卷 368-385页

作者： Minkui Tan Qi Chen Zixiong Huang Qi Wu Yuanqing Li Jiaqiu Zhou School of Software Engineering South China University of TechnologyGuangzhou 510006China Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide SA 5005Australia School of Automation Science and Engineering South China University of TechnologyGuangzhou 510006China

We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding of user requirements,where the users can hardly provide high-quality requirements without any professional knowledge;2)the design of house plan,which mainly focuses on how to capture the effective information from user *** address the above issues,we propose an automatic house design framework,called auto-3D-house design(A3HD).Unlike the previous works that consider the user requirements in an unstructured way(e.g.,natural language),we carefully design a structured list that divides the requirements into three parts(i.e.,layout,outline,and style),which focus on the attributes of rooms,the outline of the building,and the style of decoration,*** the processing of architects,we construct a bubble diagram(i.e.,graph)that covers the rooms′attributes and relations under the constraint of *** addition,we take each outline as a combination of points and orders,ensuring that it can represent the outlines with arbitrary ***,we propose a graph feature generation module(GFGM)to capture layout features from the bubble diagrams and an outline feature generation module(OFGM)for outline ***,we render 3D houses according to the given style requirements in a rule-based *** on two benchmark datasets(i.e.,RPLAN and T3HM)demonstrate the effectiveness of our A3HD in terms of both quantitative and qualitative evaluation metrics.

关键词： Automated house design user requirements understanding outline processing layout generation graph feature generation.

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Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 574-583页

作者： Qingsong Wang Siwei Li Hao Ding Ming Cheng Giuseppe Buja School of Electrical Engineering and Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast UniversityNanjing 210096China School of Software Engineering Southeast universitySuzhou 215123China

This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.

关键词： DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security software defined network Lifecycle management

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Research on Faster R-CNN material micro-defect detection method for small data volume 5

Research on Faster R-CNN material micro-defect detection met...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Wang, Zhili Yan, Hairong Zhou, Weiyu Beijing University of Technology Software Engineering Faculty of Information Technology Beijing China

ISBN: (纸本)9798350366174

Defect detection in material micro-images has a significant impact on the study of the relationship between the micro-structure and macro-properties, however, material microdefects are usually relatively small and span a wide range of scales, which increases the difficulty of defect detection. Meanwhile, since defects exist in a small number, overfitting becomes another challenge. In this paper, based on the Faster R-CNN algorithm, automated data enhancement is used to solve the overfitting, and a feature pyramid model is proposed for the defects multi-scale problem, and finally, the feasibility of the above viewpoint is verified by experiments. © 2024 IEEE.

关键词： defect detection Faster R-CNN feature pyramid model small data volume

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Steel Surface Defect Detection Based on YOLOv10

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engineering Letters 2025年第5期33卷 1220-1231页

作者： Sun, Luyu Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

To improve the accuracy of steel surface defect detection, this study proposes an improved multi-directional optimization model based on the YOLOv10n algorithm. First, we introduce innovations to the convolution (C2F) module in YOLOv10n with MSMHSA (Multi-Scale Multi-Head Self-Attention) and EMA (Enhanced Multi-Scale Attention) modules. These modules enhance learning and expression capabilities by reshaping across different scales, multiple attention heads, and channel dimensions. Second, a Multi-Dilated Channel Refinement (MDCR) module is employed. The MDCR module captures spatial features across various receptive field ranges through multiple depthwise separable convolutions, enabling more effective multi-level feature integration. Finally, a Context Aggregation module is embedded within the neck network as a generic building block for multi-head context aggregation, leveraging the inductive bias of local convolution operations to facilitate rapid convergence. Experimental results show that the improved model achieves a mean Average Precision (mAP) of 78.7% in steel surface defect detection tasks, marking a significant improvement of 4.3% over the original YOLOv10n model. In practical applications, the improved model can quickly and accurately locate and classify various steel surface defects, meeting the needs of steel surface defect detection in industrial production. © 2025, International Association of Engineers. All rights reserved.

关键词： Surface defects

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SmogMaster- LIDAR-Enhanced Driving Assistance for Beyond Visibility in Dense Fog 4

SmogMaster- LIDAR-Enhanced Driving Assistance for Beyond Vis...

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4th International Conference on Innovations in Computer Science, ICONICS 2024

作者： Saleem, Falak Huzaifa, Muhammad Iqbal, Duaa Sir Syed University of Engineering & Technology Department of Software Engineering Karachi Pakistan

ISBN: (纸本)9798331504342

This paper introduces 'SmogMaster: Your Smart Driving Companion, ' the Visual Editorial Quality Assurance, an intelligent Smart Driving Assistant System (SDAS) in areas with high foggy weather affected by heavy fog. This system is installed with a RP Lidar Al sensor, camera, and the Yolov8s model for real-time feedback. By using Lidar technology, the system identifies obstacles and objects in order to create some kind of a point of cloud, which helps to avoid collisions and accidents. The Yolov8s model is equipped with a camera to identify the objects/obstacles and to calculate the distance between preprocessors using real part identification barrier to prevent accidents. SmogM aster is specially designed to be installed/implemented in the areas which are heavily affected by fog to provide accurate and timely heavy smog warnings and thus making it safer to drivers. This paper includes the development, implementation, and testing of SmogMaster, demonstrating that it has an order of magnitude lower risk acceptance potential to low-visibility accidents. The results demonstrate that The range of systems the SmogMaster helps discriminate against drivers, raising the bar for intelligent driving systems in unfavorable weather. © 2024 IEEE.

关键词： Acceptance tests

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Infrared Small Target Detection with UAV Based on Convolutional Neural Networks

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engineering Letters 2025年第5期33卷 1505-1512页

作者： Zhang, Jingshi Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Infrared target detection is now applied in many fields, such as medical imaging, military detection, autonomous driving, and environmental monitoring with drones. Due to the small size of these targets, complex environments, and limited contextual information, target detection is challenging. Therefore, this paper proposes an improved method for detecting small infrared targets based on the YOLOv9-c ***, we propose dynamic snake convolution (DSConv), a mechanism designed to precisely model tubular structures by adaptively concentrating on elongated and intricate curvilinear features within local regions. Second, we employ a new spatial and channel synergistic attention module (SCSA), integrating the SCSA module into the neck network of YOLOv9. The SCSA framework integrates two synergistic components, the Shared Multi-Semantic Spatial Attention (SMSA) module and the Progressive Channel Self-Attention (PCSA) module. This enhances the synergy between spatial and channel attention across multiple semantic levels. Finally, we introduce the C2F module into multi-scale dilated attention, altering its original dilation rates. C2F MSDA better aggregates semantic information, effectively reducing the redundancy of self-attention mechanisms. This improvement has shown promising results on the public HIT-UAV dataset, raising the average precision to 0.902 and providing potential technological support for the detection of small infrared targets. © 2025, International Association of Engineers. All rights reserved.

关键词： Target drones

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Multi-Task Chinese Speech Recognition Method Based on the Squeezeformer Model

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第1期52卷 23-31页

作者： Guo, Ying Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Technology Liaoning Anshan114051 China

End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose deployment challenges. To address these issues, we propose a multi-task Chinese speech recognition method based on the Squeezeformer model. We replace the FMCF structure in Conformer with an MF/CF structure, leveraging the convolutional module as a local Multi- Head Attention (MHA) module to enhance efficiency. Multilevel down-sampling and up-sampling using a time-series U-Net further reduce computational costs. By eliminating redundant LayerNorm layers and employing depthwise separable convolutions, we streamline the model, reduce parameters, and lower deployment costs. An Adaptor Layer is integrated into the MHSA module to mitigate the vanishing gradient problem, and a ScaleVar Layer is added to enhance flexibility. Additionally, the RealFormer module is introduced on the decoding side to improve context understanding. Combining Connectionist Temporal Classification (CTC) with attention-based encoding and decoding models for multi-task learning improves performance and accuracy. Experimental results show that the proposed method reduces the parameters on AISHELL-1 dataset by 16% and reduces the character error rate to 5.50%. At the same time, it also shows good performance on AISHELL-2 dataset. © (2025), (International Association of Engineers). All rights reserved.

关键词： Speech recognition

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