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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Li, Dapeng School of Software Engineering Jingling Institute of Technology Nanjing China

ISBN: (纸本)9789819620456

High order repetitive control (HORC) has been reported to improve the robustness of the control system that incorporate the non-periodic disturbance. In fact, the higher the order is, the more memory cells are needed, and the more total delay time of the control system is needed. Notably, in practical applications, the reported order of HORC rarely exceeds three, and an application with infinite-order HORC has never been reported. To address this issue, a reduced infinite-order repetitive controller (RIORC), using geometric principles, is introduced in this paper. The RIORC is equivalent to a second order repetitive controller. Compared to the traditional infinite-order repetitive control, the memory cells are largely reduced, and the total delay time of the control system is less needed. The effectiveness of the proposed RIORC scheme is verified through its application in a PWM inverter. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Delay control systems

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Integrating Information Security and Hardware Defense in Medical Printers: Addressing RowHammer Vulnerabilities 24

Integrating Information Security and Hardware Defense in Med...

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4th International Conference on Signal Processing and Communication technology, SPCT 2024

作者： Han, Zongshuo School of Software Engineering Lappeenranta University of Technology Lahti Finland

ISBN: (纸本)9798400710636

With the rapid development of modern medical equipment technology, more and more diagnosis and treatment processes rely on the support of printing devices. When the doctor transfers data to the printer, the printer first stores the data in RAM to ensure the smooth progress of the printing process. However, the data in RAM faces potential security threats, especially the design defects of dynamic random access memory (DRAM) are vulnerable to RowHammer attacks. RowHammer attacks use charge interference to affect the charge state of adjacent rows by quickly and repeatedly reading a row of memory data, thereby destroying data integrity and even obtaining access to key data in the system memory, causing the risk of data leakage and tampering. In response to this security risk, this paper proposes to use RockHammer to enhance the security performance of printers. RockHammer strengthens the protection mechanism of embedded system memory, can effectively resist the interference operation of attackers, and ensure the integrity and confidentiality of data. The application of this technology can not only ensure the security of medical printing equipment data, but also provide important technical contributions to the fields of medical data security and embedded system security. © 2024 Copyright held by the owner/author(s).

关键词： Information leakage

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A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2025年第2期12卷 1-15页

作者： Kaya, Muhammed Onur Ozdem, Mehmet Das, Resul Department of Software Engineering Technology Faculty Firat University Elazig23119 Turkey Türk Telekom Ankara Turkey

This paper presents a new approach for real-time anomaly detection and visualization of dynamic network data using Wireshark, known as the most widely used network analysis tool. As the complexity and volume of network data increases, effective anomaly detection has become important to maintain network performance and improve security. The proposed method uses comprehensive network packet information from Wireshark for fast and accurate anomaly detection. The collected network packets enable easy detection and interpretation of anomalies in a dynamic manner. The presentation is enriched with graph-based representations. Thanks to this method, network administrators can understand the data more easily and make more sound decisions. The results of our study show significant improvements in the effectiveness of anomaly detection and practical applicability of visualization tools in real-time scenarios. The present study integrates the advances in the network approach and various visualization methods to provide the new ideas for network security and management for dynamic network management improvement. Copyright © 2025 M. O. Kaya et al., licensed to EAI.

关键词： Cyber Attack Graph Visualization Information Security Temporal Dynamic Networks Wireshark

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Diabetic Retinopathy Image Segmentation Method Based on Fusion DenseNet and U-Net Network

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engineering Letters 2025年第2期33卷 418-428页

作者： Zhang, Yaxuan Xu, Yang Zhai, Jinlong University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Diabetic Retinopathy (DR) is a common and significant complication in patients with diabetes, and severely affecting their quality of life. Image segmentation plays a crucial role in the early diagnosis and treatment of DR. However, traditional methods are limited in terms of segmentation accuracy and generalization capability. This paper proposes a novel image segmentation method for diabetic retinopathy based on the integration of DenseNet and U-Net networks. Firstly, DenseNet is utilized to replace the encoder part of U-Net. The dense connection mechanism enhances the efficiency of feature propagation and improves the feature extraction capability of the encoder. Secondly, we introduce Omni-Dimensional Dynamic Convolution (ODConv) to replace traditional convolutions. ODConv is used to handle diverse input features effectively. The model's adaptability and segmentation accuracy for various samples is improved. Finally, we integrate the Convolutional Block Attention Module (CBAM) into the decoder. By leveraging channel and spatial attention, the ability to capture key features is enhanced, and the segmentation accuracy and boundary recognition capabilities are improved. To validate the effectiveness and feasibility of the model. This paper conducts experiments on the DDR dataset and the IDRID dataset. The experimental results show that the proposed method improved the mDice score by 0.53% compared to traditional methods. It also increased the mIoU score by 1.12% over traditional methods. This indicates that the proposed method has better performance in segmenting diabetic retinopathy images. It also demonstrates better generalization ability. © 2025, International Association of Engineers. All rights reserved.

关键词： Image segmentation

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Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

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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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Emotion Recognition from Text and Audio Dataset Using Cumulative Attribute Graph Neural Networks (CA-GNN) 2nd

Emotion Recognition from Text and Audio Dataset Using Cumu...

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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Farooq Tayeb Alsaadawi, Hussein Das, Resul Technology Faculty Department of Software Engineering Firat University Technology Elazig23119 Turkey

ISBN: (纸本)9783031782374

Emotions play a critical role in human understanding and interpersonal communication. Deciphering emotions from text and audio sources presents significant challenges in Affective Computing and Human-Computer Interaction. The challenge intensifies for those with hearing or speech impairments, necessitating accessible technologies. Our study proposes a new approach, employing advanced AI to tackle text and audio data emotion recognition complexities. This approach directly caters to the unique needs of deaf and speech-impaired communities, striving to enhance the accuracy of emotion recognition and understanding. Our study innovates by integrating text and audio features via the CMU-Multimodal Opinion Sentiment and Emotion Intensity CMU-MOSI dataset, employing t-distributed Stochastic Neighbor Embedding (t-SNE) visualizations for comprehensive feature representation. This fusion approach aims to boost emotion detection accuracy by harnessing more information from each modality. Using CA-GNNs for classification, our method leverages the combined feature set for accurate emotion identification, thereby enhancing model effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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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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OptiMobileX: Optimizing Deep Transfer Learning Model for Accurate Human Posture Recognition Using a Deep Feature Fusion Technique

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IEEE Sensors Journal 2025年第6期25卷 9759-9766页

作者： Ogundokun, Roseline Oluwaseun Damasevicius, Robertas Maskeliunas, Rytis Kaunas University of Technology Department of Software Engineering Kaunas44249 Lithuania Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania

Human posture recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with constrained computational resources is of utmost importance to bring about significant advancements in healthcare, fitness, and technology engagement. By facilitating real-time monitoring and providing personalized solutions, this technology improves accessibility and well-being, revolutionizing our interaction with digital applications. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using two modern pretrained DTL models, MobileNetV2 and Xception, which led to our proposed OptiMobileX: A deep transfer learning (DTL) model based on a DFC of the MobileNetV2 and Xception models for the overall improvement of the identification capability of this posture. The classification accuracy of 96.42% is achieved by the OptiMobileX model, demonstrating the superiority of the proposed model over MobileNetV2, Xception, AlexNet, and InceptionV3, which only manage to obtain 88.17%, 92.47%, 90.32%, and 73.48% accuracy, respectively. The OptiMobileX model exhibits notable capabilities but faces significant limitations. Inadequate details on detecting inappropriate postures and a lack of information on required computing resources hinder the study's comprehensiveness and practical implementation. The absence of insufficient discussion on dataset biases and a lack of information on the model's generalization capacity create obstacles, raising concerns about its fairness, applicability, and real-world utility. © 2001-2012 IEEE.

关键词： Deep learning

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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 Anshan 114051 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： feature fusion object detection Steel surface defect detection YOLO

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