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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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Significant increase in thermal conductivity of cathode material LiFePO_(4) by Na substitution:A machine learning interatomic potential-assisted investigation

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Chinese Physics B 2025年第2期34卷 463-468页

作者： Shi-Yi Li Qian Liu Yu-Jia Zeng Guofeng Xie Wu-Xing Zhou School of Materials Science and Engineering Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and ConversionHunan University of Science and TechnologyXiangtan 411201China

LiFePO_(4) is a cathode material with good thermal stability,but low thermal conductivity is a critical *** this study,we employ a machine learning potential approach based on first-principles methods combined with the Boltzmann transport theory to investigate the influence of Na substitution on the thermal conductivity of LiFePO_(4) and the impact of Li-ion de-embedding on the thermal conductivity of Li_(3/4)Na_(1/4)FePO_(4),with the aim of enhancing heat dissipation in Li-ion *** results show a significant increase in thermal conductivity due to an increase in phonon group velocity and a decrease in phonon anharmonic scattering by Na *** addition,the thermal conductivity increases significantly with decreasing Li-ion concentration due to the increase in phonon *** work guides the improvement of the thermal conductivity of Li FePO_4,emphasizing the crucial roles of both substitution and Li-ion detachment/intercalation for the thermal management of electrochemical energy storage devices.

关键词： lattice thermal conductivity machine learning potential LiFePO_(4)

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On large language models safety,security,and privacy:A survey

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Journal of Electronic science and technology 2025年第1期23卷 1-21页

作者： Ran Zhang Hong-Wei Li Xin-Yuan Qian Wen-Bo Jiang Han-Xiao Chen School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation *** their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant *** challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy *** works often conflated safety issues with security *** contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of *** on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in ***,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.

关键词： Large language models Privacy issues Safety issues Security issues

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Multi-Scale Feature Optimization Point Cloud Completion Network Integrating SoftPool

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 233-243页

作者： Zhang, Wanpeng Zhou, Ziwei School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Point cloud completion is crucial in point cloud processing, as it can repair and refine incomplete 3D data, ensuring more accurate models. However, current point cloud completion methods commonly face a challenge: they fail to fully utilize multi-scale information from local features, leading to limitations in accuracy and detail preservation. To address this issue, this paper proposes a multi-scale feature optimization algorithm for point cloud completion that integrates SoftPool. Based on DGCNN, the method combines dilated convolution and bottleneck attention mechanisms to extract features at different scales, enhancing the ability to capture detailed information in point clouds. The bottleneck attention mechanism is used to optimize important detail features. The extracted local features are concatenated with their corresponding positional information to form point proxies, enhancing the effective extraction of local geometric features, resulting in more refined completed point cloud shapes. A Transformer architecture is employed to model these features. Finally, SoftPool is introduced for fine-grained feature downsampling, improving the network's ability to recover point cloud details. FoldingNet is used to reconstruct missing structures and output the completed point cloud. To validate the model's completion performance, training and testing are conducted on the PCN and ShapeNet55 datasets. Experimental results demonstrate that the model has better feature detail retention and more accurate completion results. On the PCN dataset, the average CD value is reduced by 6.5% compared to the best-performing model among the comparison methods. On the ShapeNet55 dataset, the average CD value across three difficulty levels is reduced by 6.9% compared to the best-performing model among the comparison methods. Additionally, the model also achieved a 2.1% improvement in F-score. © (2025), (International Association of Engineers). All rights reserved.

关键词： Ability testing

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Improving UAV Image Target Detection: A Novel Approach Using OptiDETR with Swin Transformer

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 771-780页

作者： Ma, Wenlong Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, and varying lighting conditions, all of which exacerbate the difficulty of recognition. In recent years, the DETR model based on the Transformer architecture has eliminated traditional post-processing steps such as NMS(Non-Maximum Suppression), thereby simplifying the object detection process and improving detection accuracy, which has garnered widespread attention in the academic community. However, DETR has limitations such as slow training convergence, difficulty in query optimization, and high computational costs, which hinder its application in practical fields. To address these issues, this paper proposes a new object detection model called OptiDETR. This model first employs a more efficient hybrid encoder to replace the traditional Transformer encoder. The new encoder significantly enhances feature processing capabilities through internal and cross-scale feature interaction and fusion logic. Secondly, an IoU (Intersection over Union) aware query selection mechanism is introduced. This mechanism adds IoU constraints during the training phase to provide higher-quality initial object queries for the decoder, significantly improving the decoding performance. Additionally, the OptiDETR model integrates SW-Block into the DETR decoder, leveraging the advantages of Swin Transformer in global context modeling and feature representation to further enhance the performance and efficiency of object detection. To tackle the problem of small object detection, this study innovatively employs the SAHI algorithm for data augmentation. Through a series of experiments, It achieved a significant performance improvement of more than two percentage points in the mAP (mean Average Precision) metric compared to current mainstream object detection models. Furthermore, ther

关键词： Decoding

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A Nonlinear Explicit Model of A Non-Circular Subsea Tunnel-Liner System with An FGM Inverted Arch Under Mechanical Loading and Fire Fields

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China Ocean engineering 2024年第5期38卷 855-865页

作者： HE Jie CHANG Guo-yong LIU Yang LI Zhao-chao School of Civil Engineering Hunan University of TechnologyZhuzhou 412007China School of Civil Engineering Changsha University of Science and TechnologyChangsha 410114China

This paper proposes an explicit scheme to analyze the failure of a subsea polyhedral tunnel-liner system with an inverted arch under mechanical loading and fire *** thin-walled liner is made of Functionally Graded Materials(FGMs),which may improve the stability behavior of the tunnel-liner *** pressure is inevitable in the liner since underground water may penetrate the cracks of the tunnel,and reach the outer surface of the *** addition,an elevated temperature loading is taken into account,considering that fire may occur in the tunnel-liner *** the combination of mechanical loading and thermal loading,the liner deforms into a single-lobe shape,which is depicted by a trigonometric *** total potential energy is expressed quantitatively after the energy approach and thin-walled shell theory are *** minimum potential energy is obtained when the critical buckling *** critical buckling pressure is calculated,which considers the effect of the thermal *** present analytical prediction is subsequently compared precisely with other closed-form ***,the effects of several parameters,such as the geometric shapes,temperature variations,and volume fraction indices,are discussed to further survey the buckling performance of the nonlinear buckling of an FGM polyhedral liner with an inverted *** may address a polyhedral liner with fewer polyhedral sides,and a lower volume fraction index is recommended to rehabilitate cracked tunnels in engineering applications.

关键词： tunnel-liner system with an inverted arch volume fraction index mechanical and thermal loadings rehabilitation buckling

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China's poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

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地球空间信息科学学报（英文版） 2024年第1期27卷 111-131页

作者： Mengjie Wang Yanjun Wang Fei Teng Shaochun Li Yunhao Lin Hengfan Cai Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanChina Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina

Poverty has always been a global concern that has restricted human *** first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the *** establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG *** paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development *** temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been *** on the SDGs global indicator framework,this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model ***,we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation ***,we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation *** results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and *** estimated model R2 is 0.65,which can identify 72.77%of China's national poverty *** 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern *** addition,the average SDG 1 evaluation value of China's districts and counties increased by 23%from 2012 to *** paper is oriented to the United Nations SDGs frame-work,explores the SDG 1 localized evalu

关键词： Multisource remote sensing data Sustainable Development Goals(SDGs) poverty indicator system partial least squares machine learning

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Principle of subtraction ghost imaging in scattering medium

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Chinese Physics B 2023年第6期32卷 250-254页

作者：付芹白艳锋谭威黄贤伟南苏琴傅喜泉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha 410205China

Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation correlation function of the ghost imaging with the disturbance of the scattering medium,which proves that the ghost image consists of two correlated results:the image of scattering medium and the target *** effect of the scattering medium can be eliminated by subtracting the correlated result between the light field after the scattering medium and the reference light from ghost image,which verifies the theoretical *** research may provide a new idea of ghost imaging in harsh environment.

关键词： ghost imaging image reconstruction techniques scattering medium

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Self-supervised Hypergraph Transformer with Alignment and Uniformity for Recommendation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 292-300页

作者： Yang, XianFeng Liu, Yang 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

Graph neural networks have proven their effectiveness for user-item interaction graph collaborative filtering. However, most of the existing recommendation models highly depended on abundant and high-quality datasets and neglected the alignment and uniformity of embedding representation. The noisy and skewed distribution of data from real-world applications is ubiquitous. The alignment and uniformity are crucial for representation learning. In this paper, we propose SHTAU (self-supervised hypergraph transformer with alignment and uniformity for the recommendation) to address the problem. Specifically, we first perform a graph neural network on the user-item interaction graph, then captures the global collaboration relationship between users and items through the hypergraph transformer structure, and use the extracted global information to generate self-supervised signals for data enhancement on the user-item interaction graph to enhance the robustness of the recommendation algorithm. Simultaneously, alignment and uniformity auxiliary task update embeddings from different angles to improve performance. Extensive experiments are conducted on Yelp, Gowalla, MovieLens, Amazon-book and Tmall datasets. The experimental results show that SHTAU has obvious advantages over the baseline methods. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Alignment

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Improvement of Prior Image for Metal Artifact Reduction of Computed Tomography

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Journal of Shanghai Jiaotong university (science) 2025年第3期30卷 446-451页

作者： Sun, Wenwu Zhuang, Tiange Chen, Siping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shenzhen University Guangdong Shenzhen518060 China

It is not easy to reduce the metal artifacts of computed tomography images. However, the pixel values inside the metal artifact regions vary smoothly, while those on the borders of the metal and the bone regions vary sharply. When the Canny operation by adaptive thresholding is conducted on the raw image, the almost continuous edges can be formed obviously on the borders of the metal and the bone regions, but this kind of information cannot be formed for the metal artifact regions. In this paper, by searching the closed areas formed by the border edges of the bone regions in the Canny image, the metal artifact regions, which are very difficult to discriminate only by intensity thresholding, can be excluded effectively. A novel prior image-based method is thus developed for metal artifact reduction. The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image. The method is suitable for the clinical application. © Shanghai Jiao Tong university 2023.

关键词： computerized tomography

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