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A Risk Prediction Model Based on Crash History Data for Railway Trams

Chinese Journal of Electronics 2023年第5期32卷 963-971页

作者： JI Wenjiang YANG Jiangcheng WANG Yichuan ZHU Lei QIU Yuan HEI Xinhong School of Computer Science and Engineering Xi'an University of Technology

Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.

关键词： Solid modeling Clustering algorithms Feature extraction Prediction algorithms Rail transportation Safety History

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Enrich Code Search Query Semantics with Raw Descriptions 19th

Enrich Code Search Query Semantics with Raw Descriptions

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Liu, Xiangzheng Liu, Jianxun Hu, Haize Liu, Yi School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search can recommend relevant source code according to the development intention (query statement) of the demander, thereby improving the efficiency of software development. In the research of deep code search model, code description is used to replace query sentences for training. However, the heterogeneity existing between the query statement and the code description will seriously affect the accuracy of the code search model. In order to make up for the shortcomings of code search, this paper proposes a sentence-integrated query expansion method—SIQE. Unlike previous query expansion methods that focus on word-level expansion, SIQE uses the entire code description fragment as the source of query expansion. And by learning the mapping relationship between the query statement and the code description, the heterogeneity problem between them is compensated. In order to verify the effect of the proposed model in code search tasks, the article conducts code search experiments and analyzes on two languages: python and java. Experimental results show that, compared with the baseline model, SIQE has higher code search results. Therefore, the SIQE model can effectively improve the search effect of query statements, improve the accuracy of code search, and further improve the development of software engineering. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Deep learning

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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A Review on Smart Healthcare employing Quantum Internet of Things

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IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies

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Chinese Journal of Electronics 2024年第2期33卷 423-435页

作者： Juncai HE Zhenxue HE Jia LIU Yan ZHANG Fan ZHANG Fangfang LIANG Tao WANG Limin XIAO Xiang WANG Hebei Agricultural University Beijing Information Science and Technology University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller(RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM(MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy(TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina(MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

关键词： Multi-output mixed polarity Reed-Muller Power optimization Combinatorial optimization problem Whale optimization algorithm Two-populations strategy Mutation strategy

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RLSM: A Strategy Matrix based Microservice Splitting Method for Reinforcement Learning

RLSM: A Strategy Matrix based Microservice Splitting Method ...

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2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Liu, Yuqiang Yu, Jianyong Han, Xue Liu, Yuqi The School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China The School of Hunan Software Vocational and Technical University Xiangtan China

We introduce a novel microservice splitting approach, the Policy Matrix-based Reinforcement Learning Splitting Method (RLSM), designed to overcome the limitations of traditional service splitting schemes by providing a solution that is both fine-grained and efficient, with a strong emphasis on automation. This method utilizes dynamic link tracking and static code analysis techniques to analyze business modules, and extract entities using data flow graphs. We use these entities as agents in reinforcement learning to construct a reinforcement learning environment model, and optimize and update the value of entities by constructing a policy matrix, replacing the Q table in traditional Q-learning algorithms. Finally, we obtained a set of entity categories and used the K-means algorithm to cluster the entities of these categories, with each clustered cluster being a split individual microservice. The experimental results show that this splitting scheme not only achieves automated splitting, but also maintains a stable compliance rate of about 97% for microservices. This is enough to demonstrate that RLSM is more efficient and flexible in completing service design and evaluation. © Published under licence by IOP Publishing Ltd.

关键词： Data flow graphs

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UGC-YOLO:Underwater Environment Object Detection Based on YOLO with a Global Context Block

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Journal of Ocean university of China 2023年第3期22卷 665-674页

作者： YANG Yuyi CHEN Liang ZHANG Jian LONG Lingchun WANG Zhenfei School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

With the continuous development and utilization of marine resources,the underwater target detection has gradually become a popular research topic in the field of underwater robot operations and target ***,it is difficult to combine the environmental semantic information and the semantic information of targets at different scales by detection algorithms due to the complex underwater *** this paper,a cascade model based on the UGC-YOLO network structure with high detection accuracy is *** YOLOv3 convolutional neural network is employed as the baseline *** fusing the global semantic information between two residual stages in the parallel structure of the feature extraction network,the perception of underwater targets is improved and the detection rate of hard-to-detect underwater objects is ***,the deformable convolution is applied to capture longrange semantic dependencies and PPM pooling is introduced in the highest layer network for aggregating semantic ***,a multi-scale weighted fusion approach is presented for learning semantic information at different *** are conducted on an underwater test dataset and the results have demonstrated that our proposed algorithm could detect aquatic targets in complex degraded underwater *** with the baseline network algorithm,the Common Objects in Context(COCO)evaluation metric has been improved by 4.34%.

关键词： object detection underwater environment semantic information semantic features deep learning algorithm

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Probing the special angle in twisted bilayer MoS_(2)via angle-dependent scanning tunneling microscopy studies

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science China(Physics,Mechanics & Astronomy) 2025年第2期68卷 153-160页

作者： Xueying Liu Shiying Liu Yulong Xiao Chang Xu Jiaxin Wu Kaihui Li Si-Yu Li Anlian Pan Key Laboratory for Micro-Nano Physics and Technology of Hunan Province Hunan Institute of Optoelectronic Integration and College of Materials Science and EngineeringHunan UniversityChangsha410082China School of Physics and Electronics Hunan Normal UniversityChangsha410081China

Twisted transition metal dichalcogenides(TMDs)homo-bilayers host unique quantum properties,which can be tuned by interlayer twist angleθ.However,the systematic evolution of their typical electronic properties with respect to the twist angleθ,which is crucial for identifying the“special angle”analogous to the“magic angle”of twisted bilayer graphene in correlation physics studies,remains incompletely ***,via scanning tunneling microscopy(STM)and spectroscopy(STS),we investigate the variation of the moirépotential,flat band,and layer polarization characteristics across a wide range of twist angleθin twisted bilayer MoS_(2)(TB-MoS_(2)).The moirépotential of the valence band exhibits a non-monotonic variation withθ,peaking at a maximum value up to 204 me Vatθ~1.7°.Concurrently,at the sameθ,the bandwidth of the flat band at theΓ_(V)point of the valence band attains its minimum,precisely signifying the“special angle”θ_(c)~1.7°in TB-MoS_(2).Interestingly,layer polarization in the moirésuperlattice is spatially visualized through the distribution of local density of states(LDOS)at the energies of bothΓ_(V)and K_(V)points of the valence band,where the polarization degree at theΓ_(V)point demonstrates a close dependency onθ.Our findings deepen understanding of twist-angle effect in TMDs,advancing both fundamental physics and practical application.

关键词： twisted bilayer MoS_(2) flat band moirépotential twist-angle dependence scanning tunneling microscopy

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A Broadband Vertical Transition from Waveguide to Microstrip Based on Narrow-Wall Excitation

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Progress in Electromagnetics Research C 2025年 154卷 105-109页

作者： Dong, Jun Zhong, Bingqing Zheng, Jing Yao, Feng Yin, Jinxin Peng, Hao College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

In this paper, a broadband vertical rectangular waveguide (RWG)-to-microstrip line (MSL) transition structure for millimeter-wave solid-state circuits is proposed. The planar circuit in this transition is composed of a V-shaped probe and tapered fin-line ground, and the probe is inserted into the waveguide through a slot on the narrow side of the RWG. To facilitate energy coupling from RWG to MSL, a back-short with a length of a quarter-wavelength is designed on the bottom side of the probe to achieve effective electric coupling. A back-to-back prototype module has been designed to verify the performance of the transition. The measurement results show that the return loss of the back-to-back transition structure is better than 13 dB across the entire Ka-band, with the insertion loss (IL) of a single transition better than 0.55 dB. The measurement results agree well with simulation ones, validating the feasibility of the proposed transition circuit. A tolerance analysis is performed through simulations to verify the reliability of this transition design. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Rectangular waveguides

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