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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Xinyue Kong, Dezhang Zhou, Zhengyan Wang, Di Chen, Shuangxi Wu, Chunming Zhejiang University College of Control Science and Engineering Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Jiaxing Vocational and Technical College Jiangxing China

ISBN: (纸本)9798350351255

Quality-of-Service (QoS) guarantees are crucial for meeting the diverse performance requirements of applications in packet networks. The Proportional Delay Differentiation (PDD) model offers relative service differentiation based on the delay requirements of different traffic classes. However, implementing PDD on current hardware switches faces challenges due to the lack of inherent queuing behavior description in switch ASICs. This paper introduces FlexPDD, a dynamic and adaptive packet prioritization mechanism designed to implement the PDD model on programmable switches. FlexPDD leverages the flexibility of programmable switch to adjust the mapping between packet classes and output queues dynamically, ensuring precise control over delay differentiation. Our implementation of FlexPDD on a Barefoot Tofino switch and an NS3 simulator demonstrates its feasibility and effectiveness. The results indicate that FlexPDD successfully maintains approximate delay differentiation among service classes proportional to their delay weights, highlighting its potential as a practical solution for achieving advanced service differentiation in modern network infrastructures. © 2024 IEEE.

关键词： Packet networks

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Escalator passenger posture anomaly detection based on improved SSD model 12

Escalator passenger posture anomaly detection based on impro...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Xu, Teng Chen, Jian Li, Zuoyong Cai, Yuanzheng Fujian University of Technology School of Electronic Electrical Engineering and Physics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation. © 2022 IEEE.

关键词： Anomaly detection

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ma, Wenqiang Hau, Yaqing Yu, Hua Tong, Xiangrong Zhu, Zexuan Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350308365

Deep neural networks are powerful and popular learning models;however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem. © 2024 IEEE.

关键词： Optimization algorithms

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Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

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computers, Materials & Continua 2023年第12期77卷 3095-3117页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of ***,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social *** bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation ***,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes ***,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their *** the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability *** process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based ***,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node ***,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关键词： Temporal social networks influence maximization voting strategy interactive properties self-similarity

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Analysis of the spatiotemporal trends and influencing factors of Hyphantria cunea in China

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Journal of Forestry Research 2024年第6期35卷 294-305页

作者： Hongwei Zhou Chaoqun Gong Xiaodong Li Yue Wang Yunbo Yan College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040People’s Republic of China Key Laboratory of National Forestry and Grassland Administration On Forest and Grassland Pest Monitoring and Warning Center for Biological Disaster Prevention and ControlNational Forestry and Grassland AdministrationShenyang 110034People’s Republic of China

In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the spa-tial distribution trends and influencing factors of *** analysis involved integrating administrative division and boundary data,distribution data of ***,and envi-ronmental variables for *** and gravity analysis techniques were employed for data processing and *** results show that *** exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern *** these years,the focal point of the infestation was in Shandong Province with a spread towards the *** such as high vegetation density in eastern China provided favorable situations for growth and development of *** China,the spatial distribution of the moth is primarily influenced by two critical factors:precipitation during the driest month and *** play a pivotal role in determining the spread of the *** on these results,suggestions are provided for a mul-tifaceted approach to prevention and control of *** infestation.

关键词： Hyphantria cunea Temporal trends GeoDetector spatial analysis Spatial heterogeneity

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Performance analysis of UAV-based mixed underwater PLC-RF systems

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Digital Communications and Networks 2024年第5期10卷 1332-1340页

作者： Shenghai Chen Liang Yang Xianfu Lei College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua418000China School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031China

In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)*** this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC *** assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician *** this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was ***,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.

关键词： ABER Average bit-error-rate ACC Average channel capacity OP Outage probability UAV Unmanned aerial vehicle UPLC Underwater power line communication

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A Positive Influence Maximization Algorithm in Signed Social Networks

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computers, Materials & Continua 2023年第8期76卷 1977-1994页

作者： Wenlong Zhu Yang Huang Shuangshuang Yang Yu Miao Chongyuan Peng College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely *** solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social *** overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)*** algorithm utilizes the ICPD model as well as the PRR set to select *** are two phases in *** is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR *** other is the node selection phase,which uses a greedy coverage algorithm to select optimal ***,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in *** on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

关键词： Positive influencemaximization polar relation information decay reverse influence sampling

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Adaptive Stabilization for Fractional-order System with Nonsymmetrical Dead-zone Input via Backstepping-based Sliding Mode control

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第2期49卷 p574-581页

作者： Tian, Xiaomin Wang, Yizhi Zhang, Yang Ge, Jiaqi Man, Chaoyuan College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Fujian Key Laboratory of Functional Marine Sensing Materials Minjiang University Fuzhou350108 China Fujian Engineering and Research Center of New Chinese Lacquer Materials and Fujian Provincial University Engineering Research Center of Green Materials and Chemical Engineering Minjiang University Fuzhou350108 China

A new backstepping-based sliding mode control strategy to realize the stabilization of a class of fractionalorder system is introduced in this paper. We assume that the system is fluctuated by unmodeled dynamics and external disturbances, meanwhile, the bounds of these uncertainties and the system parameters are unknown in advance. The effect of nonsymmetrical dead-zone input is taken into account in this paper. To deal with these unknown parameters, a sliding mode surface is proposed and some adaptive update laws are assigned. Then the indirect Lyapunov theory is given to analyze the stability of every subsystem. Simulation results are presented to prove the correctness and feasibility of the given control strategy © 2022. IAENG International Journal of computer *** Rights Reserved.

关键词： Sliding mode control

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Intelligent Maintenance Scheduling for 5G Networks with ECA-DenseNet-DQN 3

Intelligent Maintenance Scheduling for 5G Networks with ECA-...

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3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Yang, Mingye Li, Zhen Xu, Chuang Li, Chao International School Beijing University of Posts and Telecommunications Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798331529222

This paper presents an intelligent maintenance scheduling model for 5G networks using a deep reinforcement learning approach, specifically ECA-DenseNet-DQN. The proposed model optimizes the allocation of maintenance personnel by considering both travel and task completion times, aiming to minimize total task duration. Unlike traditional scheduling methods that often disregard skill levels and workloads of personnel, the model integrates Efficient Channel Attention (ECA) mechanisms into DenseNet to enhance feature extraction capabilities, thereby improving scheduling decisions. Simulation experiments demonstrate the model’s superior performance in reducing task completion time and achieving faster convergence compared to existing algorithms. ©2024 IEEE.

关键词： Scheduled maintenance

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