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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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A data representation method using distance correlation

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Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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A frequency and two-hop configuration checking-driven local search algorithm for the minimum weakly connected dominating set problem

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Neural Computing and Applications 2024年第22期36卷 13833-13852页

作者： Li, Ruizhi He, Jintao Lin, Cuisong Liu, Ying Hu, Shuli Yin, Minghao Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

The minimum weakly connected dominating set problem is a typical NP-hard problem with a wide range of applications. To solve this problem, we propose a frequency property and two-hop configuration checking strategy-driven local search algorithm (FCC2LS). In this algorithm, we first propose a lock-vertex-based initial solution construction procedure. This procedure guarantees that certain vertices, which must be included in the optimal solution, are added to the solution. Second, we propose a two-hop configuration checking strategy and a frequency property. The two-hop configuration checking strategy is a new variant of the original configuration checking strategy, which prohibits vertices with unchanged configuration from being added to the candidate solution. The frequency property is used to record the number of times for each vertex is added to the solution, which can increase the diversity of selected vertices. Third, we combine two scoring functions, Dscore and Nscore, with the above strategies and propose effective vertex selection methods to help the algorithm select suitable vertices to add into or remove from the candidate solutions. Finally, we evaluate the proposed algorithm FCC2LS with four state-of-the-art algorithms on four groups of benchmark instances. Experimental results show that our algorithm performs better on four classical benchmark instances. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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MacroTrend: A Write-Efficient Cache Algorithm for NVM-Based Read Cache

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Journal of Computer Science & Technology 2022年第1期37卷 207-230页

作者： Ning Bao Yun-Peng Chai Xiao Qin Chuan-Wen Wang Key Laboratory of Data Engineering and Knowledge Engineering Ministry of EducationBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China Samuel Ginn College of Engineering Auburn UniversityAlabama 36830U.S.A.

The future storage systems are expected to contain a wide variety of storage media and layers due to the rapid development of NVM(non-volatile memory)*** NVM-based read caches,many kinds of NVM devices cannot stand frequent data updates due to limited write endurance or high energy consumption of ***,traditional cache algorithms have to update cached blocks frequently because it is difficult for them to predict long-term popularity according to such limited information about data blocks,such as only a single value or a queue that reflects frequency or *** this paper,we propose a new MacroTrend(macroscopic trend)prediction method to discover long-term hot blocks through blocks'macro trends illustrated by their access count *** then a new cache replacement algorithm is designed based on the MacroTrend prediction to greatly reduce the write amount while improving the hit *** conduct extensive experiments driven by a series of real-world traces and find that compared with LRU,MacroTrend can reduce the write amounts of NVM cache devices significantly with similar hit ratios,leading to longer NVM lifetime or less energy consumption.

关键词： non-volatile memory(NVM) solid state disk(SSD) cache endurance

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Real-time Detection of Illegally Parked Vehicles in Roadside Parking Areas Based on Intelligent Video Terminals 24

Real-time Detection of Illegally Parked Vehicles in Roadside...

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2024 International Conference on Image Processing, Intelligent Control and Computer engineering, IPICE 2024

作者： Tang, Kang Sun, Yu Zhong, Xiaoyang Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University China

ISBN: (纸本)9798400710285

With the development of artificial intelligence technology, urban traffic management has become increasingly convenient, and the task of illegal parking detection has become a major research focus. Currently, most illegal parking detection schemes use fixed-point cameras, which not only waste resources but also have the limitation of a small detection range. To overcome this issue, we have designed a highly mobile system for detecting illegally parked vehicles. Considering the different relative positions of the tires and parking lines in roadside parking areas, we use an optimized You Only Look Once(YOLOv5) algorithm to classify the tires and determine whether the vehicle is illegally parked based on different category of tires combinations. Subsequently, we applied the above-mentioned illegal parking detection strategy to a embedded device which mounted on the mobile vehicle to realize the real-time mobile detection and information collection of illegal vehicles in the roadside parking area. © 2024 Copyright held by the owner/author(s).

关键词： Tires

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Recognition of tea sprouts based on improved deeplabV3 + model

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Multimedia Tools and Applications 2025年 1-18页

作者： Liu, Ruixin Wang, Dengzhun Chen, Zhonghui Li, Zhilin Xiao, Hui Yan, Chunyu Yan, Jianwei Lu, Ping Xie, Benliang College of Big Data and Information Engineering Guizhou University Guiyang550025 China Power Semiconductor Device Reliability Engineering Center of the Ministry of Education Guiyang550025 China College of Mechanical Engineering Guizhou University Guiyang550025 China State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education Guizhou University Guiyang550025 China

The recognition of tea sprouts is the premise of realize the intelligence of the premium tea picking. DeepLabV3 +, as the latest semantic segmentation algorithm of DeepLab family, can well recognize the tea sprouts in images. However, the model still has the problem of large number of parameters, and there is still room for improvements in segmentation accuracy and speed. To solve the multi-label recognition and segmentation of background, single bud, one leaf and two leaves in the tea sprout images, an improved deep convolutional neural network algorithm is proposed. This method realizes the recognition of different postures of high-quality tea sprouts on the premise of efficiency and accuracy. The DMResNet18 model is used as the feature extraction network to lightweight the network model and increase the segmentation speed. The feature joint attention network is added to optimize the spatial weights and channel weights of the model, which improves the accuracy of segmentation. Additionally, the low-level feature fusion method is applied to obtain rich initial global information of features and improves the overall performance of the model. The results show that the segmentation accuracy of improved DeepLabV3 + model reaches 92.58%, which is improved by 4.26% compared with the original DeepLabV3 + model. The model size and the speed of detection are improved by 80.67% and 52.24%, respectively. The proposed method offers a solution for the intelligent recognition of tea sprouts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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Computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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A level set model with shape prior constraint for intervertebral disc MRI image segmentation

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Multimedia Tools and Applications 2025年第9期84卷 6755-6783页

作者： Tian, Zhuangzhou Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Accurate intervertebral disc image segmentation is necessary for further treatment. However, existing methods are difficult to segment due to the intensity inhomogeneity of intervertebral disc MRI images and the similarity in intensity between the intervertebral disc and the surrounding regions. This paper proposes a novel level set model by introducing a shape prior constraint for automatic and accurate segmentation. We utilize a detection model and intensity change to obtain rectangle prior information, which is used to define a shape constraint term. Then, combining data and regularizing terms construct an energy function and employ the steepest descent method minimization to get segmentation results. Finally, we apply the proposed new method to segment intervertebral disc images and get promising results. Extensive experiments and comparisons show that our model is more efficient and accurate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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DeciLS-PBO: an effective local search method for pseudo-Boolean optimization

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Frontiers of Computer Science 2024年第5期18卷 239-241页

作者： Luyu JIANG Dantong OUYANG Qi ZHANG Liming ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education) Jilin UniversityChangchun 130012China

1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromi... 详细信息

关键词： Max optimization local

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Ongoing Compressional Tectonism and Regional Seismic Hazard Revealed by the 2023 Mw6.1 Jishishan Earthquake

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Journal of Earth Science 2025年第1期36卷 275-290页

作者： Xiaoning Hu Chen Yu Zhenjiang Liu Yingying Zhang Zhenhong Li Chuang Song Bingquan Han Haihui Liu Jie Li College of Geological Engineering and Geomatics Chang'an UniversityXi'an 710054China Key Laboratory of Loess Xi'an 710054China Big Data Center for Geosciences and Satellites Chang'an UniversityXi'an 710054China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of EducationXi'an 710054China China Earthquake Networks Center Beijing 100045China

On December 18,2023,a Mw6.1 earthquake struck Jishishan County,Gansu Province,china,marking the most significant earthquake in the northeastern edge of the Tibetan Plateau since *** its proximate to the Loess Plateau,which is extremely susceptible to geohazards,this earthquake raises awareness about the seismic hazard of several mega-cities such as Xi'an in Northwest *** this paper,we inferred that the rupture occurred on an east-dipping backthrust,resulting from the regional E-W contraction tectonic *** dynamic model through teleseismic waves and static model through radar displacement measurements together reveal a unilateral,along-strike rupture,encountering a slip barrier at one side of the main slip patch causing a cluster of *** also identified a high-dip structure,which is an early-stage backthrust fault whose dip becomes increasingly high due to regional compressional *** from the loaded fault segments,particularly on the fault linkage,which necessitate continuous examination,a detailed seismic hazard assessment of the west Qinling and Daotanghe-Linxia fault system identifies a seismic gap between Weiyuan and Dingxi with the potential for a Mw7.5 ***,these findings provide valuable insights into the seismic behavior of the seismogenic fault as well as guidance on hazard mitigation in its surrounding fault systems.

关键词： 2023 Jishishan Earthquake dynamic rupture process coseismic displacements seismic hazard assessment fault slips mechanics

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