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Reducing congestion and emissions via roadside unit deployment under mixed traffic flow

International Journal of Coal science & Technology 2023年第1期10卷 197-211页

作者： Yuhao Liu Zhibin Chen Siyuan Gong Han Liu Shanghai Key Laboratory of Urban Design and UrbanScience NYU ShanghaiShanghaiChina Division of Engineering and Computer Science NYUShanghaiShanghaiChina The Joint Laboratory for Internet of Vehicles Ministryof Education-China Mobile Communications CorporationXi’anShaanxiChina School of Information Engineering Chang’an UniversityXi’anShaanxiChina Central Research Institute China Coal Technologyand Engineering Group(CCTEG)BeijingChina

It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV *** paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and *** this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is ***,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information *** the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium *** active-set algorithm is presented to solve the deployment problem *** results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.

关键词： Roadside unit deployment Connected autonomous vehicle Information quality Congestion and emission

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An LSTM-based Path Switching Approach for Edge Gateways in Industrial Heterogeneous Networks 4

An LSTM-based Path Switching Approach for Edge Gateways in I...

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4th IEEE International Conference on data science and computer Application, ICDSCA 2024

作者： Chen, Zheyi Du, Dajun Hu, Huosheng Shanghai Key Laboratory of Power Station Automation Technology School of Mechanical Engineering and Automation Shanghai University 200072 China School of Computer Science and Electronic Engineering University of Essex Essex United Kingdom

ISBN: (纸本)9798350368239

Traditional path-switching methods are primarily designed for static or conventional network environments, rendering them inadequate for the dynamic nature of heterogeneous industrial networks. This paper aims to tackle the dual challenges of efficient path switching and accurate traffic prediction in such environments. First, an LSTM-based predictive model is developed to forecast critical network performance metrics, such as delay and load, facilitating proactive path adjustments. Second, a dynamic path-switching strategy is introduced, taking advantage of these predictions to minimize path oscillations and maintain stable network performance under varying traffic conditions. Finally, the performance of the proposed method is evaluated using key metrics, including latency, load distribution, and path stability. Simulation and experimental results confirm the effectiveness of the approach, demonstrating significant improvements in latency reduction, load balance, and overall network efficiency within industrial heterogeneous networks. ©2024 IEEE.

关键词： Gateways (computer networks)

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, shanghai Supercomputing Center, and computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Can Graph Learning Improve Planning in LLM-based Agents? 38

Can Graph Learning Improve Planning in LLM-based Agents?

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Xixi Shen, Yifei Shan, Caihua Song, Kaitao Wang, Siwei Zhang, Bohang Feng, Jiarui Cheng, Hong Chen, Wei Xiong, Yun Li, Dongsheng Fudan University China Microsoft Research Asia China The Chinese University of Hong Kong Hong Kong Peking University China Washington University Saint Louis United States Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural language into solvable sub-tasks, thereby fulfilling the original requests. In this context, the sub-tasks can be naturally viewed as a graph, where the nodes represent the sub-tasks, and the edges denote the dependencies among them. Consequently, task planning is a decision-making problem that involves selecting a connected path or subgraph within the corresponding graph and invoking it. In this paper, we explore graph learning-based methods for task planning, a direction that is orthogonal to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs, which is adeptly addressed by graph neural networks (GNNs). This theoretical insight led us to integrate GNNs with LLMs to enhance overall performance. Extensive experiments demonstrate that GNN-based methods surpass existing solutions even without training, and minimal training can further enhance their performance. The performance gain increases with a larger task graph size. © 2024 Neural information processing systems foundation. All rights reserved.

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A Method of Constructing and Automatically Labeling Radio Frequency Signal Training dataset for UAV 48

A Method of Constructing and Automatically Labeling Radio Fr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Chao Ma, Ruipeng Si, Zheng Chi, Mingmin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Zhengzhou University School of Cyber Science and Engineering China Zhengzhou University Henan Institute of Advanced Technology China

ISBN: (纸本)9781728163277

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%. © 2023 IEEE.

关键词： Object detection

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Few-Shot 3D Point Cloud Segmentation via Relation Consistency-Guided Heterogeneous Prototypes

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IEEE Transactions on Multimedia 2025年 27卷 3158-3170页

作者： Wei, Lili Lang, Congyan Xu, Zheming Liang, Liqian Liu, Jun Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing100044 China Beijing Jiaotong University School of Computer Science & Technology Beijing100044 China Lancaster University LancasterLA1 4YW United Kingdom

Few-shot 3D point cloud semantic segmentation is a challenging task due to the lack of labeled point clouds (support set). To segment unlabeled query point clouds, existing prototype-based methods learn 3D prototypes from point features of the support set and then measure their distances to the query points. However, such homogeneous 3D prototypes are often of low quality because they overlook the valuable heterogeneous information buried in the support set, such as semantic labels and projected 2D depth maps. To address this issue, in this paper, we propose a novel Relation Consistency-guided Heterogeneous Prototype learning framework (RCHP), which improves prototype quality by integrating heterogeneous information using large multi-modal models (e.g. CLIP). RCHP achieves this through two core components: Heterogeneous Prototype Generation module which collaborates with 3D networks and CLIP to generate heterogeneous prototypes, and Heterogeneous Prototype Fusion module which effectively fuses heterogeneous prototypes to obtain high-quality prototypes. Furthermore, to bridge the gap between heterogeneous prototypes, we introduce a Heterogeneous Relation Consistency loss, which transfers more reliable inter-class relations (i.e., inter-prototype relations) from refined prototypes to heterogeneous ones. Extensive experiments conducted on five point cloud segmentation datasets, including four indoor datasets (S3DIS, ScanNet, SceneNN, NYU Depth V2) and one outdoor dataset (Semantic3D), demonstrate the superiority and generalization capability of our method, outperforming state-of-the-art approaches across all datasets. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Enhancing Code Generation Through Retrieval of Cross-Lingual Semantic Graphs

Enhancing Code Generation Through Retrieval of Cross-Lingual...

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Asia-Pacific Conference on Software Engineering

作者： Zhijie Jiang Zejian Shi Xinyu Gao Yun Xiong School of Computer National University of Defense Technology China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

In the field of software engineering automation, code language models have made significant strides in code generation tasks. However, due to the cost of updating knowledge and the issue of hallucinations, code language models (CLMs) face challenges in practical code generation scenarios, making retrieval-augmented code generation a mainstream approach. Existing retrieval-augmented methods only build codebases for a single programming language, which is insufficient to address the lack of monolingual knowledge. To address this, we propose CodeRCSG, a novel cross-lingual retrieval-augmented code generation method. This method constructs a multilingual codebase and creates a unified cross-lingual code semantic graph to capture deep semantic information across different programming languages. By encoding the retrieved code semantic graph with GNN and combining it with input text embeddings, code language models can effectively utilize the transferred cross-lingual programming knowledge to improve the quality of generated code. Experimental results show that CodeRCSG can significantly enhance the code generation capabilities of code language models.

关键词： Knowledge engineering computer languages Codes Costs Semantics Retrieval augmented generation Programming Encoding Multilingual Software engineering

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STAGGER: Enabling All-in-One Subarray Sensing for Efficient Module-level Processing in Open-Bitline ReRAM 24

STAGGER: Enabling All-in-One Subarray Sensing for Efficient ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Chengning Feng, Dan Hu, Yuchong Tong, Wei Liu, Jingning Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage Systems Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706011

Emerging resistive RAM (ReRAM) devices can in-situ execute vector-matrix-multiplication (VMM) and is able to achieve energy-efficient in-memory scientific computing. However, the peripheral separated S&Hs and ADCs for row buffering and sensing in conventional designs are the system bottleneck. We propose an ADC-less all-in-one processing-in-ReRAM design that enables the precharge once, readout multiple-bits (PORM) functionality for overlapping the tRCD latencies of different-significance result bits sensed out on the same bitline. Specifically, to support PORM sensing, we propose a cascaded-feedback bitline sensing architecture for VMM and a buffering-and-sensing-collocated sense amplifier elemental design with the bitline and the storage node fully decoupled for enabling conflict-free column accesses. We further propose cross-level inter-leaving mechanism for successive column VMM accesses to reduce the overall latency through improving the hardware spatiotemporal utilization. Experimental results show that our proposed design achieves 297% overall performance improvement and 85.8% energy reduction, compared with an aggressive baseline. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Feedback amplifiers

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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