检索结果-内蒙古大学图书馆

CMRM: Collaborative Multi-agent Reinforcement Learning for Multi-objective Traffic Signal Control

IEEE Transactions on Consumer Electronics 2025年

作者： Nie, Lei Qi, Dandan Liu, Bingyi Li, Peng Bao, Haizhou He, Heng Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks. © 1975-2011 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Full Field-of-View and Speckleless Dmd Holographic Near-Eye Display Based on Band-Limit Double-Step Diffraction with Binary Iteration Optimization

SSRN

引用

SSRN 2023年

作者： Ding, Xian Chang, Chenliang Dai, Bo Wang, Qi Zhang, Dawei Zhuang, Songlin School of Optical-Electrical and Computer Engineering Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Laboratory of Modern Optics System University of Shanghai for Science and Technology Shanghai200093 China

Digital Micromirror Devices, owing to their rapid refresh rates, are among the most commonly used spatial light modulators in holographic three-dimensional near-eye displays. However, the modulation of Digital Micromirror Devices is typically confined to binary amplitude modulation, resulting in a noticeable presence of zero-order and conjugate noise, which significantly occupies the spatial bandwidth of the display optics and limits the achievable maximum field of view of the spatial light modulator. To address these issues, we propose a novel computational framework for generating binary computer-generated holograms for Digital Micromirror Device-based holographic near-eye displays. Our framework employs an iterative-based optimization strategy within a band-limited double-step diffraction computation, thereby enhancing the display quality while achieving a considerable field of view by eliminating zero-order wave and conjugate noise. By combining with temporal multiplexing method, we also achieve a true three-dimensional holographic near-eye display featuring low speckle noise and high contrast. © 2023, The Authors. All rights reserved.

关键词： Holographic displays

来源：评论

学校读者我要写书评

暂无评论

Two-Sided Online Task Assignment Based on Worker Portraits in Mobile CrowdSensing

Two-Sided Online Task Assignment Based on Worker Portraits i...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Zhenyang Mao Peng Li Guangzhong Liao Lei Nie Haizhou Bao Qin Liu School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China School of Cyber Science and Engineering Wuhan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Task assignment is a challenging problem in mobile crowdsensing (MCS), especially since workers and tasks are online. Existing work does not consider the portrait of the workers when assigning tasks, which may result in workers being assigned to fields they are not familiar with, thus affecting the quality of task completion. In this paper, we focus on online scenarios and identify a more practical task assignment problem, a two-sided (workers and tasks) online task assignment problem based on worker portrait in MCS. We decompose this problem into two subproblems: the worker portrait analysis problem (WPA) and the two-sided online personalized assignment problem (TOPA). To solve the WPA problem, we propose a worker portrait analysis algorithm that uses the semi-supervised model to describe the worker portrait at a fine-grained level. Then, based on the worker portrait, we propose a two-sided online personalized assignment algorithm to solve the TOPA problem. The proposed algorithm guarantees a lower bound on the assignment results by analyzing the worker portrait data. Moreover, we prove the TOPA problem is NP-hard and demonstrate the competitive ratio can achieve ln(max(u i,j )+1). Finally, we conduct extensive experiments on two datasets, and the experimental results show that our method outperforms baseline algorithms.

关键词： Analytical models Crowdsensing Federated learning Task analysis Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection

Towards Effective and Efficient Error Handling Code Fuzzing ...

引用

IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kang Chen Ming Wen Haoxiang Jia Rongxin Wu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology (HRUST) Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Jin YinHu Laboratory Wuhan China School of Informatics Xiamen University Xiamen China School of Computer Science and Technology HUST Wuhan China Cluster and Grid Computing Lab

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL- F I are effective that contribute significantly to its overall performance.

关键词： Codes computer bugs Fuzzing Software systems Software reliability Testing

来源：评论

学校读者我要写书评

暂无评论

A Node Confidence Determination Based Moving Target Defense Strategy for SDN

A Node Confidence Determination Based Moving Target Defense ...

引用

International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Shimin Sun Guowei Xu Li Han Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

With the rapid development of Internet technology, network assets have become an indispensable part of modern society. Especially in the face of high-value network assets, the attacker is faced with paying a greater cost to study the network structure. Due to the unknown attacker and attack means, the attacker has an advantage in the attack and defense situation. The moving target defense technique can flexibly change the static characteristics of the network attack surface and increase the difficulty for the attacker to detect the network, thus achieving the purpose of enhancing network security. Based on this technique, this paper proposes a defense strategy based on node confidence. It puts forward stricter constraints on the differential selection of address/port hopping paths, dynamically selects the hopping objects according to the node’s confidence, and implements the defense strategy of differential complexity. This further increases the network’s uncertainty and the difficulty for the attacker to detect the network structure. The experimental results show that in the path differentiation comparison, the maximum improvement is about 18% compared to the Dynamic Random Route Hopping strategy. In the CPU load experiment, the CPU load rate is reduced by about 4.625% on average compared to the Double Address Hopping strategy. Moreover, for larger hopping period, the proposed strategy also performs well in terms of CPU load rate. The proposed strategy also exhibits good performance in terms of CPU load ratio for large hopping cycles.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An intelligent guided-airborne virus detection 21

An intelligent guided-airborne virus detection

引用

21st IEEE International Conference on sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Hadded, Ahmed Ayed, Mossaad Ben Alshaya, Shaya A. National Engineering School of Sfax Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Electronic Industrial ENISo Sousse University Tunisia ENIS Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Majmaah University Faculty of Sciences and Humanities Sciences Department of Computer Science Saudi Arabia Prince Sultan University Dean of Digital Transformation and E-transactions Majmaah11952 Saudi Arabia

ISBN: (纸本)9781665482615

Health has recently faced many challenges, including improving a healthy environment and reducing human life's dangers and economic crises. The last pandemic COVID-19 had badly affected survivor sectors with infection and lockdown exigence. Scientists proposed several solutions to reduce the negative impact of a such pandemic by proposing systems for earlier detection of viruses. The use of metamaterials as an emerging technology in the biosensors field allows a high accuracy. This paper presents a method for detecting and capturing airborne viruses using metasurface technology. The goal is to develop a system that can identify and capture these viruses using FET sensors. The accuracy of the detection is tied to the concentration of aerosols. The model proposes a guided flow of aerosols that positively impacts the detection of viruses through the FET biosensor. The simulation results based on Concentration and airflow velocity delays prove the proposed model's performance. © 2022 IEEE.

关键词： Biosensors

来源：评论

学校读者我要写书评

暂无评论

UAV Swarm Path Planning for Sensor Data Collection via Double Pre-Partitioned Deep Q-Network

引用

IEEE Sensors Journal 2025年

作者： Zhang, Heng Gao, Yuan Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Yang, Chaoqun Cao, Xianghui Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Southeast University School of Automation Nanjing210096 China

Collision-free path planning is a significant challenge in deploying unmanned aerial vehicle (UAV) swarms for sensor data collection tasks, particularly in uncertain environments. This paper addresses the problem of designing flight strategies for energy-constrained UAV swarms using deep reinforcement learning. We propose a locally-aware double pre-partitioned deep Q-network (DPPDQN) for flight planning of multiple UAVs in large-scale uncertain industrial environments. This algorithm aims to collect data from ground sensors with communication constraints while avoiding mutual collisions. In the proposed algorithm, we first introduce a node pre-partitioning scheme using an improved clustering algorithm, which efficiently reduces the number of access points for UAVs through effective data aggregation. Subsequently, we employ an area pre-partitioning algorithm that segments the detection area into several subregions based on the number of UAVs available for operation, thereby preventing collisions among the UAV swarm during task execution. Finally, we propose a deep Q-Network algorithm incorporating a convolutional neural network, which utilizes the local perceptions of UAVs as state inputs to address the 'curse of dimensionality"occurring in large-scale environments. systematic simulation experiments validate the effectiveness and advantages of the proposed method. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Method for Removing Complex Visible Watermarks of Electronic Map Based on Conditional Generative Adversarial Nets 29

A Method for Removing Complex Visible Watermarks of Electron...

引用

29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Baode, Jiang Shaofen, Xu Liufeng, Tao Zhong, Xie National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China State Key Laboratory of Geo-information Engineering Xi'an710054 China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

With the rapid development and extensive application of the electronic map technology, complex visible watermarks have been widely used in e-maps and map tiles delivered by map service providers to protect copyright. To evaluate the robustness and effectiveness of visible watermarks, many researches on watermark removal have attracted much attention in recent years. However, most of the existing watermark removal methods have the problems of watermark residue or repaired area being indistinct and unreal. To address these difficulties, this paper presented a deep neural network model based on conditional generative adversarial networks (cGANs) to remove complex visible watermark for electronic map tiles. The model is mainly consisted of a generator and a discriminator. The generator adopts a U-Net structure with jump connections to generate watermark-free maps based on watermarked maps, and the discriminator uses a full convolutional network based on region discrimination model to distinguish the differences between the generated watermark-free maps and real watermark-free ones. Specifically, a perceptual loss function was introduced to enhance the structural details of the generated maps. The experiments show that the proposed method can remove not only simple textual watermarks, but also colorful graphic watermarks with complex structures as well, and it does not need manual intervention and can achieve fast batch removal of a wide variety of watermarks for electronic maps. In addition, the detailed information contained in map tiles can be effectively preserved while acquiring high-quality watermark-free electronic map tiles. © 2022 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

PIR: Remote Sensing Image-Text Retrieval with Prior Instruction Representation Learning

arXiv

引用

arXiv 2024年

作者： Pan, Jiancheng Ma, Muyuan Ma, Qing Bai, Cong Chen, Shengyong The Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing10084 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China The College of Science Zhejiang University of Technology Hangzhou310023 China The School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Remote sensing image-text retrieval constitutes a foundational aspect of remote sensing interpretation tasks, facilitating the alignment of vision and language representations. This paper introduces a prior instruction representation (PIR) learning paradigm that draws on prior knowledge to instruct adaptive learning of vision and text representations. Based on PIR, a domain-adapted remote sensing image-text retrieval framework PIR-ITR is designed to address semantic noise issues in vision-language understanding tasks. However, with massive additional data for pre-training the vision-language foundation model, remote sensing image-text retrieval is further developed into an open-domain retrieval task. Continuing with the above, we propose PIR-CLIP, a domain-specific CLIP-based framework for remote sensing image-text retrieval, to address semantic noise in remote sensing vision-language representations and further improve open-domain retrieval performance. In vision representation, we utilize the prior-guided knowledge of the remote sensing scene recognition by building a belief matrix to select key features for reducing the impact of semantic noise. In text representation, we use the previous time step to cyclically activate the current time step to enhance text representation capability. A cluster-wise Affiliation Loss (AL) is proposed to constrain the inter-classes and to reduce the semantic confusion zones in the common subspace. Comprehensive experiments demonstrate that PIR could enhance vision and text representations and outperform the state-of-the-art methods of closed-domain and open-domain retrieval on two benchmark datasets, RSICD and RSITMD. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Improving QoS of Workloads with CPU Pinning: A Deep Reinforcement Learning Approach

Improving QoS of Workloads with CPU Pinning: A Deep Reinforc...

引用

International Workshop on Quality of Service

作者： Xi Tao Weipeng Cao Yinghui Pan Ye Liu Zhong Ming Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In cloud computing, tenants tend to select predefined flavors with substantial resources that go beyond their needs, in order to avoid resource starvation. As a result, cloud service providers experience a high allocation ratio with a low utilization ratio of resources. To improve the resource utilization, cloud service providers offer lower-priced resources by building an overcommitted environment to reuse resources. However, the colocation interference of workloads in the overcommitted environment may cause performance anomalies and degrade quality of service (QoS). In this paper, we propose a real-time mechanism to pin a group of workloads to a certain subset of CPUs to increase resource utilization and minimize interference. To be specific, we leverage deep reinforcement learning (DRL) to automatically adjust the CPU pinning configuration in the overcommitted environment according to the performance of workloads.

关键词： Cloud computing Computational modeling Buildings Quality of service Interference Deep reinforcement learning Real-time systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：