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A Switch Architecture for Time-Triggered Transmission with Best-Effort Delivery

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arXiv 2023年

作者： Li, Zonghui Zhu, Wenlin Shin, Kang G. Wan, Hai Song, Xiaoyu Yang, Dong Ai, Bo The Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2121 United States The Software School Tsinghua University Beijing100084 China The Department of Electrical and Computer Engineering Portland State University PortlandOR United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

In Time-Triggered (TT) or time-sensitive networks, the transmission of a TT frame is required to be scheduled at a precise time instant for industrial distributed real-time control systems. Other (or best-effort (BE)) frames are forwarded in a BE manner. Under this scheduling strategy, the transmission of a TT frame must wait until its scheduled instant even if it could have been transmitted sooner. On the other hand, BE frames are transmitted whenever possible but may miss deadlines or may even be dropped due to congestion. As a result, TT transmission and BE delivery are incompatible with each other. To remedy this incompatibility, we propose a synergistic switch architecture (SWA) for TT transmission with BE delivery to dynamically improve the end-to-end (e2e) latency of TT frames by opportunistically exploiting BE delivery. Given a TT frame, the SWA generates and transmits a cloned copy with BE delivery. The first frame arriving at the receiver device is delivered with a configured jitter and the other copy ignored. So, the SWA achieves shorter latency and controllable jitter, the best of both worlds. We have implemented SWA using FPGAs in an industry-strength TT switches and used four test scenarios to demonstrate SWA's improvements of e2e latency and controllable jitter over the state-of-the-art TT transmission scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

Applications of Game Theory in Vehicular Networks: A Survey

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu 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 Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging

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arXiv 2023年

作者： Cai, Qingrui Zhu, Liuhong Zhou, Jianjun Qian, Chen Guo, Di Qu, Xiaobo National Integrated Circuit Industry Education Integration Innovation Platform School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361102 China Fudan University Xiamen361015 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

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arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies

Study of the High-tech Process Mechanical Integrity and Electrical Safety

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Study of the High-tech Process Mechanical Integrity and Elec...

International Microsystems, Packaging, Assembly and Circuits Technology (IMPACT)

作者： Kuo-Chi Chang Kai-Chun Chu Tsou-Li Chen Yueh-Lung Ward Lee Yuh-Chung Lin Trong-The Nguyen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Administration Group of Strategic Management of National Central University Taoyuan City 320 Taiwan Department of Information Technology Applications of National Quemoy University Quemoy 89250 Taiwan Management science and Engineering of University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (数字)9781728160702

ISBN: (纸本)9781728160719

The mechanical integrity of the process machine is one of the provisions of the High-Hazard Chemical Process Safety Management Act promulgated by the US federal government. This study gradually reviewed the integrity of each process machine and factory supply system to improve the safety level. The primary focus is to understand the critical equipment and its related hazard characteristics, this must be cut from the process machine, from the power supply system, monitoring system, gas or chemical supply systems, reactors etc. to understand their possibilities of high temperature, leakage poisoning, corrosion, fire, explosion, electric shock, coiling and other hazards, and finally obtain the results of risk assessment for management use in high-tech factory. However, this study explores the focus of the safety and mechanical integrity of the process equipment, from identifying the critical equipment and hazard characteristics applicable to the equipment integrity plan of the future machine to the well-written mechanical integrity insurance plan, which can be used in practice.

关键词： Fires Chemicals Production facilities Maintenance engineering Explosions

Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

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arXiv 2024年

作者： Song, Ziying Liu, Lin Jia, Feiyang Luo, Yadan Jia, Caiyan Zhang, Guoxin Yang, Lei Wang, Li School of Computer Science & Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Information Technology and Electrical Engineering The University of Queensland St LuciaQLD4072 Australia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Intelligent Green Vehicle and Mobility The School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China

In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning. The key step to this system is related to 3D object detection that utilizes vehicle-mounted sensors such as LiDAR and cameras to identify the size, the category, and the location of nearby objects. Despite the surge in 3D object detection methods aimed at enhancing detection precision and efficiency, there is a gap in the literature that systematically examines their resilience against environmental variations, noise, and weather changes. This study emphasizes the importance of robustness, alongside accuracy and latency, in evaluating perception systems under practical scenarios. Our work presents an extensive survey of camera-only, LiDAR-only, and multimodal 3D object detection algorithms, thoroughly evaluating their trade-off between accuracy, latency, and robustness, particularly on datasets like KITTI-C and nuScenes-C to ensure fair comparisons. Among these, multi-modal 3D detection approaches exhibit superior robustness, and a novel taxonomy is introduced to reorganize the literature for enhanced clarity. This survey aims to offer a more practical perspective on the current capabilities and the constraints of 3D object detection algorithms in real-world applications, thus steering future research towards robustness-centric advancements. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

Multi-View Cost-Sensitive Kernel Learning for Imbalanced Classification Problem

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SSRN

SSRN 2023年

作者： Tang, Jingjing Hou, Zhaojie Yu, Xiaotong Fu, Saiji Tian, Yingjie School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Big Data Southwestern University of Finance and Economics Chengdu611130 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

Multi-view imbalanced learning concentrates on recognizing valuable patterns from multi-view imbalanced data. There exist numerous algorithm-level multi-view imbalanced learning methods including multi-view ensemble learning methods and multi-view cost-sensitive learning methods. However, these approaches have two drawbacks. Firstly, from the multi-view representation perspective, either ignore the agreement among different views, or fail to fully exploit the complementarity information contained in multi-view data. Secondly, from the class imbalanced perspective, multi-view ensemble learning first needs to design a specific data pre-processing scheme to generate views and then fuse the local and global information with multi-view ensemble schemes. So such models are closely related to practical problems and lacks universality. In contrast, multi-view cost-sensitive learning methods possess concise formulations. But the success of such methods depends on imposing appropriate misclassification cost to the majority and minority classes. Inspired by elegant merits of asymmetric LINEX loss function, we propose a multi-view costsensitive kernel learning method with LINEX loss termed as MVCSKL. It not only fully exploits the consensus and complementary information of multiple views, but also adaptively learns and adjusts the misclassification cost of different classes through the asymmetric LINEX loss function. Besides, the introduction of the kernel function enables the MVCSKL model to handle complex nonlinear problem and further guarantees the prediction performance of the model. Furthermore, we adopt the alternating direction method of multipliers and gradient descent algorithm to solve MVCSKL. Based on the Rademacher complexity, we analyze the generalization capability of MVCSKL. Extensive experiments are conducted to verify the effectiveness of the proposed model. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

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ACM Transactions on knowledge Discovery from data 2019年第4期13卷 42-42页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen 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 College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning