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Solving the Vehicle Cooperation Problem at Signal-free Intersection via an Asynchronous Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Shuai Ding, Yuhao Ding, Xiaoqi Tan, Xiaojun Shenzhen Campus of Sun Yat-sen Univerity School of Intelligent Systems Engineering Shenzhen518107 China Guangzhou Campus of Sun Yat-sen Univerity School of Computer Science and Engineering Guangzhou510000 China

With the rapid development of modern intelligent transportation systems, connected and automated vehicles (CAVs) have garnered significant attention due to their advanced communication and decision-making capabilities. Intelligent collaborative decision-making in dynamic traffic scenarios poses significant challenges in the research of CAV technology. Strategies and methods have been developed to tackle the collaboration problem towards different scenarios. However, most existing studies have not been fully investigated the performance optimization and practicability of the vehicle collaboration at signal-free intersections. Meanwhile, the urban signal-free intersections represent a critical application scenario for vehicle cooperation, where a thorough study has not been given yet. Therefore, this study intends to solve the vehicle collaboration problem utilizing the deep reinforcement learning approach. Initially, the problem is formulated as an elaborated Markov decision process, comprising the state space, the action space, and the reward function. Then, a shared Advantage Actor-Critic (A2C) model is proposed to effectively extract temporal and spatial features through the shared network, thereby improving the consistency of feature learning processes between the Actor and Critic networks. Furthermore, the asynchronous training strategy employed in this study involves multiple training processes concurrently, thereby enhancing the model's convergence speed and stability. Finally, the effectiveness of the proposed method is verified in two typical intersection scenarios. Simulation results reveal that our method exhibits competitive performance compared with existing approaches, and an improvement up to 30% and 40% can be achieved in the retreat time and the averaged delay time. Additionally, the field experiments have been conducted on a miniaturized autonomous driving platform, verifying the considerable potential of the proposed method for real-world applicati

关键词： Deep reinforcement learning

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Optimizing Traffic Flow and Pedestrian Safety: The Role of Crosswalk Distance in Roundabout Design

Optimizing Traffic Flow and Pedestrian Safety: The Role of C...

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Global Aeronautical engineering and Satellite Technology (GAST), International Conference on

作者： Ayoub Charef Zahi Jarir Mohamed Quafafou LAMIGEP Research Laboratory EMSI Moroccan School of Engineering Sciences Marrakech Morocco Laboratory of Computer Systems Engineering (LISI) Cadi Ayyad University Marrakech Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (数字)9798350371598

ISBN: (纸本)9798350371604

This research investigates the impact of pedestrian crossings on the speed characteristics and capacity of roundabouts, with a primary focus on enhancing traffic flow. Unsignalized intersections and roundabouts conventionally grant pedestrians priority, which can substantially affect operational efficiency. The paper underscores the vital significance of strategically placing crosswalks to minimize disruptions to vehicular movement and prevent *** study collected data from roundabouts located in Marrakech, Morocco, utilizing mobile devices to record vehicle and pedestrian counts, as well as their passage times. In addition to field data, Vissim simulations were employed to improve precision. Our research specifically delves into how pedestrians influence the capacity of exit lanes at *** central objective is to ascertain the optimal spacing between roundabouts and crosswalks to maximize their capacity. The findings provide valuable insights for urban planning and traffic management, addressing the intricate relationship between pedestrian flow, crosswalk placement, and the overall efficiency of roundabouts.

关键词： Pedestrians Satellites Urban planning Oral communication Mobile handsets Data models Safety

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Three-Step Carrier Frequency Offset Estimation Algorithm for Burst-Mode Communications*

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Chinese Journal of Electronics 2023年第1期19卷 165-169页

作者： Wu Luo An Liu Qinglin Liang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronic Engineering and Computer Science Peking University Beijing China

A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

关键词： Frequency modulation Simulation Symbols Estimation Frequency estimation Signal to noise ratio

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Blood Cell Classification and Counting in Hematology through Deep Learning

Blood Cell Classification and Counting in Hematology through...

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Sustainable Expert systems (ICSES), International Conference on

作者： P. Ananthi K Nirmala Devi T. E. Ramya P. Shanmugapriya T. Aradhya Department of Computer Technology Kongu Engineering College Perundurai Erode Tamil Nadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Perundurai Erode Tamil Nadu

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Analytical models Accuracy Computational modeling Cells (biology) Real-time systems Reliability Object recognition Medical diagnosis Blood Medical diagnostic imaging

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Learnable Ophthalmology SAM

arXiv

引用

arXiv 2023年

作者： Qiu, Zhongxi Hu, Yan Li, Heng Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Segmentation is vital for ophthalmology image analysis. But its various modal images hinder most of the existing segmentation algorithms applications, as they rely on training based on a large number of labels or hold weak generalization ability. Based on Segment Anything (SAM), we propose a simple but effective learnable prompt layer suitable for multiple target segmentation in ophthalmology multimodal images, named Learnable Ophthalmology Segment Anything (SAM). The learnable prompt layer learns medical prior knowledge from each transformer layer. During training, we only train the prompt layer and task head based on a one-shot mechanism. We demonstrate the effectiveness of our thought based on four medical segmentation tasks based on nine publicly available datasets. Moreover, we only provide a new improvement thought for applying the existing fundamental CV models in the medical field. Our codes are available at website. © 2023, CC BY-NC-SA.

关键词： Ophthalmology

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Big Data for National Security in the Era of COVID-19 21st

Big Data for National Security in the Era of COVID-19

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21st International Conference on Computational science, ICCS 2021

作者： Cárdenas, Pedro Obara, Boguslaw Ivrissimtzis, Ioannis Kureshi, Ibad Theodoropoulos, Georgios Department of Computer Science Durham University Durham United Kingdom Inlecom Systems Brussels United Kingdom Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783030779603

The COVID-19 epidemic has changed the world dramatically as societies adjust their behaviour to meet the challenges and uncertainties of the new normal. These uncertainties have led to instabilities in several facets of society, most notably health, economy and public order. Increasing discontent within societies in response to government mandated measures to contain the pandemic have triggered social unrest, imposing serious threats to national security. Big Data Analytics can provide a powerful force multiplier to support policy and decision makers to contain the virus while at the same time dealing with such threats to national security. This paper presents the utilisation of a big data forecasting and analytics framework to deal with COVID-19 triggered social unrest. The framework is applied and demonstrated in two different disruptive incidents in the United States of America. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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Modeling the effect of hatched grounds on the radio-frequency performance of microstrip lines on flexible substrate

Modeling the effect of hatched grounds on the radio-frequenc...

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作者： Abdelatty, Mohamed Y. Umar, Ashraf Obeidat, Abdullah S. Enakerakpo, Emuobosan Alhendi, Mohammed Poliks, Mark D. Dept. of Systems Science and Industrial Engineering State University of New York Binghamton United States Dept. of Electrical and Computer Engineering State University of New York Binghamton United States

Cross-hatching of PCB grounds refers to a process in which certain ground planes appear as copper lattices;regular openings are placed at regular intervals. Nowadays, the efficacy of ground hatching on rigid PCBs is minimal. However, it is becoming essential for flexible hybrid electronics. Utilizing hatched grounds for flexible applications could offer some benefits. Hatched grounds can perform dual roles, act as ground surfaces, and offer structural support. Hatched grounds are more durable for bending and stretching applications. Also, hatched grounds are desirable in terms of material conservation. In this paper, we focus on studying how hatched grounds affect the RF performance of a straight microstrip line. Simulations show that cross-hatched grounds with more than 50% filling produces good radio-frequency performance. We will study the effect hatching pattern as well as the density of the hatching on the RF performance of the microstrips. The theoretical modeling is considered in this work and fabricating aerosol jet silver ink microstrip lines on PET substrates will be considered as an extension of this work. © 2022 IMAPS-International Microelectronics and Packaging Society. All rights reserved.

关键词： Microstrip lines

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A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1918-1932页

作者： Zhuoxuan Li Iakov Korovin Xinli Shi Sergey Gorbachev Nadezhda Gorbacheva Wei Huang Jinde Cao School of Mathematics Southeast UniversityNanjing 210096China Scientific Research Institute of Multiprocessor Computer Systems Southern Federal UniversityTaganrog 347928Russia School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Russian Academy of Engineering Moscow 125009Russia Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China Nanjing Modern Multimodal Transportation Laboratory Nanjing 211100China Yonsei Frontier Laboratory Yonsei UniversitySeoulKorea(South)

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

关键词： Extreme learning machine algorithm with residual correction(RELM) metaheuristic optimization oil-gas transportation RIOHTrack,rutting depth

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