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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

IEEE Transactions on Vehicular technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Secure Communication Using WFRFT-DSSS Based on Chaotic Cyclic Shift

Secure Communication Using WFRFT-DSSS Based on Chaotic Cycli...

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IEEE International Conference on Communications in China (ICCC)

作者： Chenxuan Gao Yichen Wu Yu Zhang Department of Electronic Engineering Tsinghua University Beijing P. R. China Key Laboratory of Digital TV System of Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen P. R. China Beijing National Research Center for Information Science and Technology (BNRist)

Space-air-ground-sea integrated communication is expected to be an important research direction in the future to meet the growing demand for efficiency, reliability and coverage. However, due to the open channel of wireless communication, the security of the communication network is facing great challenge. In this paper, a weighted fractional Fourier transform (WFRFT) direct sequence spread spectrum (DSSS) system based on chaotic cyclic shift is proposed. Specifically, a chaotic sequence is generated to control the cyclic shifts of the original spreading sequence and then the shifted sequences are encrypted through WFRFT before spreading the information bits. Simulation results demonstrate the practicability of chaotic cyclic shifted WFRFT (CCS-WFRFT) spreading sequence and the superior security performance of the proposed system.

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Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning

arXiv

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arXiv 2022年

作者： Wei, Shoulin Li, Yadi Lu, Wei Li, Nan Liang, Bo Dai, Wei Zhang, Zhijian Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Faculty of Science Kunming University of Science and Technology Kunming650500 China

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveysa) © 2022, CC BY.

关键词： Surveys

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EB-YOLO:An efficient and lightweight blood cell detector based on the YOLO algorithm

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Computers in Biology and Medicine 2025年第Pt A期192卷 110288-110288页

作者： Wu, Boyue Feng, Shilun Jiang, Shuyue Luo, Shaobo Zhao, Xi Zhao, Jianlong State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China School of Graduate Study University of Chinese Academy of Sciences Beijing100049 China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China GeneSense Hong Kong Limited Hong Kong Science Park Shatin N.T. HKSAR 999077 Hong Kong School of Microelectronics Shanghai University Shanghai200444 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Shanghai Frontier Innovation Research Institute Shanghai201108 China

Blood cell detection is an important part of medical diagnosis. Object detection is trending for blood cell analysis, with research focusing on high-precision neural network models. However, these models have complex architectures and high computational costs. They cannot achieve rapid detection on low-end devices. Although lightweight models can greatly enhance the detection speed and achieve the real-time detection on low-end devices, their accuracy is poor in complex tasks. The development of efficient and highly accurate blood cell detectors for environments with limited computational resources is of great practical value. This study proposes an Efficient Blood Cell Detector based on YOLO (EB-YOLO) for blood cell detection. The model uses ShuffleNet as the backbone network for feature extraction to reduce the number of parameters and computational load. It incorporates the Convolutional Block Attention Module (CBAM) to enhance feature representation. In the neck network, Adaptive Spatial Feature Fusion (ASFF) is used for feature integration to improve multi-scale target feature extraction. Depth-wise separable convolution replaces standard convolution to reduce parameters while maintaining performance. Experimental results on the BCCD dataset show that the proposed model achieves 92.1 % mAP@50 %, the computational complexity is only 0.9 GFLOPs, and the number of parameters is 0.289M. The comparison results of the inference speed on Raspberry PI 5 show that the detection speed of the model is better than the classic YOLO algorithm model. The proposed method successfully balances lightweight design and high accuracy, which shows promise for deployment on low-end embedded systems. © 2025

关键词： NP-hard

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Gesture Recognition Based on Attention and Dilated Causal Convolution

Gesture Recognition Based on Attention and Dilated Causal Co...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Zhou, Xin Liu, Jiaqing Huai, Yaping Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In recent years, based on surface electromyography (sEMG), great progress has been made in gesture recognition tasks, which is significant to the study of computer interaction and prosthesis control. Prior to this, many researches have focused on feature selection and time series modeling of data. However, it is difficult and complicated to manually perform feature extraction and feature selection on data. Besides, most of the time series modeling is based on recurrent neural networks with memory cell, that is, Long Short-term Memory (LSTM). Even though LSTM has memory gate, but cannot completely records historical information. In this paper, to address the above problems, we propose a Temporal Convolutional Network based on Attention mechanism (ATCN), in which Attention is used to characterize temporal features, and dilated causal convolution to capture more historical information. ATCN achieves 95.26% accuracy with this method on the public dataset NinaPro DB2, which exceeds the state-of-the-art accuracy of 5%. The experiments show that this approach is feasible and effective for gesture recognition tasks. Our contributions are as follows: Firstly, Attention mechanism is introduced into the field of prosthetic control. Then, ATCN is proposed for gesture recognition. Finally, ATCN achieves better results than LSTM on multiple public datasets. © 2022 IEEE.

关键词： Long short-term memory

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Joint Visual and Haptic Signal Transmission for Immersive Interactions in Human Digital Twin

Joint Visual and Haptic Signal Transmission for Immersive In...

引用

Ubiquitous Communication (Ucom), International Conference on

作者： Kun Wu Jiayuan Chen Changyan Yi Zili Liu Xiaoping Lu Junyi Wang Jun Cai College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China COSMOPlat Institute of Industrial Intelligence (Qingdao) Co. Ltd China Key Lab of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China Department of Electrical and Computer Engineering Concordia University Montréal QC Canada

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

Human digital twin (HDT) is envisioned as a system interconnecting physical twins (PTs) in the real world with virtual twins (VTs) in the digital world, enabling advanced human-centric applications. In this paper, we study an optimization of quality of experience (QoE) aware multimodal signal transmission, particularly focusing on joint visual and haptic signal feed-back transmissions from VT to its PT, for providing immersive interactions in HDT. To evaluate a synthesized performance of both visual and haptic experiences, we design a comprehensive QoE model, taking into account the video quality, continuous video quality switching rate and average haptic feedback error. Then, to maximize such QoE with a particular guarantee on synchronization between visual and haptic signal transmissions, we dynamically optimize the bandwidth allocation, bitrate and rendering mode of the video, and haptic signal's compression threshold. To this end, we propose a deep reinforcement learning based algorithm, called VisHap, which can produce an adaptive solution. Furthermore, we build an HDT multimodal interactions platform for collecting an authentic dataset, and by using it, we conduct experiments, showing that VisHap is not only feasible but also outperforms the counterparts.

关键词： Visualization Channel allocation Rendering (computer graphics) Haptic interfaces Digital twins Quality assessment Quality of experience Synchronization Optimization Video recording

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment

Identifying Appropriate Probability Density Function for Win...

引用

Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Ghulam Abbas Arshad Ali Muhammad Zubair Zohaib Mushtaq Umar Farooq Irfan A. Khan Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan Intelligent Systems Lab & Automation Facility (ISLAF) IEECE University of the Punjab Lahore Pakistan Clean and Resilient Energy Systems (CARES) Lab Texas A&M University Galveston TX USA

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures-Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2 )-are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATlab environment is used to compare the distributions and validate the effectiveness of the proposed methodology.

关键词： Wind energy generation Electric potential Wind energy Wind speed Urban areas Sea measurements Wind power generation Probability density function Probability distribution Weibull distribution

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Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

arXiv

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arXiv 2022年

作者： Wang, Xiangmeng Li, Qian Yu, Dianer Cui, Peng Wang, Zhichao Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia The School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia The Department of Computer Science and Technology Tsinghua University Beijing100084 China

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users’ true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbiased representation for users true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantics-aware disentanglement learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantics-aware representations via disentangling users true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemmed from context information, which further to align the semantics-aware representation with users true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model. © 2022, CC BY.

关键词： Semantics

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Nucleophilic hydrolysis of dichloroacetonitrile and trichloroacetonitrile disinfection byproducts by peroxymonosulfate: Kinetics and mechanisms

引用

Chemosphere 2024年 363卷 142875页

作者： Yu, Yangyi Li, Juan Zhou, Junhui Cao, Ying Guo, Qin Liu, Yongze Yang, Yi Jiang, Jin Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education School of Ecology Environment and Resources Guangdong University of Technology Guangzhou510006 China Advanced Interdisciplinary Institute of Environment and Ecology Guangdong Provincial Key Laboratory of Wastewater Information Analysis and Early Warning Beijing Normal University Zhuhai519087 China Beijing Key Lab for Sources Control Technology of Water Pollution Engineering Research Center for Water Pollution Source Control & Eco-remediation College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China University of Science and Technology of China Anhui 230026 China

In this work, it was found that peroxymonosulfate (PMS) could appreciably accelerate the transformation rates of dichloroacetonitrile (DCAN) and trichloracetonitrile (TCAN) in aqueous solutions, especially under alkaline pHs. The impact of reactive oxygen species scavengers (methyl alcohol for sulfate radical, tert-butyl alcohol for hydroxyl radical, and azide for singlet oxygen) and water matrices (chloride (Cl−), bicarbonate (HCO3−), and natural organic matter (NOM)) on DCAN and TCAN transformation by PMS is evaluated, revealing negligible effects. A nucleophilic hydrolysis pathway, as opposed to an oxidation process, was proposed for the transformation of DCAN and TCAN by PMS, supported by the hydrolyzable characteristics of these compounds and validated through density functional theory calculations. Kinetic analysis indicated that the transformation of DCAN and TCAN by PMS adhered to a second-order kinetic law, with higher reaction rates observed at elevated pH levels within the range of 7.0–10.0. Kinetic modeling incorporating the hydrolytic contributions of water, hydroxyl ion, and protonated and deprotonated PMS (i.e., HSO5− and SO52−) effectively fitted the experimental data. Species-specific second-order rate constants reveal that SO52− exhibited significantly higher reactivity towards DCAN ((1.69 ± 0.22) × 104 M−1h−1) and TCAN ((6.06 ± 0.18) × 104 M−1h−1) compared to HSO5− ((2.14 ± 0.12) × 102 M−1h−1) for DCAN;and (1.378 ± 0.11) × 103 M−1h−1 for TCAN). Comparative analysis of DCAN and TCAN transformation efficiencies by four different oxidants indicated that PMS rivaled chlorine but falls short of hydrogen peroxide, with peroxydisulfate displaying negligible reactivity. Overall, this study uncovers the nucleophilic hydrolysis characteristics of PMS, supplementing its recognized role as an oxidant precursor or mild oxidant, and underscores its significant implications for environmental remediation. © 2024 Elsevier Ltd

关键词： Sulfur compounds

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Non-Equilibrium Solidification Complexions in Additive Manufacturing Enable Exceptional Creep Resistance: An Example in Nickel-Based Superalloys

SSRN

引用

SSRN 2024年

作者： Li, Yansong Cui, Luqing Xu, Jinghao Xin, Tongzheng Jiang, Shuang Li, Yao Zhang, Honghong Dang, Xiaofeng Liang, Xiaoqing Gao, Shuang Mu, Yahang Lu, Kaiju Moverare, Johan J. He, Weifeng Shaanxi Xi’an710049 China School of Mechanical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Division of Engineering Materials Department of Management and Engineering Linköping University LinköpingSE-58183 Sweden School of Materials Science and Engineering Dalian University of Technology Dalian116024 China Key Laboratory of Electromagnetic Processing of Materials Ministry of Education Northeastern University Shenyang110819 China School of Materials Science and Engineering Chang’an University Shaanxi Xi’an710064 China School of Mechanical Engineering Xi’an Shiyou University Shaanxi Xi’an710065 China National Key Lab of Aerospace Power System and Plasma Technology Air Force Engineering University Shaanxi Xi’an710038 China School of Material Engineering Shanghai University of Engineering Science Shanghai201620 China Shenyang110016 China Defense Innovation Institute Academy of Military Science Beijing100071 China

Insufficient time-dependent properties at elevated temperatures, particularly creep resistance and ductility, are currently crucial factors impeding the use of additively manufactured Hastelloy X (HX). To address this limitation, a micro-nano olive-shaped carbide network was purposely introduced into HX via laser powder bed fusion (L-PBF) and following optimized heat treatment. The inherent chemical heterogeneity combined with the sufficient stored energy of boundaries, induced by the ultrafast cooling rate of the L-PBF process, creates favorable conditions for the formation of micro-nano precipitation networks. Compared to its untreated counterpart, the optimized HX exhibited considerably improved creep resistance, with an 83% increase in creep life and a 122% improvement in fracture ductility. Furthermore, through multiscale characterization techniques and theoretical calculations, the preferential precipitation behavior of the micro-nano carbide networks was systematically investigated from both kinetic and thermodynamic perspectives. The superior creep resistance of the L-PBF HX, decorated with carbide networks, stems from the synergistic effects of the higher cavity surface energy, greater pinning force for grain boundary sliding, and lower plasticity-assisted diffusion rate, which markedly inhibit the nucleation and growth of microvoids during high-temperature deformations. This work provides a comprehensive understanding of the strengthening mechanisms associated with non-equilibrium solidification-facilitated carbide networks, providing new insights into the targeted design and optimization of L-PBF alloys. © 2024, The Authors. All rights reserved.

关键词： Creep resistance

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