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Towards intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

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IEEE Network 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Xiangjiang Laboratory Changsha410205 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China University of Concordia Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

In intelligent transportation systems (ITSs), integrating pedestrians and vehicles into traffic management models is essential for developing realistic and safe solutions. However, current systems often fail to simulate complex, real-world scenarios due to the absence of a comprehensive digital twin framework across diverse traffic environments and effective modeling of pedestrian-vehicle interactions. In this article, we propose a surveillance video-assisted federated digital twin (SV-FDT) framework to enhance ITSs by incorporating pedestrians and vehicles into the control loop. SV-FDT improves computational efficiency and communication performance by transmitting only semantic data and agent parameters, rather than raw video streams. The proposed framework adopts three-layer architecture and constructs detailed pedestrian-vehicle interaction models using multi-source traffic surveillance videos. The three-layer architecture includes: (i) an end layer that collects surveillance videos from multiple sources;(ii) an edge layer that performs self-supervised semantic segmentation to extract interactions, converts them into executable traffic codes, and generates local digital twin systems (LDTSs) for regional traffic modeling;and (iii) a cloud layer that integrates LDTSs into a real-time global digital twin model. key design considerations, challenges, and practical implementation guidelines are discussed for SV-FDT, and a testbed evaluation is used to show that SV-FDT improves traffic flow, reduces mirroring delay, and enhances recognition accuracy and system efficiency compared to traditional terminal-server frameworks. Finally, we outline open challenges and potential directions for future research in digital twin-enabled ITS. © 1986-2012 IEEE.

关键词： Air traffic control

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Study of Star/Galaxy Classification Based on the XGBoost Algorithm

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Chinese Astronomy and Astrophysics 2019年第4期43卷 539-548页

作者： Chao, Li Wen-hui, Zhang Ji-ming, Lin College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Cognitive Radio and Information Processing the Ministry of Education Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement. © 2019

关键词： Stars: fundamental parameters—galaxies: fundamental parameters—techniques: photometric—methods: data analysis

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Predicting ICU mortality from heterogeneous clinical events with prior medical knowledge 4

Predicting ICU mortality from heterogeneous clinical events ...

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4th International Workshop on Knowledge Discovery in Healthcare data, KDH 2019

作者： Xiao, Lujing Zheng, Chuanpan Fan, Xiaoliang Xie, Yi Yu, Rongshan Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Xiamen China Computer Science Department Xiamen University Xiamen China Digital Fujian Institute of Healthcare and Biomedical Big Data Research Xiamen University Xiamen China

Mortality prediction in Intensive Care Unit (ICU) could assist clinicians to make diagnosis. Many deep learning models have been previously proposed to uncover the high order correlations among heterogeneous clinical events. However, they failed to address the importance of prior medical knowledge from experienced clinicians. In this paper, we propose a novel ICU mortality prediction method called P-BiLSTM, which combines a bidirectional Long Short-Term Memory model with prior medical knowledge of clinicians. In PBiLSTM, we first preprocess general descriptors and heterogeneous temporal events with a sophisticated data completion strategy. After that, we use a weighted block with prior medical knowledge to enhance learning and explainable abilities of deep neural networks. The performance of the proposed method is validated using a real-world dataset containing 37 types of temporal events from 4,000 ICU patients within 48-hour. Experimental results demonstrate that P-BiLSTM outperforms six baseline methods. © 2019 for this paper by its authors.

关键词： Intensive care units

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A Social-Aware Mode Selection Algorithm for D2D Communications

A Social-Aware Mode Selection Algorithm for D2D Communicatio...

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International Conference on Communications, Signal Processing, and Systems, CSPS 2018

作者： Lu, Wei-feng Lin, Wei-jun Chen, Si-guang Yang, Li-jun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China College of IoT Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

ISBN: (纸本)9789811362637

Aiming at mode selection in D2D communication, a mode selection strategy based on social-aware was proposed. In addition to physical connection status, users’ social relationships also served as an observation element. Through relationship between users, the closeness of user relationship can be judged, and then through users’ reference, the similarity between users can be obtained. In this way, by combining physical connection with social relationships, users’ aggregate utility was acquired. According to the physical connection quality and the social relationship strength, taking maximizing users’ aggregate utility as the goal, a random algorithm was designed to match users. Simulation results show that mode selection based on social perception can effectively improve users’ aggregate utility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Aggregates

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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Chinese Control and Decision Conference, CCDC

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Engineering RMIT University Melbourne VIC Australia

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This brief explores the approximation properties of a unique basis expansion based on Pascal's triangle, which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple-step predictions are analyzed. Furthermore, time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Shape Electronic mail Analytical models Mathematical analysis data models Upper bound

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

arXiv

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arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

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Distribution Pattern of Soil Organic Carbon and Its Regional Humification Constant in the Coastal Monsoon Region of Eastern China

Research Square

引用

Research Square 2021年

作者： Liu, Shutian Xie, Xiansheng Wang, Xiaochuan Feng, Xinxin Hou, Xianda Wang, Shuojin Lin, keyu Huang, Mei Jia, Shugang Hou, Yanlin Dou, Sen Guangxi Geographical Indication Crops Research Centre of Big Data Mining and Experimental Engineering Technology Nanning Normal University Nanning530001 China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning530001 China School of Geography and Planning Nanning Normal University Nanning530001 China College of Resource and Environmental Science Jilin Agricultural University Changchun130118 China

Soils are an important pool for storing organic carbon. Soil organic carbon (SOC) content is generally considered as an important indicator to evaluate farmland soil quality. The loss of SOC causes soil degradation and reduces the sustainability of farmland. In order to reveal the distribution pattern of SOC in the coastal monsoon area of eastern China, and to clarify the macro dominant factors of SOC accumulation caused by temperature and precipitation, this paper analyzed the distribution pattern of SOC in the coastal monsoon area of eastern China by using the SOC data collected from the national soil testing and formula fertilization data set, and discussed the effects of temperature and precipitation on SOC content. According to the provincial administrative divisions, the distribution of SOC in the coastal monsoon areas of eastern China from Heilongjiang Province to Hainan Province was calculated. According to the annual average temperature 20.95℃, annual average precipitation 0~400 mm, 400~800 mm, > 800 mm, the study area was divided into different regions, and the effects of annual average temperature and annual average precipitation on SOC content were studied. In the region with annual average temperature less than 10.18℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC;In the region of 10.18℃~20.95℃, the annual average temperature and annual average precipitation had a significant positive correlation, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC;In the region >20.95℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature was not related to the accumulation of SOC. In the range of 0 ~ 400 mm of annual average precipitation, the temperature had a positive correlation with the accumulation

关键词： Temperature

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Multi-Granularity Power Prediction for data Center Operations via Long Short-Term Memory Network

Multi-Granularity Power Prediction for Data Center Operation...

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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Ziyu Shen Xusheng Zhang Bin Xia Zheng Liu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets.

关键词： Power demand data centers Task analysis Predictive models data models Time series analysis Servers

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