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Research Square 2021年

作者： Zhou, Tianbai Zhang, Lingfan Cheng, Jian Wang, Jianming Zhang, Xiaoyu Li, Maoyuan Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China State Key Laboratory of Coal Mining and Clean Utilization Beijing100013 China School of Mechanics and Civil Engineering China University of Mining and Technology Beijing100083 China Institute of Geotechnical Engineering RWTH Aachen University Mies-van-der-Rohe-Str. 1 Aachen52074 Germany Ansteel Beijing Research Institute Beijing102209 China Housing and Urban Rural Development Bureau Sichuan Qionglai City611500 China

Due to long-term mining, a series of high and steep rock slopes have been formed in the open-pit mine. For high rock slopes, rainfall infiltration is the main cause of landslide. Therefore, the stability analysis of high rock slope under rainfall has become a key issue in the open-pit mine engineering. In this work, aiming at the high stress condition of high rock slope, the instantaneous internal friction angle and instantaneous cohesion of rock mass under different stress states are deduced, and the a nonlinear strength reduction method for high rock slope is established according to the relationship between normal stress and shear stress of rock mass under the Hoke-Brown criterion. The numerical calculation results show that the factor of safety (FOS) for high rock slope calculated by the proposed method is more reasonable. Taking the southwest slope of Dagushan Iron Mine as the research background, the safety factors of high rock slope under different rainfall conditions are calculated by COMSOL Multiphysics. And the stability analysis of high rock slope in open-pit mine under rainfall are carried out. © 2021, CC BY.

关键词： Rocks

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Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

arXiv

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arXiv 2020年

作者： Hao, Shijie Zhou, Yuan Guo, Yanrong Hong, Richang Cheng, Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Chinese University of Hong Kong Hong Kong

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768×1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters. The code will be released at https://***/zhouyuan888888/SGCPNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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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

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Adaptive State Continuity-Based Sparse Inverse Covariance Clustering for Multivariate Time Series

Adaptive State Continuity-Based Sparse Inverse Covariance Cl...

引用

International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Lei Li Wei Li Jianxing Liao Xuegang Hu Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

Compared with univariate time series clustering, multivariate time series (MTS) clustering has become a challenging research topic on the data mining of time series. In this paper, a novel model-based approach Adaptive State Continuity-Based Sparse Inverse Covariance Clustering (ASCSICC) is proposed for MTS clustering. Specifically, the state continuity is introduced to make the traditional Gaussian mixture model (GMM) applicable to time series clustering. To prevent overfitting, the alternating direction method of multipliers (ADMM) is applied to optimize the parameter of GMM inverse covariance. In addition, the proposed approach simultaneously segments and clusters the time series. Technically, first, the adaptive state continuity is estimated based on the distance similarity of adjacent time series data. Then, a dynamic programming algorithm of cluster assignment by adaptive state continuity is taken as the E-step, and the ADMM for solving sparse inverse covariance is taken as the M-step. E-step and M-step are combined into an Expectation-Maximization (EM) algorithm to conduct the clustering process. Finally, we show the effectiveness of the proposed approach by comparing the ASC-SICC with several state-of-the-art approaches in experiments on two datasets from real applications.

关键词： Time series analysis Clustering algorithms Covariance matrices Security Pattern analysis Feature extraction Cybernetics

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Global context-aware progressive aggregation network for salient object detection

arXiv

引用

arXiv 2020年

作者： Chen, Zuyao Xu, Qianqian Cong, Runmin Huang, Qingming University of Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing ICT CAS Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Key Lab. of Big Data Mining and Knowledge Management CAS Beijing China Peng Cheng Laboratory Shenzhen Guangdong China

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiplelevel feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Joint Resource Management for Energy-efficient UAV-Assisted SWIPT-MEC: A Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Chen, Yue Kang, Hui Li, Jiahui Sun, Geng Wang, Boxiong Wang, Jiacheng Liang, Cong Liang, Shuang Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore China Mobile Communications Group Jilin Co. Ltd China Northeast Normal University School of Information Science and Technology Changchun130117 China

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms. © 2014 IEEE.

关键词： Resource allocation

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Edge-based Differenital Big data Processing for Sensor-Cloud Systems

Edge-based Differenital Big Data Processing for Sensor-Cloud...

引用

2019 IEEE International Conference on Signal, Information and data Processing, ICSIDP 2019

作者： Mei, Yaxin Wang, Tian Ma, Ying College of Computer Science and Technology Huaqiao University Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Key Laboratory of Data Mining and Intelligent Recommendation Fujian Province University Xiamen China

ISBN: (纸本)9781728123455

The rise of big data processing and storage bring new challenges for privacy protection. More data, especially personal information, being hosted online such as in cloud, which is out of control. In this paper, we propose an edge-based model for big data processing, taking sensor-cloud data as an example, in which the raw data from wireless sensor networks (WSNs) is differentially processed by algorithms on edge servers. A small quantity of core data is stored on edge and local servers while the rest is transmitted to cloud for storage. In this way, the benefits are twofold. First, the data privacy is preserved since the original data cannot be retrieved even if the data stored in cloud is leaked. Second, implemented by a differential storage method, compared to the state of the art, the edge-based model sends less data to the cloud and reduces the cost of communication and storage. Both theoretical analyses and extensive experiments validate our proposed method. © 2019 IEEE.

关键词： data privacy

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Electric Vehicle Charging Control Based on Smart Grid Big data

Electric Vehicle Charging Control Based on Smart Grid Big Da...

引用

2020 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2020

作者： Chang, Kuo-Chi Xu, Ze Chu, Kai-Chun Chang, Fu-Hsiang Wang, Hsiao-Chuan Hsu, Tsui-Lien Lin, Yuh-Chung Zhou, Yuwen North Borneo University College Department of Business Administration Sabah Malaysia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Fujian University of Technology Department of Business Management Fuzhou China Shih-Hsin University Department of Tourism Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

ISBN: (纸本)9781728191461

At the same time, the burning of a large amount of fossil fuels has aggravated environmental pollution and the greenhouse effect. In order to solve the problems of energy shortage and environmental pollution, electric vehicle technology has been highly valued by many countries. Electric vehicles are the most promising new energy vehicles. Therefore, on the basis of fully considering the charging load characteristics of electric vehicles, how to better establish charging facilities and adjust the impact of a large number of random loads on the grid. Therefore, it is of great significance and application value to study the charging strategy and control method of electric vehicles to improve the stable operation level of the power grid and the ability to accept new energy. Here, the frequency modulation control strategy of electric vehicles considering the restriction of electricity price is studied to ensure the regulation ability and charging demand of electric vehicles. © 2020 IEEE.

关键词： Electric vehicles

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A traffic prediction method of bicycle-sharing based on long and short term memory network

Journal of Network Intelligence

引用

Journal of Network Intelligence 2019年第2期4卷 17-29页

作者： Zhang, Shun-Miao Su, Xin Jiang, Xin-Hua Chen, Ming-Long Wu, Tsu-Yang College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications & College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The prediction of bicycle-sharing traffic flow plays an important role in man-agement of the service stations for bicycle-sharing. In this paper, based on an improved long and short term memory network we propose a traffic prediction method of bicycle-sharing. Firstly, the bicycle-sharing area is divided by the probability threshold clustering algorithm. Secondly, based on the long and short term memory network the bicycle-sharing traffic model is established. In order to increase the accuracy of prediction, we optimize the related parameters by differential evolution algorithm. Finally, we adopt the traffic data set of bicycle-sharing in Yancheng city, Jiangsu province, China in our experiment. With compared to the existed methods, the experimental results are shown that our proposed method has a better effectivity and is suitable for the traffic prediction of bicycle-sharing. © 2019, Taiwan Ubiquitous Information. All rights reserevd.

关键词： Optimization

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

引用

IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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