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2nd International Workshop on High Performance Computing for Advanced modeling and simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and 1st International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018 held at the 32nd ACM International Conference on Supercomputing, ACM ICS 2018

作者： Cheng, Xiaolan Zhou, Xin Jiang, Congfeng Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase in cloud data centers for data analytics. The ubiquitous and pervasive demand for near-data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conduct to reveal the state-of-the-art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services (QoS), and Quality of Experiences (QoE) are surveyed. Resource scheduling approaches, gaming and tradeoffing among system performance and system overheads for offloading decision making are also reviewed. © Springer Nature Singapore Pte Ltd 2019.

关键词： computation offloading

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Railway Passenger Flow Forecasting Based on Time Series Analysis with Big Data

Railway Passenger Flow Forecasting Based on Time Series Anal...

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第30届中国控制与决策会议

作者： Xiangqian Xu Yajie Dou Zhexuan Zhou Tianjun Liao Yanjing Lu Yuejin Tan College of Systems Engineering National University of Defense Technology State Key Laboratory of Complex System Simulation Beijing Institute of System Engineering

With the rapid development of high-speed railway in China, the research of railway passenger flow forecasting has become a key research direction. The forecast of railway passenger flow can help formulate a reasonable price, improve the organization of passenger terminals, optimize the allocation of railway vehicles resources and improve the service capability of passenger transport equipment, which are of great significance to improve the efficiency of railway passenger transport. In this study, a comprehensive forecasting model based on time series analysis is proposed for railway passenger flow forecasting. To solve problems which cannot be handled with traditional programming model in the context of big data, time series analysis is introduced into the solution. Railway Passenger Flow Forecasting model based on Time Series Analysis is established with the combination of the long-term trend factor, the seasonal factor and the weather factor. Railway passenger flow data obtained from the Railway Bureau are used for the case study. The change rule of passenger flow was researched under different conditions, the railway passenger flow in the next two weeks was forecast, and the corresponding optimization of vehicle configuration and station docking scheme are proposed. Sensitivity analysis shows good stability and robustness of the model.

关键词： Time-Series Forecasting Big Data modeling

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Efficiency Evaluation Model Based on Belief Rule Base with Insufficient Data

Efficiency Evaluation Model Based on Belief Rule Base with I...

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第30届中国控制与决策会议

作者： Leilei Chang Zhipeng Sun Runfan Han Tianjun Liao Zhaojun Mao School of Combatant Logistics High-tech Institute of Xi'an Beijing Institute of System Engineering State Key Laboratory of Complex System Simulation

National defense is very important since it has great influence on the stabilization of a nation and the welfare of its people. To accomplish the mission and tasks of national defense, the weapon systems need to be in its best conditions and be most effective. However, it is rather difficult to directly construct an effectiveness evaluation model from a national defense level since there are multiple influential factors that are interacted and can not be analytically integrated in one single function. Moreover, there are insufficient data which makes it more difficult for the modeling and training approaches. The Armored system of systems(ASoS) is used as the combatant background. Moreover, the belief Rule Base(BRB) expert system with a training approach is applied to construct the effectiveness model. The proposed approach is validated by a Missile Armored system of system(M-ASoS) case study.

关键词： Efficiency Evaluation Model Belief Rule Base Insufficient Data

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Effect of Different Migration Strategies on Evolution of Cooperation in Spatial Prisoner's Dilemma Game

Effect of Different Migration Strategies on Evolution of Coo...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Hong Ding Xiangyu Chen Benyun Shi Yanming Ye Yizhi Ren School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China Key Lab. of Complex Syst. Modeling & Simulation Dianzi Univ. Hangzhou China

Many migration strategies have been proposed forPrisoner's Dilemma game on square lattice, such as randommigration, success-driven migration and adaptive migration. Inthis paper, we introduce three novel migration strategies: failure driven migration, hate-driven migration and global-scope migration. Based on the length of migration, individual with one of the first two strategies decides when and where to move into empty nearby places based on its performance in current site, while individual with a global-scope migration can move to anywhere. Then we compare them with the cases of no migration, random migration and success-driven migration. The simulations show that the failure-driven migration has the best performance in most circumstances. While defectors dominate in success-driven migration cases.

关键词： High definition video Big data

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Fine-grained image recognition via weakly supervised click data guided bilinear CNN model

Fine-grained image recognition via weakly supervised click d...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guangjian Zheng Min Tan Jun Yu Qing Wu Jianping Fan Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Department of Computer Science University of North Carolina at Charlotte

ISBN: (纸本)9781509060689

Bilinear convolutional neural networks (BCNN) model, the state-of-the-art in fine-grained image recognition, fails in distinguishing the categories with subtle visual differences. We design a novel BCNN model guided by user click data (C-BCNN) to improve the performance via capturing both the visual and semantical content in images. Specially, to deal with the heavy noise in large-scale click data, we propose a weakly supervised learning approach to learn the C-BCNN, namely W-C-BCNN. It can automatically weight the training images based on their reliability. Extensive experiments are conducted on the public Clickture-Dog dataset. It shows that: (1) integrating CNN with click feature largely improves the performance; (2) both the click data and visual consistency can help to model image reliability. Moreover, the method can be easily customized to medical image recognition. Our model performs much better than conventional BCNN models on both the Clickture-Dog and medical image dataset.

关键词： Reliability Visualization Image recognition Data models Training Feature extraction Computational modeling

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A GNSS Spoofing Detection Method based on Sparse Decomposition Technique

A GNSS Spoofing Detection Method based on Sparse Decompositi...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： He, Yuxin Zhuang, Yaqiang Zhuang, Xuebin Lin, Zijian School of System Science and Engineering Sun Yat-sen University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing Key Laboratory of Millimeter Wave and Terahertz Technology Beijing China Science and Technology On Complex Aviation Systems Simulation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

By broadcasting false Global Navigation Satellite system (GNSS) signals, spoofing attacks will induce false position and time fixes within the victim receiver. In this article, we propose a Sparse Decomposition (SD)-based spoofing detection algorithm in the acquisition process, which can be applied in a single-antenna receiver. In the first step, we map the Fast Fourier transform (FFT)-based acquisition result in a two-dimensional matrix, which is a distorted autocorrelation function when the receiver is under spoof attack. In the second step, the distorted function is decomposed into two main autocorrelation function components of different code phases. The corresponding elements of the result vector of the SD are the code-phase values of the spoofed and the authentic signals. Numerical simulation results show that the proposed method can not only outcome spoofing detection result, but provide reliable estimations of the code phase delay of the spoof attack. © 2022 IEEE.

关键词： Fast Fourier transforms

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Decadal climate variability during the pre-industrial Common Era: characteristics and mechanisms

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Science Bulletin 2025年

作者： Ning, Liang Liu, Zhengyu Mann, Michael E. Liu, Jian Yan, Mi Sun, Weiyi Chen, Kefan Qin, Yanmin Wen, Qin State Key Laboratory of Climate System Prediction and Risk Management Nanjing210023 China Key Laboratory for Virtual Geographic Environment Ministry of Education Nanjing210023 China State Key Laboratory Cultivation Base of Geographical Environment Evolution and Regional Response of Jiangsu Province Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China School of Geography Nanjing Normal University Nanjing210023 China Department of Geography The Ohio State University ColumbusOH43210 United States Department of Earth and Environmental Science Annenberg School for Communication University of Pennsylvania PhiladelphiaPA19104 United States Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical Science Nanjing Normal University Nanjing210023 China

In this article, we present a comprehensive review of decadal to multi-decadal climate variability during the Common Era (CE), focusing on their characteristics and mechanisms. We begin by summarizing recent advances in proxy reconstructions that reveal the paleo-evidence of decadal to multi-decadal climate variability during the CE. Decadal to multi-decadal variability has been observed in extensive sets of proxy records in the CE. Despite improvements in proxy records in the type, temporal resolution, and temporal coverage, there remains a lack of clear consistency in the preferred time scales and phases of the variability among different records. The agreements of decadal characteristics between proxy records and model simulations are higher during the periods with strong external forcings, but lower during periods of weak external forcing. We subsequently describe the recent modeling studies on the influences of external forcings and internal variability on decadal to multi-decadal climate variability with associated physical mechanisms, and some emerging research topics. Despite the improved understanding of climate variability and regional climate changes, especially over the eastern Asia summer monsoon region, several inconsistencies still exist, such as the amplitudes of responses to external forcings and relative contributions from external forcings and internal variability. The review ends with perspectives for future directions to reconcile discrepancies of decadal climate variability, such as applications of paleoclimate data assimilation and isotope-enabled transient climate modeling, and implications for projecting future decadal to multi-decadal climate changes and for improving the accuracy of decadal predictions. © 2025 Science China Press

关键词： Global warming

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Attribution of PM_(2.5) exposure in Beijing-Tianjin-Hebei region to emissions:implication to control strategies

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Science Bulletin 2017年第13期62卷 957-964页

作者： Xin Li Qiang Zhang Yang Zhang Lin Zhang Yuxuan Wang Qianqian Zhang Meng Li Yixuan Zheng Guannan Geng Timothy J.Wallington Weijian Han Wei Shen Kebin He Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University Department of Marine Earthand Atmospheric SciencesNorth Carolina State University Collaborative Innovation Center for Regional Environmental Quality Laboratory for Climate and Ocean-Atmosphere Sciences Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking University Department of Marine Sciences Texas A&M University at Galveston Department of Atmospheric Sciences Texas A&M UniversityCollege Station National Satellite Meteorological Center China Meteorological Administration Research and Advanced Engineering Ford Motor Company Asia Pacific Research Ford Motor Company State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua University

The Beijing-Tianjin-Hebei （BTH） region is one of the most heavily polluted regions in China, with both high PM2.5 concentrations and a high population density. A quantitative source-receptor relationship can provide valuable insights that can inform effective emission control strategies. Both source appor- tionment （SA） and source sensitivity （SS） can provide such information from different perspectives. In this study, both methods are applied in northern China to identify the most significant emission cate- gories and source regions for PMz5 exposure in BTH in 2013. Despite their differences, both models show similar distribution patterns for population and simulated PM2.5 concentrations, resulting in overall high PM2.5 exposure values （approximately 110 Ixg/m3） and particularly high exposure values during the win- ter （approximately 200 ktg/m3）. Both methods show that local emissions play a dominant role （70%）, with some contribution from surrounding provinces （e.g., Shandong） via regional transport. The two methods also agree on the priority of local emission controls： both identify industrial, residential, and agricultural emissions as the top three categories that should be controlled locally. In addition, the effect of control- ling agricultural ammonia emissions is approximately doubled when the co-benefits of reducing nitrate are considered. The synthesis of SA and SS for addressing specific categories of emissions provides a quantitative basis for the development of emission control strategies and policies for controlling PM2.5 in China.

关键词： PM2.5 exposureSource apportionmentSource sensitivityControl priorityChina

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Touching PBSData-An exploratory study of visual analytics for Public Bicycle system supporting Multi-touch device

Touching PBSData-An exploratory study of visual analytics fo...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Xiaoying Shi Zhenhai Yu Xinyu Qu Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Zhejiang Institute of Communications

Public bicycle sharing services are becoming popular over the past decade. The behavior of using public bicycle system(PBS) produces numerous hire records involving citizens' movement. This paper presents a visual analytics system-Touching PBSData, to help both traffic administrators and ordinary citizens visually explore and understand PBS data on multi-touch devices. Tasks and design principles are put forward firstly. Four views are designed to show different aspects of the dataset, including the geographical distribution, the spatio-temporal pattern and personal relevant information. In order to enhance the flexibility and usability of system, the organization mode of views and rich user interaction suitable for multi-touch device are further studied. By adopting real PBS dataset, the results of case studies certify that our method is able to help analyzers to find stations' main function and citizens' travel habits. Moreover, the feedback from the participants with various backgrounds is very affirmative through the user study.

关键词： traffic system management public bicycle system multi-touch device human mobility analysis visual analytics

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NWD-YOLOv5: A YOLOv5 Model for Small Target Detection Based on NWD Loss 6

NWD-YOLOv5: A YOLOv5 Model for Small Target Detection Based ...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhou, Liliang Ran, Huaming Xiong, Rongling Tan, Ruijie National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China 10th Research Institute China Electronics Technology Group Corporation Chengdu610036 China School of Mathematics Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798331541699

Target detection technology is vital in modern social management and urban security, with small target detection—accurately identifying tiny objects in large scenes—posing a key challenge for improving surveillance system accuracy and real-time performance. The YOLO series is still the mainstream target detection model, for the YOLOv5 model, as the IoU metric it adopts still uses the geometrical overlap of the detection frame as the basis of the loss calculation, while the experiments have proved that this metric is unable to effectively utilize the small-scale characteristics of small targets. Therefore, improving the loss metric in the existing framework can help the model to pay more attention to the spatial semantic features of small targets in the image in the training phase of the network, respectively, and thus improve the detection effect. In this paper, we propose the NWDYOLOv5 model, i.e., we introduce the NWD loss that measures the difference in the distribution of the detection frames for the existing target detection framework YOLOv5, which ensures that the loss of the small targets is not neglected in the calculation of the forward propagation loss of the model, and we purposefully modify the structure of the feature extraction network in the original framework by adding a feature extraction layer for the detection of the small targets, which enhances the ability of the model to capture the subtle features thus improving the accuracy of small target detection. Through comparative experiments, we evaluated the detection performance of the original YOLOv5 model, the YOLOv5 model using only the NWD loss, the YOLOv5 model with only the additional small-target detection layer, and the full NWD-YOLOv5 model for small-targeted human bodies on the screened COCO2017 public dataset, respectively. The experimental results show that compared to the original YOLOv5 model, the model with only the NWD loss improves the mAP@.5 metrics on small-target detection by 16.5

关键词： Digital elevation model

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