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International Conference on big data and Smart Computing (bigCOMP)

作者： Zhouyi Sun Siang Gao Bingqing Liu Yufei Wang Tong Yang Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application (PKU) China Department of Computer Science Peking University China International School Beijing University of Posts and Telecommunications China

Given d sets without intersection, the multi-set membership query problem is to decide which set an incoming item e belongs to. Multi-set membership query is a fundamental problem in a variety of fields of computer science, especially computer networking. Although several approaches have been proposed in the literature to address this problem, they cannot achieve a high processing speed, a high accuracy, and a small memory usage at the same time. In this paper, we propose a novel data structure, namely Magic Cube Bloom Filter (MCBF), which outperforms the state-of-the-art in terms of accuracy and query speed with a limited memory usage. The key idea of the Magic Cube Bloom filter is that items from the same set are stored in different Bloom filters, improving the accuracy by the redistribution of items. The MC-BF also improves the query speed by utilizing spatial locality. Experimental results show that the MC-BF outperforms the state-of-the-art BUFFALO by up to 148.5 times in terms of accuracy, and up to 3.16 times in terms of query speed. We have made the source code of our MC-BF available on Github [1].

关键词： Random access memory Hash functions Servers Memory management Switches Arrays

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An anomaly detection-based dynamic OD prediction framework for urban networks

An anomaly detection-based dynamic OD prediction framework f...

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2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020

作者： Liu, Jing Zheng, Fangfang Zuylen, Henk Van Li, Jie Luo, Jie Southwest Jiaotong University School of Transportation and Logistics 611756 China Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology 611756 China Delft University of Technology Department of Transport and Planning Delft Netherlands Hunan University Civil Engineering College Changsha410082 China

ISBN: (数字)9781728195032

ISBN: (纸本)9781728195032

The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results. © 2020 IEEE.

关键词： Forecasting

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Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

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International Journal of Agricultural and Biological engineering 2024年第4期17卷 265-275页

作者： Weixin Zhai Zhi Xu Jinming Liu Xiya Xiong Jiawen Pan Sun-Ok Chung Dionysis Bochtis Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Department of Smart Agricultural Systems Graduate SchoolChungnam National UniversityDaejeon 34134Republic of Korea Institute for Bio-Economy and Agri-Technology(iBO) Centre for Research and Technology-Hellas(CERTH)Thessaloniki 57001Greece

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory *** the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was ***,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory ***,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature ***,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory *** validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural *** the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.

关键词： road-field trajectory classification efficientNet feature deformation network multi-range feature enhancement agricultural machinery operation mode recognition

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Research on recognition algorithm for gesture page turning based on wireless sensing

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Intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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ICSTrace: A Malicious IP Traceback Model for Attacking data of Industrial Control System

arXiv

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arXiv 2019年

作者： Xiao, Feng Xu, Qiang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Electronic Engineering Institute of Hefei China

Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new services. The model extracts the function codes and their parameters from the attack data according to the format of industrial control protocol, and employs a short sequence probability method to transform the function codes and their parameter into a vector, which characterizes the attack pattern of malicious IP addresses. Furthermore, a Partial Seeded K-Means algorithm is proposed for the pattern’s clustering, which helps in tracing the attacks back to an organization. ICSTrace is evaluated basing on the attack data captured by the large-scale deployed honeypots for industrial control system, and the results demonstrate that ICSTrace is effective on malicious IP traceback in industrial control system. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

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Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay 5

Bayesian Optimization of Double-SAC Robotic Arm Path Plannin...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Yuan, Shuai Wen, Ke Zhang, Feng Xiong, Jianqiang Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center School of Computer Science and Engineering Shengyang110168 China Shenyang Jianzhu University School of Electrical and Control Engineering Shengyang110168 China

ISBN: (纸本)9798350307320

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance. © 2023 IEEE.

关键词： Motion planning

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of engineering and technology. All rights reserved.

关键词： Genetic algorithms

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Path Tracking Control for Connected Automated Vehicle Using Lateral Stability Envelope at Handling Limits

Path Tracking Control for Connected Automated Vehicle Using ...

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International Conference on Intelligent Transportation

作者： Linbin Chen Yafei Liu Zhanbo Sun Lei Yang National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu China

To improve the lateral stability and accuracy of path tracking control for connected automated vehicle (CAV) at handling limit conditions, the paper develops a model predictive control (MPC) method ultizing a lateral stability envelope. Specifically, the lateral stability envelope for vehicle state variables (i.e., sideslip angle and yaw rate) is derived by analyzing dynamic changes in the limit slip angle of the front wheel. This envelope varies with the steering angle and is incorporated into the proposed MPC controller as a constraint, which can guarantee lateral stability at handling limits. To further enhance the tracking accuracy, a reference for the yaw rate is derived by backstepping control considering the lateral displacement error. This reference is formulated as the objective for the MPC controller to regulate tracking errors. Simulation experiments demonstrate that the proposed controller can decrease the yaw rate and steering angle fluctuations in path tracking with low road adhesion compared with the nominal controller.

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Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms

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Journal of Geriatric Cardiology 2022年第6期19卷 445-455页

作者： Hu XU Wen-Zhe CAO Yong-Yi BAI Jing DONG He-Bin CHE Po BAI Jian-Dong WANG Feng CAO Li FAN Chinese PLA Medical School Chinese PLA General HospitalBeijingChina Department of Cardiology the Second Medical CenterNational Clinical Research Center for Geriatric DiseasesChinese PLA General HospitalBeijingChina Department of General Surgery the First Medical CenterChinese PLA General HospitalBeijingChina Institute of Geriatrics the Second Medical CenterChinese PLA General HospitalBeijingChina Medical Big Data Research Center&National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijingChina Department of Respiratory Diseases Chinese PLA Rocket Force Characteristic Medical CenterBeijingChina

OBJECTIVE To establish a prediction model of coronary heart disease(CHD)in elderly patients with diabetes mellitus(DM)based on machine learning(ML)*** Based on the Medical big data Research Centre of Chinese PLA General Hospital in Beijing,China,we identified a cohort of elderly inpatients(≥60 years),including 10,533 patients with DM complicated with CHD and 12,634 patients with DM without CHD,from January 2008 to December *** collected demographic characteristics and clinical *** selecting the important features,we established five ML models,including extreme gradient boosting(XGBoost),random forest(RF),decision tree(DT),adaptive boosting(Adaboost)and logistic regression(LR).We compared the receiver operating characteristic curves,area under the curve(AUC)and other relevant parameters of different models and determined the optimal classification *** model was then applied to 7447 elderly patients with DM admitted from January 2018 to December 2019 to further validate the performance of the *** Fifteen features were selected and included in the ML *** classification precision in the test set of the XGBoost,RF,DT,Adaboost and LR models was 0.778,0.789,0.753,0.750 and 0.689,respectively;and the AUCs of the subjects were 0.851,0.845,0.823,0.833 and 0.731,*** the XGBoost model with optimal performance to a newly recruited dataset for validation,the diagnostic sensitivity,specificity,precision,and AUC were 0.792,0.808,0.748 and 0.880,*** The XGBoost model established in the present study had certain predictive value for elderly patients with DM complicated with CHD.

关键词： patients admitted coronary

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Mass Transfer during Hematitization and Implications for Uranium Mineralization in the Zoujiashan Deposit,Xiangshan Volcanic Basin

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Journal of Earth Science 2022年第2期33卷 422-434页

作者： Teng Deng Guoxiang Chi Xiongjie Zhang Zenghua Li Deru Xu Shengmiao Li Pengfei Du Pei Shang Shaohao Zou Wanpeng Zhou Ke Xu Hai Yan MaWen Zhengpeng Ding State Key Laboratory of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China Key Laboratory of Uranium Geology China Geological SurveyTianjin 300170China School of Earth Sciences East China University of TechnologyNanchang 330013China Department of Geology University of ReginaRegina S4S 0A2Canada Engineering Research Center of Nuclear Technology Application Ministry of EducationEast China University of TechnologyNanchang 330013China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of TechnologyNanchang330013China Geological Bureau of Hunan Province Changsha 410014China Geologic Party No.208 China National Nuclear CorporationBaotou 410014China

The Zoujiashan uranium deposit in the Xiangshan ore field is the largest volcanic-related uranium deposit in ***-and fluorite-type ores are the predominant mineralization *** in the Xiangshan ore field is closely associated with uranium mineralization,mainly occurring as hematitized rocks enclosing fluorite-type vein ores developed in pre-ore illitized porphyritic *** petrographic and mass balance calculation studies were conducted to evaluate the mechanisms for uranium precipitation and mass transfer during *** observations suggest that in the hematitized rocks,orthoclase is more altered than plagioclase,and quartz dissolution is common,whereas in the illitized rocks,pyrite commonly occurs within the altered biotite grains,and chlorite grains are locally *** balance calculations indicate that Na2O and U were gained,K2O,Ca O and Si O2were lost,whereas Fe2O3-t remained more or less constant during *** observations suggest that the hydrothermal fluids were Na-and U-rich and Ca-K-poor,and the Fe2+used for hematitization was locally derived,most likely from biotite,pyrite and chlorite in the host *** Fe2+is inferred to have played the role of reductant to precipitate uranium,and calculation indicates that oxidation of Fe2+provided by host rocks is sufficient to form ores of economic ***,the hematite-type ore is interpreted to be generated by the reaction between oxidized ore fluids and reduced components in host *** development of calcite and pyrite in the fluorite ores suggests that perhaps mixing between the U-rich fluid and another fluid carrying reduced sulfur and carbon may have also contributed to uranium mineralization,in addition to temperature and pressure drop associated with the veining.

关键词： Xiangshan uranium ore field mass transfer uranium precipitation hematitization redox reaction ore deposit geology

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