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Robust and Trustworthy data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

Computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy *** distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high *** address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage *** architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and *** on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw *** off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data *** provide a unified access interface for user-friendly system *** testing validates the system’s reliability and stable *** proposed approach significantly enhances storage capacity compared to standalone blockchain *** reliability tests consistently yield positive *** average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

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HyperCalm Sketch: One-Pass Mining Periodic Batches in data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

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Deep learning approaches in flow visualization

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Advances in Aerodynamics 2022年第1期4卷 327-340页

作者： Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Key Laboratory of Machine Perception(Ministry of Education) and School of AIPeking UniversityBeijingChina China Aerodynamics Research and Development Center MianyangChina National Engineering Laboratory for Big Data Analysis and Application Peking UniversityBeijingChina

With the development of deep learning(DL)techniques,many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL *** reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these *** also analyzed the prospects of the development of flow visualization with the help of deep learning.

关键词： Deep learning Flow visualization data management Feature extraction Particle tracing

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Efect of Pre‑stretch Strain at High Temperatures on the Formability of AZ31 Magnesium Alloy Sheets

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Acta Metallurgica Sinica(English Letters) 2023年第1期36卷 48-60页

作者： Yongqiao Li Lifei Wang Xiaohuan Pan Qiang Zhang Guangsheng Huang Bin Xing Weili Cheng Hongxia Wang Kwang Seon Shin College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China Chongqing Innovation Center of Industrial Big-Data Co.Ltd. National Engineering Laboratory for Industrial Big-Data Application TechnologyChongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National UniversitySeoul 881416Republic of Korea

High-temperature pre-stretching experiments were carried out on the AZ31 Mg alloy at 723 K with strain levels of 2.54%,6.48%,10.92%,and 19.2%to alter the microstructure and texture for improving room-temperature *** results showed that the strain-hardening coefcient increased,while the Lankford value *** addition,the Erichsen values of all pre-stretch sheets were enhanced compared with that of the as-received *** maximum Erichsen value increased from 2.38 mm for the as-received sample to 4.03 mm for the 10.92%-stretched sample,corresponding to an improvement of 69.32%.This improvement was mainly attributed to the gradual increase in grain size,and the(0001)basal texture was weakened due to the activated non-basal slip as the high-temperature pre-stretching strain levels *** visco-plastic self-consistent analysis was performed on the as-received and high-temperature pre-stretched *** confrmed the higher activity of the prismatic slip in 10.92%-stretched sample,leading to divergence and weakening of basal texture *** results in an augmentation of the Schmid factor under diferent slip ***,it can be concluded that high-temperature pre-stretching technology provided an efective method to enhance the formability of Mg alloy sheets.

关键词： High-temperature deformation Grain growth Magnesium alloys Texture Visco-plastic self-consistent modeling Formability

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Identification and mapping of soybean and maize crops based on Sentinel-2 data

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International Journal of Agricultural and Biological engineering 2020年第6期13卷 171-182页

作者： Bao She Yuying Yang Zhigen Zhao Linsheng Huang Dong Liang Dongyan Zhang School of Geomatics Anhui University of Science&TechnologyHuainan 232001AnhuiChina National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China

Soybean and maize are important raw materials for the production of food and livestock *** mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage *** this study,two towns in Guoyang County,Anhui Province,China,were selected as the study area,and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing *** data obtained on August 18(early pod-setting stage of soybean)was determined to be the most applicable to soybean and maize mapping by means of the Jeffries-Matusita(JM)***,three machine-learning algorithms,i.e.,random forest(RF),support vector machine(SVM)and back-propagation neural network(BPNN)were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth *** appeared that RF with a Kappa of 0.83 was superior to the other two ***,twenty candidate features containing the reflectance of ten spectral bands(spatial resolution at 10 m or 20 m)and ten remote-sensing indices were input into the RF algorithm to conduct an important *** features were screened out and served as the optimum subset,the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle(UAV)images covering six ground *** optimum feature-subset achieved high-accuracy crop mapping,with a reduction of data volume by 65%compared with the total twenty features,which also overrode the performance of ten spectral ***,feature-optimization had great potential in the identification of the two ***,the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.

关键词： soybean and maize crop identification Sentinel-2 data machine learning feature selection

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Dynamic train dwell time forecasting:a hybrid approach to address the influence of passenger flow fluctuations

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Railway engineering Science 2023年第4期31卷 351-369页

作者： Zishuai Pang Liwen Wang Shengjie Wang Li Li Qiyuan Peng School of Transportation and Logistics Southwest Jiaotong UniversityChengdu 610031China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong UniversityChengdu 610031China

Train timetables and operations are defined by the train running time in sections,dwell time at stations,and headways between *** estimation of these factors is essential to decision-making for train delay reduction,train dispatching,and station capacity *** the present study,we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem(e.g.,dynamics over time,heavy-tailed distribution of data,and spatiotemporal relationships of factors)for real-time train *** averaging mechanism in the present study is based on multiple state-of-the-art base predictors,enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data ***,considering the influence of passenger flow on train dwell time,we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations(e.g.,passenger soars in peak hours or passenger plunges during regular periods).We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway *** results show that due to the advantages over the base predictors,the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and ***,the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes,i.e.,15.4%and 15.5%corresponding to the mean absolute error and root mean square error,*** on the proposed predictor,a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell ***,planned time has positive influen

关键词： Train operations Dwell time Passenger flow Averaging mechanism Dynamic smoothing

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Immobilized ZnO based nanostructures and their environmental applications

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Progress in Natural Science:Materials International 2021年第6期31卷 821-834页

作者： Wei Gaoa Yangsi Liu Junzhe Dong Department of Chemical and Materials Engineering The University of Auckland Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing Guyue New Materials Research Institute Beijing University of Technology

In the research of semiconductor metal oxides, zinc oxide(ZnO) nanomaterials have been committed to design and exploration because of their unique physical and chemical properties. ZnO has many structural characteristics and has considerable practical applications in the field of environmental technology. However, ZnO nanomaterials used for environmental functions are often presented in the form of powders. After use, they need to go through recovery processes such as centrifugation and filtration, which is also easy to cause secondary *** above problems can be avoided by fixing ZnO nanostructures on substrates to form easily movable ***, we present a review of our work and some others on the different synthetic strategies, formation mechanism, modification, performance and diversifications of immobilized ZnO based nanostructures, and highlight their photocatalytic related activities for environmental remediation. Various immobilized ZnO based nanostructures including nanorod arrays, 3D nanostructures, and composite heterostructures have been focused. We hope it may serve as a useful reference for researchers interested in immobilized semiconductor nanostructures and their photocatalysis and other environmental applications.

关键词： ZnO Nanostructure Immobilization Photocatalysis Environmental application

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Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm

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International Journal of Agricultural and Biological engineering 2020年第2期13卷 182-188页

作者： Linsheng Huang Hansu Zhang Chao Ruan Wenjiang Huang Tingguang Hu Jinling Zhao National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefeiChina Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina

A new method was proposed to extract sensitive features and to construct a monitoring model for wheat scab based on in situ hyperspectral data of wheat ears to achieve effective prevention and control and provide theoretical support for its large-scale *** sensitive features were selected through correlation analysis and wavelet *** features were as follows:three original bands of 350-400 nm,500-600 nm,and 720-1000 nm;three vegetation indices of modified simple ratio(MSR),normalized difference vegetation index,and structural independent pigment index;and two wavelet features of WF01 and *** combining the selected sensitive features with support vector machine(SVM)and SVM optimized by genetic algorithm(GASVM),a total of 16 monitoring models were built,and the monitoring accuracies of the two types of models were *** ability of the monitoring models built by GASVM to identify scab was better than that of SVM algorithm under the same characteristic *** the 16 models,MSR combined with GASVM had an overall accuracy of 75%and a Kappa coefficient of *** can be used to monitor wheat scab and its application can improve the accuracy of disease monitoring.

关键词： wheat scab hyperspectral data correlation analysis genetic algorithm wavelet transform support vector machine

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An improved cellular automata model of traffic flow with look-ahead potential

An improved cellular automata model of traffic flow with loo...

引用

2021 China Automation Congress, CAC 2021

作者： Ma, Xiao Liu, Yu-Hu School of Mathematics Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9781665426473

This paper presents an improved automata model for traffic flow based upon models for cellular automata and traffic flow. The proposed model is concerned with the headway and multiple velocities. The proposed model enforces the stochastic rules for moving a car on the basis of traffic in front of it. Rules for updating the velocity of car relies upon the location of the car as well as front traffic and their velocities. In particular, by using numerical simulations, the model proposed in this study can reach the steady-state more quickly for random perturbations. © 2021 IEEE

关键词： Cellular automata

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Prediction of high-speed train delay propagation based on causal text information

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Railway engineering Science 2023年第1期31卷 89-106页

作者： Qianyi Liu Shengjie Wang Zhongcan Li Li Li Jun Zhang Chao Wen National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong UniversityChengdu 610031China China Railway Chengdu Group Co. LtdChengdu 610031China

The delay-causing text data contain valuable information such as the specific reasons for the delay,location and time of the disturbance,which can provide an efficient support for the prediction of train delays and improve the guidance of train control *** on the train operation data and delay-causing data of the Wuhan-Guangzhou high-speed railway,the relevant algorithms in the natural language processing field are used to process the delay-causing text *** also integrates the train operatingenvironment information and delay-causing text information so as to develop a cause-based train delay propagation prediction *** Word2vec model is first used to vectorize the delay-causing text description after word *** mean model or the term frequency-inverse document frequency-weighted model is then used to generate the delay-causing sentence vector based on the original word ***,the train operating-environment features and delay-causing sentence vector are input into the extreme gradient boosting(XGBoost)regression algorithm to develop a delay propagation prediction *** this work,4 text feature processing methods and 8 regression algorithms are *** results demonstrate that the XGBoost regression algorithm has the highest prediction accuracy using the test features processed by the continuous bag of words and the mean *** with the prediction model that only considers the train-operating-environment features,the results show that the prediction accuracy of the model is significantly improved with multi-ple regression algorithms after integrating the delay-causing feature.

关键词： High-speed rail Delay propagation Cause of delay Word2vec Natural language processing

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