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Big Earth Data 2022年第1期6卷 77-89页

作者： Jinhu Bian Ainong Li Xi Nan Guangbin Lei Zhengjian Zhang Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina

Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda.As one of the indicators,the Mountain Green Cover Index(MGCI)datasets can provide consis-tent and comparable status of green vegetation in mountainous areas,which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time.The produc-tion of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales.In this paper,the MGCI datasets with 500-meter spatial resolutions,covering the economic corridors of the Belt and Road Initiative(BRI),were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform.The validation of green vegeta-tion cover with the ground-truth samples indicated that the data-sets can achieve an overall accuracy of 94.06%,with well-detailed spatial and temporal variations.The archived datasets include the MGCI of each BRI economic corridor,matched to a geospatial layer denoting the economic corridor boundaries.The essential informa-tion of the datasets and their limitations,along with the production flow,were described in this paper.

关键词： Sustainable development goals(SDGs) mountain green cover index(MGCI) dataset Belt and Road Landsat

dataset of human motion status using IR-UWB through-wall radar

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Journal of Systems Engineering and Electronics 2021年第5期32卷 1083-1096页

作者： ZHU Zhengliang YANG Degui ZHANG Junchao TONG Feng Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education Xia-men UniversityXiamen 361005China School of Aeronautics and Astronautics Central South UniversityChangsha 410083China College of Ocean and Earth Sciences Xiamen UniversityXiamen 361005China

Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home.Although many target detection methods of UWB through-wall radar based on machine learning have been proposed,there is a lack of an opensource dataset to evaluate the performance of the algorithm.This published dataset is measured by impulse radio UWB(IR-UWB)through-wall radar system.Three test subjects are measured in different environments and several defined motion status.Using the presented dataset,we propose a human-motion-status recognition method using a convolutional neural network(CNN),and the detailed dataset partition method and the recognition process flow are given.On the well-trained network,the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%.The dataset presented in this paper considers a simple environment.Therefore,we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.

关键词： impulse radio ultra-wideband(IR-UWB) through-wall radar human motion status dataset convolutional neural network(CNN)

MBB-IoT:Construction and Evaluation of IoT DDoS Traffic dataset from a New Perspective

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Computers, Materials & Continua 2023年第8期76卷 2095-2119页

作者： Yi Qing Xiangyu Liu Yanhui Du Department of Artificial Intelligence People’s Public Security University of ChinaBeijing100038China Department of Cybersecurity People’s Public Security University of ChinaBeijing100038China

Distributed Denial of Service(DDoS)attacks have always been a major concern in the security field.With the release of malware source codes such as BASHLITE and Mirai,Internet of Things(IoT)devices have become the new source of DDoS attacks against many Internet applications.Although there are many datasets in the field of IoT intrusion detection,such as Bot-IoT,ConstrainedApplication Protocol–Denial of Service(CoAPDoS),and LATAM-DDoS-IoT(some of the names of DDoS datasets),which mainly focus on DDoS attacks,the datasets describing new IoT DDoS attack scenarios are extremely rare,and only N-BaIoT and IoT-23 datasets used IoT devices as DDoS attackers in the construction process,while they did not use Internet applications as victims either.To supplement the description of the new trend of DDoS attacks in the dataset,we built an IoT environment with mainstream DDoS attack tools such as Mirai and BASHLITE being used to infect IoT devices and implement DDoS attacks against WEB servers.Then,data aggregated into a dataset namedMBB-IoTwere captured atWEBservers and IoT nodes.After the MBB-IoT dataset was split into a training set and a test set,it was applied to the training and testing of the Random Forests classification algorithm.The multi-class classification metrics were good and all above 90%.Secondly,in a cross-evaluation experiment based on Support Vector Machine(SVM),Light Gradient Boosting Machine(LightGBM),and Long Short Term Memory networks(LSTM)classification algorithms,the training set and test set were derived from different datasets(MBB-IoT or IoT-23),and the test performance is better when MBB-IoT is used as the training set.

关键词： Intrusion detection IoT malware botnet DDoS dataset

Long-term daily dataset of surface sensible heat flux and latent heat release over the Tibetan Plateau based on routine meteorological observations

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Big Earth Data 2022年第4期6卷 480-491页

作者： Anmin Duan Senfeng Liu Wenting Hu Die Hu Yuzhuo Peng State Key Laboratory of Marine Environmental Science College of Ocean and Earth SciencesXiamen UniversityXiamen China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(Lasg) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing China College of Earth Science University of Chinese Academy of SciencesBeijingChina Tropical Marine Science Institute National University of SingaporeSingapore

As the main components of the atmospheric heat source/sink over the Tibetan Plateau(TP),up-to-date spatiotemporal fields of surface sensible heat flux and latent heat release by precipitation are vital for investigating the local land-atmosphere interaction and the effect of the thermal forcing of the TP on global weather and climate.This study recalculates the long-term daily dataset of surface sensible heat flux and latent heat release of condensation over the TP based on 293 routine meteorological observations,with the latest date being 31 December 2019.Most stations have adequate and valid records during the period 1981-2019,and the results for 1951-1980 are also calculated if the observations are available.Moreover,a brief evaluation of the climatology and long-term variation during 1981-2019 is conducted.By providing the most continuous and longest set of observational surface sensible heat flux and latent heat release of condensation data over the TP with a high degree of credibility,this new dataset will support research concerning the multi-timescale variation of diabatic heating/cooling over the TP and its remote influence.It is openly available on the LASG data-sharing platform(http://***/TPSHLH/).

关键词： Atmospheric heat source/sink dataset Tibetan Plateau sensible heat latent heat

AWSD:An Aircraft Wing dataset Created by an Automatic Workflow for Data Mining in Geometric Processing

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2935-2952页

作者： Xiang Su Nan Li Yuedi Hu Haisheng Li School of Arti􀀀cial Intelligence Beijing Technology and Business UniversityBeijing100048China School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing100048China

This paper introduces an aircraft wing simulation data set(AWSD)created by an automatic workflow based on creating models,meshing.simulating the wing flight flow field solution,and parameterizing solution results.AWSD is a flexible,independent wing collection of simulations with specific engineering requirements.The data set is applicable to handle computer geometry processing tasks.In contrast to the existing 3D model data set,there are some advantages the scale of this data set is not limited by the collection source,the data files have high quality,no defects,redundancy,and other problems,and the models and simulation are all designed for the specific actual engineering demand.Moreover,AWSD has the characteristics of rich information and a similar model structure,which contributes to the construction of the surrogate model.On the other hand,this data set is suitable for advancing research of data mining in computational geometry graphics.To solve the problem that the CFD flows field results are not intuitive,this paper used the resampling method of surface data to sample the result to the model surface,then segmented the re sampled 3D mesh surface,and compared with the diferences among K-means algorithm,Mini_Batch K means algorithm,and Spectral Clustering algorithm.AWSD provides 300 sets of models,meshes,CFD simulation results,and parametric results based on ARAP(As-Rigid-As Posible)and Harmonic mapping for advancing the construction of engineering surrogate models,3D mesh segmentation,surface resampling,and related geometric processing tasks.

关键词： dataset CFD mesh parameterization parametric modeling

InStereo2K: a large real dataset for stereo matching in indoor scenes

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Science China(Information Sciences) 2020年第11期63卷 136-146页

作者： Wei BAO Wei WANG Yuhua XU Yulan GUO Siyu HONG Xiaohu ZHANG School of Electrical Engineering and Automation Hefei University of Technology School of Electronics and Communication Engineering Sun Yat-sen University College of Electronic Science and Technology National University of Defense Technology School of Aeronautics and Astronautics Sun Yat-sen University

Deep neural networks have shown great success in stereo matching in recent years. On the KITTI datasets, most top performing methods are based on neural networks. However, on the Middlebury datasets,these methods usually do not perform well. The KITTI datasets are collected in outdoor scenes while the Middlebury datasets are collected in indoor scenes. It is commonly believed that the community still lacks a large labelled dataset for stereo matching in indoor scenes. In this paper, we introduce a new stereo dataset called InS tereo2K. It contains 2050 pairs of stereo images with highly accurate groundtruth disparity maps, including 2000 pairs for training and 50 pairs for test. Experimental results show that our dataset can significantly improve the performance of several latest networks(including StereoNet and PSMNet) on the Middlebury 2014 dataset. The large scale, high accuracy and rich diversity of the proposed InS tereo2K dataset provide new opportunities to researchers in the area of stereo matching and beyond. It also takes end-to-end stereo matching methods a step towards practical applications.

关键词： stereo matching depth estimation convolutional neural network dataset

Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using dataset Approach and Water Cycle Algorithm

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Journal of Modern Power Systems and Clean Energy 2020年第1期8卷 86-93页

作者： Munir Azam Muhammad Hazlie Mokhlis Kanendra Naidu Adil Amin John Fredy Franco Mohamadariff Othman Department of Electrical Engineering Faculty of EngineeringUniversity of Malaya50603 Kuala LumpurMalaysia Electrical Technology Section University Kuala LumpurBritish Malaysian InstituteBt.8Jalan Sungai Pusu53100 GombakSelangor Darul EhsanMalaysia Sao Paulo State University Campus of Rosana19274-000RosanaBrazil

The integration of network reconfiguration and distributed generation(DG)can enhance the performances of overall networks.Thus,proper sizing and siting of DG need to be determined,otherwise it will cause degradation in system performance.However,determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space.This search space mostly contains non-radial network configurations.Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution.To reduce the searching complexity,this paper considers the discretized network reconfiguration via dataset approach.Water cycle algorithm(WCA)is used to obtain the near optimal solution of network reconfiguration,and sizing and sitting of DG.In addition,the power factor of DG is also optimized to reduce the power loss.The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor.The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima.The proposed method outperforms other technique such as harmony search algorithm(HSA),fireworks algorithm(FWA),Cuckoo search algorithm(CSA)and uniform voltage distribution based constructive algorithm(UVDA)and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20%and 27.88%,respectively.

关键词： Reactive power injection sizing and siting of distributed generation(DG) dataset approach active distribution network power system

A new dataset of dog breed images and a benchmark for fine-grained classification

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Computational Visual Media 2020年第4期6卷 477-487页

作者： Ding-Nan Zou Song-Hai Zhang Tai-Jiang Mu Min Zhang Department of Computer Science and Technology BNRistTsinghua UniversityBeijing 100084China NaJiu Company Hunan 410022China Harvard Medical School Brigham and Women’s HospitalBostonMA 02115USA

In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater,with between 200 and 7000+ images for each breed.Annotation of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://***/ThuDogs/.

关键词： fine-grained classification dog dataset benchmark

A Potential Risk Index dataset for Landfalling Tropical Cyclones over the Chinese Mainland(PRITC dataset V1.0)

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Advances in Atmospheric Sciences 2021年第10期38卷 1791-1802页

作者： Peiyan CHEN Hui YU Kevin K.W.CHEUNG Jiajie XIN Yi LU Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030China Key Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration Shanghai 20030China The Joint Laboratory for Typhoon Forecasting Technique Applications between Shanghai Typhoon Institute and Wenzhou Meteorological Bureau Wenzhou 325027China Climate and Atmospheric Science NSW Department of Planning Industry and EnvironmentSydney 2000Australia

A dataset entitled“A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland”(PRITC dataset V1.0)is described in this paper,as are some basic statistical analyses.Estimating the severity of the impacts of tropical cyclones(TCs)that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study,including an index combining TC-induced precipitation and wind(IPWT)and further information,such as the corresponding category level(CAT_IPWT),an index of TC-induced wind(IWT),and an index of TC-induced precipitation(IPT).The current version of the dataset includes TCs that made landfall from 1949-2018;the dataset will be extended each year.Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased,as embodied by the annual mean IPWT values,and increases in TC-induced precipitation are the main contributor to this increase.TC Winnie(1997)and TC Bilis(2006)were the two TCs with the highest IPWT and IPT values,respectively.The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

关键词： tropical cyclone risk dataset China

SciCN:A Scientific dataset for Chinese Named Entity Recognition

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Computers, Materials & Continua 2024年第3期78卷 4303-4315页

作者： Jing Yang Bin Ji Shasha Li Jun Ma Jie Yu College of Computer National University of Defense TechnologyChangsha410073China

Named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent years.The abundant annotated English NER datasets have significantly promoted the NER research in the English field.By contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER datasets.To alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific papers.We manually annotate a total of 62,059 entities,and these entities are classified into six types.Compared to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research fields.To investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate SciCN.Experimental results show that SciCN is more challenging than other Chinese NER datasets.In addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER models.Motivated by this fact,we provide a scientific domain-specific lexicon.Validation results demonstrate that our lexicon delivers better performance gains than lexicons of other domains.We hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future research.The dataset and lexicon are available at:https://***/yangjingla/SciCN.git.

关键词： Named entity recognition dataset scientific information extraction lexicon