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QpefBD:A benchmark dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

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Journal of Meteorological Research 2022年第1期36卷 93-106页

作者： Anyuan XIONG Na LIU Yujia LIU Shulin ZHI Linlin WU Yongjian XIN Yan SHI Yunjian ZHAN National Meteorological Information Center China Meteorological AdministrationBeijing 100081 Jiangxi Meteorological Observatory Nanchang 330096 Anhui Weather Modification Office Hefei 230061

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological ***-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific *** present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October *** individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic *** data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also *** present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and *** on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and *** results can serve as the benchmark reference for the performance evaluation of machine learning models using this *** paper also gives some suggestions and scenarios of the QpefBD *** believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.

关键词： machine learning benchmark dataset quantitative precipitation estimation precipitation forecast

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DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

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Earthquake Science 2023年第2期36卷 84-94页

作者： Ming Zhao Zhuowei Xiao Shi Chen Lihua Fang Institute of Geophysics China Earthquake AdministrationBeijing 100081China Beijing Baijiatuan Earth Sciences National Observation and Research Station Beijing 100095China Institute of Geology and Geophysics Chinese Academy of SciencesBeijing 100029China Key Laboratory of Earthquake Source Physics China Earthquake AdministrationBeijing 100081China

In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of *** amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology *** this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time *** were recorded using broadband and short-period *** obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity *** waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an *** three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise *** magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,*** dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion *** research will further promote the development and application of artificial intelligence in seismology.

关键词： artificial intelligence benchmark dataset earthquake detection seismic phase identification first-motion polarity

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Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

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Frontiers of Computer Science 2023年第3期17卷 91-102页

作者： Mingdi HU Long BAI Jiulun FAN Sirui ZHAO Enhong CHEN School of Communications and Information Engineering&School of Artificial Intelligence Xi’an University of Posts&TelecommunicationsXi’an 710121China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation ***,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual ***,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in *** address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance ***,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth *** former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with ***,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR *** extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail *** Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

关键词： vehicle color recognition benchmark dataset multi-scale feature fusion long-tail distribution improved smooth l1 loss

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维普期刊数据库

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Data Augmentation Using Contour Image for Convolutional Neural Network

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Computers, Materials & Continua 2023年第6期75卷 4669-4680页

作者： Seung-Yeon Hwang Jeong-Joon Kim Department of Computer Engineering Anyang UniversityAnyang-si14058Korea Department of ICT Convergence Engineering Anyang UniversityAnyang-si14058Korea

With the development of artificial intelligence-related technologies such as deep learning,various organizations,including the government,are making various efforts to generate and manage big data for use in artificial ***,it is difficult to acquire big data due to various social problems and restrictions such as personal information *** are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning ***,this study proposes a mixed contour data augmentation technique,which is a data augmentation technique using contour images,to solve a problem caused by a lack of ***,a famous convolutional neural network(CNN)architecture,and CIFAR-10,a benchmark data set,are used for experimental performance evaluation to prove the superiority of the proposed *** to prove that high performance improvement can be achieved even with a small training dataset,the ratio of the training dataset was divided into 70%,50%,and 30%for comparative *** a result of applying the mixed contour data augmentation technique,it was possible to achieve a classification accuracy improvement of up to 4.64%and high accuracy even with a small amount of data *** addition,it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets.

关键词： Data augmentation image classification deep learning convolutional neural network mixed contour image benchmark dataset

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Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning

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Chinese Journal of Polymer Science 2023年第3期41卷 422-431,I0009页

作者： Fang Ding Lun-Yang Liu Ting-Li Liu Yun-Qi Li Jun-Peng Li Zhao-Yan Sun State Key Laboratory of Polymer Physics and Chemistry Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China Department of Polymer Materials and Engineering College of Materials and MetallurgyGuizhou UniversityGuiyang 550025China State Key Laboratory of Advanced Technologies for Comprehensive Utilization of Platinum Metals Sino-Platinum Metals Co.Ltd.Ltdunming 650106China

Bridging the gap between the computation of mechanical properties and the chemical structure of elastomers is a long-standing *** fill the gap,we create a raw dataset and build predictive models for Young’s modulus,tensile strength,and elongation at break of polyurethane elastomers(PUEs).We then construct a benchmark dataset with 50.4%samples remained from the raw dataset which suffers from the intrinsic diversity problem,through a newly proposed recursive data elimination *** coefficients of determination(R^(2)s)from predictions are improved from 0.73-0.78 to 0.85-0.91 based on the raw and the benchmark *** fitting of stress-strain curves using the machine learning model shows a slightly better performance than that for one of the well-performed constitutive models(e.g.,the Khiêm-Itskov model).It confirmed that the black-box machine learning models are feasible to bridge the gap between the mechanical properties of PUEs and multiple factors for their chemical structures,composition,processing,and measurement *** accurate prediction for these curves is still a *** release the raw dataset and the most representative benchmark dataset so far to call for more attention to tackle the longstanding gap problem.

关键词： Mechanical properties Stress-strain curves Polyurethane elastomers Machine learning benchmark dataset

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