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Ride-hailing origin-destination demand prediction with spatiotemporal information fusion

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transportation Safety and Environment 2024年第2期6卷 63-74页

作者： Ning Wang Liang Zheng Huitao Shen Shukai Li School of Traffic and transportation Engineering Central South UniversityChangsha 410075China State key laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China

Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.

关键词： intelligent transport system ride-hailing generative adversarial networks spatiotemporal dependencies origin-destination(OD)demand prediction

Digitization of Astronomical Photographic Plates of China and Astrometric Measurement of Single-exposure Plates

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Research in Astronomy and Astrophysics 2024年第5期24卷 103-116页

作者：商正君于涌王亮亮杨美婷杨静沈世银刘敏徐权峰崔辰州樊东卫唐正宏赵建海 Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China University of Chinese Academy of Sciences Beijing 100049China key lab for Astrophysics Shanghai200234China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China National Astronomical data Center Chinese Academy of SciencesBeijing 100101China

From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates were captured. These historical plates play an irreplaceable role in conducting long-term, time-domain astronomical research. To preserve and explore these valuable original astronomical observational data, Shanghai Astronomical Observatory has organized the transportation of plates, taken during nighttime observations from various stations across the country, to the Sheshan Plate Archive for centralized preservation. For the first time, plate information statistics were calculated. On this basis, the plates were cleaned and digitally scanned, and finally digitized images were acquired for 29,314 plates. In this study, using Gaia DR2 as the reference star catalog, astrometric processing was carried out successfully on 15,696 single-exposure plates, including object extraction, stellar identification,and plate model computation. As a result, for long focal length telescopes, such as the 40 cm double-tube refractor telescope, the 1.56 m reflector telescope at Shanghai Astronomical Observatory, and the 1m reflecting telescope at Yunnan Astronomical Observatory, the astrometric accuracy obtained for their plates is approximately 0 1–0 3. The distribution of astrometric accuracy for medium and short focal length telescopes ranges from 0 3 to 1 0. The relevant data of this batch of plates, including digitized images and a stellar catalog of the plates, are archived and released by the National Astronomical data Center. Users can access and download plate data based on keywords such as station, telescope, observation year, and observed celestial coordinates.

关键词： methods data analysis-techniques image processing-astronomical databases miscellaneous-astrometry

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

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International Journal of mining Science and Technology 2024年第4期34卷 545-556页

作者： Zhengyu Liu Jue Kou Zengxin Yan Peilong Wang Chang Liu Chunbao Sun Anlin Shao Bern Klein School of Civil and Resources Engineering University of Science and TechnologyBeijing 100083China Institute of Minerals Research University of Science and TechnologyBeijing 100083China State key laboratory of Mineral Processing Beijing 102628China Ansteel Group mining Co.Ltd. Anshan 114000China Norman B.Keevil Institute of mining Engineering University of British ColumbiaVancouverBC V6T 1Z4Canada

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

关键词： XRF sensor-based sorting PSO-SVM algorithm Copper ore pebble Receiver operating curve(ROC) Net smelter return(NSR)

A STAMP-Game model for accident analysis in oil and gas industry

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Petroleum Science 2024年第3期21卷 2154-2167页

作者： Huixing Meng Xu An Daiwei Li Shijun Zhao Enrico Zio Xuan Liu Jinduo Xing State key laboratory of Explosion Science and Safety Protection Beijing Institute of Technology School of Emergency Management and Safety Engineering China University of Mining and Technology (Beijing) Energy Department Politecnico di Milano Mines Paris PSL Centre de Recherche sur les Risques et les Crises School of Mechanical-Electronic and Vehicle Engineering Beijing University of Civil Engineering and Architecture

Accidents in engineered systems are usually generated by complex socio-technical factors. It is beneficial to investigate the increasing complexity and coupling of these factors from the perspective of system safety. Based on system and control theories, System-Theoretic Accident Model and Processes(STAMP) is a widely recognized approach for accident analysis. In this paper, we propose a STAMP-Game model to analyze accidents in oil and gas storage and transportation systems. Stakeholders in accident analysis by STAMP can be regarded as players of a game. Game theory can, thus, be adopted in accident analysis to depict the competition and cooperation between stakeholders. Subsequently, we established a game model to study the strategies of both supervisory and supervised entities. The obtained results demonstrate that the proposed game model allows for identifying the effectiveness deficiency of the supervisory entity, and the safety and protection altitudes of the supervised entity. The STAMP-Game model can generate quantitative parameters for supporting the behavior and strategy selections of the supervisory and supervised entities. The quantitative data obtained can be used to guide the safety improvement, to reduce the costs of safety regulation violation and accident risk.

关键词： Accident analysis STAMP System engineering Game theory Oil and gas storage and transportation systems

Quantitative geochemical reconstruction of Eocene paleoenvironment in Fushun Basin,northeast China

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Acta Geochimica 2024年第3期43卷 571-587页

作者： Yuanji Li Pingchang Sun Qiang Zhang Junxian Wang Department of Safety Engineering Heilongjiang University of Science and TechnologyHarbin 150022China National Professional Center lab of Safety Basic Research for Hydrocarbon Gas Pipeline transportation Network Harbin 150022China College of Earth Sciences Jilin UniversityChangchun 130061JilinChina beijing Research Institute of Uranium Geology Beijing 100029China

The quantitative estimation of key parameters of paleotemperature and paleoprecipitation is crucial for paleoclimate reconstruction.Geochemical data from mod-ern sediments are highly consistent with climate data,and their relationship can provide an important reference for the quantitative reconstruction of the paleoclimate.In this study,detailed inorganic geochemical analysis was carried out using high-precision sampling of the Paleogene(LFD-1 well)Guchengzi,Jijuntun and Xiloutian Formations in the Fushun Basin located in the mid-latitudes of the Northern Hemisphere.The Eocene Guchengzi Formation(54.51–47.8 Ma)and Jijuntun Formation(47.8–41.2 Ma)in the Fushun Basin were found to have been deposited under a humid climate.The lower(41.2–40.1 Ma)and upper(40.1–37.8 Ma)parts of the Xiloutian Formation were character-ized by semiarid and semihumid–semiarid climates,respec-tively,which is very similar to the paleoclimatic information reflected by organic carbon isotopes.The Eocene Thermal Maximum 2(ETM2,~53.7 Ma),Early Eocene Climatic Optimum(EECO,~53.1–46.5 Ma),Eocene Thermal Maxi-mum 3(ETM 3,~52.8 Ma),and Middle Eocene Climatic Optimum(MECO,~40.7–40.1 Ma)events significantly enhanced chemical weathering during these periods.The rapid increase in pCO_(2)concentration leads to an increase in temperature,precipitation,and surface runoff,exhibiting strong chemical weathering.The mean annual temperature(MATa)and mean annual precipitation(MAPa,MAPb,and MAPc)were estimated using parameters,such as the corrosion index without potassium(CIA-K),CaO/Al_(2)O_(3),and(Na_(2)O+K_(2)O)/Al_(2)O_(3).Comparing MAPa,MAPb,and MAPc with the MAP estimated using pollen data,MAPa and MAPb were found to be more sensitive to the climate during high precipitation periods(precipitation>1000 mm,Guchengzi Formation),and the recovered average precipi-tation was similar to MAP.In contrast,MAPc was more sensitive to the climate during low precipitation periods(precipitation<1000 mm,Jijuntun,and Xiloutian Forma-tions),with higher accuracy.To fully co

关键词： Eocene Fushun Basin Major element Paleoclimate Geochemical reconstruction

Siamese transformer with hierarchical concept embedding for fine-grained image recognition

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Science China(Information Sciences) 2023年第3期66卷 188-203页

作者： Yilin LYU Liping JING Jiaqi WANG Mingzhe GUO Xinyue WANG Jian YU School of Computer and Information Technology Beijing Jiaotong University beijing key lab of Traffic data analysis and mining Beijing Jiaotong University Alibaba Group

Distinguishing the subtle differences among fine-grained images from subordinate concepts of a concept hierarchy is a challenging task. In this paper, we propose a Siamese transformer with hierarchical concept embedding(STr HCE), which contains two transformer subnetworks sharing all configurations,and each subnetwork is equipped with the hierarchical semantic information at different concept levels for fine-grained image embeddings. In particular, one subnetwork is for coarse-scale patches to learn the discriminative regions with the aid of the innate multi-head self-attention mechanism of the transformer. The other subnetwork is for finer-scale patches, which are adaptively sampled from the discriminative regions, to capture subtle yet discriminative visual cues and eliminate redundant information. STr HCE connects the two subnetworks through a score margin adjustor to enforce the most discriminative regions generating more confident predictions. Extensive experiments conducted on four commonly-used benchmark datasets, including CUB-200-2011, FGVC-Aircraft, Stanford Dogs, and NABirds, empirically demonstrate the superiority of the proposed STr HCE over state-of-the-art baselines.

关键词： fine-grained image recognition transformer hierarchical concept embedding adaptive sampling Siamese network

Leveraging implicit social structures for recommendation via a Bayesian generative model

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Science China(Information Sciences) 2022年第4期65卷 269-271页

作者： Huafeng LIU Jingxuan WEN Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University beijing key lab of Traffic data analysis and mining Beijing Jiaotong University

Dear editor,Social recommendation leverages the social relations between users and their past behaviors to model user preferences. While social recommendation is a core component in recommendation systems, discovering... 详细信息

关键词： Recommendation system Collaborative filtering Social recommendation Social networks Generative model

Numerical analysis on mechanical difference of sandstone under in-situ stress,pore pressure preserved environment at depth

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International Journal of mining Science and Technology 2023年第11期33卷 1339-1350页

作者： Hongwei Zhou Mingyuan Lu Heping Xie Wenhao Jia Ruidong Peng Yimeng Wang Bocen Chen Pengfei Jing School of Energy and mining Engineering China University of Mining and Technology-BeijingBeijing 100083China State key laboratory of Coal Resources and Safe mining China University of Mining and Technology-BeijingBeijing 100083China State key laboratory of Hydraulics and Mountain River Engineering College of Water Resource and HydropowerSichuan UniversityChengdu 610065China Guangdong Provincial key laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization Institute of Deep Earth Sciences and Green EnergyCollege of Civil and Transportation EngineeringShenzhen UniversityShenzhen 518060China Shenzhen key laboratory of Deep Underground Engineering Sciences and Green Energy Shenzhen UniversityShenzhen 518060China School of Mechanics and Civil Engineering China University of Mining and Technology-BeijingBeijing 100083China

Deep in-situ rock mechanics considers the influence of the in-situ environment on mechanical properties,differentiating it from traditional rock mechanics.To investigate the effect of in-situ stress,pore pressure preserved environment on the mechanical difference of sandstone,four tests are numerically modeled by COMSOL:conventional triaxial test,conventional pore pressure test,in-situ stress restoration and reconstruction test,and in-situ pore pressure-preserved test(not yet realized in the laboratory).The in-situ stress restoration parameter is introduced to characterize the recovery effect of in-situ stress on elastic modulus and heterogeneous distribution of sandstone at different depths.A random function and nonuniform pore pressure coefficient are employed to describe the non-uniform distribution of pore pressure in the in-situ environment.Numerical results are compared with existing experimental data to validate the models and calibrate the numerical parameters.By extracting mechanical parameters from numerical cores,the stress-strain curves of the four tests under different depths,in-situ stress and pore pressure are compared.The influence of non-uniform pore pressure coefficient and depth on the peak strength of sandstone is analyzed.The results show a strong linear relationship between the in-situ stress restoration parameter and depth,effectively characterizing the enhanced effect of stress restoration and reconstruction methods on the elastic modulus of conventional cores at different depths.The in-situ pore pressurepreserved test exhibits lower peak stress and peak strain compared to the other three tests,and sandstone subjected to non-uniform pore pressure is more prone to plastic damage and failure.Moreover,the influence of non-uniform pore pressure on peak strength gradually diminished with increasing depth.

关键词： In-situ pore pressure-preserved environment Numerical simulation approach Deep in-situ rock mechanics In-situ stress restoration and reconstruction

On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection

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Cybersecurity 2018年第1期1卷 933-944页

作者： Yixiang Wang Shaohua lv Jiqiang Liu Xiaolin Chang Jinqiang Wang beijing key laboratory of Security and Privacy in Intelligent transportation Beijing Jiaotong University3Shangyuancun100044 BeijingChina beijing key lab of Traffic data analysis and mining Beijing Jiaotong University3 Shangyuancun100044 BeijingChina

Deep learning(DL)has exhibited its exceptional performance in fields like intrusion detection.Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models.However,the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion.Previously,the seq2seq model has been explored to augment system-call sequences.Following this work,we propose a gated convolutional neural network(GCNN)model to thoroughly extract the potential information of augmented sequences.Also,in order to enhance themodel’s robustness,we adopt adversarial training to reduce the impact of adversarial examples on the model.Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm.The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance.Furthermore,GCNN with adversarial training can enhance robustness significantly.

关键词： data augmentation Intrusion detection system Machine learning algorithms System call