The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1...The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.展开更多
A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably m...A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.展开更多
A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depi...A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depict the shape of the whole seismograms. However, unlike some previous efforts which completely abandon the DIAL approach, i.e., signal detection, phase identifi- cation, association, and event localization, and seek to use envelope cross-correlation to detect seismic events directly, our technique keeps following the DIAL approach, but in addition to detect signals corresponding to individual seismic phases, it also detects continuous wave-trains and explores their feature for phase-type identification and signal association. More concrete ideas about how to define wave-trains and combine them with various detections, as well as how to measure and utilize their feature in the seismic data processing were expatiated in the paper. This approach has been applied to the routine data processing by us for years, and test results for a 16 days＇ period using data from the Xinjiang seismic station network were presented. The automatic processing results have fairly low false and missed event rate simultaneously, showing that the new technique has good application prospects for improvement of the automatic seismic data processing.展开更多
文摘The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.
文摘A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.
文摘A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depict the shape of the whole seismograms. However, unlike some previous efforts which completely abandon the DIAL approach, i.e., signal detection, phase identifi- cation, association, and event localization, and seek to use envelope cross-correlation to detect seismic events directly, our technique keeps following the DIAL approach, but in addition to detect signals corresponding to individual seismic phases, it also detects continuous wave-trains and explores their feature for phase-type identification and signal association. More concrete ideas about how to define wave-trains and combine them with various detections, as well as how to measure and utilize their feature in the seismic data processing were expatiated in the paper. This approach has been applied to the routine data processing by us for years, and test results for a 16 days＇ period using data from the Xinjiang seismic station network were presented. The automatic processing results have fairly low false and missed event rate simultaneously, showing that the new technique has good application prospects for improvement of the automatic seismic data processing.
