The Extreme Ultraviolet Camera (EUVC) onboard the Chang'e-3 (CE-3) lander is used to observe the structure and dynamics of Earth's plasmasphere from the Moon. By detecting the resonance line emission of helium i...The Extreme Ultraviolet Camera (EUVC) onboard the Chang'e-3 (CE-3) lander is used to observe the structure and dynamics of Earth's plasmasphere from the Moon. By detecting the resonance line emission of helium ions (He+) at 30.4 nm, the EUVC images the entire plasmasphere with a time resolution of 10 min and a spatial resolution of about 0.1 Earth radius (RE) in a single frame. We first present details about the data processing from EUVC and the data acquisition in the commissioning phase, and then report some initial results, which reflect the basic features of the plas- masphere well. The photon count and emission intensity of EUVC are consistent with previous observations and models, which indicate that the EUVC works normally and can provide high quality data for future studies.展开更多
In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone (TC) with differ...In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone (TC) with different return periods to guarantee the safety in projected operating life period. Based on the 71-year (1945-2015) TC data in the Northwest Pacific (NWP) by the Joint Typhoon Warning Center (JTWC) of US, a notable growth of the TC intensity is observed in the context of climate change. The fact implies that the traditional stationary model might be incapable of predicting parameters in the extreme events. Therefore, a non-stationary model is proposed in this study to estimate extreme wind speed in the South China Sea (SCS) and NWP. We find that the extreme wind speeds of different return periods exhibit an evident enhancement trend, for instance, the extreme wind speeds with different return periods by non- stationary model are 4.1%-4.4% higher than stationary ones in SCS. Also, the spatial distribution of extreme wind speed in NWP has been examined with the same methodology by dividing the west sea areas of the NWP 0°-45°N, 105°E-130°E into 45 subareas of 5° × 5°, where oil and gas resources are abundant. Similarly, remarkable spacial in-homogeneity in the extreme wind speed is seen in this area: the extreme wind speed with 50-year return period in the subarea (15°N-20°N, 115°E-120°E) of Zhongsha and Dongsha Islands is 73.8 m/s, while that in the subarea of Yellow Sea (30°N-35°N, 120°E-125°E) is only 47.1 m/s. As a result, the present study demonstrates that non-stationary and in-homogeneous effects should be taken into consideration in the estimation of extreme wind speed.展开更多
The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command La...The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.展开更多
A novel distributed passive vehicle tracking technology is proposed and demonstrated. This technology is based on a phase-sensitive optical time domain reflectometer(Φ-OTDR) that can sense and locate vibrations. Tw...A novel distributed passive vehicle tracking technology is proposed and demonstrated. This technology is based on a phase-sensitive optical time domain reflectometer(Φ-OTDR) that can sense and locate vibrations. Two algorithms, dynamic frequency-space image and 2D digital sliding filtering, are proposed to distinguish a car's moving signals from severe environmental noises and disturbances. This technology is proved effective by field experiments for tracking a single car and multiple cars. This work provides a new distributed passive way for real-time vehicle tracking and this technology will be extremely important for traffic controlling and public safety in modern society.展开更多
Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable perform...Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.展开更多
文摘The Extreme Ultraviolet Camera (EUVC) onboard the Chang'e-3 (CE-3) lander is used to observe the structure and dynamics of Earth's plasmasphere from the Moon. By detecting the resonance line emission of helium ions (He+) at 30.4 nm, the EUVC images the entire plasmasphere with a time resolution of 10 min and a spatial resolution of about 0.1 Earth radius (RE) in a single frame. We first present details about the data processing from EUVC and the data acquisition in the commissioning phase, and then report some initial results, which reflect the basic features of the plas- masphere well. The photon count and emission intensity of EUVC are consistent with previous observations and models, which indicate that the EUVC works normally and can provide high quality data for future studies.
基金financially supported by the Ministry of Science and Technology(863 program)(2006AA09A103-4)the National Natural Science Foundation of China(11232012)the Chinese Academy of Sciences(CAS)knowledge innovation program(KJCXYW-L02)
文摘In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone (TC) with different return periods to guarantee the safety in projected operating life period. Based on the 71-year (1945-2015) TC data in the Northwest Pacific (NWP) by the Joint Typhoon Warning Center (JTWC) of US, a notable growth of the TC intensity is observed in the context of climate change. The fact implies that the traditional stationary model might be incapable of predicting parameters in the extreme events. Therefore, a non-stationary model is proposed in this study to estimate extreme wind speed in the South China Sea (SCS) and NWP. We find that the extreme wind speeds of different return periods exhibit an evident enhancement trend, for instance, the extreme wind speeds with different return periods by non- stationary model are 4.1%-4.4% higher than stationary ones in SCS. Also, the spatial distribution of extreme wind speed in NWP has been examined with the same methodology by dividing the west sea areas of the NWP 0°-45°N, 105°E-130°E into 45 subareas of 5° × 5°, where oil and gas resources are abundant. Similarly, remarkable spacial in-homogeneity in the extreme wind speed is seen in this area: the extreme wind speed with 50-year return period in the subarea (15°N-20°N, 115°E-120°E) of Zhongsha and Dongsha Islands is 73.8 m/s, while that in the subarea of Yellow Sea (30°N-35°N, 120°E-125°E) is only 47.1 m/s. As a result, the present study demonstrates that non-stationary and in-homogeneous effects should be taken into consideration in the estimation of extreme wind speed.
基金This work was financially supported by the Key Research and Development Project of Shandong Province(Grant No.2020CXGC010702).
文摘The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.
基金supported by the National Natural Science Foundation of China (No. 61405227)the National High Technology Research and Development Program of China (No. 2012AA041203)the Science and Technology Commission of Shanghai Municipality (Nos. 11DZ1140202 and 13XD1425400)
文摘A novel distributed passive vehicle tracking technology is proposed and demonstrated. This technology is based on a phase-sensitive optical time domain reflectometer(Φ-OTDR) that can sense and locate vibrations. Two algorithms, dynamic frequency-space image and 2D digital sliding filtering, are proposed to distinguish a car's moving signals from severe environmental noises and disturbances. This technology is proved effective by field experiments for tracking a single car and multiple cars. This work provides a new distributed passive way for real-time vehicle tracking and this technology will be extremely important for traffic controlling and public safety in modern society.
基金supported by National Natural Science Foundation of China (Nos. 61203102 and 60874057)Postdoctoral Science Foundation of China (No. 20100471464)
文摘Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.