The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and t...The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions.The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE.However,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.展开更多
A fragility calculation scheme is estabtished in this paper for porcelain-type equipments subjected to random earthquake ground motions. All steps of the method are illustrated by the seismic damage analysis of GW4-11...A fragility calculation scheme is estabtished in this paper for porcelain-type equipments subjected to random earthquake ground motions. All steps of the method are illustrated by the seismic damage analysis of GW4-110 disconnect switch. The model of the equipment is built applying the finite element method with flexible joints, and the seismic response of the equipment is analyzed using elastic time history method. On the base, according to the strength damage index and Monte-Carlo Method, the seismic damage ratios are counted and the seismic fragility curves are presented. Then the seismic damage of GW4-110 disconnect switch can be predicted.展开更多
We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Se...We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.展开更多
A random synthesis procedure based on finite fault model is adopted for near field strong ground motion simulation in this paper. The fault plane of the source is divided into a number of sub-sources, the whole moment...A random synthesis procedure based on finite fault model is adopted for near field strong ground motion simulation in this paper. The fault plane of the source is divided into a number of sub-sources, the whole moment magnitude is also divided into more sub-events. The Fourier spectrum of ground motion caused by a sub-event in given sub-source, then can be derived by means of taking the point source spectrum, attenuation with distance, energy dissipation, and near surface effect, into account. A time history is synthesized from this amplitude spectrum and a random phase spectrum, and being combined with an envelope function. The ground motion is worked out by superposition of all time histories from each sub-event in each sub-source, with time lags determining by the differences between the triggering times of sub-events and distances of the sub-sources. From the example of simulations at 21 near field points in a scenario earthquake with 4 dip angles of the fault plane, it is illustrated that the procedure can describe the rupture directivity and hanging wall effect very well. To validate the procedure, the response spectra and time histories recorded at three near fault stations MCN, LV3 and PCD during the Northridge earthquake in 1994, are compared with the simulated ones.展开更多
Seismic random vibration analysis of stochastic truss structures is presented. A new method called random factor method is used for dynamic analysis of structures with uncertain parameters, due to variability in their...Seismic random vibration analysis of stochastic truss structures is presented. A new method called random factor method is used for dynamic analysis of structures with uncertain parameters, due to variability in their material properties and geometry. Using the random factor method, the natural frequencies and modeshapes of a stochastic structure can be respectively described by the product of two parts, corresponding to the random factors of the structural parameters with uncertainty, and deterministic values of the natural frequencies and modeshapes obtained by conventional finite element analysis. The stochastic truss structure is subjected to stationary or non-stationary random earthquake excitation. Computational expressions for the mean and standard deviation of the mean square displacement and mean square stress are developed by means of the random variable＇s functional moment method and the algebra synthesis method. An antenna and a truss bridge are used as practical engineering examples to illustrate the application of the random factor method in the seismic response analysis of random structures under stationary or non-stationary random earthquake excitation.展开更多
A novel approach is introduced to generate simulated ground motion records by considering spectral acceleration correlations at multiple periods. Most of the current reliable Ground Motion Record(GMR) simulation proce...A novel approach is introduced to generate simulated ground motion records by considering spectral acceleration correlations at multiple periods. Most of the current reliable Ground Motion Record(GMR) simulation procedures use a seismological model including source, path and site characteristics. However, the response spectrum of simulated GMR is somewhat different when compared with the response spectrum based on recorded GMRs. More specifi cally, the correlation between the spectral values at multiple periods is a characteristic of a record which is usually different between simulated and recorded GMRs. As this correlation has a signifi cant infl uence on the structural response, it is needed to investigate the consistency of the simulated ground motions with actual records. This issue has been investigated in this study by incorporating an optimization algorithm within the Boore simulation technique. Eight seismological key parameters were optimized in order to achieve approximately the same correlation coeffi cients and spectral acceleration between two sets of real and simulated records. The results show that the acceleration response spectra of the synthetic ground motions also have good agreement with the real recorded response spectra by implementation of the proposed optimized values.展开更多
An analysis of a base-isolated structure for multi-component random ground motion is presented. The mean square respond of the system is Obtained under different parametric variations. The effectiveness of main param...An analysis of a base-isolated structure for multi-component random ground motion is presented. The mean square respond of the system is Obtained under different parametric variations. The effectiveness of main parameters and the torsional component during an earthquake is quantified with the help of the response ratio and the root mean square response with and without base isolation. It is observed that the base isolation has considerable influence on the response and the effect of the torsional component is not ignored.展开更多
Because of the increasing attention on environmental issues, especially air pollution, predicting whether a day is polluted or not is necessary to people’s health. In order to solve this problem, this research is cla...Because of the increasing attention on environmental issues, especially air pollution, predicting whether a day is polluted or not is necessary to people’s health. In order to solve this problem, this research is classifying ground ozone level based on big data and machine learning models, where polluted ozone day has class 1 and non-ozone day has class 0. The dataset used in this research was derived from the UCI Website, containing various environmental factors in Houston, Galveston and Brazoria area that could possibly affect the occurrence of ozone pollution [1]. This dataset is first filled up for further process, next standardized to ensure every feature has the same weight, and then split into training set and testing set. After this, five different machine learning models are used in the prediction of ground ozone level and their final accuracy scores are compared. In conclusion, among Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine (SVM), the last one has the highest test score of 0.949. This research utilizes relatively simple methods of forecasting and calculates the first accuracy scores in predicting ground ozone level;it can thus be a reference for environmentalists. Moreover, the direct comparison among five different models provides machine learning field an insight to determine the most accurate model. In the future, Neural Network can also be utilized to predict air pollution, and its test scores can be compared with the previous five methods to conclude the accuracy of Neuron Network.展开更多
Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possi...Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.展开更多
基金supported by the National Natural Science Foundation of China Youth Fund(12105234)。
文摘The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions.The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE.However,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.
基金supported by the Science and Technology Commission of Shanghai Municipality (Grant No.04JC14035)
文摘A fragility calculation scheme is estabtished in this paper for porcelain-type equipments subjected to random earthquake ground motions. All steps of the method are illustrated by the seismic damage analysis of GW4-110 disconnect switch. The model of the equipment is built applying the finite element method with flexible joints, and the seismic response of the equipment is analyzed using elastic time history method. On the base, according to the strength damage index and Monte-Carlo Method, the seismic damage ratios are counted and the seismic fragility curves are presented. Then the seismic damage of GW4-110 disconnect switch can be predicted.
文摘We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.
基金Earthquake Science Foundation under Contract No.201009
文摘A random synthesis procedure based on finite fault model is adopted for near field strong ground motion simulation in this paper. The fault plane of the source is divided into a number of sub-sources, the whole moment magnitude is also divided into more sub-events. The Fourier spectrum of ground motion caused by a sub-event in given sub-source, then can be derived by means of taking the point source spectrum, attenuation with distance, energy dissipation, and near surface effect, into account. A time history is synthesized from this amplitude spectrum and a random phase spectrum, and being combined with an envelope function. The ground motion is worked out by superposition of all time histories from each sub-event in each sub-source, with time lags determining by the differences between the triggering times of sub-events and distances of the sub-sources. From the example of simulations at 21 near field points in a scenario earthquake with 4 dip angles of the fault plane, it is illustrated that the procedure can describe the rupture directivity and hanging wall effect very well. To validate the procedure, the response spectra and time histories recorded at three near fault stations MCN, LV3 and PCD during the Northridge earthquake in 1994, are compared with the simulated ones.
文摘Seismic random vibration analysis of stochastic truss structures is presented. A new method called random factor method is used for dynamic analysis of structures with uncertain parameters, due to variability in their material properties and geometry. Using the random factor method, the natural frequencies and modeshapes of a stochastic structure can be respectively described by the product of two parts, corresponding to the random factors of the structural parameters with uncertainty, and deterministic values of the natural frequencies and modeshapes obtained by conventional finite element analysis. The stochastic truss structure is subjected to stationary or non-stationary random earthquake excitation. Computational expressions for the mean and standard deviation of the mean square displacement and mean square stress are developed by means of the random variable＇s functional moment method and the algebra synthesis method. An antenna and a truss bridge are used as practical engineering examples to illustrate the application of the random factor method in the seismic response analysis of random structures under stationary or non-stationary random earthquake excitation.
文摘A novel approach is introduced to generate simulated ground motion records by considering spectral acceleration correlations at multiple periods. Most of the current reliable Ground Motion Record(GMR) simulation procedures use a seismological model including source, path and site characteristics. However, the response spectrum of simulated GMR is somewhat different when compared with the response spectrum based on recorded GMRs. More specifi cally, the correlation between the spectral values at multiple periods is a characteristic of a record which is usually different between simulated and recorded GMRs. As this correlation has a signifi cant infl uence on the structural response, it is needed to investigate the consistency of the simulated ground motions with actual records. This issue has been investigated in this study by incorporating an optimization algorithm within the Boore simulation technique. Eight seismological key parameters were optimized in order to achieve approximately the same correlation coeffi cients and spectral acceleration between two sets of real and simulated records. The results show that the acceleration response spectra of the synthetic ground motions also have good agreement with the real recorded response spectra by implementation of the proposed optimized values.
文摘An analysis of a base-isolated structure for multi-component random ground motion is presented. The mean square respond of the system is Obtained under different parametric variations. The effectiveness of main parameters and the torsional component during an earthquake is quantified with the help of the response ratio and the root mean square response with and without base isolation. It is observed that the base isolation has considerable influence on the response and the effect of the torsional component is not ignored.
文摘Because of the increasing attention on environmental issues, especially air pollution, predicting whether a day is polluted or not is necessary to people’s health. In order to solve this problem, this research is classifying ground ozone level based on big data and machine learning models, where polluted ozone day has class 1 and non-ozone day has class 0. The dataset used in this research was derived from the UCI Website, containing various environmental factors in Houston, Galveston and Brazoria area that could possibly affect the occurrence of ozone pollution [1]. This dataset is first filled up for further process, next standardized to ensure every feature has the same weight, and then split into training set and testing set. After this, five different machine learning models are used in the prediction of ground ozone level and their final accuracy scores are compared. In conclusion, among Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine (SVM), the last one has the highest test score of 0.949. This research utilizes relatively simple methods of forecasting and calculates the first accuracy scores in predicting ground ozone level;it can thus be a reference for environmentalists. Moreover, the direct comparison among five different models provides machine learning field an insight to determine the most accurate model. In the future, Neural Network can also be utilized to predict air pollution, and its test scores can be compared with the previous five methods to conclude the accuracy of Neuron Network.
文摘Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.
