Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distribu...Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.展开更多
An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variat...An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.展开更多
In order to improve the accuracy of prediction when using the empirical orthogonal function （EOF） method, this paper describes a novel approach for two-dimensional （2D） EOF analysis based on extrapolating both the...In order to improve the accuracy of prediction when using the empirical orthogonal function （EOF） method, this paper describes a novel approach for two-dimensional （2D） EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component.展开更多
The false discovery proportion (FDP) is a useful measure of abundance of false positives when a large number of hypotheses are being tested simultaneously. Methods for controlling the expected value of the FDP, namely...The false discovery proportion (FDP) is a useful measure of abundance of false positives when a large number of hypotheses are being tested simultaneously. Methods for controlling the expected value of the FDP, namely the false discovery rate (FDR), have become widely used. It is highly desired to have an accurate prediction interval for the FDP in such applications. Some degree of dependence among test statistics exists in almost all applications involving multiple testing. Methods for constructing tight prediction intervals for the FDP that take account of dependence among test statistics are of great practical importance. This paper derives a formula for the variance of the FDP and uses it to obtain an upper prediction interval for the FDP, under some semi-parametric assumptions on dependence among test statistics. Simulation studies indicate that the proposed formula-based prediction interval has good coverage probability under commonly assumed weak dependence. The prediction interval is generally more accurate than those obtained from existing methods. In addition, a permutation-based upper prediction interval for the FDP is provided, which can be useful when dependence is strong and the number of tests is not too large. The proposed prediction intervals are illustrated using a prostate cancer dataset.展开更多
The modern language teaching theory highly emphasizes the effect of"listening"on the learning and the use of language. The teaching of English listening is not only a teaching of language, but also a kind of...The modern language teaching theory highly emphasizes the effect of"listening"on the learning and the use of language. The teaching of English listening is not only a teaching of language, but also a kind of training of ability. In the four skills of listening, writing, reading and speaking, listening is considered as the basic, and the most important skill. It isn't only the main way of acquiring language information, but the base of improving other skills. Listening practice is especially important in classrooms where English is taught as a foreign language, that is, in situations where English is not the language of the country and where opportunities for hearing English outside the classroom are few. Students in such environments often learn to read and write English, and even to speak English, but they frequently have great difficulty comprehending English spoken to them.Plenty of Chinese students claim that the listening test is the most difficult part of English test. As a result, teachers should take particular pains to avoid such simplification and to provide opportunities for students to hear the kind of language that will prepare them for situations outside the classroom. Teachers should try to anticipate the kind of English the students will hear when the opportunity arises for them to be in an English-speaking country. In this article, I start with the factors which may exert an influence on listening comprehension, shows ways to improve listening comprehension and design some useful activities in the class. And I also focus on the activities of English listening teaching and insist that the activities should be both emphasized on the process of listening and the result of listening, due to different situations.展开更多
A method based on the diffraction theory for estimating the three-dimensional (3D) focusing performance of the compound refractive X-ray lenses is presented in this paper. As a special application, the 3D X-ray intens...A method based on the diffraction theory for estimating the three-dimensional (3D) focusing performance of the compound refractive X-ray lenses is presented in this paper. As a special application, the 3D X-ray intensity distribution near the focus is derived for a plano-concave compound refractive X-ray lens. Moreover, the computer codes are developed and some results of 3D focusing performance for a compound refractive X-ray lens with Si material are shown and discussed.展开更多
文摘Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.
基金The National Natural Science Foundation of China under contract No.51379002the Fundamental Research Funds for the Central Universities of China under contract Nos 3132016322 and 3132016314the Applied Basic Research Project Fund of the Chinese Ministry of Transport of China under contract No.2014329225010
文摘An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.
基金the School of Engineering at Cardiff University for providing the financial support of a Ph D studentship to accomplish the research
文摘In order to improve the accuracy of prediction when using the empirical orthogonal function （EOF） method, this paper describes a novel approach for two-dimensional （2D） EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component.
文摘The false discovery proportion (FDP) is a useful measure of abundance of false positives when a large number of hypotheses are being tested simultaneously. Methods for controlling the expected value of the FDP, namely the false discovery rate (FDR), have become widely used. It is highly desired to have an accurate prediction interval for the FDP in such applications. Some degree of dependence among test statistics exists in almost all applications involving multiple testing. Methods for constructing tight prediction intervals for the FDP that take account of dependence among test statistics are of great practical importance. This paper derives a formula for the variance of the FDP and uses it to obtain an upper prediction interval for the FDP, under some semi-parametric assumptions on dependence among test statistics. Simulation studies indicate that the proposed formula-based prediction interval has good coverage probability under commonly assumed weak dependence. The prediction interval is generally more accurate than those obtained from existing methods. In addition, a permutation-based upper prediction interval for the FDP is provided, which can be useful when dependence is strong and the number of tests is not too large. The proposed prediction intervals are illustrated using a prostate cancer dataset.
文摘The modern language teaching theory highly emphasizes the effect of"listening"on the learning and the use of language. The teaching of English listening is not only a teaching of language, but also a kind of training of ability. In the four skills of listening, writing, reading and speaking, listening is considered as the basic, and the most important skill. It isn't only the main way of acquiring language information, but the base of improving other skills. Listening practice is especially important in classrooms where English is taught as a foreign language, that is, in situations where English is not the language of the country and where opportunities for hearing English outside the classroom are few. Students in such environments often learn to read and write English, and even to speak English, but they frequently have great difficulty comprehending English spoken to them.Plenty of Chinese students claim that the listening test is the most difficult part of English test. As a result, teachers should take particular pains to avoid such simplification and to provide opportunities for students to hear the kind of language that will prepare them for situations outside the classroom. Teachers should try to anticipate the kind of English the students will hear when the opportunity arises for them to be in an English-speaking country. In this article, I start with the factors which may exert an influence on listening comprehension, shows ways to improve listening comprehension and design some useful activities in the class. And I also focus on the activities of English listening teaching and insist that the activities should be both emphasized on the process of listening and the result of listening, due to different situations.
基金This work was supported by the National Natural Science Foundation of China (No. 10174079)the fund for the qualified researchers in the Zhejiang University of Technology, P. R. China.
文摘A method based on the diffraction theory for estimating the three-dimensional (3D) focusing performance of the compound refractive X-ray lenses is presented in this paper. As a special application, the 3D X-ray intensity distribution near the focus is derived for a plano-concave compound refractive X-ray lens. Moreover, the computer codes are developed and some results of 3D focusing performance for a compound refractive X-ray lens with Si material are shown and discussed.
