In this paper, a discussion is devoted to the theory and method of a reversed-frame normalization design to robust flight control system. The robust stability theory of the normal transfer function matrix with the sam...In this paper, a discussion is devoted to the theory and method of a reversed-frame normalization design to robust flight control system. The robust stability theory of the normal transfer function matrix with the same characteristic gain loci is proved. An example of flight control system design shows the application and advantage of this method.展开更多
In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong...In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (】0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (】0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.展开更多
The FY-2C meteorological satellite had been working well for 8 years as of October 19, 2012. Though the satellite was originally designed to work for 3 years, it still operates steadily. As the nation's first oper...The FY-2C meteorological satellite had been working well for 8 years as of October 19, 2012. Though the satellite was originally designed to work for 3 years, it still operates steadily. As the nation's first operational first-generation geo- stationary meteorological satellite, FY-2C works together with the following satellites FY-2D, FY-2E and FY-2F, in a operational mode of two operational plus two backup ones. FY-2C, FY-2D and FY-2E were the second batch satellites of FY-2 series展开更多
Robust optimization approach for aerodynamic design has been developed and applied to supercritical wing aerodynamic design. The aerodynamic robust optimization design system consists of genetic optimization algorithm...Robust optimization approach for aerodynamic design has been developed and applied to supercritical wing aerodynamic design. The aerodynamic robust optimization design system consists of genetic optimization algorithm, improved back propagation （BP） neural network and deformation grid technology. In this article, the BP neural network has been improved in two major aspects to enhance the training speed and precision. Uniformity sampling is adopted to generate samples which will be used to establish surrogate model. The testing results show that the prediction precision of the improved BP neural network is reliable. On the assumption that the law of Mach number obeys normal distribution, supercritical wing configuration considering fuselage interfering of a certain aerobus has been taken as a typical example, and five design sections and twist angles have been optimized. The results show that the optimized wing, which considers robust design, has better aerodynamic characteristics. What＇s more, the intensity of shock wave has been reduced.展开更多
This paper obtains asymptotic normality for double array sum of linear time series zeta(t), and gives its application in the regression model. This generalizes the main results in [1].
To develop uniform and seismic environment-dependent design spectrum,common acceleration response spectral characteristics need to be identified.In this paper,a bi-normalized response spectrum (BNRS) is proposed,which...To develop uniform and seismic environment-dependent design spectrum,common acceleration response spectral characteristics need to be identified.In this paper,a bi-normalized response spectrum (BNRS) is proposed,which is defined as a spectrum of peak response acceleration normalized with respect to peak acceleration of the excitation plotted vs.the natural period of the system normalized with respect to the spectrum predominant period,Tp.Based on a statistical analysis of records from the 1999 Chi-Chi earthquake,the conventionally normalized response spectrum(NRS) and the BNRS are examined to account for the effects of soil conditions,epicentral distance,hanging wall and damping.It is found that compared to the NRS the BNRS is much less dependent on these factors.Finally,some simple relationships between the BNRS for a specified damping ratio and that for a damping ratio of 5%,and between the spectra predominant period and epicentral distance for different soil types are provided.展开更多
Using neural networks for supervised learning means learning a function that maps input <em>x</em> to output <em>y</em>. However, in many applications, the inverse learning is also wanted, <...Using neural networks for supervised learning means learning a function that maps input <em>x</em> to output <em>y</em>. However, in many applications, the inverse learning is also wanted, <em>i.e.</em>, inferring <em>y</em> from <em>x</em>, which requires invertibility of the learning. Since the dimension of input is usually much higher than that of the output, there is information loss in the forward learning from input to output. Thus, creating invertible neural networks is a difficult task. However, recent development of invertible learning techniques such as normalizing flows has made invertible neural networks a reality. In this work, we applied flow-based invertible neural networks as generative models to inverse molecule design. In this context, the forward learning is to predict chemical properties given a molecule, and the inverse learning is to infer the molecules given the chemical properties. Trained on 100 and 1000 molecules, respectively, from a benchmark dataset QM9, our model identified novel molecules that had chemical property values well exceeding the limits of the training molecules as well as the limits of the whole QM9 of 133,885 molecules, moreover our generative model could easily sample many molecules (<em>x</em> values) from any one chemical property value (<em>y</em> value). Compared with the previous method in the literature that could only optimize one molecule for one chemical property value at a time, our model could be trained once and then be sampled any multiple times and for any chemical property values without the need of retraining. This advantage comes from treating inverse molecule design as an inverse regression problem. In summary, our main contributions were two: 1) our model could generalize well from the training data and was very data efficient, 2) our model could learn bidirectional correspondence between molecules and their chemical properties, thereby offering the ability to sample any number of molecules from any <em>y</em> values. In conclusion, our findings revealed the efficiency and effectiveness of using invertible neural networks as generative models in inverse molecule design.展开更多
This paper proposes a new kind of generalized Friendman's urn model,which with adaptive nonhomogeneous generating matrix.This model may be applied in sequential medical experiment.In this model some limit theorems...This paper proposes a new kind of generalized Friendman's urn model,which with adaptive nonhomogeneous generating matrix.This model may be applied in sequential medical experiment.In this model some limit theorems (strong consistency and asymptot- ical normality) have been obtained.展开更多
基金This paper has been supported by the National Natural Science Foundation of Chaina.
文摘In this paper, a discussion is devoted to the theory and method of a reversed-frame normalization design to robust flight control system. The robust stability theory of the normal transfer function matrix with the same characteristic gain loci is proved. An example of flight control system design shows the application and advantage of this method.
基金supported by National Natural Science Foundation of China (Grant Nos. 51135003, U1234208, 51205050)New Teachers' Fund for Doctor Stations of Ministry of Education of China (Grant No.20110042120020)+1 种基金Fundamental Research Funds for the Central Universities, China (Grant No. N110303003)China Postdoctoral Science Foundation (Grant No. 2011M500564)
文摘In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (】0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (】0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.
文摘The FY-2C meteorological satellite had been working well for 8 years as of October 19, 2012. Though the satellite was originally designed to work for 3 years, it still operates steadily. As the nation's first operational first-generation geo- stationary meteorological satellite, FY-2C works together with the following satellites FY-2D, FY-2E and FY-2F, in a operational mode of two operational plus two backup ones. FY-2C, FY-2D and FY-2E were the second batch satellites of FY-2 series
文摘Robust optimization approach for aerodynamic design has been developed and applied to supercritical wing aerodynamic design. The aerodynamic robust optimization design system consists of genetic optimization algorithm, improved back propagation （BP） neural network and deformation grid technology. In this article, the BP neural network has been improved in two major aspects to enhance the training speed and precision. Uniformity sampling is adopted to generate samples which will be used to establish surrogate model. The testing results show that the prediction precision of the improved BP neural network is reliable. On the assumption that the law of Mach number obeys normal distribution, supercritical wing configuration considering fuselage interfering of a certain aerobus has been taken as a typical example, and five design sections and twist angles have been optimized. The results show that the optimized wing, which considers robust design, has better aerodynamic characteristics. What＇s more, the intensity of shock wave has been reduced.
基金the National Natural ScienceFoundation of China(19971001)
文摘This paper obtains asymptotic normality for double array sum of linear time series zeta(t), and gives its application in the regression model. This generalizes the main results in [1].
基金Heilongjiang Natural Science Foundation Under Project No.ZGJ03-03the Research Fund for the Doctoral Program of Higher Education of China Through Project No.20030213042
文摘To develop uniform and seismic environment-dependent design spectrum,common acceleration response spectral characteristics need to be identified.In this paper,a bi-normalized response spectrum (BNRS) is proposed,which is defined as a spectrum of peak response acceleration normalized with respect to peak acceleration of the excitation plotted vs.the natural period of the system normalized with respect to the spectrum predominant period,Tp.Based on a statistical analysis of records from the 1999 Chi-Chi earthquake,the conventionally normalized response spectrum(NRS) and the BNRS are examined to account for the effects of soil conditions,epicentral distance,hanging wall and damping.It is found that compared to the NRS the BNRS is much less dependent on these factors.Finally,some simple relationships between the BNRS for a specified damping ratio and that for a damping ratio of 5%,and between the spectra predominant period and epicentral distance for different soil types are provided.
文摘Using neural networks for supervised learning means learning a function that maps input <em>x</em> to output <em>y</em>. However, in many applications, the inverse learning is also wanted, <em>i.e.</em>, inferring <em>y</em> from <em>x</em>, which requires invertibility of the learning. Since the dimension of input is usually much higher than that of the output, there is information loss in the forward learning from input to output. Thus, creating invertible neural networks is a difficult task. However, recent development of invertible learning techniques such as normalizing flows has made invertible neural networks a reality. In this work, we applied flow-based invertible neural networks as generative models to inverse molecule design. In this context, the forward learning is to predict chemical properties given a molecule, and the inverse learning is to infer the molecules given the chemical properties. Trained on 100 and 1000 molecules, respectively, from a benchmark dataset QM9, our model identified novel molecules that had chemical property values well exceeding the limits of the training molecules as well as the limits of the whole QM9 of 133,885 molecules, moreover our generative model could easily sample many molecules (<em>x</em> values) from any one chemical property value (<em>y</em> value). Compared with the previous method in the literature that could only optimize one molecule for one chemical property value at a time, our model could be trained once and then be sampled any multiple times and for any chemical property values without the need of retraining. This advantage comes from treating inverse molecule design as an inverse regression problem. In summary, our main contributions were two: 1) our model could generalize well from the training data and was very data efficient, 2) our model could learn bidirectional correspondence between molecules and their chemical properties, thereby offering the ability to sample any number of molecules from any <em>y</em> values. In conclusion, our findings revealed the efficiency and effectiveness of using invertible neural networks as generative models in inverse molecule design.
基金This work is supported by a grant of National University of Singapore(RP 3972712)by partially National Science Foundation of
文摘This paper proposes a new kind of generalized Friendman's urn model,which with adaptive nonhomogeneous generating matrix.This model may be applied in sequential medical experiment.In this model some limit theorems (strong consistency and asymptot- ical normality) have been obtained.
