This paper takes Gan Fuxi model as the theoretical basis, firstly verifies its feasibility for predicting the density and elastic modulus properties of glass fibers, and then deduces the density and ...elastic modulus additivity coefficients of TFe2O3 of the iron series glass fibers system and predicts the density and elastic modulus of this system, which lays a foundation for the Gan Fuxi model to be used in the design of the composition of the iron series glass fibers and prediction of their properties. The results of the study show that VFe=6.8142NTFe2O32−79.6131NTFe2O3+276.0759 when 3.5 % ≤ NTFe2O3 ≤ 5.6 % and VFe=43.4 when 5.6 % ≤ NTFe2O3≤ 7.17 %, and the prediction relative errors are 0 ∼ 2.15 %. When NTFe2O3 ≤ 9 %, the modulus of elasticity additivity coefficient of TFe2O3 is ETFe2O3×10−5=−19kg/cm2, and within the studied components, 73.9 % of the component relative errors fall in the range of 0 ∼ 10 %.
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The conventional Monte Carlo simulation may not be efficient enough for reliability evaluation of composite power systems. The cross-entropy (CE) algorithm is a promising state-of-the-art fast ...sampling method, while it has not been well developed in this field due to the implicit probability distributions of penetrated renewable energies. Specifically, the CE sampling requires the distributions of interest to be explicit and parametric, while some preconceived probabilistic distribution functions (PDFs) such as the Weibull distribution of wind speed make the results to deviate from the reality sometimes. In this study, a data-driven efficient approach for reliability evaluation of power systems with wind penetration is proposed utilising generative adversarial networks (GANs) and CE sampling. The distributions of wind speeds in multiple wind farms are estimated by GANs considering their spatial correlation without any prior knowledge. With the trained generative network mapping from the explicit Gaussian noise to the raw wind speed data, the CE sampling is successfully enabled to efficiently sample the system states with implicit PDFs, which are associated with wind speeds and component failures. A real wind speed dataset and the RTS testing system are utilised to verify the proposed integrated method, including the accuracy of distribution estimation and reliability evaluation result, as well as the speed-up efficiency of sampling.
The modulation transfer function (MTF) of a medical imaging display is typically determined by measuring its response to square waves (bar patterns), white noise, and/or line stimuli. However, square ...waves and white noise methods involve capture and analysis of multiple images and are thus quite tedious. Measurement of the line-spread function (LSF) offers a good alternative. However, as previously reported, low-frequency response obtained from the LSF method is not as good as that obtained from measurement of edge-spread function (ESF). In this paper, we present two methods for evaluating the MTF of a medical imaging display from its ESF. High degree of accuracy in the higher frequency region (near the Nyquist frequency of the system) was achieved by reducing the noise. In the first method, which is a variant of the Gans' original method, the periodic raster noise is reduced by subtracting a shifted ESF from the ESF. The second method employs a low-pass differentiator (LPD). A novel near maximally flat LPD with the desired cut-off frequency was designed for this purpose. Noise reduction in both the methods was also achieved by averaging over large portions of the image data to form the ESF. Experimental results show that the MTF obtained by these methods is comparable to that obtained from the square wave response. Furthermore, the MTFs of rising and falling edges of a cathode ray tube (CRT) were measured. The results show that the rising and falling vertical MTFs are practically the same, whereas the rising horizontal MTF is poorer than the falling horizontal MTF in the midfrequency region.
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