Fluctuations of Turbulent Bed Shear Stress

With the assumption that the bed shear stress fluctuates in a lognormal fashion, the probability density function (PDF) of the standardized bed shear stress is derived as a function of the relative shear stress intensity. The PDF is more skewed with larger relative intensities, but approaches a Gaussian function when the relative intensity is small. The computed PDF agrees well with the reported experimental data for flows over a smooth boundary. The higher-order moments of the bed shear stress, skewness, and kurtosis, are shown analytically to be also dependent on the relative intensity. The theoretical dependencies are then compared to a number of measurements available in the literature. The Reynolds number effect on the relative intensity is also discussed.     

基于背景噪声的图像盲篡改检测

数字图像在成像过程中会产生特定的背景噪声,如果两幅不同噪声的图像拼接在一起,篡改区域和其他区的噪声会有差异。提出一种基于偏度统计特性的背景噪声估计算法,其通过对图像分块计算每块的噪声标准差,从而检测出噪声异常部分以达到篡改检测的目的。算法利用DCT变换去除原图细节部分,利用偏度统计特性估计噪声,利用条件最小值法求出噪声的标准差。算法改进了迭代求条件最小值法,利用微分方法求取最小值,避免了初始值设定问题,提高了算法的准确率。实验结果表明,提出的噪声估计算法正确率高,且对拼接篡改图像篡改检测有明显效果。     

Statistical Analysis for Decision Making

In this article we consider a generalization of the univariate g-and-h distribution to the multivariate situation with the aim of providing a flexible family of multivariate distributions that incorporate skewness and kurtosis. The approach is to modify the underlying random variables and their quantiles, directly giving rise to a family of distributions in which the quantiles rather than the densities are the foci of attention. Using the ideas of multivariate quantiles, we show how to fit multivariate data to our multivariate g-and-h distribution. This provides a more flexible family than the skew-normal and skew-elliptical distributions when quantiles are of principal interest. Unlike those families, the distribution of quadratic forms from the multivariate g-and-h distribution depends on the underlying skewness. We illustrate our methods on Australian athletes data, as well as on some wind speed data from the northwest Pacific.     

项目活动时间估算的适用性研究

在项目活动时间三次估算公式的基础上,根据活动时间分布,试图减少一个极端值的估算,得出两次估算方法,以此降低时间估算中的主观性和时间估算的次数,并对两种方法推导出项目活动时间均值和方差的计算路径,同时构造了样本假设检验的方法,在试验的基础上对两种估算进行比较,从而判断和改善项目活动时间估算的适用性.最后通过实证得出当偏差系数小于-0.4或大于0.2时,活动时间估算采用三次比较适宜;当偏差系数位于二者之间时,采用二次估算比较适宜.     

Modelling time-varying higher moments with maximum entropy density

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model of Engle [R. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50 (1982) 987–1007], the literature of modelling the conditional second moment has become increasingly popular in the last two decades. Many extensions and alternate models of the original ARCH have been proposed in the literature aiming to capture the dynamics of volatility more accurately. Interestingly, the Quasi Maximum Likelihood Estimator (QMLE) with normal density is typically used to estimate the parameters in these models. As such, the higher moments of the underlying distribution are assumed to be the same as those of the normal distribution. However, various studies reveal that the higher moments, such as skewness and kurtosis of the distribution of financial returns are not likely to be the same as the normal distribution, and in some cases, they are not even constant over time. These have significant implications in risk management, especially in the calculation of Value-at-Risk (VaR) which focuses on the negative quantile of the return distribution. Failed to accurately capture the shape of the negative quantile would produce inaccurate measure of risk, and subsequently lead to misleading decision in risk management. This paper proposes a solution to model the distribution of financial returns more accurately by introducing a general framework to model the distribution of financial returns using maximum entropy density (MED). The main advantage of MED is that it provides a general framework to estimate the distribution function directly based on a given set of data, and it provides a convenient framework to model higher order moments up to any arbitrary finite order k. However this flexibility comes with a high cost in computational time as k increases, therefore this paper proposes an alternative model that would reduce computation time substantially. Moreover, the sensitivity of the parameters in the MED with respect to the dynamic changes of moments is derived analytically. This result is important as it relates the dynamic structure of the moments to the parameters in the MED. The usefulness of this approach will be demonstrated using 5 min intra-daily returns of the Euro/USD exchange rate.     

Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models

Successful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values.     

Gradient histogram: Thresholding in a region of interest for edge detection

Selecting a threshold from the gradient histogram, a histogram of gradient magnitudes, of an image plays a crucial role in a gradient based edge detection system. This paper presents a methodology to determine the threshold from a gradient histogram generated using any kind of linear gradient operator on an image. We consider the image as a random process with dependent samples, model the gradient histogram using theories of random process and random input to a system, and determine a region of interest in the gradient histogram using certain properties of a probability density function. Standard histogram thresholding techniques are then used within the region of interest to get the threshold value. To obtain the edges, this threshold value is then used as the upper threshold of the hysteresis thresholding technique that follows the non-maximum suppression operation applied on the gradient magnitude image. The proposed methodology of determining a threshold in a gradient histogram is deduced through rigorous analysis and hence it helps in achieving consistently appreciable edge detection performance. Experimental results using different real-life and benchmark images are shown to demonstrate the effectiveness of the proposed technique.     

Eddy Taxonomy Methodology around a Submerged Barb Obstacle within a Fixed Rough Bed

Past research in environmental hydraulics has established the consideration that small- and large-scale turbulent eddy structures correspond to fast and slow fluctuations within a velocity time series measured at a fixed location. This work embraces this concept and develops an eddy taxonomy methodology to classify the prominent small- and large-scale eddies in the vicinity of an obstacle within a fixed rough bed. The previously documented visual interpretation technique is used in conjunction with a novel technique, which utilizes the statistical skew parameter, to quantify the moving-average time step at which large-scale eddies may be isolated from small-scale eddies. Thereafter, triple decomposition theory is employed and prominent spatial and temporal scales (i.e., integral length scales and periodicity) of small- and large-scale eddies are calculated. The eddy taxonomy methodology is implemented using acoustic Doppler velocimeter time-series measurements captured in the vicinity of an experimental model of a submerged barb obstacle—a hydraulic structure used for bank protection and increasing aquatic diversity. Implementation of the eddy taxonomy methodology using the streamwise velocity (u) time series and streamwise-vertical Reynolds stress (uw) time series provide similar results for the time step necessary to decompose large- from small-scale eddies. Eddy taxonomy results indicate the presence of large-scale, macroturbulent eddies throughout the barb test section with periodicity and length scales that agree with literature reported values. Additionally, small-scale bed derived eddies are most pronounced in the deflected flow regions where the barb obstacle has less influence upon the flow, while multiple small-scale eddies, including ejection, wake, and Kelvin–Helmotz associated eddies, persist in the downstream overtopping and wake regions of the barb obstacle.     

3D Characterization of super-smooth surfaces of diamond turned OFHC copper mirrors

In this study, super-smooth surfaces of oxygen-free high-conductivity copper mirrors obtained by single point diamond turning were characterized with an atomic force microscope from the three-dimensional (3D) point of view. The machining conditions were also studied. A program developed in this study for a unified approach to 3D surface finish assessment proposed by the Commission of European Community was used to obtain the 3D parameters. The feed rate was changed from 0.2 to 2.0 μm per revolution and 0.3-3.0 μm per revolution, respectively, in two experiments. By 3D roughness characterization of these surfaces, the optimal feed rate was found to be 1.4 μm per revolution when cutting depth was 1 μm or 0.9-1.2 μm per revolution at a cutting depth of 2 μm, where an S_q of 2.6 and 2.3 nm could be obtained, respectively. The suggested 3D roughness characterization parameters may include S_q, S_sk, S_ku, S_tr, S_al, S_Δq, and one functional parameter, such as S_bi.     

3D characterization of super-smooth surfaces of diamond turned OFHC copper mirrors

In this study, super-smooth surfaces of oxygen-free high-conductivity copper mirrors obtained by single point diamond turning were characterized with an atomic force microscope from the three-dimensional (3D) point of view. The machining conditions were also studied. A program developed in this study for a unified approach to 3D surface finish assessment proposed by the Commission of European Community was used to obtain the 3D parameters. In two experiments, the feed rate was changed from 0.2 to 2.0 μm per revolution and 0.3 to 3.0 μm per revolution, respectively. By 3D roughness characterization of these surfaces, the optimal feed rate was found to be 1.4 μm per revolution when the cutting depth was 1 μm or 0.9-1.2 μm per revolution at a cutting depth of 2 μm, where an S_q of 2.6 and 2.3 nm could be obtained, respectively. The suggested 3D roughness characterization parameters may include S_q, S_sk, S_ku, S_tr, S_al, S_Δq, and one functional parameter, such as S_bi.