A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms

Portfolio selection is a key issue in the business world and financial fields. This article presents a new decision making method of portfolio optimization (PO) issues in different risk measures by using new evolutionary computing method and cardinality constrains which is mentioned as hybrid meta-heuristic algorithms. Based on mean–variance (MV) Method by Markowitz we collected three risk levels; mean absolute deviation (MAD), semi variance (SV) and variance with skewness (VWS). The developed algorithms are Electromagnetism-like algorithm (EM), particle swarm optimization (PSO), genetic algorithm (GA), genetic network programming (GNP) and simulated annealing (SA). Also a diversification mechanism strategy is implemented and hybridized with the developed algorithms to increase the diversity and overcome local optimality. The sustainability of this proposed model is verified by 50 factories on the Iranian stock exchange. Finally, experimental results of proposed algorithms with cardinality constraint are compared with each other by four effective metrics in which the algorithms performance for achieving the optimal solution discussed. In addition, we have done the analysis of variance technique to confirm the validity and accurately analyze of the results which the success of this method was proved.     

The Identification of Data Distribution in Oil Production

Abstract

This article suggests nonparametric criteria for diagnosing changes in process condition based on analysis of data variation.

The developed criteria allow diagnosing changes in conditions of the studied process in cases of data distribution skewness, as well as cases when application of other criteria is not correct or complicated.

The suggested statistical criteria allow simplification of process diagnosis, which are described by multifractal, chaotic fluctuations, and their calculation algorithm can be easily realized.

The applicability of the diagnostic criteria has been proven both in modeled and practical examples of oil production.     

基于有偏t分布ARIVIAX模型的短期电价预测

基于正态分布假设的时间序列分析模型不能有效地处理电价的有偏厚尾性,在对电力市场现货电价的影响因素和波动规律综合分析的基础上,提出了一种基于有偏学生t分布ARMAX模型的短期电价预测方法。该方法可同时考虑电价分布的有偏厚尾性、多重周期性及其与负荷之间的非线性相关性。对PJM电力市场历史数据的算例研究表明,该方法计算量小,待估参数少。     

高层建筑脉动风压的非高斯峰值因子方法

为了更好地确定建筑表面的设计极值风压,以具有“靴型”造型的复杂超高层建筑为例,研究非高斯峰值因子的计算方法.在风洞试验的基础上对各风向角下的风压系数时程数据进行三阶、四阶矩统计量分析,研究该复杂超高层建筑表面风压的非高斯分布特性.分别通过基于零穿越率的峰值因子法、改进峰值因子法、Sadek Simiu法和改进Gumbel法对各风向角下风压数据的峰值因子进行估计,并在改进峰值因子法的基础上提出偏度非高斯峰值因子法.对各种方法的适用性及计算结果进行对比分析表明,改进峰值因子法在峰度小于3的情况下不适用;偏度非高斯峰值因子法与Sadek Simiu法相比,两者的计算结果十分吻合.     

基于偏态和峰度的丁胞传热管性能分析

建立含不同参数的正六边形截面丁胞传热管有限元分析模型,仿真研究雷诺数为5000~40000时丁胞组合类型、深度和直径对传热管中流体流动及传热性能的影响。此外,采用离散方法计算并分析丁胞参数对传热管表面偏态和峰度的影响。最终基于丁胞参数对偏态和峰度的影响规律对丁胞传热管的性能进行分析。研究结果表明,凹坑或凸起丁胞通过增强流体的回流效果及削弱流体边界层厚度,能够有效增强传热管内流体的流动及传热性能,且凹坑丁胞的影响优于凸起丁胞。此外,不同丁胞组合类型的影响表现为偏态越小、传热管的流动及传热综合性能越好。然而,对于凹坑丁胞传热管,其表面偏态越大、峰度越小,传热管的流动及传热综合性能越好,增加凹坑丁胞深度和直径可增大偏态、减小峰度。     

Four‐parameter beta distribution estimation and skewness test

Sequential tolerance control (STC) is a tolerance control methodology used in discrete parts manufacturing. Recently, an adaptive sphere‐fitting method for STC (ASF–STC) was developed to account for potential skewness in manufacturing operations' distributions, a factor not considered in conventional STC. ASF–STC offers significant improvements over conventional STC when such skewness exists. The direction of skewness of an operations' distribution is a necessary input to ASF–STC. Thus, a novel approach to determining the skewness of a distribution for small sample sizes is presented here. ASF–STC has an additional requirement of distribution information for each operation. The beta distribution is an ideal candidate here, as it is very flexible in shape. The literature on four‐parameter beta estimation is very limited, and their performance for small sample sizes is poor. STC was designed for low‐volume production, thus the estimation for small sample sizes is necessary here. This study presents a heuristic, based on the method of moments estimates for a beta distribution, that estimates the four parameters for a beta distribution with small sample size. Several computational results are provided to compare this heuristic to the best‐known procedure, with the heuristic found to perform better for the test problems considered. Copyright © 2002 John Wiley & Sons, Ltd.     

偏度与峰度在脉冲激光回波分析中的应用研究

将波形序列做平移处理,并结合其坐标信息可以计算波形的偏度与峰度,两者分别反映了波形的对称性和尖峭程度。在脉冲激光探测中,偏度和峰度可以用于对回波信号的特征分析,目标表面的反射率变化和起伏通常不会引起回波波形偏度和峰度的较大变化,只有当目标表面反射率或起伏出现突变时回波的偏度和峰度才会呈现出较大的值,而且不同突变情况下的回波的两参数的值也是不同的,这为脉冲激光探测的回波特征分析提供了重要依据。     

The Normal Probability Plot as a Tool for Understanding Data: A Shape Analysis from the Perspective of Skewness,Kurtosis, and Variability

Continuous quality improvement is an effort to improve the quality of products, processes, or services. A program intended to effectively implement such efforts begins with the collection and analysis of data. The primary purpose of the normal probability plot, which is one of the most frequently used graphical tools by quality practitioners and researchers, is for normality testing; however, the plot offers other valuable insights into data analysis that have rarely been addressed in the research community. This article provides an overview of distributional characteristics in the context of the four sample moments and investigates how variations in these moments affect the normal probability plot, focusing primarily on the presence of skewness and kurtosis and the effects of variability. This article then lays out a comprehensive analysis of how various statistical characteristics within a data set can influence the shape and corresponding properties of a normal probability plot, demonstrating how variations in the characteristics of the data can reveal or mask the degree of concavity, convexity, or the S shape in the plot, as well as the spread of the data about the mean and in the tails. This can provide engineers with a better understanding of the ways in which data “communicate” through the plot, thereby providing a better basis for initial assumptions, as well as facilitating more accurate model estimation and optimization results thereafter. Copyright © 2011 John Wiley & Sons, Ltd.     

Experimental analysis on particle fluctuation velocity in a horizontal air–solid two-phase pipe flow having a dune model

To further elucidate the mechanism of energy-conserving conveying in horizontal pneumatic conveying with the dune model, the high-speed particle image velocimetry is applied to measure particle fluctuation velocity near the minimum conveying velocity of the conventional pneumatic conveying. This study focuses on the effect of mounting dune models on the horizontal pneumatic conveying in terms of power spectrum, autocorrelation coefficients, two-point correlation coefficients, fluctuation intensity of particle velocity, skewness factor, and probability density function. It is found that the power spectrum peaks with the dune model are larger than those of the nondune system, suggesting the acceleration and suspending efficiency of the dune model, especially dune models mounted at the bottom of the pipe. Meanwhile, the profiles of particle fluctuation velocity intensity indicate that the large particle fluctuating energy is generated due to mounting the dune model so that the particles are more easily accelerated and suspended. This is one of the important reasons why the mounted dune model results in a low pressure drop and low minimum conveying velocity. Based on the distribution of skewness factor and probability density function, it is found that the particle fluctuation velocities of all cases follow the Gaussian distribution in the lower and middle parts of the pipe. The particle fluctuation velocities in the case of the dune models mounted at the bottom of the pipe obey the Gaussian-type fluctuation more.     

低信噪比条件下的调制方式识别

传统的调制方式识别方法通常假设接收信号是平稳随机过程,一般要在信噪比不低于10dB时才能取得较高的识别率。本文根据模拟调制信号的谱特性和谱相关特性,给出了低信噪比下的调制方式识别算法,理论分析和仿真结果表明,本文提出的算法,在低信噪比(3dB)条件下,仍然具有很高的识别率。