Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system

This paper presents a genetic algorithm (GA) for parallel redundancy optimization in series-parallel power systems exhibiting multi-state behavior, optimizing the reliability subject to constraints. The components are binary and chosen from a list of products available in the market, and are being characterized by their feeding capacity, reliability, cost and weight. System reliability is defined as the ability to satisfy consumer demand and is presented as a piecewise cumulative load curve. In GA, to handle infeasible solutions penalty strategies are used. Penalty technique keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from sides of, both, feasible and infeasible regions. We here present a dynamic adaptive penalty function which helps the algorithm to search efficiently for optimal/near optimal solution. To evaluate system reliability, a fast procedure, based on universal generating function, is used. An example considering a multi-state series-parallel power system is solved considering both homogeneous and heterogeneous types of redundancy. Also an example considering price discounts is solved. The effectiveness of the penalty function and the proposed algorithm is studied and shown graphically.     

基于fused惩罚的稀疏主成分分析

本文旨在研究基于fused惩罚的稀疏主成分分析方法，以适用于相邻变量之间高度相关甚至完全相等的数据情形。首先，从回归分析角度出发，提出一种求解稀疏主成分的简便思路，给出一种广义的稀疏主成分模型—— GSPCA模型及其求解算法，并证明在惩罚函数取1-范数时，该模型与现有的稀疏主成分模型——SPC模型的求解结果一致。其次，本文提出将fused惩罚与主成分分析相结合，得到一种fused稀疏主成分分析方法，并从惩罚性矩阵分解和回归分析两个角度，给出两种模型形式。在理论上证明了两种模型的求解结果是一致的，故将其统称为FSPCA模型。模拟实验显示，FSPCA模型在处理相邻变量之间高度相关甚至完全相等的数据集上的表现良好。最后，将FSPCA模型应用于手写数字识别，发现与SPC模型相比，FSPCA模型所提取的主成分具备更好的解释性，这使得该模型更具实用价值。     

An m-estimation-based model selection criterion with a data-oriented penalty

In Wu and Zen (1999), a linear model selection procedure based on M-estimation is proposed, which includes many classical model selection criteria as its special cases, and it is shown that the selection procedure is strongly consistent for a variety of penalty functions. In this paper, we will investigate its small sample performances for some choices of fixed penalty functions. It can be seen that the performance varies with the choice of the penalty. Hence, a randomized penalty based on observed data is proposed, which preserves the consistency property and provides improved performance over a fixed choice of penalty functions.     

索赔次数的开放式混合泊松分布研究

本文建立了索赔次数的多风险类别混合泊松模型。首先,考虑索赔次数的零膨胀、厚尾性和异质性等特征,建立风险类别待定的开放式混合泊松模型,开放式结构使该模型对实际数据的多样特征和风险类别具有良好的自适应性;其次,定义混合权重参数的iSCAD惩罚函数,实现对权重参数的筛选;最后,借助EM算法求得模型参数,实现对各风险类别下索赔次数的估计。借助iSCAD惩罚函数,给出最优混合数,避免传统混合模型中主观选择的弊端,克服传统混合模型中结构复杂、参数估计没有显式表达式、估计结果不便于解释等问题。基于三组风险特征多样数据的实证分析,本文发现该模型可以显著改进现有模型的拟合效果。     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

中国传统刑罚思想对毛泽东刑罚思想的影响

长期以来，中外研究毛泽东思想的学者，多注重于对毛泽东哲学思想、军事思想、建党学说、文艺理论等方面的研究，而对毛泽东法律思想和法制实践，一直没有有组织地开展全面系统的研究工作。笔者认为，毛泽东法律思想，尤其是刑罚思想具有很强的研究价值。本文将从中国传统的刑罚思想入手来阐述毛泽东的刑罚思想，试图解析中国传统刑罚思想对毛泽东刑罚思想的影响。     

论中国古代的司法时令制度

“刑以秋冬”的司法时令思想有确切文字记载的是春秋时期,但这一思想的起源还应早于此。自汉代起,“刑以秋冬”的思想逐渐发展成我国古代的一项司法制度,并最终在唐代得到完善。在发展的过程中,轻罪与时令的关系逐渐断绝,而关于死刑行刑的规定却越来越详细。司法时令制度的哲学基础是“天人合一”、“天人相关”的思想,其现实基础是以农业为主的经济状况。     

试论西周初期的“德”观念——以今文《周书》为中心的考察

周初之“德”是一个较为宽泛的观念,它可以分为以德配天、敬德保民、明德慎罚、任德尚贤四个重要组成部分,其价值与意义主要体现在政治领域,“德”是周人为论证政权的合法性及谋求政权的持久性而提出的。从内涵上来看,“德”字在周初尽管有了道德的意义,但“德”观念在人自身内在心灵的领域尚未达到深入的程度,“德”的外在意义在周初占有主导地位,它与后世具有普遍意义的个体心性道德还有一定的差距,并不等同于我们今天所说的“道德”。     

Mixture Models in View of Evidential Analysis

In some practical inferential situations, it is needed to mix some finite sort of distributions to fit an adequate model for multi-modal observations. In this article, using evidential analysis, we determine the sample size for supporting hypotheses about the mixture proportion and homogeneity. An Expectation-Maximization algorithm is used to evaluate the probability of strong misleading evidence based on modified likelihood ratio as a measure of support.     

宪法学上的一种死刑观——以美国宪法原理和判例为中心的考察

时至今日,不论人们是否赞同死刑废止论,死刑的存废之争已具有毋庸置疑的学理价值和实践意义。我国虽然也存在这种讨论,但长期以来,这些讨论主要是在刑法学界中展开的,最终形成的主流观点是主张贯彻少杀、慎杀的刑事政策,其间似乎欠缺坚实的实定法学框架之内的学理论证,而仅囿于单纯的道德、政治或刑事政策上的考量与论说。有鉴于此,笔者力图从宪法学出发另辟蹊径,论述宪法上的酷刑禁止原理,并主要通过分析以现代美国的判例与学说为代表的死刑酷刑论以及这种论证的结构和策略,揭示现代宪法的死刑观,并透视其对完善我国死刑制度所可能具有的借镜意义。