A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

The exponential distribution analog of the Grubbs–Weaver method

Abstract

Grubbs and Weaver (1947 Grubbs, F. E., and C. L. Weaver. 1947. The best unbiased estimate of population standard deviation based on group ranges. Journal of the American Statistical Association 42 (238):224–41. doi: 10.2307/2280652.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggest a minimum-variance unbiased estimator for the population standard deviation of a normal random variable, where a random sample is drawn and a weighted sum of the ranges of subsamples is calculated. The optimal choice involves using as many subsamples of size eight as possible. They verified their results numerically for samples of size up to 100, and conjectured that their “rule of eights” is valid for all sample sizes. Here we examine the analogous problem where the underlying distribution is exponential and find that a “rule of fours” yields optimality and prove the result rigorously.     

Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil

Motivated by a proposal of the local authority for improving the existing healthcare system in the Parana State in Brazil, this article presents an optimization-based model for developing a better system for patients by aggregating various health services offered in the municipalities of Parana into some microregions. The problem is formulated as a multi-objective partitioning of the nodes of an undirected graph (or network) with the municipalities as the nodes and the roads connecting them as the edges of the graph. Maximizing the population homogeneity in the microregions, maximizing the variety of medical procedures offered in the microregions, and minimizing the inter-microregion distances to be traveled by patients are considered as three objective functions of the problem. An integer-coded multi-objective genetic algorithm is adopted as the optimization tool, which yields a significant improvement to the existing healthcare system map of the Parana State. The results obtained may have a strong impact on the healthcare system management in Parana. The model proposed here could be a useful tool to aid the decision-making in health management, as well as for better organization of any healthcare system, including those of other Brazilian States.     

Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.     

A new metamodel-based method for solving semi-expensive simulation optimization problems

In this article, a new algorithm for rather expensive simulation problems is presented, which consists of two phases. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is replaced by a valid metamodel. In addition, a new optimization algorithm is presented. To evaluate the performance of the proposed algorithm, it is applied to the (s,S) inventory problem as well as to five test functions. Numerical results show that the proposed algorithm leads to better solutions with less computational time than the corresponding metamodel-based algorithm.     

Multinomial Selection in the Presence of Infinite Alternatives

We propose a new procedure for the multinomial selection problem to solve a real problem of any modern Air Force: the elaboration of better air-to-air tactics for Beyond Visual Range air-to-air combat that maximize its aircraft survival probability H(θ, ω), as well as enemy aircraft downing probability G(θ, ω). In this study, using a low-resolution simulator with generic parameters for the aircraft and missiles, we could increase an average success rate of 16.69% and 16.23% for H(θ, ω) and G(θ, ω), respectively, to an average success rate of 76.85% and 79.30%. We can assure with low probability of being wrong that the selected tactic has greater probability of yielding greater success rates in both H(θ, ω) and G(θ, ω) than any simulated tactic.     

中国决策情境下的多源流模型及其优化研究

作为当今公共政策研究领域最炙手可热的理论模型之一,约翰·金登的多源流分析理论业已得到国内外学者的广泛应用与认可。在对该模型的相关内容进行简单铺陈后,通过辨析国内外不同的政策主体参与逻辑,进一步厘清了我国公共决策的独有特征。研究重点是在整合提炼西方既有修正思路的基础上,沿袭模型的模糊性假设,结合我国具体实践案例,对多源流模型进行优化与补充。研究表明,中国决策情境下的多源流模型优化主要有提高源流间的融合性、政治源流的核心化、焦点事件的独立化以及特殊情况下的临时决策等四种路径,四种优化与补充路径的有机结合将为进一步提升模型适用性提供帮助。     

采矿工程专业人才培养方案优化与实践

随着全球经济一体化的发展,我国煤炭行业得到了快速发展,煤炭工业需要大量的高素质复合型采矿工程专业毕业生,因此,修订和完善采矿专业人才培养方案具有重要意义。河南理工大学采矿工程专业为了不断提高人才培养质量,对采矿工程专业人才培养方案进行了优化,2010年10月通过全国工程教育专家委员会的认证工作。新的人才培养方案已经得到实施,效果明显。     

大学生专业操作能力优化教学模式的构建——以人力资源管理专业课程为例

高校人才培养与企业实际需求存在匹配度较低的问题,因而有必要构建新的大学生专业操作能力优化教学模式.新的教学模式涵盖目标描述、目标支撑、流程管理、经验教训、工具与表单五个方面.它可以让学生的学习从抽象到具体,从理论到实践,从宏观到微观,从被动接受到主动思考,达到对该模块更深刻的学习、掌握与运用,从而更加适应企业与社会的需求.     

地方高校大学英语课程结构的优化

本文阐述了地方高校大学英语教学长期延用的课程结构体系中存在的一些主要问题,并针对这些问题,提出了建立新型课程结构应该考虑的五个方面。