Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains

In the real-world manufacturing/distribution planning decision (MDPD) integration problems in supply chains, the environmental coefficients and parameters are normally imprecise due to incomplete and/or unavailable information. This work presents a fuzzy linear programming approach based on the possibility theory. It applies this approach to solve multi-product and multi-time period MDPD problems with imprecise goals and forecast demand by considering the time value of money of related operating cost categories. The proposed approach attempts to minimize the total manufacturing and distribution costs by considering the levels of inventory, subcontracting and backordering, the available machine capacity and labor levels at each source, forecast demand and available warehouse space at each destination. This study utilizes an industrial case study to demonstrate the feasibility of applying the proposed approach to practical MDPD problems. The primary contribution of this paper is a fuzzy mathematical programming methodology for solving the MDPD integration problems in uncertain environments.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

Identity-based trapdoor mercurial commitments and applications

In this paper, we first introduce the notion of identity-based trapdoor mercurial commitment which enjoys the advantages of both the identity-based trapdoor commitment and trapdoor mercurial commitment, while using the idea of “Customized Identity”. Inherently, an identity-based trapdoor mercurial commitment is an underlying building block for constructing identity-based (non-interactive) zero-knowledge sets. That is, a prover can commit to a set S in a way that reveals nothing about S and prove to a verifier, in zero-knowledge, statements of the form x∈S and x∉S. Besides, although the (non-interactive) proof is publicly verifiable, it is also bound to the identity of the prover in a way which is recognizable to any verifier.     

Conservative and aggressive rough SVR modeling

Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ?-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.     

Effective dimensions and relative frequencies

Consider the problem of calculating the fractal dimension of a set X consisting of all infinite sequences S over a finite alphabet Σ that satisfy some given condition P on the asymptotic frequencies with which various symbols from Σ appear in S. Solutions to this problem are known in cases where

(i)

the fractal dimension is classical Hausdorff or packing dimension (by work of Volkmann and Olsen), or

(ii)

the fractal dimension is effective (even finite-state) and the condition Pcompletely specifies an empirical distribution π over Σ, i.e., a limiting frequency of occurrence for every symbol in Σ.

In this paper, we show how to calculate the finite-state dimension (equivalently, the finite-state compressibility) of such a set X when the condition P only imposes partial constraints on the limiting frequencies of symbols. Our results automatically extend to less restrictive effective fractal dimensions (e.g., polynomial-time, computable, and constructive dimensions), and they have the classical results (i) as immediate corollaries. Our methods are nevertheless elementary and, in most cases, simpler than those by which the classical results were obtained.     

基于优势-直觉模糊粗糙模型的IT项目绩效审计评价规则获取

针对我国政府审计机关对政府投资的I`I}项目进行绩效审计评价规则知识获取的困难,考虑了条件属性取值 为优势精确值、分类结果为直觉模糊值的决策系统规则获取问题。首先比较条件属性值的大小,构建对象的优势部 域,再由对象邻域的直觉模糊值确定对象的上下近似;根据对象的上下近似和不同对象的直觉模糊值确定对象间的区 分关系,利用分辫矩阵给出知识约简和规则提取算法;最后将直觉模糊粗糙模型应用于政府I"I'项目绩效审计评价规 则的获取,得到了较为合理的IT项目绩效评价规则。     

巧用FANUC系统的刀具半径补偿功能

介绍了对刀尖补偿功能的作用、原理和使用方法 ,以车轮轮缘踏面的加工程序为例 ,说明刀具半径补偿功能的具体应用     

基于模糊理论的SAR图像分割算法研究与实现

SAR图像存在强烈的相干斑噪声,传统方法不能很好对其分割。文章基于模糊理论,通过选择图像特征,构造模糊集,借助最大隶属度原则进行了SAR图像分割算法的设计,并借助SAR图像分别进行了参数调节和窗口选择的实验,获得了满意的分割结果。实验结果表明,该算法对于SAR图像分割可行有效。     

On the complexity of binary samples

Consider a class of binary functions h: X→{ − 1, + 1} on an interval . Define the sample width of h on a finite subset (a sample) S ⊂ X as ω _S (h) =  min _x ∈ S |ω _h (x)| where ω _h (x) = h(x) max {a ≥ 0: h(z) = h(x), x − a ≤ z ≤ x + a}. Let be the space of all samples in X of cardinality ℓ and consider sets of wide samples, i.e., hypersets which are defined as Through an application of the Sauer-Shelah result on the density of sets an upper estimate is obtained on the growth function (or trace) of the class , β > 0, i.e., on the number of possible dichotomies obtained by intersecting all hypersets with a fixed collection of samples of cardinality m. The estimate is .