A connection between computer science and fuzzy theory: midpoints and running time of computing

Following the mathematical formalism introduced by M. Schellekens [Elec- tronic Notes in Theoret. Comput. Sci. 1 (1995), 211-232] in order to give a common foundation for Denotational Semantics and Complexity Analysis, we obtain an application of the theory of midpoints for asymmetric distances de ned between fuzzy sets to the complexity analysis of algorithms and pro- grams. In particular we show that the average running time for the algorithm known as Largetwo is exactly a midpoint between the best and the worst case running time of computingPeer Reviewe

A least squares approach to Principal Component Analysis for interval valued data

Principal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA) recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.Principal Component Analysis, Least squares approach, Interval valued data, Chemical data

Quick Response Practices at the Warehouse of Ankor

In the warehouse of Ankor, a wholesaler of tools and garden equipment, various problems concerning the storage and retrieval of products arise. For example, heavy products have to be retrieved prior to light products to prevent damage. Furthermore, the layout of the warehouse differs from the layout generally assumed in literature. The goal of this research was to determine storage locations for the products and a routing method to obtain sequences in which products are to be retrieved from their locations. It is shown that despite deviations from the "normal" case, similar savings in route length can be obtained by adapting existing solution techniques. Total labor savings are far less than expected on basis of assumptions made in literature. With a minimum of adaptations to the current situation the average route length can be decreased by 30 %. There is no need for complex techniques.storage;warehousing;optimization;case study;routing

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

Approximate range searching☆☆A preliminary version of this paper appeared in the Proc. of the 11th Annual ACM Symp. on Computational Geometry, 1995, pp. 172–181.

AbstractThe range searching problem is a fundamental problem in computational geometry, with numerous important applications. Most research has focused on solving this problem exactly, but lower bounds show that if linear space is assumed, the problem cannot be solved in polylogarithmic time, except for the case of orthogonal ranges. In this paper we show that if one is willing to allow approximate ranges, then it is possible to do much better. In particular, given a bounded range Q of diameter w and ε>0, an approximate range query treats the range as a fuzzy object, meaning that points lying within distance εw of the boundary of Q either may or may not be counted. We show that in any fixed dimension d, a set of n points in Rd can be preprocessed in O(n+logn) time and O(n) space, such that approximate queries can be answered in O(logn(1/ε)d) time. The only assumption we make about ranges is that the intersection of a range and a d-dimensional cube can be answered in constant time (depending on dimension). For convex ranges, we tighten this to O(logn+(1/ε)d−1) time. We also present a lower bound for approximate range searching based on partition trees of Ω(logn+(1/ε)d−1), which implies optimality for convex ranges (assuming fixed dimensions). Finally, we give empirical evidence showing that allowing small relative errors can significantly improve query execution times

Pattern Recognition in Stock Data

Finding patterns in high dimensional data can be difficult because it cannot be easily visualized. There are many different machine learning methods to fit data in order to predict and classify future data but there is typically a large expense on having the machine learn the fit for a certain part of a dataset. We propose a geometric way of defining different patterns in data that is invariant under size and rotation. Using a Gaussian Process, we find that pattern within stock datasets and make predictions from it