Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Reason maintenance in constraint satisfaction

Research effort in constraint satisfaction has traditionally been devoted to curbing the exponential cost of search through the methods of backtracking and problem reduction. These methods serve the overall goal of avoiding redundant computations and reduce the search space needed to derive a solution. The advent of reason maintenance systems (or RMSs) in recent years have provided the necessary machinery to dynamically determine the causes of failure, revise assumptions and avoid redundancy in backtracking. In addition, RMS-based CSP solvers promote program design clarity by separating control and inference mechanisms. However, it is well known that classical breadth-first control of the RMS incurs an exponential amount of work when only a few solutions are required. Furthermore, research effort in reason maintenance technology has neither consolidated nor clearly defined a direction for improving its performance. The deployment of such an RMS-based solver for CSPs is also a topic that has only been theoretically evaluated against classical constraint satisfaction techniques. Their derived similarities on a propositional level have promoted the inter-migration of solutions and ideas from both fields, but an evaluation of their empirical performance and comparison of respective problem solving models remains an area that has shown little growth.Doctor of Philosophy (SCE

Sciences]- biology and genetics.

Researchers of HIV-1 are today, still unable to determine exactly the biological mechanisms that cause AIDS. Various mechanisms have been hypothesized and their existences have been experimentally verified, but whether they are sufficient to account for the observed disease progression is still in question. To better understand the phenomena, HIV-1 researchers turn to scientific models for hypothesis verification. Modeling methods which rely on differential calculus to describe population dynamics, can be inconvenient for predicting nonuniform interactions on a spatial dimension. Multi-Agent (or MA) modeling approaches, on the other hand, views the immune system as a hierarchical structure of cooperating and competing agents, operating with highly coupled behaviours to exhibit emergent complexity. We adopt the latter approach to simulate the pathogenesis of HIV-1. We show the model design and the emergent results for four well-know

A hybrid agent-based approach for modeling microbiological systems

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 103 cells and 1.2×106 molecules. The model produces cell migration patterns that are comparable to laboratory observations