Quality Evaluation in Flexible Machining Systems: A Flexible Fixture Case Study
Most of the studies in flexible manufacturing systems address the issues of flexibility, productivity, cost, etc. The impact of flexible lines on product quality is less studied. This paper presents a quantitative model based on Markov chain analysis to evaluate quality performance of a flexible machining system. A case study of flexible fixture is provided to illustrate the applicability of the method.

title: Bottlenecks with respect to due-time performance in pull serial production lines
abstract: The problem of satisfying customers demand in pull serial production lines is addressed. The level of demand satisfaction is quantified by the due-time performance (DTP), which is defined as the probability to ship to the customer a required number of parts during a fixed time interval. The definitions of DTP bottlenecks are introduced and a method for their identification is developed.

title: Text and picture segmentation by the distribution analysis of wavelet coefficients
abstract: This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.

title: Construct robust rule sets for classification
abstract: We study the problem of computing classification rule sets from relational databases so that accurate predictions can be made on test data with missing attribute values. Traditional classifiers perform badly when test data are not as complete as the training data because they tailor a training database too much. We introduce the concept of one rule set being more robust than another, that is, able to make more accurate predictions on test data with missing attribute values. We show that the optimal class association rule set is as robust as the complete class association rule set. We then introduce the k-optimal rule set, which provides predictions exactly the same as the optimal class association rule set on test data with up to k missing attribute values. This leads to a hierarchy of k-optimal rule sets in which decreasing size corresponds to decreasing robustness, and they all more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated by the optimal association rule mining approach performs better than that by the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data.

title: The Empirical Studies on Quality Benefits of Reusing Software Components
abstract: The benefits of reusing software components have been studied for many years. Several previous studies have concluded that reused components have fewer defects in general than non-reusable components. However, few of these studies have gone a further step, i.e., investigating which type of defects has been reduced because of reuse. Thus, it is suspected that making a software component reusable will automatically improve its quality. This paper presents an on-going industrial empirical study on the quality benefits of reuse. We are going to compare the defects types, which are classified by ODC (orthogonal defect classification), of the reusable component vs. the non-reusable components in several large and medium software systems. The intention is to figure out which defects have been reduced because of reuse and the reasons of the reduction.

title: Using fixed point theorems to model the binding in protein-protein interactions
abstract: The binding in protein-protein interactions exhibits a kind of biochemical stability in cells. The mathematical notion of fixed points also describes stability. A point is a fixed point if it remains unchanged after a transformation by a function. Many points may not be a fixed point, but they may approach a stable status after multiple steps of transformation. In this paper, we define a point as a protein motif pair consisting of two traditional protein motifs. We propose a function and propose a method to discover stable motif pairs of this function from a large protein interaction, sequence data set. There are many interesting properties for this function (for example, the convergence). Some of them are useful for gaining much efficiency in the discovery of those stable motif pairs; some are useful for explaining why our proposed fixed point theorems are a good way to model the binding of protein interactions. Our results are also compared to biological results to elaborate the effectiveness, of our method.

title: Rule Evaluations, Attributes, and Rough Sets: Extension and a Case Study
abstract: Manually evaluating important and interesting rules generated from data is generally infeasible due to the large number of rules extracted. Different approaches such as rule interestingness measures and rule quality measures have been proposed and explored previously to extract interesting and high quality association rules and classification rules. Rough sets theory was originally presented as an approach to approximate concepts under uncertainty. In this paper, we explore rough sets based rule evaluation approaches in knowledge discovery. We demonstrate rule evaluation approaches through a real-world geriatric care data set from Dalhousie Medical School. Rough set based rule evaluation approaches can be used in a straightforward way to rank the importance of the rules. One interesting system developed along these lies in HYRIS (HYbrid Rough sets Intelligent System). We introduce HYRIS through a case study on survival analysis using the geriatric care data set.