Vision-based neural network classifiers and their applications

A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel

On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

Box transformer substation (BTS) is an important power distribution environment. To ensure the safe and stable operation of the power distribution system, it is critical to monitor the BTS operation and diagnose its faults in a reliable manner. In the Internet of Things (IoT) environment, this paper aims to develop a real-time and accurate online strategy for BTS monitoring and fault diagnosis. The framework of our strategy was constructed based on the IoT technique, including a sensing layer, a network layer and an application layer. On this basis, a BTS fault diagnosis method was established with variable precision rough set (VPRS) as the pre-network and the radial basis function neural network (RBFNN) as the back-fed network. The VPRS and the RBFNN were selected, because the BTS faults have many characteristic parameters, with complex nonlinear relationship with fault modes. Finally, a prototype of our strategy was developed and applied to the fault diagnosis of an actual BTS. The results fully demonstrate the effectiveness and feasibility of our strategy

AN EFFICIENT MULTI-CRITERIA DECISION-MAKING APPROACH BASED ON HYBRIDIZING DATA MINING TECHNIQUES AN EFFICIENT MULTI-CRITERIA DECISION-MAKING APPROACH BASED ON HYBRIDIZING DATA MINING TECHNIQUES

Multiple-criteria decision-making (MCDM) that deals with multiple criteria in decision-making environments has been explicitly applied to various decision-making fields. Nevertheless, the critical issues of uncertainty and inaccuracy generally and gradually exists in the majority of the MCDM processes because of (1) prejudice and preference of decision-makers or experts as well as (2) the insufficiency information of the input and output. Therefore, this research efficiently proposed a novel method, FVM-index method, to resolve the limitations happened when MCDM is applied. The FVM-index approach, which consists of the fuzzy set theory (FST), the variable precision rough set (VPRS), and the cluster validity index (CVI) function, not only provides optimized classification results for the datasets but also filters out the uncertainty and inaccuracy instances from surveyed datasets by VPRS theory. Because the datasets are refined by the proposed FVM-index method, the decision makers will be able to effectively obtain the suitable results of MCD

Enabling decision trend analysis with interactive scatter plot matrices visualization

© 2015 Elsevier Ltd. This paper presents a new interactive scatter plot visualization for multi-dimensional data analysis. We apply Rough Set Theory (RST) to reduce the visual complexity through dimensionality reduction. We use an innovative point-to-region mouse click concept to enable direct interactions with scatter points that are theoretically impossible. To show the decision trend we use a virtual Z dimension to display a set of linear flows showing approximation of the decision trend. We conducted case studies to demonstrate the effectiveness and usefulness of our new technique for analyzing the property of three popular data sets including wine quality, wages and cars. The paper also includes a pilot usability study to evaluate parallel coordinate visualization with scatter plot matrices visualization with RST results

Rough Set Soft Computing Cancer Classification and Network: One Stone, Two Birds

Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article

Variable precision rough set theory decision support system: With an application to bank rating prediction

This dissertation considers, the Variable Precision Rough Sets (VPRS) model, and its development within a comprehensive software package (decision support system), incorporating methods of re sampling and classifier aggregation. The concept of /-reduct aggregation is introduced, as a novel approach to classifier aggregation within the VPRS framework. The software is applied to the credit rating prediction problem, in particularly, a full exposition of the prediction and classification of Fitch's Individual Bank Strength Ratings (FIBRs), to a number of banks from around the world is presented. The ethos of the developed software was to rely heavily on a simple 'point and click' interface, designed to make a VPRS analysis accessible to an analyst, who is not necessarily an expert in the field of VPRS or decision rule based systems. The development of the software has also benefited from consultations with managers from one of Europe's leading hedge funds, who gave valuable insight, advice and recommendations on what they considered as pertinent issues with regards to data mining, and what they would like to see from a modern data mining system. The elements within the developed software reflect each stage of the knowledge discovery process, namely, pre-processing, feature selection, data mining, interpretation and evaluation. The developed software encompasses three software packages, a pre-processing package incorporating some of the latest pre-processing and feature selection methods a VPRS data mining package, based on a novel "vein graph" interface, which presents the analyst with selectable /-reducts over the domain of / and a third more advanced VPRS data mining package, which essentially automates the vein graph interface for incorporation into a re-sampling environment, and also implements the introduced aggregated /-reduct, developed to optimise and stabilise the predictive accuracy of a set of decision rules induced from the aggregated /-reduct

Rough sets, their extensions and applications

Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Despite its recency, the theory and its extensions have been widely applied to many problems, including decision analysis, data-mining, intelligent control and pattern recognition. This paper presents an outline of the basic concepts of rough sets and their major extensions, covering variable precision, tolerance and fuzzy rough sets. It also shows the diversity of successful applications these theories have entailed, ranging from financial and business, through biological and medicine, to physical, art, and meteorological