Fuzzy machine vision based inspection

Machine vision system has been fostered to solve many realistic problems in various fields. Its role in achieving superior quality and productivity is of paramount importance. But, for such system to be attractive, it needs to be fast, accurate and cost-effective. This dissertation is based on a number of practical machine vision based inspection projects obtained from the automotive industry. It presents a collection of developed efficient fuzzy machine vision approaches endorsed with experimental results. It also covers the conceptual design, development and testing of various fuzzy machine vision based inspection approaches for different industrial applications. To assist in developing and evaluating the performance of the proposed approaches, several parts are tested under varying lighting conditions. This research deals with two important aspects of machine vision based inspection. In the first part, it concentrates on the topics of component detection and component orientation identification. The components used in this part are metal clips mounted on a dash panel frame that is installed in the door of trucks. Therefore, we propose a fuzzy machine vision based clip detection model and a fuzzy machine vision based clip orientation identification model to inspect the proper placement of clips on dash panels. Both models are efficient and fast in terms of accuracy and processing time. In the second part of the research, we are dealing with machined part defects such as broken edge, porosity and tool marks. The se defects occur on the surface of die cast aluminum automotive pump housings. As a result, an automated fuzzy machine vision based broken edge detection method, an efficient fuzzy machine vision based porosity detection technique and a neuro-fuzzy part classification model based on tool marks are developed. Computational results show that the proposed approaches are effective in yielding satisfactory results to the tested image databases. There are four main contributions to this work. The first contribution is the development of the concept of composite matrices in conjunction with XOR feature extractor using fuzzy subtractive clustering for clip detection. The second contribution is about a proposed model based on grouping and counting pixels in pre-selective areas which tracks pixel colors in separated RGB channels to determine whether the orientation of the clip is acceptable or not. The construction of three novel edge based features embedded in fuzzy C-means clustering for broken edge detection marks the third contribution. At last, the fourth contribution presents the core of porosity candidates concept and its correlation with twelve developed matrices. This, in turn, results in the development of five different features used in our fuzzy machine vision based porosity detection approach

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Corrosion Behavior of H-Pile Steel in Different Soils

This dissertation aimed to study the corrosion performance of carbon steel in different soils, collected from the state of Wisconsin. Carbon steel specimens (as-received) as well as steel embedded in mortar (steel-mortar) specimens, to simulate the realistic H-pile design in bridges, were used in this investigation. Both as-received steel and steel-mortar specimens were embedded in as-received soils, with different physiochemical properties, i.e. pH, moisture content, resistivity, chloride content, sulfate and sulfite contents, and the mean total organic carbon concentration, for more than one year. Both specimen types were also embedded in the same as-received soils, but with increased chloride content to 3% by weight of chloride ions for more than one year. In addition, the surface of three identical as-received specimens was modified using the sandblasting method for 5 minutes. These specimens were embedded in one of the collected soils. Different electrochemical measurements were conducted on the specimens to evaluate the corrosion activity of the steel in these soils. The results showed a comparable corrosion activity of the steel-mortar specimens in all soils compared to the as-received specimens in the same soil both with and without chlorides, except for soils collected from Wausau. No correlation between the available physiochemical data and the observed results was determined. No information on the type and population of the bacteria in the collected soils was available. Perhaps, this information could explain the observed results. In all cases, there was a galvanic current flowing between specimens in chloride-free and chloride contaminated soils. In addition, corrosion potential values of all specimens remained relatively stable both before and after addition of chlorides, suggesting just measuring the corrosion potential may not be an efficient method to monitor the change of corrosion behavior of steel in the soil. The results of electrochemical experiments also showed significant improvement in corrosion resistance of sandblasted specimens compared to the as-received specimens

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems