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

Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature.This work has been supported by University of Alicante projects GRE14-02 and Smart University

Multi-Stakeholder Consensus Decision-Making Framework Based on Trust and Risk

Indiana University-Purdue University Indianapolis (IUPUI)This thesis combines human and machine intelligence for consensus decision-making, and it contains four interrelated research areas. Before presenting the four research areas, this thesis presents a literature review on decision-making using two criteria: trust and risk. The analysis involves studying the individual and the multi-stakeholder decision-making. Also, it explores the relationship between trust and risk to provide insight on how to apply them when making any decision. This thesis presents a grouping procedure of the existing trust-based multi-stakeholder decision-making schemes by considering the group decision-making process and models. In the first research area, this thesis presents the foundation of building multi-stakeholder consensus decision-making (MSCDM). This thesis describes trust-based multi-stakeholder decision-making for water allocation to help the participants select a solution that comes from the best model. Several criteria are involved when deciding on a solution such as trust, damage, and benefit. This thesis considers Jain's fairness index as an indicator of reaching balance or equality for the stakeholder's needs. The preferred scenario is when having a high trust, low damages and high benefits. The worst scenario involves having low trust, high damage, and low benefit. The model is dynamic by adapting to the changes over time. The decision to select is the solution that is fair for almost everyone. In the second research area, this thesis presents a MSCDM, which is a generic framework that coordinates the decision-making rounds among stakeholders based on their influence toward each other, as represented by the trust relationship among them. This thesis describes the MSCDM framework that helps to find a decision the stakeholders can agree upon. Reaching a consensus decision might require several rounds where stakeholders negotiate by rating each other. This thesis presents the results of implementing MSCDM and evaluates the effect of trust on the consensus achievement and the reduction in the number of rounds needed to reach the final decision. This thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in the stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the distance of the choices made by the stakeholders. Trust is useful in decreasing the distances. In the third research area, this thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the perturbation in the rating matrix. Trust is useful in increasing the rating matrix perturbation. Such perturbation helps to decrease the number of rounds. Therefore, trust helps to increase the speed of agreeing upon the same decision through the influence. In the fourth research area, this thesis presents Rating Aggregation operators in the implemented MSCDM framework. This thesis addresses the need for aggregating the stakeholders' ratings while they negotiate on the round of decisions to compute the consensus achievement. This thesis presents four aggregation operators: weighted sum (WS), weighted product (WP), weighted product similarity measure (WPSM), and weighted exponent similarity measure (WESM). This thesis studies the performance of those aggregation operators in terms of consensus achievement and the number of rounds needed. The consensus threshold controls the performance of these operators. The contribution of this thesis lays the foundation for developing a framework for MSCDM that facilitates reaching the consensus decision by accounting for the stakeholders' influences toward one another. Trust represents the influence

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

An investigation of decision support knowledge production, transfer and adoption for it outsourcing

Information Technology Outsourcing (ITO) is a widely-adopted strategy for IT governance. ITO decisions are very complicated and challenging for many organisations. During the past three decades of ITO research, numerous decision support artefacts (e.g. frameworks, models, tools) to support organisational ITO decisions have been described in academic publications. However, the scope, rigour, relevance and adoption of this research by industry practitioners had not been assessed. This study investigates the production, transfer and adoption of academic research-generated knowledge for ITO decision support through multiple perspectives of ITO researchers and practitioners (e.g. IT managers, IT consultants) to provide insights into the research problem. A mixed-methods research approach underpinned by the critical realism paradigm is employed in this study. The study comprised three phases. In Phase A, the scope of extant research for supporting ITO decisions is identified through a systematic literature review and critical assessment of the rigour and relevance of this body of research is conducted using a highly regarded research framework. One hundred and thirty three articles on IT outsourcing (including cloud sourcing) were identified as ITO decision support academic literature. These articles suggested a range of Multiple Criteria Decision Making (MCDM), optimisation and simulation methods to support different IT outsourcing decisions. The assessment of these articles raised concerns about the limited use of reference design theories, validation and naturalistic evaluation in ITO decision support academic literature. Recommendations to enhance the rigour and relevance of ITO decision support research are made in this thesis. Phase B involved interviewing and surveying academic researchers who published academic literature on ITO decision support artefacts. This phase reports researchers’ reflections on their ITO research experience and knowledge transfer activities undertaken by them. The findings indicate researchers’ motivations, knowledge transfer mechanisms, and communication/ interaction channels with industry may explain effective knowledge transfer. Impact-minded researchers were significantly more effective than publication-minded researchers in knowledge transfer. In Phase C, interviews and a survey of practitioners engaged in IT outsourcing shed light on use of academic-generated knowledge. Academic research was the least used source of decision-making knowledge among ITO practitioners. Practitioners preferred to seek advice from their peers, IT vendors and consultants to inform their ITO decision making. Two communities of users and non-users of academic research were identified in our sample of ITO practitioners, with non-users forming the majority. Six factors that may influence the use of academic research by practitioners were identified. Non-users of academic research held perceptions that academic research was not timely, required too much time to read, was far from the real world and that it was not a commonly-used knowledge source for practitioners. Also, non-users of academic research read academic research less frequently and did not perceive themselves as an audience for academic research. This study engaged two fields of research: ITO decision support and academic knowledge transfer/utilisation (including research-practice gap). ITO decision support research provide the specific context for a critical assessment of academic knowledge production, transfer and adoption. For ITO DSS, this study identified the scope, rigour and relevance of the field, and improvement opportunities. This study confirms that a research-practice gap exists in the ITO decision support field as previously suggested by some scholars. Also, this study made a significant contribution to the highly complex and contested field of research utilisation and the research-practice gap. The relationship between research and practice in terms of knowledge production, transfer and utilisation is modelled using social system theory. Multiple theories are applied through a retroductive (abductive) analysis to shed light on the root causes of the research-practice gap. This study suggests that the lack of adequate appreciation of research relevance in academic reward schemes and the academic publishing structure are the main root causes of the research-practice gap in the knowledge production side. Moreover, various institutional mechanisms exist in knowledge transfer and adoption domains that influence the knowledge adoption channels of practitioners. As a result, academic research does not become a priority source of ITO decision support knowledge for practitioners. This study suggests that to overcome the barriers to academic research adoption by practitioners, the effective structural coupling mechanism between the system of science (knowledge production domain) and organisation systems (knowledge consumption domain) needs to be identified and activated

Quality Control in Criminal Investigation

Edited by Xabier Agirre Aranburu, Morten Bergsmo, Simon De Smet and Carsten Stahn, this 1,108-page book offers detailed analyses on how the investigation and preparation of fact-rich cases can be improved, both in national and international jurisdictions. Twenty-four chapters organized in five parts address, inter alia, evidence and analysis, systemic challenges in case-preparation, investigation plans as instruments of quality control, and judicial and prosecutorial participation in investigation and case-preparation. The authors include Antonio Angotti, Devasheesh Bais, Olympia Bekou, Gilbert Bitti, Leïla Bourguiba, Thijs B. Bouwknegt, Ewan Brown, Eleni Chaitidou, Cale Davis, Markus Eikel, Shreeyash Uday Lalit, Moa Lidén, Tor-Geir Myhrer, Trond Myklebust, Matthias Neuner, Christian Axboe Nielsen, Gilad Noam, Gavin Oxburgh, David Re, Alf Butenschøn Skre, Usha Tandon, William Webster and William H. Wiley, in addition to the four co-editors. There are also forewords by Fatou Bensouda and Manoj Kumar Sinha, and a prologue by Gregory S. Gordon.The book follows from a conference at the Indian Law Institute in New Delhi, and is the main outcome of the third leg of a research project of the Centre for International Law Research and Policy (CILRAP) known as the 'Quality Control Project'. Other books produced by the project are Quality Control in Fact-Finding (Second Edition, 2020) and Quality Control in Preliminary Examination: Volumes 1 and 2 (2018). Covering three distinct phases - documentation, preliminary examination and investigation - the volumes consider how the quality of each phase can be improved. Emphasis is placed on the nourishment of an individual mindset and institutional culture of quality control.bookExploring the Frontiers of International La