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Graduation date: 1956

Thesis (M.S.)--Ohio University, June, 1994. / Title from PDF t.p.

Thesis--University of Paris.

Infrared thermography was used to evaluate Carbon Fiber Reinforced Panels (CFRP) and optimize the inspection process so that a set of guidelines can be established in order to be efficient and effective. It has been shown in previous work that when a material is heated up the heat will diffuse through the material at a constant rate. However, if there is a defect in the material, such as a delamination, this defect will act like an insulator. When this happens the heat cannot penetrate as quickly as the rest of the material so the surface above the defect shows up hotter than the rest of the material. An operator looks for inconsistent temperatures in the sample to determine the quality of the piece. Samples with simulated defects were made and modeled using a finite element program. Heat will be applied to the models and the temperature profiles analyzed. Along with changing the heat and time, different post-processing techniques were used to improve the method in determining defects in the sample. Once this has been optimized, actual CFRP with the same simulated defects was experimentally tested using the conditions from the analytical model. The analytical and experimental data was compared to insure that the testing process has been optimized. A standardized process was developed for evaluating the CFRPs using infrared thermography.

This thesis is concerned with the problem of colour shade grading for Industrial Inspection and attempts to find accurate and robust solutions to this problem. The application we are interested in, is the automation of the ceramic tiles manufacturing process so as to replace the human inspectors responsible for the quality control of the product. Therefore our aim is to perform the colour grading in a way which is consistent with what the human experts and subsequently the clients would perceive. First an overview of colour vision, colour measurement and colour constancy is given. Then a method that tackles the problem of colour grading of uniform and patterned surfaces is proposed. This method is the first step towards colour grading since it involves various corrections of the data, so as to provide the necessary precision for any further attempt. The problem of colour grading of random textures is then addressed. A method based on the comparison between colour histograms is proposed, and various statistical aspects involved in the comparison of distributions such as the colour histograms are discussed. Since the real-time implementation of any industrial inspection method should be taken into account, we use a space-effective method of storing colour histograms. Having solved the problem of colour grading for the majority of uniform and textured surfaces, we then try to optimise the performance of the proposed techniques, for cases where it fails. We attribute that to the fact that every electronic sensor captures colour and patterns in a way which only approximates what the human vision system would perceive. First we propose a method of perceptual colour grading of uniform surfaces, which transforms the camera data to data as they would have been recorded by the human eye. This method makes use of metameric data, to determine the relation between the human and the electronic sensors. We use various methods of generating metamers, and we show how the need of a spectrophotometer can be overcome. In a similar way, we propose a method of perceptual colour grading of random textures, which involves the restoration of the electronically acquired data and then their transformation to a colour space which expresses the way we perceive colour texture. We test both methods with real data, and we compare them with the non-perceptual ones. All the methods proposed in this thesis have been tested with real data, from the ceramic tiles manufacturing industry, previously colour graded by human inspectors. The consistency of the methods has been tested by using various sets of all sorts of tiles, and by repeating the acquisition and grading processes many times for every set of tiles. Further, these experiments have been carried out using different apparatuses, thus allowing us to draw conclusions about their quality and to make our methods as hardware independent as possible.

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A dissertation submitted in partial fulfilment of the requirements for the Master of Arts Degree in Clinical Psychology at the University of Zululand, South Africa, 2011. / This study was used to compare how supervisors and supervisees experience supervision at the University of Zululand in the department of psychology. A survey was conducted with the use of Likert type questionnaires. The researcher used the data that was gathered during the pilot study (which was conducted in 2008) as well as the recommendations to formulate a 5-point scale. Items were in a counterbalanced order to identify and control response bias. Participants were then asked to suggest what they thought should be done to improve supervision in an open-ended question. Each statement was presented individually in terms of percentages of respondents and their level of agreement with the statement. Statements were designed to elicit particular themes so the researcher condensed them so as to better describe self-perceived experiences of both supervisors and supervisees. The findings were descriptive of the reality of supervision (what is happening) as perceived by the participants. These findings were then compared with the existing literature on supervision (what should be happening). The findings showed a more positive picture than what had been anticipated by the researcher. Nevertheless, a few gaps were identified and recommendations were made in an attempt to bridge them. This study has provided valuable information on how supervision is experienced by both supervisors and supervisees. Moreover, it is of value to the university departments that incorporate supervision as part of their training process, especially the helping professions. Furthermore, the study helped in the identification of factors that supervisors and supervisees view as attributes in the promotion of professional development.

The integration of Artificial Intelligence (AI) in collaborative tasks has gained momentum, with particular implications for critical infrastructure maintenance. This study examines the assurance goals of AI—security, explainability, and trustworthiness—within Virtual Reality (VR) environments for bridge maintenance. Adopting a within-subjects design approach, this research leverages VR environments to simulate real-world bridge maintenance scenarios and gauge user interactions with AI tools. With the industry transitioning from paper-based to digital bridge maintenance, this investigation underscores the imperative roles of security and trust in adopting AI-assisted methodologies. Recent advancements in AI assurance within critical infrastructure highlight its monumental role in ensuring safe, explainable, and trustworthy AI-driven solutions. / Master of Science / In today's rapidly advancing world, the traditional methods of inspecting and maintaining our bridges are being revolutionized by digital technology and artificial intelligence (AI). This study delves into the emerging role of AI in bridge maintenance, a field historically reliant on manual inspection. With the implementation of AI, we aim to enhance the efficiency and accuracy of assessments, ensuring that our bridges remain safe and functional. Our research employs virtual reality (VR) to create a realistic setting for examining how users interact with AI during bridge inspections. This immersive approach allows us to observe the decision-making process in a controlled environment that closely mimics real-life scenarios. By doing so, we can understand the potential benefits and challenges of incorporating AI into maintenance routines. One of the critical challenges we face is the balance of trust in AI. Too little trust could undermine the effectiveness of AI assistance, while too much could lead to overreliance and potential biases. Furthermore, the use of digital systems introduces the risk of cyber threats, which could compromise the security and reliability of the inspection data. Our research also investigates the impact of AI-generated explanations on users' decisions. In essence, we explore whether providing rationale behind AI's recommendations helps users make better judgments during inspections. The ultimate objective is to develop AI tools that are not only advanced but also understandable and reliable for those who use them, even if they do not have a deep background in technology. As we integrate AI into bridge inspections, it's vital to ensure that such systems are protected against cyber threats and that they function as reliable companions to human inspectors. This study seeks to pave the way for AI to become a trusted ally in maintaining the safety and integrity of our infrastructure.

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