Process control for WAAM using computer vision

This study is mainly about the vision system and control algorithm programming for wire arc additive manufacturing (WAAM). Arc additive manufacturing technology is formed by the principle of heat source cladding produced by welders using molten inert gas shielded welding (MIG), tungsten inert gas shielded welding (TIG) and layered plasma welding power supply (PA). It has high deposition efficiency, short manufacturing cycle, low cost, and easy maintenance. Although WAAM has very good uses in various fields, the inability to control the adding process in real time has led to defects in the weld and reduced quality. Therefore, it is necessary to develop the real-time feedback through computer vision and algorithms for WAAM to ensure that the thickness and the width of each layer during the addition process are the same

IMPROVING UNDERSTANDABILITY AND UNCERTAINTY MODELING OF DATA USING FUZZY LOGIC SYSTEMS

The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty. Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague terms that rely on human understandable terms. The use of linguistic terms and simple consequential rules increase the understandability of system behavior as well as data. Use of vague terms and modeling data from non-discrete prototypes enables modeling of uncertainty. However, due to recent trends, the primary research of fuzzy logic have been diverged from the basic concept of understandability. Furthermore, high computational costs to achieve robust uncertainty modeling have led to restricted use of such fuzzy systems in real-world applications. Thus, the goal of this dissertation is to present algorithms and techniques that improve understandability and uncertainty modeling using Fuzzy Logic Systems. In order to achieve this goal, this dissertation presents the following major contributions: 1) a novel methodology for generating Fuzzy Membership Functions based on understandability, 2) Linguistic Summarization of data using if-then type consequential rules, and 3) novel Shadowed Type-2 Fuzzy Logic Systems for uncertainty modeling. Finally, these presented techniques are applied to real world systems and data to exemplify their relevance and usage

Multi-scale approaches for the statistical analysis of microarray data (with an application to 3D vesicle tracking)

The recent developments in experimental methods for gene data analysis, called microarrays, provide the possibility of interrogating changes in the expression of a vast number of genes in cell or tissue cultures and thus in depth exploration of disease conditions. As part of an ongoing program of research in Guy A. Rutter (G.A.R.) laboratory, Department of Biochemistry, University of Bristol, UK, with support from the Welcome Trust, we study the impact of established and of potentially new methods to the statistical analysis of gene expression data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Elements of change in the evolution of solid-state hydrogen storage technology

The search for satisfycing hydrogen storage materials (HSMs) is in an exploratory phase of development. This phase is associated with large uncertainties, and technological change that is diffcult to anticipate. Nevertheless, it is a common (and necessary) practice to make claims about and form strategies around the perceived prospects of HSMs. Which of these diversely construed anticipations are reliable? This thesis aims to contribute a perspective, theoretically and empirically informed, that is valuable to an objective assessment of the prospects for materials-based hydrogen storage. Instead of offering a simplifed narrative of future developments in hydrogen storage, the exploratory approach taken has addressed important aspects of a complex process. Three important evolutionary principles of technological change - variation, learning, and selection - have been represented. Each chapter draws on a different set of concepts to address diverse questions. I study the extent of variation activity in research, and review prominent directions of search for fitter hydrogen storage materials. I ask about the relationship between progress, and expectations of progress embodied by the research community. I look at expert judgement as a source of bettering our understanding of hydrogen storage prospects. I also explore the possibility of anticipating a subset of the selection pressures, that will determine likely "survivors" among competing concepts. Insights are gained that inform us on hydrogen storage prospects in various dimensions. For example, I argue that the dynamics of expectations is key to understanding the historic "trajectory of progress". An implication of expert foresight is that investments into a portfolio of research trajectories is compelling. A trend of convergence toward compressed hydrogen technology is evident, an option I show to be wholistically superior to solid-state concepts, assuming a variety of selection pressures. In all, the adopted perspective proves a useful framework for thinking about processes of technological change

2003 July, University of Memphis bulletin

Vol. 89 of the University of Memphis bulletin containing the undergraduate catalog for 2003-04, 2003 July.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1190/thumbnail.jp