33,986 research outputs found

    Transportation Life Cycle Assessment Synthesis: Life Cycle Assessment Learning Module Series

    Get PDF
    The Life Cycle Assessment Learning Module Series is a set of narrated, self-advancing slideshows on various topics related to environmental life cycle assessment (LCA). This research project produced the first 27 of such modules, which are freely available for download on the CESTiCC website http://cem.uaf.edu/cesticc/publications/lca.aspx. Each module is roughly 15- 20 minutes in length and is intended for various uses such as course components, as the main lecture material in a dedicated LCA course, or for independent learning in support of research projects. The series is organized into four overall topical areas, each of which contain a group of overview modules and a group of detailed modules. The A and α groups cover the international standards that define LCA. The B and β groups focus on environmental impact categories. The G and γ groups identify software tools for LCA and provide some tutorials for their use. The T and τ groups introduce topics of interest in the field of transportation LCA. This includes overviews of how LCA is frequently applied in that sector, literature reviews, specific considerations, and software tutorials. Future modules in this category will feature methodological developments and case studies specific to the transportation sector

    Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

    No full text
    This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

    Supporting Focus and Context Awareness in 3D Modelling Tasks Using Multi-Layered Displays

    Get PDF
    Most 3D modelling software have been developed for conventional 2D displays, and as such, lack support for true depth perception. This contributes to making polygonal 3D modelling tasks challenging, particularly when models are complex and consist of a large number of overlapping components (e.g. vertices, edges) and objects (i.e. parts). Research has shown that users of 3D modelling software often encounter a range of difficulties, which collectively can be defined as focus and context awareness problems. These include maintaining position and orientation awarenesses, as well as recognizing distance between individual components and objects in 3D spaces. In this paper, we present five visualization and interaction techniques we have developed for multi-layered displays, to better support focus and context awareness in 3D modelling tasks. The results of a user study we conducted shows that three of these five techniques improve users' 3D modelling task performance

    Application of wavelet analysis in tool wear evaluation using image processing method

    Get PDF
    Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The re-sults showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301
    corecore