349 research outputs found

    A research agenda for the retrofitting of residential buildings in China – A case study

    Get PDF
    The high-rise residential buildings of China will soon need retrofitting and any such retrofitting should include consideration of new energy saving methods and ‘green’ technologies. A research agenda is needed to meet this challenge. This paper presents a research agenda for the ‘green’ retrofitting of residential buildings. The agenda is based on the input of 25 national and international experts which was produced by a novel methodology specifically designed to discuss the key questions relating to the retrofitting of residential buildings. This methodology, based on Problem Tree Analysis, proved an effective method of producing an agenda for the research that is needed to facilitate such change. The research needs are presented under six headings. Stages for undertaking the research activities under each of these headings have been identified. The agenda highlights that the challenge of retrofitting is holistic and includes not just engineering and construction actions but economic, social and governmental requirements. Key aspects of the research agenda include the need for better macro-economic and micro-economic models and a better understanding of people's needs and expectations. These topics are discussed together with recent research findings both from China and other countries

    1D profiles of the images reconstructed by different algorithms using noise-free projections.

    No full text
    <p>(a) Horizontal profiles (240th row, 200th column to the 300th column); (b) Vertical profiles (258th column, 180th row to the 260th row).</p

    Summary of Welch’s t test analysis results of performance evaluations of image quality between different algorithms (with 100 iterations from noise-free projections for 500 slices of the FORBILD head phantom).

    No full text
    <p>Summary of Welch’s t test analysis results of performance evaluations of image quality between different algorithms (with 100 iterations from noise-free projections for 500 slices of the FORBILD head phantom).</p

    Geometrical scanning parameters for limited-angle CT.

    No full text
    <p>Geometrical scanning parameters for limited-angle CT.</p

    A typical slice of the FORBILD head phantom.

    No full text
    <p>A typical slice of the FORBILD head phantom.</p

    A typical phase of the NCAT phantom.

    No full text
    <p>A typical phase of the NCAT phantom.</p

    1D profiles of the reconstructed images shown in Fig 6 by different algorithms.

    No full text
    <p>The top row and bottom row show the result from scanning angular ranges [0, 90°] and [0, 120°], respectively. (a) and (c) are Horizontal profiles (32th row); (b) and (d) are Vertical profiles(90th column).</p

    <i>ℓ</i><sub>0</sub> Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography

    No full text
    <div><p>In medical and industrial applications of computed tomography (CT) imaging, limited by the scanning environment and the risk of excessive X-ray radiation exposure imposed to the patients, reconstructing high quality CT images from limited projection data has become a hot topic. X-ray imaging in limited scanning angular range is an effective imaging modality to reduce the radiation dose to the patients. As the projection data available in this modality are incomplete, limited-angle CT image reconstruction is actually an ill-posed inverse problem. To solve the problem, image reconstructed by conventional filtered back projection (FBP) algorithm frequently results in conspicuous streak artifacts and gradual changed artifacts nearby edges. Image reconstruction based on total variation minimization (TVM) can significantly reduce streak artifacts in few-view CT, but it suffers from the gradual changed artifacts nearby edges in limited-angle CT. To suppress this kind of artifacts, we develop an image reconstruction algorithm based on <i>ℓ</i><sub>0</sub> gradient minimization for limited-angle CT in this paper. The <i>ℓ</i><sub>0</sub>-norm of the image gradient is taken as the regularization function in the framework of developed reconstruction model. We transformed the optimization problem into a few optimization sub-problems and then, solved these sub-problems in the manner of alternating iteration. Numerical experiments are performed to validate the efficiency and the feasibility of the developed algorithm. From the statistical analysis results of the performance evaluations peak signal-to-noise ratio (PSNR) and normalized root mean square distance (NRMSD), it shows that there are significant statistical differences between different algorithms from different scanning angular ranges (<i>p</i><0.0001). From the experimental results, it also indicates that the developed algorithm outperforms classical reconstruction algorithms in suppressing the streak artifacts and the gradual changed artifacts nearby edges simultaneously.</p></div
    corecore