A model based on the assessment of intrinsic creep resistance concept

In the present study a simple model is proposed to assess creep behavior. The model is applied to experimental results performed on austenitic steel X8 CrNiMoNb 16 16. The model is based on a modification of the Levy-Mises equation for plasticity to consider creep time effects, introducing as a parameter the intrinsic creep resistance. The assessment of creep behavior applied for monotonic and two stages loading data is good. The model could assess negative creep strain rates as well as damage accumulation observed as an increase of the minimum creep rate after each reloading at the same stress level in two stages tests

Fusion of Aerial Images with Mean Shift-based Upsampled Elevation Data for Improved Building Block Classification

Nowadays there is an increasing demand for detailed 3D modeling of buildings using elevation data such as those acquired from LiDAR airborne scanners. The various techniques that have been developed for this purpose typically perform segmentation into homogeneous regions followed by boundary extraction and are based on some combination of LiDAR data, digital maps, satellite images and aerial orthophotographs. In the present work, our dataset includes an aerial RGB orthophoto, a DSM and a DTM with spatial resolutions of 20cm, 1m and 2m respectively. Next, a normalized DSM (nDSM) is generated and fused with the optical data in order to increase its resolution to 20cm. The proposed methodology can be described as a two-step approach. First, a nearest neighbor interpolation is applied on the low resolution nDSM to obtain a low quality, ragged, elevation image. Next, we performed a mean shift-based discontinuity preserving smoothing on the fused data. The outcome is on the one hand a more homogeneous RGB image, with smoothed terrace coloring while at the same time preserving the optical edges and on the other hand an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. Besides the apparent visual assessment of the increased accuracy of building boundaries, the effectiveness of the proposed method is demonstrated using the processed dataset as input to five supervised classification methods. The performance of each method is evaluated using a subset of the test area as ground truth. Comparisons with classification results obtained with the original data demonstrate that preprocessing the input dataset using the mean shift algorithm improves significantly the performance of all tested classifiers for building block extraction

Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed

URBAN DENSITY INDICES USING MEAN SHIFT-BASED UPSAMPLED ELEVATION DATA

Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed

A new Method for Segmenting Newspaper Articles

the results of our research associated with the stage of segmentation of the various regions - the image consists of - as well as the identification of text regions which have to be separated from other regions, i.e. figures, drawings or line regions. The main region segmentation techniques are based on two fundamental approaches: firstly, on the smearing and labeling of regions [1-2], and secondly on the image profiling in various directions [3-4]. Both techniques have not been successful in achieving newspaper segmentation because of the haphazard lay out of newspaper articles and their very close contact. Furthermore, the first approach results in great computational cost. Aiming at a solution of these particular problems accruing from the newspaper segmentation, we suggest a new technique based on 2 B. Gatos et al. horizontal and vertical image projections which provides a quick region segmentation as well as identification of text areas. The proposed technique consists of thre

Should a diagnosis of Alzheimer's disease be disclosed?

There is evidence that some health practitioners may be reluctant to disclose a diagnosis of Alzheimer's disease (AD) to patients (Clafferty, Brown, & McCabe, 1998; Drickamer & Lachs, 1992; Fortinsky, Leighton, & Wasson, 1995; Kirby & Maguire, 1998; Maguire et al., 1996; Rice & Warner, 1994; Rice, Warner, Tye & Bayer, 1997). However, this reluctance towards disclosure may not be in accordance with patient expectation (Erde, Evan, Nadal, & Scholl, 1988; Holroyd, Snustad, & Chalifoux, 1996; Kirby & Maguire, 1998; Maguire et al., 1996; Vassilas & Donaldson, 1998). This study examined the attitudes of 100 undergraduate psychology students towards disclosure practices in relation to AD, before and after exposure to AD education. After AD education, 93% of participants indicated a desire to be informed of a diagnosis of AD, and 95% of participants were in favour of telling a close relative a diagnosis of AD. Results are discussed in terms of the relationship between age and attitudes towards AD diagnosis. It is concluded that the high rate of support for disclosure of AD diagnoses to patients among younger adults may reflect a change in the information preferences of patients brought about by a shift away from a patriarchal medical model, toward a more autonomous model of health