Our study suggests that IFNβ-1b may trigger severe exacerbation in patients with the NMO spectrum. In INFβ-1b therapy, cases in NMO spectrum should be carefully excluded.
Recently, renovations of plant equipment have been more frequent because of the shortened lifespans of the products, and as-built models from large-scale laser-scamied data is expected to streamline rebuilding processes. However, the laser-scanned data of an existing plant has an enormous amount ofpoints, captures inmcate objects, and includes a high noise level, so the manual reconstmction of a 3D model is very time-consuming and costly. Among plant equipment, piping systems account for the greatest proportion. Therefore, the purpose of this research was to propose an algorithm which could automatically recognize a piping system from the terrestrial laser- scanned data plant equipment. The straight pomon pipes, connecting parts, and connection relationship ofthe piping system can be recognized in this algorithm. Normal-based region growing and cylinder surface fitting can extract all possible locations ofpipes, including straight pipes, elbows, and junctions. Tracing the axes of a piping system enables the recognition of the positions of these elements and their connection relationship. Using only point clouds, the recognition algorithm can be performed in a fUlly automatic way. The algorithm was applied to large-scale scamied data of an oil rig and a chemical plant. Recognition rates of about 86%, 88%, and 71% were achieved straight pipes, elbows, andjunctions, respectively.
ABSTRACT:With the spread of the Mobile Laser Scanning (MLS) system, the demands for the management of road and facilities using MLS point clouds have increased. Especially, pole-like objects such as streetlights, utility poles, street signs and etc. are in high demand as facilities to be managed. We propose a method for recognizing pole-like objects from MLS point clouds. Our method is based on Laplacian smoothing using the k-nearest neighbors graph, Principal Component Analysis for recognizing points on pole-like objects, and thresholding for the degree of pole-like objects. Our method can robustly recognize pole-like objects with various radii and tilt angles from MLS point clouds. For correctly segmented objects, accuracy of pole-like object recognition is on average 97.4%.
ABSTRACT:Recently, by Mobile Mapping System (MMS) with laser scanners, a GPS and IMU (Inertial Measurement Unit), 3D point clouds of urban areas (MMS point clouds) are easily acquired. When the same areas are scanned several times by the MMS, the point clouds often have differences in the range of several hundreds of millimetres. Such differences are caused by inertial drifts of IMU and losses of GPS signals in urban areas. In this paper, we propose an automatic accurate registration method of MMS point clouds using a new variant of ICP (Iterative Closest Point) algorithm for MMS point clouds and trajectory modification. Our method consists of four steps. Firstly, some trajectory points are automatically extracted by analyzing the trajectory. Secondly, the differences of point clouds are derived at the extracted trajectory points in the overlapping scan region by our new ICP algorithm which minimizes pointto-plane and point-to-point distances simultaneously and filters incorrect correspondences based on a point classification by PCA (Principle Component Analysis). Thirdly, the modified positions and rotation parameters at all extracted trajectory points are derived by a least squares method for positioning and registration constraints. Finally, each point in the point clouds is modified by coordinate transformations which are derived from linear interpolation of the modified positions and rotation parameters of the extracted trajectory points. Our method was applied to MMS point clouds and trajectory and the performances were evaluated.
Owing to our rapidly aging society, accessibility evaluation to enhance the ease and safety of access to indoor and outdoor environments for the elderly and disabled is increasing in importance. Accessibility must be assessed not only from the general standard aspect but also in terms of physical and cognitive friendliness for users of different ages, genders, and abilities. Meanwhile, human behavior simulation has been progressing in the areas of crowd behavior analysis and emergency evacuation planning. However, in human behavior simulation, environment models represent only “as-planned” situations. In addition, a pedestrian model cannot generate the detailed articulated movements of various people of different ages and genders in the simulation. Therefore, the final goal of this research was to develop a virtual accessibility evaluation by combining realistic human behavior simulation using a digital human model (DHM) with “as-is” environment models. To achieve this goal, we developed an algorithm for generating human-like DHM walking motions, adapting its strides, turning angles, and footprints to laser-scanned 3D as-is environments including slopes and stairs. The DHM motion was generated based only on a motion-capture (MoCap) data for flat walking. Our implementation constructed as-is 3D environment models from laser-scanned point clouds of real environments and enabled a DHM to walk autonomously in various environment models. The difference in joint angles between the DHM and MoCap data was evaluated. Demonstrations of our environment modeling and walking simulation in indoor and outdoor environments including corridors, slopes, and stairs are illustrated in this study. Highlights An adaptive walking simulation algorithm of the digital human was developed. The environment models are automatically generated from laser-scanned point clouds. A digital human can walk autonomously in various as-built environment models. Simulated walking motion of the digital human is similar to one of real human. Elapsed time of modeling and simulation is short enough for practical application.
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