• Wind and Structures
• /
• v.29 no.5
• /
• pp.323-337
• /
• 2019

Wind tunnel testing technique has been established as a powerful experimental method for predicting wind-induced loads on high-rise buildings. Accurate assessment of the design wind load combinations for tall buildings on the basis of wind tunnel tests is an extremely important and complicated issue. The traditional design practice for determining wind load combinations relies partly on subjective judgments and lacks a systematic and reliable method of evaluating critical load cases. This paper presents a novel optimization-based framework for determining wind tunnel derived load cases for the structural design of wind sensitive tall buildings. The peak factor is used to predict the expected maximum resultant responses from the correlated three-dimensional wind loads measured at each wind angle. An optimized convex hull is further developed to serve as the design envelope in which the peak values of the resultant responses at any azimuth angle are enclosed to represent the critical wind load cases. Furthermore, the appropriate number of load cases used for design purposes can be predicted based on a set of Pareto solutions. One 30-story building example is used to illustrate the effectiveness and practical application of the proposed optimization-based technique for the evaluation of peak resultant wind-induced load cases.

• The KIPS Transactions:PartA
• /
• v.10A no.6
• /
• pp.665-670
• /
• 2003

In the applications like automatic masks generation for semiconductor production, a drawing consists of lots of polygons that are partitioned into trapezoids. The addition/deletion of a polygon to/from the drawing is performed through geometric operations such as insertion, deletion, and search of trapezoids. Depending on partitioning algorithm being used, a polygon can be partitioned differently in terms of shape, size, and so on. So, It's necessary to invent some comparison algorithm of sets of trapezoids in which each set represents interested parts of a drawing. This comparison algorithm, for example, may be used to verify a software program handling geometric objects consisted of trapezoids. In this paper, given k sets of trapezoids in which each set forms the regions of interest of each drawing, we present how to compare the k sets to see if all k sets represent the same geometric scene. When each input set has the same number n of trapezoids, the algorithm proposed has O(2$^{k-2}$ $n^2$(log n＋k)) time complexity. It is also shown that the algorithm suggested has the same time complexity O( $n^2$ log n) as the sweeping-based algorithm when the number k(<< n) of input sets is small. Furthermore, the proposed algorithm can be kn times faster than the sweeping-based algorithm when all the trapezoids in the k input sets are almost the same.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.4
• /
• pp.256-262
• /
• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• Journal of IKEEE
• /
• v.9 no.1 s.16
• /
• pp.7-18
• /
• 2005

A key component of an autonomous mobile robot is to localize itself accurately and build a map of the environment simultaneously. In this paper, we propose a vision-based mobile robot localization and mapping algorithm using scale-invariant features. A camera with fisheye lens facing toward to ceiling is attached to the robot to acquire high-level features with scale invariance. These features are used in map building and localization process. As pre-processing, input images from fisheye lens are calibrated to remove radial distortion then labeling and convex hull techniques are used to segment ceiling region from wall region. At initial map building process, features are calculated for segmented regions and stored in map database. Features are continuously calculated from sequential input images and matched against existing map until map building process is finished. If features are not matched, they are added to the existing map. Localization is done simultaneously with feature matching at map building process. Localization. is performed when features are matched with existing map and map building database is updated at same time. The proposed method can perform a map building in 2 minutes on $50m^2$ area. The positioning accuracy is ${\pm}13cm$, the average error on robot angle with the positioning is ${\pm}3$ degree.