• Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.67-72
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• 2015

Recently, along with the improvements of geometry modeling methods using sketch-based interface, there have been a lot of developments in research about generating surface model from 3D curves. However, surfacing a 3D curve network remains an ambiguous problem due to the lack of geometric information. In this paper, we propose a new algorithm for estimating the normal vectors of the 3D curves which accord closely with user intent. Bending energy is defined by utilizing RMF(Rotation-Minimizing Frame) of 3D curve, and we estimated this minimal energy frame as the one that accords design intent. The proposed algorithm is demonstrated with surface model creation of various curve networks. The algorithm of estimating geometric information in 3D curves which is proposed in this paper can be utilized to extract new information in the sketch-based modeling process. Also, a new framework of 3D modeling can be expected through the fusion between curve network and surface creating algorithm.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• Atmosphere
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• v.30 no.4
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• pp.347-360
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• 2020

The automatic algorithm optimized for the Korean Peninsula was developed to detect and track the center of typhoon based on a geometrical method using high-resolution retrieved WISSDOM (WInd Syntheses System using DOppler Measurements) wind and reflectivity data. This algorithm analyzes the center of typhoon by detecting the geometric circular structure of the typhoon's eye in radar reflectivity and vorticity 2D field data. For optimizing the algorithm, the main factors of the algorithm were selected and the optimal thresholds were determined through sensitivity experiments for each factor. The center of typhoon was detected for 5 typhoon cases that approached or landed on Korean Peninsula. The performance was verified by comparing and analyzing from the best track of Korea Meteorological Administration (KMA). The detection rate for vorticity use was 15% higher on average than that for reflectivity use. The detection rate for vorticity use was up to 90% for DIANMU case in 2010. The difference between the detected locations and best tracks of KMA was 0.2° on average when using reflectivity and vorticity. After the optimization, the detection rate was improved overall, especially the detection rate more increased when using reflectivity than using vorticity. And the difference of location was reduced to 0.18° on average, increasing the accuracy.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1330-1338
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• 2024

Information on isotopic composition and geometric structure is necessary for identifying a true warhead. Nevertheless, such classified information should be protected physically or electronically. With a novel Hash encryption algorithm, this paper presents a Monte Carlo-based design of a neutron activation analysis verification module. The verification module employs a thermal neutron source, a non-uniform mask (physically encrypting information about isotopic composition and geometric structure), a gamma detector array, and a Hash encryption algorithm (for electronic encryption). In the physical field, a non-uniform mask is designed to distort the characteristic gamma rays emitted by the inspected item. Furthermore, as part of the Hash algorithm, a key is introduced to encrypt the data and improve the system resolution through electronic design. In order to quantify the difference between items, Hamming distance is used, which allows data encryption and analysis simultaneously. Simulated inspections of simple objects are used to quantify system performance. It is demonstrated that the method retains superior resolution even with 1% noise level. And the performances of anti-statistical attack and anti-brute force cracking are evaluated and found to be very excellent. The verification method lays a solid foundation for nuclear disarmament verification in the upcoming era.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.94-101
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• 2005

This paper presents a data-mining aided heuristic algorithm development. The developed algorithm includes three steps. The steps are a uniform selection, development of feature functions and clustering, and a decision tree making. The developed algorithm is employed in designing an optimal multi-station fixture layout. The objective is to minimize the sensitivity function subject to geometric constraints. Its benefit is presented by a comparison with currently available optimization methods.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Korean Journal of Computational Design and Engineering
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• v.20 no.2
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• pp.104-113
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• 2015

In developing a tire, many CAE analyses are performed to make a better tire. But its meshing work is not easy, and it takes much time. One of the reasons of taking much time is that there are many overlapped geometric entities in CAD data that are modeled in CAD system by CAD engineers. In this study, we studied about the features of the overlapped geometric entities, and the method to find out and delete them. I developed a program using the proposed algorithm, and applied it in meshing tire pattern and tire case. I proved that the time in meshing a tire reduced dramatically by removing overlapped geometric entities by using the developed program.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.271-275
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• 2003

In this paper, we propose a method that corrects the geometric distortion of mouth in an image. the method is composed of two steps - detecting key points and correcting geometric distortion. First, key points of lips in source and destination images are found by using lips detection algorithm. Then, the two images are mapped by using affine transformation and information found in first step. In experiment result for various mouths with different geometric distortion, we found that the proposed method have satisfactory efficiency.

• Journal of Advanced Marine Engineering and Technology
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• v.18 no.1
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• pp.92-100
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• 1994

For generating NC machining data automatically, it is important to handle computer models such as geometric shape data including engineering specifications for the mechanical part to be manufactured. We proposed unique CAM system for a personal computer that can define the geometric shape in an ease manner and machine the sculptured surfaces of a mold cavity. In this paper, the theoretical basis of generation of high precision machining data for a mold cavity is obtained. The first is geometric modelling, and the second is high precision machining with an optimized tool path algorithm satisfying given tolerance limits. Especially, the bicubic Bezier basis function is adopted for a geometric modelling.