• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.2
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• pp.3-10
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• 2018

Damage location and extent of structure could be detected by the inverse analysis on dynamic response properties such as frequencies and mode shapes. In practice the measured difference of natural frequencies represent the stiffness change reliably, however the measured mode shape is insensitive for stiffness change, but provides spatial information of damage. The damage detection index on shear building structures is formulated in this study. The damage detection index could be estimated from mode shape and srory stiffness of undamaged structure and frequency difference between undamaged and damaged structure. For the verification of the observed damage detection method, the numerical analysis of Matlab and MIDAS and shacking table test were performed. In results, the damage index of damaged story was estimated so higher than undamaged stories that indicates the damaged story apparently.

• Journal of the Korean Magnetics Society
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• v.20 no.2
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• pp.45-51
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• 2010

We suggest the algorithm that it detects volume and shape according with a variation of magnetic field in non-contact electromagnetic measurement system. It is possible to assess an object shape through a variation of magnetic field. The basic idea is compared a length difference with a variation of magnetic field in a detected object and a circle which modeled equivalent area. And the shape is detected to many calibration process that it is similar to signal pattern between a length difference and a variation of magnetic field in object and equivalent circle. This is the shape detection algorithm that use only the variation of magnetic field. In this paper, it has application to the shape detection algorithm about the object as hexagon, pentagon, rectangle, trigon. we can detect the object shape easily because the shape detection algorithm is only used to the variation of magnetic field.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.725-744
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• 2015

In this paper, a new damage indicator based on mode shape data is introduced to identify damage in beam structures. In order to construct the indicator proposed, the mode shape, mode shape slope and mode shape curvature of a beam before and after damage are utilized. Mode shape data of the beam are first obtained here using a finite element modeling and then the slope and curvature of mode shape are evaluated via the central finite difference method. In order to assess the robustness of the proposed indicator, two test examples including a simply supported beam and a two-span beam are considered. Numerical results demonstrate that using the proposed indicator, the location of single and multiple damage cases having different characteristics can be accurately determined. Moreover, the indicator shows a better performance when compared with a well-known indicator found in the literature.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.99-103
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• 2021

Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1311-1317
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• 2021

In this study, an airborne alpha detection system, which consists of a passivated implanted planar silicon (PIPS) detector and an air filter, was developed. A collimator applied to the alpha detection system showed an enhancement in resolution and a degradation in detection efficiency. The resolution and detection efficiency were compared and analyzed to evaluate the performance of the collimator. Thus, the resolution was found to be more important than the efficiency as a determining factor of the detection system performance, from the viewpoint of radionuclide identification. The performance was evaluated on three properties of the collimator: hole shape, hole length, and the ratio between the hole and frame pitches. From the hole shape performance evaluation, a hexagonal collimator showed the highest resolution. Further, the collimator with a hole pitch of 14 mm was found to have the highest resolution while that with a frame pitch of 4-6 mm (i.e., 1.2-1.4 times longer than the hole pitch) showed the highest resolution.

• 전기의세계
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• v.19 no.6
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• pp.18-25
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• 1970

The object of this project is to manifest the mechanism of deterioration phenomena for dielectrics causing corona discharge and applies it for determine the standard corona detection technique. As the results, we observed that corona discharges may occur more strongly around cylindrical shape electrode in air than hemisphere shape electrode in vacuum, so that it depends on effects such as shape of the electrode, moisture, surface coditions, etc. According to observed the deterioration of dielectrics takes place in following stages. At first the attacked surface by an electron avalanche is uniformly eroded; then pits are formed; after that sharp channels are formed which lead to break-down as a treeing. The test are accelerated with higher frequencies by the cylindrical bar shape electrode in the pulse stright detection method more sensitive than Lissajous patterns. Lissajous patterns detection method is simple but usually insensitive and has disadvantage that the magnitude of the individual discharge is not measured.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.575-578
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• 1999

The principal features of a face are as follows : skin-tone, symmetry, and requisites such as shape of ellipse, eyes, nose, mouth. Also, faces have different size, various shape and position. In case of application of face recognition and detection without preprocessing, efficiency of the performance is decreased. In addition, face itself, complex background, image quality, etc. are included. Therefore, previous face recognition methods are implemented on the base of specific constraints of the face image. In this paper, we propose the efficient and automatic face detection algorithm for minimizing influence such as complex background, image quality, etc. This face detection technique consists of skin-tone, candidate face region and face region extractions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.429-435
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• 2010

Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We earned out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.