• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.159-162
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• 2000

Selecting locally optimum thresholds, based on optimizing a criterion composed of the area variation rate and the compactness of the segmented shape, is presented. The method is shown to have the shape-resolving property in the subtraction image, so that overlapped objects may be resolved into bright and dark evidences characterizing each object. As an application a vehicle detection algorithm robust to the operating conditions could be realized by applying simple merging rules to the geometrically correlated bright and dark evidences obtained by this local thresholding.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1997년도 가을 학술발표회 논문집
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• pp.791-796
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• 1997

Deterioration of pavements or tunnels primarily from the existence of voids under the pavements or tunnel linings. To detect these voids effectively by non-destructive testes, a method using radar was proposed. In this research, the detection of shape of voids by radar image processing is investigate. The experiments and simulation were conducted to detect voids in or under concrete pavements for tunnel linings) with reinforcing bars. From the results, the fundamental algorithm for tracing the voids, improving the horizontal resolution of the object image and detecting shape of objects, was verified.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.399-408
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• 2012

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

• Steel and Composite Structures
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• 제42권5호
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• pp.685-697
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• 2022

In this paper, a vibration-based method using the change ratios of modal data and the experience-based learning algorithm is presented for quantifying the position, size, and interface layer of delamination in laminated composites. Three types of objective functions are examined and compared, including the ones using frequency changes only, mode shape changes only, and their combination. A fine three-dimensional FE model with constraint equations is utilized to extract modal data. A series of numerical experiments is carried out on an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam for investigating the influence of the objective function, the number of modal data, the noise level, and the optimization algorithms. Numerical results confirm that the frequency-and-mode-shape-changes-based technique yields excellent results in all the three delamination variables of the composites and the addition of mode shape information greatly improves the accuracy of interface layer prediction. Moreover, the EBL outperforms the other three state-of-the-art optimization algorithms for vibration-based delamination detection of composites. A laboratory test on six CFRP beams validates the frequency-and-mode-shape-changes-based technique and confirms again its superiority for delamination detection of composites.

• Wind and Structures
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• 제21권6호
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• pp.657-675
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• 2015

In this study, the feasibility of vibration-based damage detection methods for the wind turbine tower (WTT) structure is evaluated. First, a frequency-based damage detection (FBDD) is outlined. A damage-localization algorithm is visited to locate damage from changes in natural frequencies. Second, a mode-shape-based damage detection (MBDD) method is outlined. A damage index algorithm is utilized to localize damage from estimating changes in modal strain energies. Third, a finite element (FE) model based on a real WTT is established by using commercial software, Midas FEA. Several damage scenarios are numerically simulated in the FE model of the WTT. Finally, both FBDD and MBDD methods are employed to identify the damage scenarios simulated in the WTT. Damage regions are chosen close to the bolt connection of WTT segments; from there, the stiffness of damage elements are reduced.

• 대한의용생체공학회:의공학회지
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• 제28권5호
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• pp.665-675
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• 2007

The morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. Generally ST segment deviation is concerned with myocardial abnormality. The aim of this study is to detect the change of ST in shape using a polynomial approximation method and the reference ST type. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The detection of QRS complex is accomplished using it's the morphological characteristics such as the steep slope and high amplitude. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes of the specified points between the reference ST set and the least square curve. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

• Advanced Composite Materials
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• 제17권1호
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• pp.41-56
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• 2008

In this research, optical fiber sensors and shape memory alloys (SMA) were incorporated into sandwich panels for development of a smart honeycomb sandwich structure with damage detection and shape recovery functions. First, small-diameter fiber Bragg grating (FBG) sensors were embedded in the adhesive layer between a CFRP face-sheet and an aluminum honeycomb core. From the change in the reflection spectrum of the FBG sensors, the debonding between the face-sheet and the core and the deformation of the face-sheet due to impact loading could be well detected. Then, the authors developed the SMA honeycomb core and bonded CFRP face-sheets to the core. When an impact load was applied to the panel, the cell walls of the core were buckled and the face-sheet was bent. However, after the panel was heated over the reverse transformation finish temperature of the SMA, the core buckling disappeared and the deflection of the face-sheet was relieved. Hence the bending stiffness of the panel could be recovered.

• 방송공학회논문지
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• 제18권3호
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• pp.393-400
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• 2013

손 모양 인식은 자연스러운 인간-컴퓨터 상호작용을 위한 기반 기술이다. 본 논문에서는 Kinect 기반 손 모양 인식을 위해 효과적으로 손 영역을 검출하기 위한 방법에 대해 논의한다. Kinect는 컬러 영상과 적외선 영상(혹은 깊이 영상)을 동시에 획득할 수 있는 카메라이기 때문에, 손 영역을 검출하는 과정에서 컬러 정보와 깊이 정보를 활용할 수 있다. 즉, 손 영역은 스킨 컬러를 가지는 영역으로 검출될 수도 있으며, 일정한 깊이 값을 가지는 영역으로 검출될 수도 있다. 그러므로, 이러한 방법들의 성능을 분석하여, 손 영역의 실루엣이 깔끔하게 도출될 수 있도록 적절히 결합하는 방법이 마련되어야 한다. 이는 손 모양 인식률을 크게 좌우하기 때문이다. 최종적으로 일반적인 환경에서 손 영역 검출 방법의 차이에 따른 손 모양 인식률을 비교함으로써, 성능이 우수한 손 영역 검출 방법을 제안한다.

• 로봇학회논문지
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• 제4권4호
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• pp.257-264
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• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.

• 디지털융복합연구
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• 제11권9호
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• pp.311-318
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• 2013

얼굴 검출 기법들의 경우 조명과 배경에 따라 검출의 정확도가 떨어지는 현상이 발생하여, 이를 해결하기 위한 기법들이 요구되고 있다. 본 연구에서는 얼굴의 눈과 입의 성분을 분석하여 인간의 감성 정보를 추출하기 위한 데이터를 획득하고자 한다. 이를 위해 처리속도가 빠르고 환경 요소들에 강인한 검출율을 보이는 얼굴 특징 검출 방법을 제안하였다. 본 방법은 적분 이미지를 적용한 Haar-like Feature기법을 이용하여 얼굴 성분(두 눈, 입)을 검출한 후, 색상 정보를 바탕으로 검출된 성분들을 이진화하고 피부영역과 얼굴 성분영역을 구분한다. 그 후, 빠르고 정확한 shape를 생성하기 위해 베지어 곡선을 이용하여 검출된 성분들의 shape를 생성한다. 제안된 방법의 성능을 평가하기 위하여 Face Recognition Homepage의 데이터를 이용하여 실험을 진행하였으며, 이를 통해 정교한 얼굴 성분 검출이 가능함을 확인하였다.