• 한국공간구조학회논문집
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• 제14권4호
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• pp.55-64
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• 2014

In this study, Intersecting Tensegrity System that is integrated solid compression members with tension members was presented. This system is set up by connecting upper and lower compression members of pyramid shape with exterior tension members. In this system, the solid compression members are intersected each other and connected by a tension member in the center. This system is a variation of Tensegrity system, has a improved feature that the system is able to induce prestresses in all of tension members easily by adjusting the distance of a tension member in the center. The proposed system was studied by modeling, and the structural behavior of the system was investigated by mechanical analysis of the model. Furthermore, the features of the structural behavior variations was investigated when the composition elements(total height, size of surface, intersection length, etc.) are changed variously. It was also showed that the system is able to be used as a temporary space structure system with a membrane roof of inverse conical shape.

• 한국공간구조학회논문집
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• 제13권1호
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• pp.87-96
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• 2013

This study investigated characteristics of buckling load and effective buckling length by member rigidity of dome-typed space frame which was sensitive to initial conditions. A critical point and a buckling load were computed by analyzing the eigenvalues and determinants of the tangential stiffness matrix. The hexagonal pyramid model and star dome were selected for the case study in order to examine the nodal buckling and member buckling in accordance with member rigidity. From the numerical results, an effective buckling length factor of adopted models was bigger than that of Euler buckling for the case of fixed boundary. These numerical models indicated that the influence of nodal buckling was greater than that of member buckling as member rigidity was higher. Besides, there was a tendency that the bifurcation appeared on the equilibrium path before limit point in the member buckling model.

• 대한원격탐사학회지
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• 제17권1호
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• pp.57-69
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• 2001

본 연구는 무감독 영상분류를 위하여 공간지역 확장을 통하여 영상을 분할한 후 분할된 집단을 한정된 수의 클래스로 분류하는 다중단계 기법을 제안하고 있다. 제안된 알고리듬은 무감독 분석을 위하여 작은 집단들을 단계적으로 큰 집단들로 합병해 가는 계층집단연결 기법에 기반을 두고 있다. 다중단계 기법의 영상분할 단계는 공간적으로 근접하고 있는 이웃지역간의 결합을 통하여 최종적으로 전체영상 공간내의 모든 집단에 대해서 서로 이웃하고 있는 집단들의 물리적 특성이 서로 다르도록 영상을 분할하는 과정이고, 영상분류 단계는 결합 지역의 공간적 제약 없이 영상 분할 단계에서 분할된 지역을 상대적으로 적은 수의 클래스로 분류하는 과정이다. 제안 된 알고리듬에서 사용하고 있는 계층집단연결 기법의 계산/기억 상의 복잡성을 완화시키기 위해 상호최근사 이웃쌍과 다중창 작업을 사용하고 있다. 모의 자료를 사용하여 제단 된 알고리듬 대한 평가와 효율성을 검증하였고 경기도 용인.능평지역의 LANDSAT ETM+ 자료에 적용한 결과를 예시하고 있다.

• 한국멀티미디어학회논문지
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• 제22권3호
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• pp.335-348
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• 2019

Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1614-1632
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• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2011년도 제40회 동계학술대회 초록집
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• pp.47-47
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• 2011

There have been numerous efforts to enhance the efficiency of light-emitting diodes (LEDs) by using low dimensional structures such as quantum dots (QDs), wire (QWRs), and wells (QWs). We demonstrate QD/QWR/QW hybrid structured LEDs by using nano-scaled pyramid structures of GaN with ~260 nm height. Photoluminescence (PL) showed three multi-peak spectra centered at around 535 nm, 600 nm, 665 nm for QWs, QWRs, and QDs, respectively. The QD emission survived at room temperature due to carrier localization, whereas the QW emission diminished from 10 K to 300 K. We confirmed that hybrid LEDs had zero-, one-, and two-dimensional behavior from a temperature-dependent time-resolved PL study. The radiative lifetime of the QDs was nearly constant over the temperature, while that of the QWs increased with increasing temperature, due to low dimensional behavior. Cathodoluminescence revealed spatial distributions of InGaN QDs, QWRs, and QWs on the vertices, edges, and sidewalls, respectively. We investigated the blue-shifted electroluminescence with increasing current due to the band-filling effect. The hybrid LEDs provided broad-band spectra with high internal quantum efficiency, and color-tunability for visible light-emitting sources.

• 대한공간정보학회지
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• 제16권2호
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• pp.49-56
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• 2008

영상등록은 영상모자�掠茱� 중 중요한 기술로 인식되고 있으며, 파노라마 영상생성이나 비디오 모니터링, 영상복원 등과 같은 다양한 분야에서 사용될 수 있다. 영상등록에서 중요한 처리과정은 많은 시간이 소요되는 특징점 검출과 추적이다. 본 연구에서는 연속된 영상자료에서 특징점을 검출하고 추적하기 위해서 KLT 특징점 추적자를 제안하였으며, 무인헬기에서 촬영된 연속영상프레임의 영상등록에 적용하여 효용성을 입증하였다. 그 결과 KLT추적자에 의한 반복처리는 연속영상의 첫 번째 프레임에서 추출된 특징점을 이용하여 전체 프레임에 걸쳐 성공적으로 추적할 수 있었다. 또한, 회전, 축척, 이동량이 다른 각각의 프레임들간의 특징점 추적은 KLT영상피라미드와 처리조건의 선택에 의해 정확도를 향상시킬 수 있었다.

• 한국광학회지
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• 제16권2호
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• pp.113-120
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• 2005

LCD-BLU(liquid crystal device-back light unit)에 사용되는 도광판의 미세 산란패턴을 만드는 새로운 방법으로서, 실리콘 웨이퍼의 비등방 식각에 의해 자연적으로 형성되는 3차원 결정면 구조를 이용하는 방법을 제안하였다. 실리콘 3차원 결정면을 갖는 도광판과 프리즘 시트의 원판을 설계 및 제작하였고, casting 공정을 통해 PDMS 재질로 복제된 도광판을 제작하여 특성을 분석하였다. 측정 결과, 기존 인쇄형 도광판에 비해 실리콘피라미드 패턴의 도광판이 $10\%$ 증가된 정면 휘도 효율을 가질 수 있음을 실험적으로 검증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1057-1060
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• 2004

We propose new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering, combined with the Mahalanobis distance. The procedure verifies an impact of random noise as well as an interesting and less known impact of noise due to spatial transformations. The recognition accuracy of the proposed techniques has been tested with the preceding moment invariants as well as with some wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low noise, 8% for the average noise and 15% for high level noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.