• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.821-824
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• 1999

In distributed arithmetic-based architecture for an inner product between two length-N vectors, the size of the ROM increases exponentially with N. Moreover, the ROMs are generally the bottleneck of speed, especially when their sire is large. In this paper, a ROM size reduction technique for DA (Distributed Arithmetic) is proposed. The proposed method is based on modified OBC (Offset Binary Coding) and control circuit reduction technique. By simulations, it is shown that the use of the proposed technique can result in reduction in the number of gates up to 50%.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.70-74
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• 2008

In the field of camera motion research, it is widely held that the position (movement) and pose (rotation) of cameras are correlated and cannot be independently separated. A new equation based on inner product is proposed here to independently separate the position and pose. It is proved that the position and pose are not correlated and the equation is applied to estimation of the camera exterior parameters using a real image and 3D data.

• Annual Conference of KIPS
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• 2008.11a
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• pp.782-784
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• 2008

급속하게 확산된 비즈니스 웹 사이트로 인해 웹상에 상품의 정보가 기하급수적으로 증가하여 정보 과부하 문제가 발생하였다. 이를 극복하기 위해 내용 기반 추천 시스템, 협업 필터링 추천 시스템 등의 개인화 추천 시스템이 발전했으나 사용자의 성향과 아이템의 성향을 반영하지 못하고 있다. 본 연구에서는 웹상에서 사용자의 행동을 관찰하여 상품의 구매경로와 판매의 상관관계에 따라 각 사용자의 성향과 그룹의 성향, 아이템의 성향을 측정한 뒤 벡터의 내적을 이용하여 사용자의 성향에 가장 적합한 상품의 유사도를 계산하고 추천하는 시스템을 제안한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.375-381
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• 2007

A novel method to register images onto 3D data, such as 3D point cloud, 3D vectors, and 3D surfaces, is proposed. The proposed method estimates the exterior orientation of a camera with respective to the 3D data though fitting pairs of the normal vectors of two planes passing a focal point and 2D and 3D lines extracted from an image and the 3D data, respectively. The fitting condition is that the angle between each pair of the normal vectors has to be zero. This condition can be represented as a numerical formula using the inner product of the normal vectors. This paper demonstrates the proposed method can estimate the exterior orientation for the image registration as simulation tests.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.167-179
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• 2002

In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs) in order to compensate the weakness of HMM assumptions. The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function because the single frame feature cannot represent the temporal dynamics of speech signals effectively. To apply the segmental features to pattern classification, we adopted segmental HMM(SHMM) which is known as the effective method to represent the trend of speech signals. SHMM separates observation probability of the given state into extra- and intra-segmental variations that show the long-term and short-term variabilities, respectively. To consider the segmental characteristics in acoustic model, we present segmental-feature HMM(SFHMM) by modifying the SHMM. The SFHMM therefore represents the external- and internal-variation as the observation probability of the trajectory in a given state and trajectory estimation error for the given segment, respectively. We conducted several experiments on the TIMIT database to establish the effectiveness of the proposed method and the characteristics of the segmental features. From the experimental results, we conclude that the proposed method is valuable, if its number of parameters is greater than that of conventional HMM, in the flexible and informative feature representation and the performance improvement.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.735-742
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• 2004

We present a new use of common graphics hardware to perform a faster artificial neural network. And we examine the use of GPU enhances the time performance of the image processing system using neural network, In the case of parallel computation of multiple input sets, the vector-matrix products become matrix-matrix multiplications. As a result, we can fully utilize the parallelism of GPU. Sigmoid operation and bias term addition are also implemented using pixel shader on GPU. Our preliminary result shows a performance enhancement of about thirty times faster using ATI RADEON 9800 XT board.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.71-77
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• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.23-23
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• 2020

하구 관측은 조사 방법 및 주기에 따라 크게 두 가지로 구분되는데, 첫째는 현장에서 직접 주기적으로 자료를 수집하는 정기 현장관측과 다른 하나는 고정된 지점에 관측소를 설치하여 실시간으로 연속된 자료를 수집하는 실시간 관측으로 분류된다. 본 연구는 하구 관측망 체계를 확립하기 위한 기초 연구로서 금강하구역을 대상으로 모의된 수치 모델 자료를 이용하여 관측망을 설계하기 위한 대표 모니터링 지표를 선정하고, 이를 기반으로 관측 지점을 설계하기 위한 전략을 제시하였다. 대표 모니터링 지표는 실제 현장에서 일반적으로 취득할 수 있는 6가지 항목(수온, 염분, 용존산소, 클로로필a, 총질소, 총인)을 대상으로 EOF 분석을 실시하여 해역의 시공간 분포를 대표할 수 있다고 판단되는 2개의 항목을 선정하였다. 대표 모니터링 지점은 2개의 대표 모니터링 지표에 대한 고유 벡터 사이의 각도를 벡터의 내적으로 계산하고 이를 설계변수로 활용하여 도식최적화 기법을 통해 각 모니터링 항목들에 대한 공간 분포를 가장 잘 재현해 낼 수 있는 지점의 개수와 위치를 선정하였다. 선정된 모니터링 지점들을 이용하여 재구성된 공간 분포를 참값(수치모델)과 비교하여 통계적 적정성 여부를 평가하였으며, 이를 통해 금강하구의 대표 모니터링 지점들을 도출 해 내었다. 금강하구의 정기 현장 관측에 대한 대표 모니터링 지점은 7개로 선정되었으며, 이들은 6가지 관측 항목들에 대해서 매우 높은 공간분포 재현율을 확보할 수 있음을 확인하였다. 또한, 담수가 비정기적으로 방류되는 금강하구 시스템의 지역적 특성에 대한 시계열 정보를 연속적으로 가장 잘 취득할 수 있는 실시간 관측소 설치 영역을 결정하기 위하여, 7개의 대표 모니터링 지점에서의 시계열 정보를 금강하구둑 전면과 외해의 시계열 정보와 비교분석하여 설치가능 지점을 영역으로 제언하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.87-88
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• 2023

본 논문에서는 COVID-19 팬데믹으로 인해 사회적 거리두기 및 규제조치가 시행되면서 다양한 분야에서 큰 영향을 가져왔다. 변화된 홈트레이닝 분야는 운동기구를 구비하여 개인운동을 통해 건강을 유지하고 있으나 전문적인 교육을 받지 않은 홈트레닝으로 부상 위험에 노출 되고 있다. 요가는 호흡운동과 명상을 지향하는 운동으로 요가의 효과를 얻기 위해 올바른 움직임과 자세가 중요 하다. 본 논문에서는 실시간으로 입력된 영상 프레임을 OpenCV와 MediaPipe를 통해 추출된 주요좌표 값을 벡터 내적공식을 대입, 코사인2법칙을 통해 요가의 올바른 자세를 분석하여 종합적인 정보를 제공하는 요가교정 모델이다.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.9
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• pp.597-604
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• 2016

A parallel algorithm of bi-conjugate gradient method was developed based on CUDA for parallel computation of the incompressible Navier-Stokes equations. The governing equations were discretized using splitting P2P1 finite element method. Asymmetric stenotic flow problem was solved to validate the proposed algorithm, and then the parallel performance of the GPU was examined by measuring the elapsed times. Further, the GPU performance for sparse matrix-vector multiplication was also investigated with a matrix of fluid-structure interaction problem. A kernel was generated to simultaneously compute the inner product of each row of sparse matrix and a vector. In addition, the kernel was optimized to improve the performance by using both parallel reduction and memory coalescing. In the kernel construction, the effect of warp on the parallel performance of the present CUDA was also examined. The present GPU computation was more than 7 times faster than the single CPU by double precision.