• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.500-504
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• 2001

The present approach based on the genetic algorithm with improved selections of bonds was adopted to extract a bridged T equivalent circuit elements of $\times10\mu m^2$InGaP/GaAs HBT. the small-signal model parameters were extracted using the genetic algorithm from S-parameters measured at different frequencies under multiple forward-active biases, which demonstrate physically meaningful values and consistency. The agreement between the measured and modeled S-parameters is excellent over the frequency range of 2 to 26.5GHz.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.3
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• pp.26-34
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• 1999

A new silicon-based IC interconnect transmission line parameter extraction methodology is presented and experimentally examined. Unlike the PCB or MCM interconnects, a dominant energy propagation mode in the silicon-based IC interconnects is not quasi-TEM but slow wave mode(SWM). The transmission line parameters are extracted taking the silicon substrate effect (i.e., slow wave mode) into account. The capacitances are calculated considering silicon substrate surface as a ground. Whereas the inductances are calculated by using an effective dielectric constant. In order to verify the proposed method, test patterns were designed. Experimental data have agreement within 10%. Further, crosstalk noise simulation shows excellent agreements with the measurements which are performed with high-speed time domain measurement ( i.e., TDR/TDT measurements) for test pattern, while RC model or RLC model without silicon substrate effect show about 20~25% underestimation error.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.35-41
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• 2002

There are many image segmentation methods having been published as the results of research so far, but it is difficult to be partitioned to each similar range that should be extracted into the accurate parameters of image information on the images with noises. Also if it is used to fractal coding, according to amount of noise in image, the image segmentation leads to decreasing of the compression ratio. In this paper, we propose the new quad-tree image segmentation using the box-counting dimension which can estimate the effective image information parameters against the noise properties and apply this method to fractal image coding. As the result of simulation, we confirm that the image segmentation is improved to 31.10% for parameter detection of image information and compression ratio is enhanced to 38.93% for fractal image coding when tested on 10% Gaussian white noise image by the proposed quad-tree method compared with method using existing quad-tree. 

• The Journal of the Korea Contents Association
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• v.4 no.2
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• pp.21-27
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• 2004

In an urban area telecommunication using wireless system, the accurate prediction and analysis of wave propagation characteristics are very important to determine the service area optimized selection of base station, and eel design, etc. In the stage of these analyses, we have to present the propagation prediction mood which is varied with the type of antenna, directional angle, and configuration of the ground in our urban area in addition we need to perform an analysis of the conventional mode which is similar to ours and dig out the parameters to evaluate the wave environment before the cell design for the selected area. In this paper, we propose a wave propagation prediction model concerning the topography and obstacles in our urban area. We extract the parameters and apply them to the proposed wave environment for the simulation analyzing the propagation characteristics. Throughout these analyzing procedure, we extracted the essential parameters such as the position of the base station, the height of topography, and adequate type and height of the antenna with our preferable cuteness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.1-8
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• 2005

There are many image segmentation methods having bon published as the results of research so far, however these are for the noise images which can process an image under the general white noise environments. Therefore, these methods has the disadvantages because it is difficult to extract only the accurate parameters, which can distinguish between image and impulsive noise, from image with impulsive noises. So it has a problem about the potential decreasing of the performance according to the impulsive noise for all applications using the present quadtree segmentation. In this paper, we propose new quadtree segmentation using quartile deviation which can extract effectively the image information parameters from a noise image. Therefore our method can apply for various image processing fields because it has a advantage to distinguish an image information from noise image. As the result of simulation, we confirm that the proposed quadtree segmentation is more efficient than the present method when tested on impulsive noise image.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.324-329
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• 2006

Non-negative matrix factorization(NMF) 기법은 음이 아닌 값으로 구성된 데이터를 두 종류의 양의 행렬의 곱의 형식으로 분할하는 데이터 분석기법으로서, 텍스트마이닝, 바이오인포매틱스, 멀티미디어 데이터 분석 등에 활용되었다. 본 연구에서는 기본 NMF 기법에 기반하여 텍스트 문서로부터 토픽을 추출하고 동시에 이를 가시적으로 도시하기 위한 Topographic NMF (TNMF) 기법을 제안한다. TNMF에 의한 토픽 가시화는 데이터를 전체적인 관점에서 보다 직관적으로 파악하는데 도움이 될 수 있다. TNMF는 생성모델 관점에서 볼 때, 2개의 은닉층을 갖는 계층적 모델로 표현할 수 있으며, 상위 은닉층에서 하위 은닉층으로의 연결은 토픽공간상에서 토픽간의 전이확률 또는 이웃함수를 정의한다. TNMF에서의 학습은 전이확률값의 연속적 스케줄링 과정 속에서 반복적 파리미터 갱신 과정을 통해 학습이 이루어지는데, 파라미터 갱신은 기본 NMF 기반 학습 과정으로부터 유사한 형태로 유도될 수 있음을 보인다. 추가적으로 Probabilistic LSA에 기초한 토픽 가시화 기법 및 희소(sparse)한 해(解) 도출을 목적으로 한 non-smooth NMF 기법과의 연관성을 분석, 제시한다. NIPS 학회 논문 데이터에 대한 실험을 통해 제안된 방법론이 문서 내에 내재된 토픽들을 효과적으로 가시화 할 수 있음을 제시한다.

• Proceedings of the KIPE Conference
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• 2008.10a
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• pp.39-41
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• 2008

본 논문에서는 PEM(Proton Exchange Membrane) 연료전지 스택의 동적 특성에 관한 시뮬레이션에 대하여 기술한다. 연료전지의 출력은 부하 변동에 따른 가스 압력의 변화와 동작 온도의 변화 등에 민감하게 반응하는 특성을 갖고 있다. 본 논문에서는 부하 변화에 따른 스택 내부 채널의 가스 압력 변화를 계산하고 이를 Nernst 방정식에 적용하여 출력전압의 변동을 계산한 뒤 부하에 따른 손실을 계산하는 방법으로 동적 모델링을 수행하고 이를 이용한 시뮬레이션을 실시하였다. 스택의 각종 파리미터는 실험을 통해 추출되었고, 이를 적용한 Matlab/Simulink 시뮬레이션을 통해 제안된 모델이 연료전지 스택의 정특성 및 동특성을 적절하게 추종함을 확인하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.112-113
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• 2018

스트레스는 스트레스 요인에 반응하는 신체와 정신의 변화과정으로, 외부의 자극을 받으면 혈압, 심박동수, 호흡수가 증가하게 된다. 이러한 생체신호로 인간의 스트레스 요인을 측정할 수 있다. 본 연구에서는 선박조종시뮬레이션을 이용해 예비(견습)도선사가 도선접안 중 체감하는 스트레스의 정도와 특성을 생체신호 변화를 활용하여 실증적으로 분석하였다. 선박조종시뮬레이션에서 추출한 엔진 사용량, 타각 사용량, 속력 및 예선 사용량과 스트레스 분석을 위해 Heart BPM, SDNN, RMSSD의 3가지 심박변이도 파리미터의 상관관계를 분석하였다. 분석결과, 6회 중 4회의 시뮬레이션에서 Heart BPM은 지속적으로 상승하고 SDNN은 하강하여 시간에 따라 스트레스가 상승하는 것으로 분석되었다. 특히 예선 사용시점에서 변화의 폭이 큰 것을 확인하였다. RMSSD는 단기주기(1분간격) 측정 파라메터로 실험자가 느끼는 순간의 위험에 따라 그래프 변화폭이 심한 것으로 분석되었다. 전체적으로 엔진, 타각, 예선의 사용시점에 따라 큰 변화를 보였으며, 실험자들의 선박운용 행위에 따라 생체신호 변화를 보였다고 사료된다. 분석결과를 바탕으로 도선사에게 스트레스를 미치는 요소를 판별하여 도선접안 중 스트레스가 높아지는 순간에 인적사고를 예방할 수 있는 시스템적 보완을 마련하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.29-39
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• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-6
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• 2009

One of the most important problems in bioinformatics is how to extract the useful information from a huge amount of data, and make a decision in diagnosis, prognosis, and medical treatment applications. This paper proposes a weighted kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a learning method based on genetic algorithm. The weights of basis kernel functions in proposed kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions.