• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.935-937
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• 2009

본 논문에서는 MicroTec을 이용한 채널 길이에 따른 20nm이하 MOSFET의 전달컨덕턴스의 특성을 분석하였다. 전달컨덕턴스는 게이트 전압의 변화에 의한 드레인 전류의 변화이다. MicroTec의 이동도 모델중 Lombardi, Constant, Yamaguchi 모델을 선택하여 이동도 모델에 따른 gm(전달컨덕턴스)를 비교하였다. 인가전압은 소스 0V, 기판 0V, 드레인 0.1V, 게이트는 -2.5V에서 4.5V까지 증가시켰다. 채널의 길이가 줄어들수록 gm(전달컨덕턴스)의 최대값과 드레인 전류가 증가함을 알 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.65-84
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• 2006

In this paper, we present an improved image formation model and propose a color image enhancement based on the model. In the presented image formation model, an input image is represented as a product of global illumination, local illumination, and reflectance. In the proposed color image enhancement, an input RGB color image is converted into an HSV color image. Under the assumption of white-light illumination, the H and S component images are remained as they are and the V component image only is enhanced based on the image formation model. The global illumination is estimated by applying a linear LPF with wide support region to the input V component image and the local illumination by applying a JND (just noticeable difference)-based nonlinear LPF with narrow support region to the processed image, where the estimated global illumination is eliminated from the input V component image. The reflectance is estimated by dividing the input V component image by the estimated global and local illuminations. After performing the gamma correction on the three estimated components, the output V component image is obtained from their product. Histogram modeling is next executed such that the final output V component image is obtained. Finally an output RGB color image is obtained from the H and S component images of the input color image and the final output V component image. Experimental results for the test image DB built with color images downloaded from NASA homepage and MPEG-7 CCD color images show that the proposed method gives output color images of very well-increased global and local contrast without halo effect and color shift.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.639-644
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• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.121-130
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• 2020

This paper suggests evaluating the military closed network data as an image which is generated by Generative Adversarial Network (GAN), applying an image evaluation method such as the InceptionV3 model-based Inception Score (IS) and Frechet Inception Distance (FID). We employed the famous image classification models instead of the InceptionV3, added layers to those models, and converted the network data to an image in diverse ways. Experimental results show that the Densenet121 model with one added Dense Layer achieves the best performance in data converted using the arctangent algorithm and 8 * 8 size of the image.

• Journal of the Korean Vacuum Society
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• v.9 no.3
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• pp.242-248
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• 2000

BST thin films were prepared with various deposition conditions by rf-magnetron sputtering. As substrate temperature increases and Ar/$O_2$ratio decreases, the electrical properties of the BST films improve. The conventional Schottky model and modified-Schottky model were introduced in order to investigate the leakage-current-conduction mechanisms of the deposited films. It was found that the modified-Schottky model better describes the current-conduction mechanism in the BST films than the conventional Schottky model. From the modified-Schottky model, optical dielectric constant ($\varepsilon$), electronic drift mobility ($\mu$), and barrier height $({\phi}_b)are calculated as $\varepsilon$=4.9, $\mu$=0.019 $\textrm{cm}^2$/V-s, and ${\phi}_b=0.79 eV.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.34-43
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• 2008

The state-space average model is extended to buck-boost, and buck-boost topology switching mode DC/DC converters and modified to have higher precision without increment of computation. The modified model is used in continuous conduction mode(CCM) switching DC/DC converters and some significant conclusions are derived. This paper discusses the discontinuous conduction mode(DCM) modeling of DC/DC converter and critical characteristic using average model of switch. Average model of switch approach is expended to the modeling of boundary conduction mode DC/DC converters that operate at the boundary between continuous conduction mode(CCM) and discontinuous conduction mode(DCM). Frequency responses predicted by the average model of switch are verified by simulation and experiment. A prototype featuring 15[V] input voltage, 24[V] output voltage, and 24[W] output power using MOSFET.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.213-218
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• 2019

기계독해는 자연어로 표현된 질문과 단락이 주어졌을 때, 해당 단락 내에 표현된 정답을 찾는 태스크이다. 최근 기계독해 태스크도 다른 자연어처리 태스크와 유사하게 BERT, XLNet, RoBERTa와 같이 사전에 학습한 언어모델을 이용하고 질문과 단락이 입력되었을 경우 정답의 경계를 추가 학습(fine-tuning)하는 방법이 우수한 성능을 보이고 있으며, 특히 KorQuAD v1.0 데이터셋에서 학습 및 평가하였을 경우 94% F1 이상의 높은 성능을 보이고 있다. 본 논문에서는 현재 최고 수준의 기계독해 기술이 학습셋과 유사한 평가셋이 아닌 일반적인 질문과 단락 쌍에 대해서 가지는 일반화 능력을 평가하고자 한다. 이를 위하여 첫번째로 한국어에 대해서 공개된 KorQuAD v1.0 데이터셋과 NIA v2017 데이터셋, 그리고 엑소브레인 과제에서 구축한 엑소브레인 v2018 데이터셋을 이용하여 데이터셋 간의 교차 평가를 수행하였다. 교차 평가결과, 각 데이터셋의 정답의 길이, 질문과 단락 사이의 오버랩 비율과 같은 데이터셋 통계와 일반화 성능이 서로 관련이 있음을 확인하였다. 다음으로 KorBERT 사전 학습 언어모델과 학습 가능한 기계독해 데이터 셋 21만 건 전체를 이용하여 학습한 기계독해 모델에 대해 블라인드 평가셋 평가를 수행하였다. 블라인드 평가로 일반분야에서 학습한 기계독해 모델의 법률분야 평가셋에서의 일반화 성능을 평가하고, 정답 단락을 읽고 질문을 생성하지 않고 질문을 먼저 생성한 후 정답 단락을 검색한 평가셋에서의 기계독해 성능을 평가하였다. 블라인드 평가 결과, 사전 학습 언어 모델을 사용하지 않은 기계독해 모델 대비 사전 학습 언어 모델을 사용하는 모델이 큰 폭의 일반화 성능을 보였으나, 정답의 길이가 길고 질문과 단락 사이 어휘 오버랩 비율이 낮은 평가셋에서는 아직 80%이하의 성능을 보임을 확인하였다. 본 논문의 실험 결과 기계 독해 태스크는 특성 상 질문과 정답 사이의 어휘 오버랩 및 정답의 길이에 따라 난이도 및 일반화 성능 차이가 발생함을 확인하였고, 일반적인 질문과 단락을 대상으로 하는 기계독해 모델 개발을 위해서는 다양한 유형의 평가셋에서 일반화 평가가 필요함을 확인하였다.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.480-486
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• 2017

This study was conducted to develop the merchantable stem volume models to predict the volume up to upper diameter or upper height out of the total stem volume, targeting on Pinus densiflora, Pinus koraiensis, and Larix kaempferi in South Korea. The 131 stemmed sample trees for stem analysis were used as the data for developing the models. The six kinds of merchantable volume equations including merchantable volume ratio form, ratio form, and exponential ratio form were examined to develop the best models. The two models were finally selected as the best models to predict the merchantable volume: $V_d=V_t\{{\exp}[{\alpha}_1(d^{{\alpha}_2}/D^{{\alpha}_3})]\}$ for upper diameter and $V_h=V_t\{1+{\beta}_1(P^{{\beta}_2}/H^{{\beta}_3})\}$ for upper height. By rearranging the best model equations, implicit taper functions were derived, and the estimation was performed for the upper height by upper diameter and upper diameter by upper height. Because of not only the high accuracy but also the convenience, the models developed in this study were considered to be easily applicable in the field of forestry.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.776-781
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• 2023

K-UAM will be commercialized through maturity after 2035. Since the Urban Air Mobility (UAM) corridor will be used vertically separating the existing helicopter corridor, the corridor usage is expected to increase. Therefore, a system for monitoring corridors is also needed. In recent years, object detection algorithms have developed significantly. Object detection algorithms are largely divided into one-stage model and two-stage model. In real-time detection, the two-stage model is not suitable for being too slow. One-stage models also had problems with accuracy, but they have improved performance through version upgrades. Among them, YOLO-V5 improved small image object detection performance through Mosaic. Therefore, YOLO-V5 is the most suitable algorithm for systems that require real-time monitoring of wide corridors. Therefore, this paper trains YOLO-V5 and analyzes whether it is ultimately suitable for corridor monitoring.K-uam will be commercialized through maturity after 2035.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.16-22
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• 2023

This paper presents a proactive longitudinal motion planning algorithm for improving the safety of an automated bus. Since the field of view (FOV) of the autonomous vehicle was limited depending on onboard sensors' performance and surrounding environments, it was necessary to implement vehicle-to-vehicle (V2V) communication for overcoming the limitation. After a virtual V2V-equipped target was constructed considering information obtained from V2V communication, the reference motion of the ego vehicle was determined by considering the state of both the V2V-equipped target and the sensor-detected target. Model predictive control (MPC) was implemented to calculate the optimal motion considering the reference motion and the chance constraint, which was deduced from manual driving data. The improvement in driving safety was confirmed through vehicle tests along actual urban roads.