• Journal of the Korean Graphic Arts Communication Society
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• v.19 no.1
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• pp.17-27
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• 2001

플라티늄포르피린 핵과 주위에 8, 16, 32 그리고 64 개의 벤질 단위를 갖는 새로운 덴드리머를 압력 감지형 페인트에 사용할 발광체로 합성하였다. 플라티늄포르피린 핵을 갖는 제 1 세대의 덴드리머는 Lindsey형 합성법을 이용하여 제조하였으며, 제 2 세대에서 제 4 세대까지의 플라티늄포르피린 핵을 갖는 덴드리머는 플라티늄 테트라키스(3,5-디히드록시페닐)포르피린을 적합한 덴드론 브로마이드와 Williamson 에테르 합성법에 따라 알킬화반응시켜 제조하였다. 이러한 에테르 연결의 생성 반응들은 $K_2$CO$_3$와 18-크라운-6를 사용하여 아세톤 용매에서 질소 기류 하에서 6$0^{\circ}C$에서 수행하였을 때 가장 좋은 결과를 주었다. 그리고 이렇게 합성한 덴드리머들을 $^1$H-NMR, $^{13}$C-NMR, Mass spectrum 이용하여 구조를 확인하고, 그리고 UV-VIS spectroscopy를 이용하여 분광학적인 특성을 조사하였다.

• Laser Solutions
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• v.12 no.2
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• pp.26-30
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• 2009

Scanning Electron Microscopy (SEM) includes high voltage generator, electron gun, column, secondary electron detector, scan coil system and image grabber. Column includes electron lenses (condenser lens and objective lens). Condenser lens generates fringe field, makes focal length and control spot size. Focal length represents property of lens. Objective lens control focus. Most of the electrons emitted from the filament, are captured by the anode. The portion of the electron current that leaves the gun through the hole in the anode is called the beam current. Electron beam probe is called the focused beam on the specimen. Because of the lens and aperture, the probe current becomes smaller than the beam current. It generate various signals(backscattered electron, secondary electron) in an interaction with the specimen atoms. In this paper, we describe the result of research to develop the core elements for low-resolution SEM.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.174-179
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• 2005

This paper presents 3D line segment extraction method, which can be used in generating 3D rooftop model. The core of our method is that 3D line segment is extracted by using line fitting of elevation data on 2D line coordinates of ortho-image. In order to use elevations in line fitting, the elevations should be reliable. To measure the reliability of elevation, in this paper, we employ the concept of self-consistency. We test the effectiveness of the proposed method with a quantitative accuracy analysis using synthetic images generated from Avenches data set of Ascona aerial images. Experimental results indicate that the proposed method shows average 30 line errors of .16 - .30 meters, which are about $10\%$ of the conventional area-based method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.37-41
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• 2016

Localization is one of the key issues of demandable applications, especially smart services. Beside the traditional GPS based localization technique, the localization issue by visible light communications is promising market because of possibility of combining visible light communications with positioning technique for a high accurate, especially indoor localization service. This paper provides the overview and new image sensor scheme for localization issue based on visible light communication. The survey is introduced from core techniques to enhancement issues of localization. We hope these will be the essential references for the impact selection method in implementation and standardization issues.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1387-1395
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• 2018

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

• 중국학논총
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• no.66
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• pp.227-264
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• 2020

In Sanyue, the identity of the state and the individual is discussed in terms of the class, ideology, system, moral ethics, tactical strategy, national view, Talent image, etc. and It pursues a historical approach in the world history and an attempt to self-realize in the history of mankind. This literature expresses the strength of military theory and discusses the main core points of the country as a military political and tactical strategy as follows: "Shangyue, zhongyue, and Xiayue. In the Shangyue in Sanyue, it describes how to distinguish the heroes who have been appointed through the ceremony, and it shows the accomplishment of the reason. In zhongyue, we discuss how to distinguish the contingency plan the meaning of change. In Xiayue, we discuss practical thinking of moral ethics and explain the possibility of All the officials and sages angry by considering the safety of the country."

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.595-598
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• 2020

IoU (Intersection over Union) is the most commonly used index in target detection. The core requirement of target detection is what is in the image and where. Based on these two problems, classification training and positional regression training are needed. However, in the process of position regression, the most commonly used method is to obtain the IoU of the predicted bounding box and ground-truth bounding box. Calculating bounding box regression losses should take into account three important geometric measures, namely the overlap area, the distance, and the aspect ratio. Although GIoU (Generalized Intersection over Union) improves the calculation function of image overlap degree, it still can't represent the distance and aspect ratio of the graph well. As a result of technological progress, Bounding-Box is no longer represented by coordinates x,y,w and h of four positions. Therefore, the IoU can be further optimized with the center point and aspect ratio of Bounding-Box.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.117-120
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• 2000

This study is to construct a general purpose library for a computer vision system development. The library includes many core algorithms required for computer vision systems such as image Processing algorithms, feature extracting methods, neural networks and etc. We have experimented the efficiency of the library by building a vehicle plate recognition system and the overall time and effort in development could be reduced to a certain extent.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.685-688
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• 1998

Measurement of optical flow is a core problem of matching through the analysis of image sequences. In this paper, horn's and anandan's techniques are analyzed to derive a better technique for matching. Experimental results show that Horn's technique has low accuracy in measuring the velocity of optical flow while anandan's technique has poor performaance for diverging images. Based upon this observation, a new technique for the measurement of optical flow is proposed.