• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1041-1047
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• 2017

There are many methods for surveying the status of a road, and the use of unmanned aerial vehicle (UAV) photo is one such method. When the UAV images are too large to be processed and suspected to be redundant, a texture extraction technique is used to transform the data into a reduced set of feature representations. This is an important task in 3D simulation using UAV images because a huge amount of data can be inputted. This paper presents a texture extraction method from UAV images to obtain high-resolution images of bridges. The proposed method is in three steps: firstly, we use the 3D bridge model from the V-World database; secondly, textures are extracted from oriented UAV images; and finally, the extracted textures from each image are blended. The result of our study can be used to update V-World textures to a high-resolution image.

• Journal of Food Hygiene and Safety
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• v.33 no.4
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• pp.248-254
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• 2018

To assess the risk of V. parahaemolyticus infection caused by consumption of raw oysters in Korea, contamination levels during the retail-to-table route of oysters was modeled to predict V. parahaemolyticus growth based on temperature and time. The consumed amount data of the KNHANES and the standard recipe of RDA were applied. A consumption scenario for exposure assessment was developed and combined with a Beta-Poisson dose-response model. The estimated probability of illness from consumption of pathogenic V. parahaemolyticus in raw oysters during three separate months (April, October, and November) was $5.71{\times}10^{-5}$ (within the 5th and 95th percentile ranges of $2.71{\times}10^{-8}$ to $1.03{\times}10^{-4}$). The results of the quantitative microbial-risk assessment indicated that the major factors affecting the probability of illness were the initial contamination level at the retailer, the consumed amount, the prevalence of pathogenic strains [tdh or trh genes], and exposure temperature and time.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.1
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• pp.154-159
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• 1987

In the previously reported Part I, the behavior of the flank wear for carbide tool was studied as a preceeding step to present a simple method for Quick Tool-Life Testing, and the following general equation was obtained $W_{f}$ =(a+bt) $V^{m}$ . In this study the following step-cutting formula for the constants a, b and m in the above general model is derived by using step-cutting data [a numerical formula] To check the validity of the above formula, the comparison is made between the tool-life equation inferred in this method and that inferred in the conventional tool-life testing method, when the wear criterion is 0.3mm. The equation obtained in the present method is V(T')$^{0.57}$=1763 whereas the equation obtained in the conventional tool-life testing method is V(T)$^{0.56}$=1605 The results of the above two formula are satisfactory and also verify the validity of the present research.earch.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.72-77
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• 2017

This paper focuses on the development of a supporting S/W tool for the automated design of an automotive press trim die. To define the die design process based on automation, we analyze the press die design process of the current industry and group repetitive works in the 3D modeling process. The proposed system consists of two modules, namely the template models of the trim steel parts and UI function for their auto-positioning. Four kinds of template models are developed to adapt to various situations and the rules of the interaction formula which are used for checking and correcting the directions of the datum point, datum curve, datum plane are implemented to eliminate errors. The system was developed using CATIA Knowledgeware, CAA(CATIA SDK) and Visual C++, in order for it to function as a plug-in module of CATIA V5, which is one of the major 3D CAD systems in the manufacturing industry. The developed system was tested by applying it to various panels of current automobiles and the results showed that it reduces the time-cost by 74% compared to the traditional method.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.9-16
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• 2019

The goal of this study is to propose an efficient model for recognizing and classifying tree images to measure the accuracy that can be applied to smart devices during class. From the 2009 revised textbook to the 2015 revised textbook, the learning objective to the fourth-grade science textbook of elementary schools was added to the plant recognition utilizing smart devices. In this study, we compared the recognition rates of trees before and after retraining using a pre-trained inception V3 model, which is the support of the Google Inception V3. In terms of tree recognition, it can distinguish several features, including shapes, bark, leaves, flowers, and fruits that may lead to the recognition rate. Furthermore, if all the leaves of trees may fall during winter, it may challenge to identify the type of tree, as only the bark of the tree will remain some leaves. Therefore, the effective tree classification model is presented through the combination of the images by tree type and the method of combining the model for the accuracy of each tree type. I hope that this model will apply to smart devices used in educational settings.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.457-457
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• 2014

오존발생방법은 다양한 방식으로 구현이 가능하나 대용량 장치를 만들기 위해서는 DBD (Dielectric barrier discharge) 구조의 형태의 가지고 있다. 이러한, DBD는 반도체의 MOS (Metal On Semiconductor)의 반대 구조를 가진 SOM (Semiconductor On Metal)의 형태를 가지고 있으며 대부분이 Oxidation 산화물을 가지고 구현한다. 오존발생기는 반도체 공정, 환경 및 정화 등 다양한 분야에 사용이 되고 있는 상황으로 성능개선을 위한 연구가 필요한 상황이다. 대표적으로 사용되는 물질인 $SiO_2$를 가지고 있는 상황이며 Silicon은 에너지 Bandgap이 1.1 eV로 금속위에 증착되어 통상적으로 사용되는 문턱전압은 0.7 V에 해당이 된다. 현재 점차적으로 연구가 진행되고 있는 $Al_2O_3$는 8.8 eV의 bandgap을 가지고 있으며 유전 상수가 9로 $SiO_2$인 3.9보다 높은 유전률 특징을 가지고 있다. 따라서, 본 연구는 오존 발생장치에 사용되는 방전관을 기존의 $SiO_2$에서 $Al_2O_3$ 방식으로 대체하므로써 실제적인 유전율의 값의 차이와 오존 발생시 오존변화율 증대에 관하여 연구하였다. $SiO_2$ 방전관은 Fe 메탈위에 약 3 mm정도의 두께를 binding시켜 N4L사의 PSM1700 모델 LCR meter를 사용하여 1.3 kHz시 7.2 pF의 유전율 확인 할 수 있으며 동일한 조건의 금속 메탈위에 $Al_2O_3$를 binding 시켜 측정한 결과 1.07 kHz시 10.7 pF의 유전율을 가지게 되어 40% 이상 높은 유전율을 가지게 되는 것을 확인 할 수 있다. 오존발생을 위하여 가변 주파수형 트랜스 드라이버를 통한 공진 주파수를 생성하여 방전 증폭을 위한 Amplifier를 통하여 변환률을 높이는 방식을 적용하여 MIDAC사의 I1801모델 적외선 분광기(FT-IR)를 통한 오존발생량을 측정하여 기존의 $SiO_2$의 방전관은 시간당 54 g의 오존 발생률 가지게 된다. $Al_2O_3$는 시간당 70 g 정도의 오존 발생률 가지므로 기존의 $SiO_2$ 보다 발생률 높은 것을 확인 할 수 있다.

• Proceedings of the Korea Crystallographic Association Conference
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• 2002.11a
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• pp.40-41
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• 2002

차세대 반도체 및 나노소자 산업에 대한 국제적 기술은 고밀도 직접화의 추세에 따라서 .게이트 산화막의 두께가 급속히 작아지는 추세이다. 지금까지 이산화규소(A1₂O₃)가 게이트 산화막으로 주로 사용되어 왔으나 점차 SiON 혹은 high k 박막으로 바뀌고 있다. 본 연구에서는 차세대 반도체 소자에 사용될 게이트 산화막 물질인 SiON 박막과 Al₂O₃박막에 대한 SE(Spectroscopic Ellipsometry)분석 모델을 확립하였고, SE 측정결과를 TEM, MEIS, XRR의 결과들과 비교하였다. SiON 박막의 굴절률 값은 Si₃N₄와 SiO₂가 물리적으로 혼합되어 있다고 가정하여 Bruggeman effective medium approximation을 사용하여 구하였다. 동일한 시료를 절단하여 TEM, MEIS, 그리고 XRR에 의하여 SiON 박막의 두께를 측정하였으며, 그 결과 SE와 XRR에 의해 얻어진 박막두께가 TEM과 MEIS의 결과 값보다 약 0.5 nm 크게 주어짐을 알 수 있었다(Table 1 참조). 본 연구결과는 비파괴적이며 비접촉식 측정방법인 SE가 2～4nm 두께의 초미세 SiON 박막의 두께와 N 농도의 상대적 값을 빠르고 쉽게 구할 수 있는 유용한 측정방법 임을 보여주었다. 기존의 게이트 산화물인 SiO₂를 대체할 후보 물질들 중의 하나인 A1₂O₃의 유전함수를 구하기 위하여 8 inch, p-type 실리콘 기판 위에 성장된 5 nm, 10 nm, 및 20 nm 두께의 A1₂O₃ 박막의 유전함수와 두께를 측정하였다. 이 시료들에 대한 SE data는 vacuum-UV spectroscopic ellipsometer를 사용하여 세 개의 입사각에서 0.75 eV에서 8.75 eV까지 0.05 eV 간격으로 측정되었다. A1₂O₃ 박막의 유전함수와 두께를 얻기 위하여 공기층/A1₂O₃ 박막/Si 기판으로 구성된 3상계 모델을 사용하였다. Si 기판에 대한 복소 유전함수는 문헌상의 값(1)을 사용하였고, A1₂O₃ 박막의 유전함수는 5개의 미지상수를 갖는 Tauc- Lorentz(TL) 분산함수(2)를 사용하였다. A1₂O₃ 박막의 경우 두께가 증가함에 따라서 굴절률이 커짐을 알 수 있었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.177-182
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• 2024

In this paper, the purpose of this paper is to predict and prevent the risk of crowd concentration in advance for possible future crowd accidents based on the Itaewon crush accident in Korea on October 29, 2022. In the case of a single CCTV, the administrator can determine the current situation in real time, but since the screen cannot be seen throughout the day, objects are detected using YOLOv4, which learns images taken with CCTV angle, and safety accidents due to crowd concentration are prevented by notification when the number of clusters exceeds. The reason for using the YOLO v4 model is that it improves with higher accuracy and faster speed than the previous YOLO model, making object detection techniques easier. This service will go through the process of testing with CCTV image data registered on the AI-Hub site. Currently, CCTVs have increased exponentially in Korea, and if they are applied to actual CCTVs, it is expected that various accidents, including accidents caused by crowd concentration in the future, can be prevented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.31-40
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• 2024

Recently, static analysis-based signature and pattern detection technologies have limitations due to the advanced IT technologies. Moreover, It is a compatibility problem of multiple architectures and an inherent problem of signature and pattern detection. Malicious codes use obfuscation and packing techniques to hide their identity, and they also avoid existing static analysis-based signature and pattern detection techniques such as code rearrangement, register modification, and branching statement addition. In this paper, We propose an LLVM IR image-based automated static analysis of malicious code technology using machine learning to solve the problems mentioned above. Whether binary is obfuscated or packed, it's decompiled into LLVM IR, which is an intermediate representation dedicated to static analysis and optimization. "Therefore, the LLVM IR code is converted into an image before being fed to the CNN-based transfer learning algorithm ResNet50v2 supported by Keras". As a result, we present a model for image-based detection of malicious code.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.109-126
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• 2017

In the various kinds of intelligent services, many studies for detecting emotion are in progress. Particularly, studies on emotion recognition at the particular time have been conducted in order to provide personalized experiences to the audience in the field of exhibition though facial expressions change as time passes. So, the aim of this paper is to build a model to predict the audience's emotion from the changes of facial expressions while watching an exhibit. The proposed model is based on both sequential neural network and the Valence-Arousal model. To validate the usefulness of the proposed model, we performed an experiment to compare the proposed model with the standard neural-network-based model to compare their performance. The results confirmed that the proposed model considering time sequence had better prediction accuracy.