• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.25 no.1
• /
• pp.109-116
• /
• 2012

This paper presents a study on the nonlinear behavior of an innovative bridge girder made from concrete-filled and tied tubular steel arch (CFTA) under static loading. Manufacturing of the CFTA girder may have defects which may highly affect the symmetry and performance of the structure. A simple method is proposed by using stiffness extracted from static test data to detect manufacturing defects of the CFTA girder. A three-dimensional finite element model was used in the numerical analysis in order to verify the method. The proposed method was experimentally validated through static tests of the CFTA girder. The application of the proposed method showed that it is effective in identifying invisible manufacturing defects of the CFTA girder, especially for mass production of a standard type in the factory.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.04a
• /
• pp.59-62
• /
• 2002

최근 네트워크 구성이 복잡해짐에 따라 정책기반의 네트워크 관리기술에 대한 필요성이 증가하고 있으며, 특히 네트워크 보안관리를 위한 새로운 패러다임으로 정책기반의 네트워크 관리 기술이 도입되고 있다. 보안정책 서버는 새로운 정책을 입력하거나 기존의 정책을 수정, 삭제하는 기능과 보안정책 결정 요구 발생시 정책결정을 수행하여야 하는데 이를 위해서는 보안정책 실행시스템에서 보내온 경보 메시지에 대한 분석 및 관리가 필요하다. 따라서 이 논문에서는 정책기반 네트워크 보안관리 프레임워크의 구조 중에서 보안정책 서버의 효율적인 보안정책 수림 및 수행을 지원하기 위한 경보데이터 관리기를 설계하고 구현한다. 그리고 경보 데이터 저장과 분석을 위해서 데이터베이스 스키마를 설계하고 저장된 경보데이터를 분석하는 모듈을 구현한다. 또한 불량사용자나 호스트의 관리를 위하여 블랙 리스트 매니져를 구현하며 블랙리스트 매니져는 위험한 불량사용자와 호스트를 탐지하여 관리하는 기능을 제공한다 구현된 경보 관리기나 고수준 분석기는 효율적인 보안정책관리를 지원하게 된다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.477-480
• /
• 2022

반도체 성능 향상으로 신호를 전달하는 회로의 단위가 마이크로 미터에서 나노미터로 미세화되어 선폭(linewidth)이 점점 좁아지고 있다. 이러한 변화는 검출해야 할 불량의 크기가 작아지고, 정상 공정상태와 비정상 공정상태의 차이도 상대적으로 감소되어, 공정오차 및 공정조건의 허용범위가 축소되었음을 의미한다. 따라서 검출해야 할 이상징후 탐지가 더욱 어렵게 되어, 높은 정밀도와 해상도를 갖는 검사공정이 요구되고 있다. 이러한 이유로, 미세 공정변화를 파악할 수 있는 신규 검사 및 계측 공정이 추가되어 TAT(Turn-around Time)가 증가하게 되었고, 웨이퍼가 가공되어 완제품까지 도달하는데 필요한 공정시간이 증가하여 제조원가 상승의 원인으로 작용한다. 본 논문에서는 웨이퍼의 검계측 데이터가 아닌, 제조공정 과정에서 발생하는 다양한 센서 및 장비 데이터를 기반으로 웨이퍼 제조 결과가 양품인지 그렇지 않으면 불량인지 구별할 수 있는 가상계측 모델을 제안한다. 기계학습의 여러 알고리즘 중에서 다양한 장점을 갖는 XGBoost 알고리즘을 이용하여 예측모델을 구축하였고, 데이터 전처리(data-preprocessing), 주요변수 추출(feature selection), 모델 구축(model design), 모델 평가(model evaluation)의 순서로 연구를 수행하였다. 결과적으로 약 94% 이상의 정확성을 갖는 모형을 구축하는데 성공하였으나 더욱 높은 정확성을 확보하기 위해서는 반도체 공정과 관련된 Domain Knowledge 를 반영한 모델구축과 같은 추가적인 연구가 필요하다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.2
• /
• pp.95-111
• /
• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• Journal of Korean Society for Quality Management
• /
• v.25 no.3
• /
• pp.74-85
• /
• 1997

In this paper we calculate the subgroup size necessary for detecting the change of percent defective with several detection probabilities for orginal population fraction nonconforming p, changed population fraction nonconforming $p^*$, and the ratio k=$p^*$/p in the usage of p control charts. From our calculation we can know the error level of normal a, pp.oximation in detection probability calculation and recommend the subgroup size with lower error levels of normal a, pp.oximation, and then we show the reasonable subgroup size necessary for p, $p^*$, k, and the detection probability of the change of fraction nonconforming in a process. The information that we here show in tables will be useful when p control chart users decide the subgroup size in the p control chart users decide the subgroup size in the p control chart.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2000.10a
• /
• pp.277-280
• /
• 2000

Credit evaluation is one of the most important and difficult tasks fur credit card companies, mortgage companies, banks and other financial institutes. Incorrect credit judgement causes huge financial losses. This work describes the use of an evolutionary-fuzzy system capable of classifying suspicious and non-suspicious credit card transactions. The paper starts with the details of the system used in this work. A series of experiments are described, showing that the complete system is capable of attaining good accuracy and intelligibility levels for real data.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.05c
• /
• pp.168-173
• /
• 2001

배전용 애자는 전기적, 열적, 기계적 스트레스 등 내 외부 서지에 의한 균열이 서서히 발생되며 장시간 사용시 절연파괴에 의한 지락사고로 진전되는 경우가 많다. 이러한 사고로 인하여 순간정전이나 장시간 정전에 의한 피해를 최소화하기 위해 열화된 애자를 조기에 검출함으로써 전력공급의 신뢰성 향상을 기하고자 한다. 이를 위해 열화된 애자에서 나타나는 물리적 현상에 의해 변화되는 주파수 스펙트럼 분포 해석을 통해 방전 전자파가 갖는 주기성 파형(120Hz)을 검출하여 열화된 애자를 탐지 추적하는 장치를 개발하고자 한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.1044-1049
• /
• 2001

Classical deconvolution methods for source identification following linear filtering can only be used if the transfer function of the system is known. For many practical situations, however, this information is not accessible and/or is time varying. The problem addressed here is that of reconstruction of the original input from only the measured signal. This is known as 'blind deconvolution'. By using Higher Order Statistics (HOS), the restoration of the input signal is established through the maximisation of higher order moments (cumulants) with respect to the characteristics of the signals concerned. This restoration is achieved by constructing an inverse filter considering the choice of the initial inverse filter type. As a practical application, an experimental verification is carried out for the restoration of our impacting signal arising in the response of a cantilever beam with an end stop when randomly excited.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.3
• /
• pp.125-130
• /
• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.1
• /
• pp.54-58
• /
• 2023

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.