• Applied Microscopy
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• v.48 no.4
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• pp.126-127
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• 2018

Carbon has numerous allotropes and various crystalline forms with full dimensionalities such as diamond, graphite, fullerenes, and carbon nanotubes leading a wide range of applications. Since the emerge of graphene consisting of a single atomic layer of carbon atoms, a fabrication of all-carbon-based device with combination of one-, two-, and three-dimensional carbons has become a hot issue. Here, we introduce an ultimate one-dimensional carbon atomic chain. Carbon atomic chains were experimentally created by removing atoms from monolayer graphene sheet under electron beam inside transmission electron microscope (TEM). A series of TEM images demonstrate the dynamics of carbon atomic chains over time from the formation, transformation, and then breakage.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.27-30
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• 2019

Factory monitoring systems are gaining importance in wide areas of industry. Especially, there have been many efforts in implementation of vibration measurement and analysis for monitoring the status of rotating machines. In this paper, a digital signal processor (DSP) based monitoring system dedicated to the vibration monitoring and analysis on rotating machines is discussed. Vibration signals are acquired and processed for the continuous monitoring of the machine status. Time domain signals and fast Fourier transform (FFT) are used for vibration analysis. All of the signal processing procedures are done in the DSP to reduce the production and maintenance cost. The developed system could also provide remote and mobile monitoring capabilities to operator via internet connection. This paper describes the overview of the functional blocks of the implemented system. Test results based on signals from small-size single phase motors are discussed for monitoring and defect diagnosis of the machine status.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.988-999
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• 2021

AOI (Automatic Optical Inspection) of PCB (Printed Circuit Board) is a very important step to guarantee the product performance. The process of registering components called teaching mode is first perform, and AOI is then carried out in a testing mode that checks defects, such as recognizing and comparing the component mounted on the PCB to the stored components. Since most of registration of the components on the PCB is done manually, it takes a lot of time and there are many problems caused by mistakes or misjudgement. In this paper, A components classifier is proposed using YOLO (You Only Look Once) v2's object detection model that can automatically register components in teaching modes to reduce dramatically time and mistakes. The network of YOLO is modified to classify small objects, and the number of anchor boxes was increased from 9 to 15 to classify various types and sizes. Experimental results show that the proposed method has a good performance with 99.86% accuracy.

• International journal of steel structures
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• v.18 no.5
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• pp.1710-1722
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• 2018

Investigating remained damages from terrible earthquakes, it could be concluded that some events including explosion because of defect and failure in the building mechanical facilities or caused by gas leak, firing, aftershocks, etc., which are occurred during or a few time after the earthquake, will increase the effects of damages. In this paper, by introducing a complete risk analysis which included direct and indirect risks for earthquake (the main shock) and aftershock, the corresponding robustness index was created that called as "robustness index sequential critical events risk-based". One of the main properties of the intended robustness index is using progressive collapse percentage in its evaluation. Then, in a numerical example for a 4-storey moment resisting steel frame structure, a method is presented for obtaining all effective parameters in robustness index evaluation based on the intended risk and at last its results were reported.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.237.3-238
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• 2003

Tissue engineering may be adequately defined as the science of persuading the body to regenerate or repair tissue that fail to regenerate or heal spontaneously. In the various techniques of cartilage tissue engineering, the use of 3-dimensional polymeric scaffolds implanted at a tissue defect site is usually involved. These scaffolds provided a framework for cells to attach, proliferate, and form extracellular matrix(ECM). The scaffolds may also serve as carriers for cells and/or growth factors. In the ideal case, scaffold absorb at a predefined rate so that the 3-dimensional space occupied by the initial scaffold is replaced by regenerated host tissue. (omitted)

• Journal of the Korean Society of Visualization
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• v.18 no.3
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• pp.109-115
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• 2020

Five layers in mean flow are proposed by using the direct numerical simulation data of turbulent pipe flow up to Reτ = 3008. Viscous sublayer, buffer layer, mesolayer, log layer and core region are investigated. In the buffer layer, the viscous force is counterbalanced by the turbulent inertia from the streamwise mean momentum balance, and a log law occurs here. The overlap layer is composed of the mesolayer and the log layer. Above the buffer layer, the non-negligible viscous force causes the power law, and this region is the mesolayer, where it is the lower part of the overlap layer. At the upper part of the overlap layer, where the viscous force itself becomes naturally negligible, the log layer will appear due to that the acceleration force of the large-scale motions increases as the Reynolds number increases. In the core region, the velocity-defect form is satisfied with the power-law scaling.

• Annual Conference of KIPS
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• 2009.11a
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• pp.811-812
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• 2009

프로젝트 개발에서 소프트웨어의 품질을 높이기 위한 방법 중 하나는 소스코드에 대한 잠재적인 결함을 초기에 발견하는 것이다. 이를 실현하기 위해 정형화된 기법으로 코드 인스펙션을 자동화하였으며, 개발자들이 ACI 규칙을 수립하였다. 논문에서는 실제 진행 중인 프로젝트를 기반으로 하여 결함 점검 수행에 따른 결함 발견 건수와 결함밀도가 감소되는 증명을 다룬다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.248-251
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• 2020

전 반도체 제조 및 검사 공정 과정을 자동화하는 스마트 팩토리의 실현에 있어 제품 검수를 위한 검사 장비는 필수적이다. 하지만 딥 러닝 모델 학습을 위한 데이터 처리 과정에서 엔지니어가 전체 웨이퍼 영상에 대하여 결함 항목 라벨을 매칭하는 것은 현실적으로 불가능하기 때문에 소량의 라벨 (labeled) 데이터와 나머지 라벨이 없는 (unlabeled) 데이터를 적절히 활용해야 한다. 또한, 웨이퍼 영상에서 결함이 발생하는 빈도가 결함 종류별로 크게 차이가 나기 때문에 빈도가 적은 (minor) 결함은 잡음처럼 취급되어 올바른 분류가 되지 않는다. 본 논문에서는 소량의 라벨 데이터와 대량의 라벨이 없는 데이터를 동시에 활용하면서 결함 사이의 발생 빈도 불균등 문제를 해결하는 점진적 데이터 평준화 (progressive pseudo-labeling balancer)를 제안한다. 점진적 데이터 평준화를 이용해 분류 네트워크를 학습시키는 경우, 기존의 테스트 정확도인 71.19%에서 6.07%-p 상승한 77.26%로 약 40%의 라벨 데이터가 추가된 것과 같은 성능을 보였다.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.82-90
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• 1995

This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.

• Annual Conference of KIPS
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• 2023.11a
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• pp.595-596
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• 2023

스마트팩토리의 핵심 설비 기기인 협동로봇의 유지보수를 위해 다양한 센서 데이터를 활용한 딥러닝 기반 결함 진단 연구가 확대되고 있다. 하지만 협동로봇은 기계적 특성과 수행하는 작업의 다양성으로 인해 내부 센서 데이터의 복잡도가 매우 높아 고정적인 결함 진단 기법을 적용하기 어렵다. 따라서 본 논문은 협동로봇의 동작 패턴을 직관적이고 신속하게 인지할 수 있는 비전 기술을 활용하여, 동작 오류 진단을 기반으로 원인이 되는 조인트 결함 위치를 추적하는 딥러닝 기법을 제안한다.