• 분석과학
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• 제16권3호
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• pp.226-231
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• 2003

국내와 국외에서 생산된 두 가지 종류의 AAS 기기를 이용하여 대기분진 시료를 분석하였다. 국내에서 생산된 제품의 경우, 단색화 장치의 성능면에서 선택된 파장들에 대한 광량의 세기가 우수하게 관찰되었다. 그리고 원자화장치의 경우, 재현성을 높이기 위하여 미세방울을 제외한 모든 큰 방울을 폐기 잔유물이 남지 않도록 하였다. 기기의 검출부에서는 저역 통과 filter를 사용하여, 데이터의 노이즈를 줄였다. Au 표준 용액을 이용한 검출한계 실험 분석치는 약 $0.015{\mu}g/L$ 수준의 높은 감도로서 외국사의 성능과 뚜렷한 큰 차이를 구분하기는 어려웠다. 본 연구진은 이러한 분석기법을 활용하여 서울시내 7개 지역 주요 관측점을 중심으로 2001년부터 2002년 봄까지 일년 동안 대기보전시료를 채취하였다. 이들 시료를 이용하여 분진 중에 결합된 중금속성분의 (Pb, Cu, Mn, Cd, Ni, Fe, Cr, Co, Mg, Al) 농도를 분석하였다.

• 전기학회논문지
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• 제67권9호
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• pp.1216-1223
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• 2018

As field of application of AMI, AMR uses the power line as the primary means of communication. PLC has a big merit without installation of the new network for communication in a field using the power line which is the existing equipment. However, there is a serious obstacle in commercialization for the instability by noise and communication environment. Therefore, the technical method for maintaining the communication state which overcome such demerit and was stabilized is required essentially. PLC routing technology is applied with the alternative plan now. The routing technology currently managed by field includes many problems by applying the algorithm of an elementary level. PLC routing path search problem can be modeled with the problem of searching for optimal solution as similar to such as optimal routing problem and TSP(Travelling salesman problem). In this paper, in order to search for a PLC routing path efficiently and to choose the optimal path, GA(Genetic Algorithm) was applied. Although PLC was similar in optimal solution search as compared with typical GA, it also has a difference point by the characteristic of communication, and presented the new methodology over this. Moreover, the validity of application technology was verified by showing the experimental result to which GA is applied and analyzing as compared with the existing algorithm.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2009년도 제38회 동계학술대회 초록집
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• pp.464-464
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• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• 비파괴검사학회지
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• 제20권6호
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• pp.511-520
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• 2000

초음파 펄스-에코법을 매우 얇은 층을 갖는 다층구조물에 적용할 때 그 얇은 층의 상하면에서의 반사파가 중첩되게 되면 검사가 곤란하게 된다. 이런 문제는 반도체 내부에서의 심한 감쇠를 피하기 위해 20MHz 이하의 비교적 저주파수를 사용하는 초음파 현미경으로 반도체의 얇은 실리콘 칩을 검사하는 경우에 쉽게 볼 수 있다. 기존에 이런 초음파 신호의 중첩을 분리하기 위해 디컨볼루션 기법이 사용되어 왔으나, 송신파의 파형이 전파하면서 왜곡되어 수신되는 경우에는 적절치 못하다. 본 논문에서는 기존의 디컨볼루션 기법에 비하여 우수한 성능으로 중첩 신호를 분리해 낼 수 있는 새로운 신호처리 기법으로서 웨이브렛 변환 기반 디컨볼루션 (WTBD) 기법을 제안하였다. 여기서 웨이브렛 변환은 송신파와 왜곡된 수신 신호의 공통 파형을 추출하기 위해 사용되고 추출된 공통 파형에 대해 디컨볼루션 처리한다. 제안하는 방법의 성능은 모형신호에 대한 컴퓨터 시뮬레이션과 인위적으로 실리콘 칩 상면에 들뜸 결함을 만든 반도체 시편에 대한 실험을 통해 검증되었다.

• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• 한국기계가공학회지
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• 제18권9호
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• pp.72-80
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• 2019

In this task, the tool dynamometer design and manufacture, and the Ansys S/W structural analysis program for tool attachment that satisfies the cutting force measurement requirements of the tool dynamometer system are used to determine the cutting force generated by metal cutting using 3-axis static structural analysis and the LabVIEW system. The cutting power in a cutting process using a milling tool for processing metals provides useful information for understanding the processing, optimization, tool status monitoring, and tool design. Thus, various methods of measuring cutting power have been proposed. The device consists of a strain-gauge-based sensor fitted to a new design force sensing element, which is then placed in a force reduction. The force-sensing element is designed as a symmetrical cross beam with four arms of a rectangular parallel line. Furthermore, data duplication is eliminated by the appropriate setting the strain gauge attachment position and the construction of a suitable Wheatstone full-bridge circuit. This device is intended for use with rotating spindles such as milling tools. Verification and machining tests were performed to determine the static and dynamic characteristics of the tool dynamometer. The verification tests were performed by analyzing the difference between strain data measured by weight and that derived by theoretical calculations. Processing test was performed by attaching a tool dynamometer to the MCT to analyze data generated by the measuring equipment during machining. To maintain high productivity and precision, the system monitors and suppresses process disturbances such as chatter vibration, imbalances, overload, collision, forced vibration due to tool failure, and excessive tool wear; additionally, a tool dynamometer with a high signal-to-noise ratio is provided.

• 한국군사과학기술학회지
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• 제22권5호
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• pp.599-606
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• 2019

The Arena Fragmentation Test(AFT) is one of the important tasks for designing a high-explosive warhead. In order to measure the statistics of a warhead in the test, fragments of a warhead that penetrate steel plates are detected by using complex and expensive measuring equipment. In this paper, instead of using specific hardware to measure the statistics of a warhead, we propose to use an image processing based object detection algorithm to detect fragments in AFT. To this end, we use a hard-thresholding method with a brightness feature and apply a morphology filter to remove noise components. We also propose a simple yet effective temporal filtering method to detect only the first penetrating fragments. We show that the performance of the proposed method is comparable to that of a hardware system under the same experimental conditions. Furthermore, the proposed method can produce better results in terms of finding exact positions of fragments.

• 한국자기학회지
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• 제21권5호
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• pp.167-173
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• 2011

함정에 의한 수중 정자기장 신호는 수중 방사소음 신호에 비해 상대적으로 거리에 따라 급격히 감소되는 특성을 가지지만 근거리에서 정확한 표적 탐지가 가능하므로 감응 기뢰 체계에서 기폭 신호를 제공하는 신호원으로 널리 사용되고 있다. 본 논문에서는 상용 전자기 해석 도구를 활용하여 함정 선체, 내부 구조물 및 주요 탑재장비들에 의한 수중 정자기장 신호 특성 예측 결과에 대해 상세히 기술하였으며, 추가로 대상 함정에 소자코일을 배치하여 소자 효과도 분석을 수행하였다.

• 임상간호연구
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• 제17권2호
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• pp.228-238
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• 2011

Purpose: The purpose of this study was to investigate parent expectation and satisfaction with respect to pediatric inpatient care and to identify the variables related to parent satisfaction. Methods: The study was conducted in pediatric wards of a tertiary children's hospital in Korea. The participants were 361 parents of children who were inpatients. Data were collected using a structured questionnaire (The Pediatric Family Satisfaction Questionnaire) at the time of discharge. Results: The highest parent expectation domain was medical service. The parents were most satisfied with nursing service and least satisfied with general hospital service and accommodation. The parents expressed lower satisfaction with hospital facilities, equipment, noise, cleanliness, and communication by health care professionals. Parents with younger children reported higher expectation from the complete hospital service and those who had a longer length of stay reported higher expectation from the nursing service. Conclusion: To improve the quality of hospital services, we need to understand parent expectation and improve and provide clear communication. In addition, the general hospital service and accommodation should not be overlooked for improvement.

• 한국빅데이터학회지
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• 제6권1호
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• pp.197-206
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• 2021

GPR(Ground Penetrating Radar)에서 수집된 데이터는 지하 탐사를 위해 사용된다. 이 때, 지반 아래의 시설물들이 GPR을 반사하는 경우가 종종 발생하여 수집된 데이터는 전문가에 경험에 의존하여 해석된다. 또한, GPR 데이터는 수집 장비, 환경 등에 따라 데이터의 노이즈, 특성 등이 다르게 나타난다. 이로 인해 정확한 레이블을 가지는 데이터가 충분히 확보되지 못하는 경우가 많다. 일반적으로 이미지 분류 문제에서 높은 성능을 보이는 인공신경망 모델을 적용하기 위해서는 많은 양의 학습 데이터가 확보되어야 한다. 그러나 GPR 데이터의 특성 상 데이터에 정확한 레이블을 붙이는 것은 많은 비용을 필요로 하여 충분한 데이터를 확보하기가 어렵다. 이는 결국 일반적으로 활용되는 지도학습 방법을 기반으로 인공신경망을 적절히 학습시킬 수 없게 한다. 본 논문에서는 각 레이블의 정확도가 유사한 수준을 갖도록 하는 것을 목표로 데이터 특성을 바탕으로 하는 이미지 분류 방법을 제안한다. 제안 방법은 준지도학습을 기반으로 하고 있으며, 인공신경망으로부터 이미지의 특징값을 추출한 후 클러스터링 기법을 활용하여 이미지를 분류한다. 이 방법은 라벨링 된 데이터가 충분하지 않은 경우 라벨링할 때 뿐 만 아니라 데이터에 달린 레이블의 신뢰도가 높지 않은 경우에도 활용할 수 있다.