• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.186-186
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• 2016

산업안전보건법에 따르면 결함수 분석기법(Fault Tree Analysis, FTA)은 주로, 설계 또는 운전단계에 있는 공정 위험성 평가 시 사고의 발생빈도와 예상사고 시나리오를 추정하는데 적용되어왔다. 따라서 일반적인 결함수 분석에서 정상 사상은 시스템 자체의 사용불능을 야기하는 가장 큰 원인을 의미한다. 그러나 상수도의 유지관리를 위해 FTA를 적용한 경우 정상 사상은 일반적인 FTA에서의 정상 사상과는 다르게 정의되어야 한다. 즉, 용수공급의 경우 관망 전체에 영향을 미치는 정상 사상이란 수원지의 오염이나, 수원지에서 직접적으로 나오는 관로에 문제가 생겼다거나 하는 사건을 의미하므로, 이러한 방식의 정상 사건 정의는 관망의 관로 및 절점에서 발생되는 문제를 분석하기 어렵게 만든다. 따라서 본 연구에서는 관망의 특정 지점에 용수공급이 원활하게 되지 않는 경우를 FTA의 정상사상으로 정의하였다. 또한, 개개의 관로 노후도 평가 점수와 FTA를 이용하여 관망의 특정 절점에 용수를 공급하기 위한 관로들의 노후도 평가 평균 점수를 산출하였다. 개발한 프로그램에서는 평균 노후도 평가 점수를 산출하기 위해서, 특정 절점 값을 입력 받고 각 파이프와 그로 인해 생기는 고립구간(세그먼트 + 비의도적 고립구간) 에 포함되는 절점들의 관계를 나타낸 자료를 이용하여 파이프에 문제가 생길 시 단수되는 절점을 역으로 검색해서 각 파이프의 노후도 평가 점수를 산술평균 하였다. 본 연구에서는 특정지점에 용수 공급이 원활하지 못 한 정상사상에 대하여 관로의 수리 또는 교체를 해야 하는 구간을 찾아 노후도 평가 점수를 산정하는 것이 목표이다. 이를 위해 특정 절점에 용수를 공급하기 위하여 지나가는 관로들에 대해서 평균 점수를 구함으로써, 절점에 용수가 원활히 공급 될 가능성을 산정하여 FTA와 관로 노후도 평가 점수를 이용하고 특정 절점에 용수를 공급하기 위하여 지나가는 관로들에 대해서 평균 점수를 산정하였다. 절점에서 용수를 공급하는 중요도에 따라 절점으로 이어지는 관로의 평균 노후도를 일정 점수 이하로 유지시키는 방법을 이용함으로써 관로의 유지관리 필요성에 대해 지표로써 사용할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.114-116
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• 2012

본 논문에서는 대규모 무기체계에서 정보 모델 기반의 통합 관리 및 모니터링 시스템을 위한 결함허용 방법을 제시한다. 정보 모델 기반의 통합 관리 및 모니터링 시스템은 이기종 분산 환경으로 이루어지는 대규모 무기체계의 하드웨어 및 애플리케이션을 추상화된 정보 모델을 이용하여 관리할 수 있는 중앙 관리 및 통제 시스템이다. 대규모 무기체계에서는 하나의 시스템에서 오류가 발생하게 되면 시스템 전체에 영향을 줄 수 있기 때문에 중앙 관리 및 통제 시스템에서의 결함허용 방법이 필요하다. 이 문제를 해결하기 위해 정보 모델을 관리하기 위한 결함허용 그룹을 정의하고, 결함허용 그룹마다 서로 다른 결함허용 방법을 설정하여 애플리케이션의 중요도에 따라 다양한 방법을 이용하여 결함허용을 수행한다.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.433-447
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• 1999

검사관련 능력과 문항점수사이의 관계를 모형화하기 위해 사용한 순환모형에서 관측불능인 능력상대변수를 비롯한 모든 변수들이 범주형 변수라 가정하자. 이 범주형 자료를 위한 모수추정문제를 다루기 위해 EM 방법을 이용했는데, EM 방법은 사용하기에 편리하지만 순환모형에 대한 추정값이 적절하지 않는 경우가 발생한다. 그 주된 원인중의 하나로 초기값 선정의 잘못을 들 수 있는데, 본 논문에서는 이 외에 구조상의 결함도 그 원인이 됨을 경험적으로 보았다. 따라서 구조적 결함을 먼저 해결하면 보다 효과적인 초기값을 선정할 수 있으리가 기대한다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.5
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• pp.83-93
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• 2017

This study proposed the new weighting values and fault points of condition assessment items for reinforced earth retaining walls based on the combination the inspection data and hybrid weighting technique. Utilizing the inspection data of 161 reinforced earth retaining walls, multi regression analysis and entropy technique were applied to gain the weighting values of condition assessment items. In addition, the weighting values by AHP technique was analyzed based on the opinion of experts. By appling hybrid weighting technique to the calculated weighting values obtained by the individual technique, the new weighting values of condition assessment items were proposed, and the fault points and fault indices of reinforced earth retaining walls were proposed. Results showed that the rank of the weighting value of the condition evaluation items was fluctuated according to the multiple regression analysis, AHP technique, and entropy technique. There was no duplication of the rank of the weighting value while the current weighting value was overlapped. Specially, in the rsults of multi regression analysis, two condition assessment items were occupied 70% of the total weights. When the proposed weighting values were applied to existing reinforced earth retaining wall of 161, 16 reinforced earth retaining walls showed the increased risk rank and 31 represented the decreased risk rank.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.6
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• pp.619-628
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• 2017

As the 3D laser scanning technology capable of databaseing large sewage box culverts becomes possible, it is necessary to develop a standardization manual that can clearly distinguish the structural and operational defect types of box culver and analyze the defect data. In this study, we collected and analyzed defects in sewage box culverts of 14,827m in total by selecting three districts in Korea. The major defects were surface damages, and their defect densities were $2.17m^2/m$, $0.27m^2/m$ and $0.10m^2/m$ for aggregate exposure, Steel reinforcement exposure, and Steel reinforcement projecting. In order to support the decision of the box culverment management, it was divided into five grades and each defect code and defect score were allocated. The results of this study are useful for the diagnosis of the sewage box culverts in Korea and it is expected to support a decision making for management.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.1
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• pp.21-28
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• 2017

This study was performed to propose the sewer defect scoring, and grading protocols for sewer condition assessment. For this, sewer defect scoring methods were comparatively analyzed and reviewed for four international condition assessment protocols, which are established based on WRc manual. As a result, we proposed a new protocol for sewer condition assessment, in which characteristics of sewer pipes are considered by segment. In reference to the PIM-3, the extent of ground subsidence was adopted to be of importance, and renewal scores increased in accordance with weighting of defects causing structural backfill materials. Also, defect grades of 'Hole' were extended to 5 levels of the grading, and 'Surface Damage' was excluded in defect assessment. The addition of 'Buckling' resulted in reduction of weights in 'Surface Damage' and 'Lining Defects'.

• Phonetics and Speech Sciences
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• v.16 no.2
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• pp.83-90
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• 2024

This study aimed to explore phonological retrieval and phonological memory skills in second to third graders with dyslexia, poor comprehension, and typical development. The participants included 17 children with dyslexia, 17 children with poor comprehension, and 24 typically developing children. Children with dyslexia scored below 85 on the word decoding test, poor comprehender scored above 90 on the word decoding, and below 85 on the reading comprehension test and typical children scored above 90 on both reading tests. All participants were assessed on rapid automatized naming (RAN) and nonword repetition (NWR). The result indicated that children with dyslexia performed significantly worse on RAN and NWR tasks than other groups. However, there was significant differences between poor comprehender and typically developing children. Furthermore, only RAN were significantly correlated with word decoding and reading comprehension in children with dyslexia. For typically developing children, RAN was correlated with word decoding and reading comprehension, while NWR had a significant correlation with reading comprehension. No correlations were found between these variables for poor comprehender. The finding suggests that children with dyslexia showed difficulties on phonological retrieval and phonological memory, which are essential for reading development while poor comprehender do not have difficulties with phonological processing skills. Phonological processing deficits may underlie word decoding difficulties in dyslexia.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.299-308
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• 2018

This study was conducted to examine the mediating effect of executive function deficiencies on the relationship between smartphone addiction and memory impairment and to suggest treatment implications for adults with smartphone addiction symptoms. For this study, 300 adults(146 males and 154 females) were conducted smartphone addiction scale, Barkley Deficits in Executive Functioning Scale-Short Form(BDEFS-SF), and Multifactorial Memory Questionnaire(MMQ). The results are as follows. First, correlation analyses showed that smartphone addiction, executive function deficiencies, and memory impairment were positively correlated. Second, Smartphone addiction group showed significantly higher scores on all of the subfactors of executive function and memory impairment. Third, Stepwise regression analyses revealed that self-organization/problem solving, self-management to time, self-restraint, and withdrawal were the best predictors of memory impairment. Forth, Executive function deficiencies fully mediated the relationship between smartphone addiction and memory impairment. These findings were considered to suggest that treatment programs for smartphone addiction group needed to include training for cognitive improvement such as executive function rehabilitation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.68-76
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• 2016

The present study proposes the definition of service life and the end-of-life criterion for bridge maintenance. Bridges begin to deteriorate as soon as they are put into service. Effective bridge maintenance requires sound understanding of the deterioration mechanism as well as the expected service life. In order to determine the expected service life of a bridge for effective bridge maintenance, it is necessary to have a clear definition of service life and end-of-life. However, service life can be viewed from several perspectives based on literature review. The end of a bridge's life can be also defined by more than one perspective or performance measure. This study presents definition of service life which can be used for bridge maintenance and the end-of life criterion using the performance measure such as a damage score. The regression model can predict an average service life of bridges using the proposed end-of-life criterion.