• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.277-283
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• 2019

Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.04a
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• pp.262-266
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• 2011

본 연구에서는 건축물 내장 재료에 대한 연소독성평가와 독성지수연구로써 FT-IR을 이용한 연소가스분석실험을 하였다. 독성지수를 산정하기 위한 실험의 화재모델로 콘 칼로리미터 화재모델(KS F ISO/TR 9122-4)을 사용하였으며 ISO 19702의 절차에 따라 FT-IR을 이용하여 건축 재료의 연소로부터 발생되는 가스의 분석을 수행하였다. 국제규격에서 제시하고 있는 몇 가지 독성지수 산정법 중 ISO 13344에서 규정하는 방법에 따라 FED 값을 산정하였으며, 30분간 시험동물에 노출 시 대상의 50%가 사망하는 농도인 $LC_{50}$을 기준으로 하여 3가지 재료의 독성지수화를 통해 상대적인 독성 위험도를 평가하였다.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.399-400
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• 2018

인덕션 히터(Induction Heater)는 PTC 히터의 비해 빠른 온도 상승 효과를 가지고 있다. 인덕션 히터는 LC공진 회로로 구성된다. 커패시터의 정전 용량은 가격과 중량에 비례한다. 따라서 인덕턴스를 향상시켜서 정전용량을 줄여야 한다. 인덕턴스 향상을 위해 인덕션 히터의 구조를 변경하고 다구찌 기법과 유한요소법(FEM:Finite Element Method) 시뮬레이션을 통해 최적화 모델을 도출한다.

• The Korean Journal of Pesticide Science
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• v.16 no.2
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• pp.151-162
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• 2012

This study was carried out to establish Korean total diet study (TDS) model for estimating pesticide residue in food samples. In addition, pesticide residues of food samples were monitored by setting the standards of established Korean total diet study model. For monitoring, first step were selection of total 102 species food samples, second step were selection of total 70 species food samples, and third step were selection of total 12 representative diet and 109 species food samples. Ninety-eight pesticides were analyzed using $GC-{\mu}ECD$, GC-MS, and LC-MS/MS after QuEChERS sample preparation method. The residue levels in detected food samples were below the maximum residue limit (MRL). Establishment of the Korean total diet study model means providing safe food for consumers, secure the safety of food samples and provide ongoing information to agricultural producers about use of pesticides.

• Journal of radiological science and technology
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• v.46 no.6
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• pp.535-542
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• 2023

This study aimed to implement a deep learning model that can perform quantitative quality control through ACTS software used for quantitative evaluation of ACR phantom in CT quality control and evaluate its usefulness. By changing the scanning conditions, images of three modules of the ACR phantom's slice thickness (ST), low contrast resolution (LC), and high contrast resolution (HC) were obtained and classified as ACTS software. The deep learning model used ResNet18, implementing three models in which ST, HC, and LC were learned with epoch 50 and an integrated model in which three modules were learned with Epoch 10, 30, and 50 at once. The performance of each model was evaluated through Accuracy and Loss. When comparing and evaluating the accuracy and loss function values of the deep learning models by ST, LC, and HC modules, the Accuracy and Loss of the HC model were the best with 100% and 0.0081, and in the integrated model according to the Epoch value, Accuracy and Loss with epoch 50 were the best with 96.29% and 0.1856. This paper showed that quantitative quality control is possible through a deep learning model, and it can be used as a basis and evidence for applying deep learning to the CT quality control.

• Korean Journal of Human Ecology
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• v.18 no.3
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• pp.749-756
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• 2009

Keeping abreast with the latest consumer's trends, industries are focusing on sensibility aspects of products to meet consumer's needs. The car(?) seat cover fabrics are more closely related to human senses than anything else. This study attempted to investigate which seat cover fabric can give good feeling to consumers and to analyze their characteristics. Twelve kinds of jacquard fabric used for seat cover were selected. The Kawabata Evaluation System was used to measure the mechanical properties of 12 jacquard fabrics, and tactile sensibility(TS), and preference(P) determined by subjective evaluation of 160 participants were also utilized. The stepwise regression analysis was made to select the most significant mechanical properties, and some models for predicting tactile sensibility and preference was developed. The results are briefly summarized as follows: the most important parameter to choose seat cover fabric is a "hygienic property" and the other parameters are 'materials with color fastness', 'compressive property', 'color', 'antibacterial property', 'easy-care property'. The LogSMD, LogB, LC, EM were selected as significant mechanical properties affecting tactile sensibility. Also, the LC, LogB, LogSMD, LogWC, LogMMD were selected as significant mechanical properties affecting preference.

• KSBB Journal
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• v.14 no.4
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• pp.408-413
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• 1999

To separate mixtures analytically and preparatively by LC (Liquid Chormatography), the operating conditions of analytical chromatography should be determined. The HCI program was utilized to find the optimum operating condition accurately and rapidly, and to reduce the number of experiments. In an analytical chromatography, based on the resolution and analysis time, the experimental conditons of deoxyribonucleosides and phopholipids were fixed in terms of taxol was calculated, and the collection time was predicted for the mixture of 5'-IMP and 5'-GMP from the elution profile when and purity wer known.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.327-329
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• 2017

개체명 인식(Named Entity Recognition) 시스템은 문서에서 인명(PS), 지명(LC), 단체명(OG)과 같은 개체명을 가지는 단어나 어구를 해당 개체명으로 인식하는 시스템이다. 개체명 인식 시스템을 개발하기 위해 딥러닝 기반의 워드 임베딩(word embedding) 자질과 문장의 형태적 특징 및 기구축 사전(lexicon) 기반의 자질 구성 방법을 제안하고, bi-directional LSTM, CNN, CRF과 같은 모델을 이용하여 구성된 자질을 학습하는 방법을 제안한다. 실험 데이터는 2017 국어 정보시스템 경진대회에서 제공한 2016klpNER 데이터를 이용하였다. 실험은 전체 4258 문장 중 학습 데이터 3406 문장, 검증 데이터 426 문장, 테스트 데이터 426 문장으로 데이터를 나누어 실험을 진행하였다. 실험 결과 본 연구에서 제안하는 모델은 BIO 태깅 방식의 개체 청크 단위 성능 평가 결과 98.9%의 테스트 정확도(test accuracy)와 89.4%의 f1-score를 나타냈다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.327-329
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• 2017

개체명 인식(Named Entity Recognition) 시스템은 문서에서 인명(PS), 지명(LC), 단체명(OG)과 같은 개체명을 가지는 단어나 어구를 해당 개체명으로 인식하는 시스템이다. 개체명 인식 시스템을 개발하기 위해 딥러닝 기반의 워드 임베딩(word embedding) 자질과 문장의 형태적 특징 및 기구축 사전(lexicon) 기반의 자질 구성 방법을 제안하고, bi-directional LSTM, CNN, CRF과 같은 모델을 이용하여 구성된 자질을 학습하는 방법을 제안한다. 실험 데이터는 2017 국어 정보시스템 경진대회에서 제공한 2016klpNER 데이터를 이용하였다. 실험은 전체 4258 문장 중 학습 데이터 3406 문장, 검증 데이터 426 문장, 테스트 데이터 426 문장으로 데이터를 나누어 실험을 진행하였다. 실험 결과 본 연구에서 제안하는 모델은 BIO 태깅 방식의 개체 청크 단위 성능 평가 결과 98.9%의 테스트 정확도(test accuracy)와 89.4%의 f1-score를 나타냈다.