• 한국산학기술학회논문지
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• 제21권5호
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• pp.409-416
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• 2020

4차 산업 혁명의 핵심 동력으로 각광받고 있는 인공지능은 고성능 하드웨어와 빅데이터의 활용, 데이터 처리기술, 학습방법 및 알고리즘의 발전에 따라 단순한 학문적 지식 수준을 넘어 스마트 공장, 자율주행 등 다양한 산업분야에서 활용되며 영역을 넓혀가고 있다. 국방 분야에서도 국방 예산 감축, 병역 자원 감소, 무인 전투체계의 보편화 등 안보 환경이 변화함에 따라 선진국을 중심으로 상황 인식, 결심 지원, 업무 프로세스 간소화, 효율적 자원 활용 등 인공지능을 국방 업무에 접목하기 위한 정책 및 기술에 대한 연구가 활발히 이루어지고 있다. 이러한 이유에서 잠재력 있는 미래 국방기술의 발굴 및 연구개발을 위해 기술주도형 기획과 조사의 중요성 또한 증대되고 있다. 본 연구에서는 미래 국방기술 도출을 위해 진행되었던 연구 자료를 바탕으로 인공지능 분야 미래기술에 관한 특성 평가지표를 분석하고 실증연구를 수행하였다. 이를 통해 국방 인공지능 분야 미래기술에서는 무기체계 적용성, 경제적 파급효과가 유망도와 유의미한 관련성을 나타낸다는 것을 확인할 수 있었다.

• 대한원격탐사학회지
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• 제37권2호
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• pp.359-365
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• 2021

천리안위성2A호 기상탑재체 AMI(Advanced Meteorological Imager) 센서 검출기의 최상의 요소들로 구성된 Best Detector Select (BDS) 맵은 발사 전 확정되어 AMI에 업로드 되어 있다. 위성 발사 이후 급격한 온도 변화 환경에 노출되면 검출기의 성능에 변화가 생길 수 있으며, 발사 및 탑재체 아웃개싱 이후에 BDS맵의 성능을 다시 분석하고 필요시 업데이트가 필요하다. 검출기 요소 전체에 대한 성능을 검증하기 위한 분석 작업이 탑재체 개발업체(미 L3HARRIS사)가 제공한 BDS맵 분석 기술 문서를 기반으로 진행되었다. BDS맵 분석이란 탑재체 검출기가 기준 목표물(심우주와 탑재체 내부 보정 타겟)을 응시하는 동안 얼마나 안정적인 신호를 보이는 지를 평가하는 것이다. 이러한 목적으로 LTS(Long Time Series) 및 V-V(Output Voltage vs. Bias Voltage)라 부르는 검증법이 이용된다. LTS는 30초 동안, V-V는 2초 동안 목표물을 응시하고 이 때의 검출기 노이즈 성분을 계산한다. 자료를 획득하기 위해서는 탑재체의 운영을 멈추고 특별 관측을 실시하여야 하기 때문에, 정상 운영 전인 궤도상 시험기간 중에 해당 작업이 이루어지게 된다. 천리안위성2A호 기상탑재체 궤도상 시험 기간 동안 획득한 자료를 바탕으로 BDS맵의 상태를 평가하였다. 발사 전 지상 시험에서 평가된 BDS맵의 전체 성분들 중에 약 1%에 해당하는 요소들이 성능 변화를 보였으며, 이를 다른 요소들 중 최상의 성능을 보이는 성분으로 교체하였다. 새로운 BDS맵을 적용한 결과 BDS문제로 인해 야기된 기상탑재체 원시영상에 나타나는 노이즈 성분(줄무늬)이 완전하게 제거되었다.

• Spatial Information Research
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• 제1권1호
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• pp.39-53
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• 1993

지리 정보시스템(Geographical Information System)을 이용하여 도심지 Non Pount Source오염 물질의 양이 오염원 종류별로 확인되고 적절하한 오염감소를 위한 대책이 마련되었다. 경험에 의한 공해물질 예측모델을 운용하기 위한 모든 입력 자료들이 도심지의 거리 구획별(Street block)별로 제공되어 각 거리 구획별 오염량이 계산되었다. 계산된 오염량은 각 우수 배출구별로 합산되어 오염량이 많은 지역이 판명되었다. 또한 오염량을 줄이기 위하여 인공호수를 만들기 위한 적지분석이 수행되었으며, 그에 따른 비용분석이 이루어졌다. 본 연구는 지형정보시스템의 도심지 공해연구에의 기여도를 입증시켜 주었다.

• Asia pacific journal of information systems
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• 제17권3호
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• pp.131-146
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• 2007

Recently, the role of IT service providers has been enlarged from managing a single function or system to reconstructing entire information management processes in new ways to contribute to shareholder value across the enterprise. This movement toward extensive and complex outsourcing agreements has been driven by the assumption that outsourcing information technology functions is a reliable approach to maximizing resource productivity. Hiring external IT service providers to manage part or all of its information-related services helps a firm focus on its core business and provides better services to its clients, thus obtaining sustainable competitive advantage. This practice of focusing on the strategic aspect of outsourcing is referred to as strategic sourcing where the focus is capability sourcing, not procurement. Given the importance of the strategic outsourcing, however, to our knowledge, there is little empirical research on the relationship between the strategic outsourcing orientation and outsourcing performance. Moreover, there is little research on the factor that makes the strategic outsourcing effective. This study is designed to investigate the relationship between strategic IT outsourcing orientation and IT outsourcing performance and the process through which strategic IT outsourcing orientation influences outsourcing performance, Based on the framework of strategic orientation-performance and core competence based management, this study first identifies core competency orientation as a proper strategic orientation pertinent to IT outsourcing and IT outsourcing process management capability as the mediator to affect IT outsourcing performance. The proposed research model is then tested with a sample of 200 firms. The findings of this study may contribute to the literature in two ways. First, it draws on the strategic orientation - performance framework in developing its research model so that it can provide a new perspective to the well studied phenomena. This perspective allows practitioners and researchers to look at outsourcing from an angle that emphasizes the strategic decision making to outsource its IT functions. Second, by separating the concept of strategic orientation and outsourcing process management capability, this study provides practices with insight into how the strategic orientation can work effectively to achieve an expected result. In addition, the current study provides a basis for future studies that examine the factors affecting IT outsourcing performance with more controllable factors such as IT outsourcing process management capability rather than external hard-to-control factors including trust and relationship management. This study investigates the major factors that determine IT outsourcing success. Based on strategic orientation and core competency theories, we develop the proposed research model to investigate the relationship between core competency orientation and IT outsourcing performance and the mediating role of IT outsourcing process management capability on IT outsourcing performance. The model consists of two independent variables (core-competency-orientation and IT outsourcing process management capability), and two dependent variables (outsourced task complexity and IT outsourcing performance). Comprehensive data collection was conducted through an outsourcing association. The survey data were analyzed using a structural analysis method. IT outsourcing process management capability was found to mediate the effect of core competency orientation on both outsourced task complexity and IT outsourcing performance. Further analysis and findings are discussed.

• 대한토목학회논문집
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• 제38권4호
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• pp.579-586
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• 2018

단기 통행속도 예측을 위해 데이터 기반 비모수적 기법들을 활용한 다양한 연구들이 수행되고 있다. 그럼에도 교통신호 및 교차로로 인한 복잡한 동적 특성을 가지는 도시부의 예측 연구는 상대적으로 부족한 실정이다. 본 연구는 도시부 통행 속도를 예측하기 위해 앙상블 경험적 모드 분해법(EEMD)과 인공신경망(ANN)을 이용한 하이브리드 접근법을 제안하는 것을 목적으로 한다. EEMD는 통행속도의 시계열 자료를 고유모드함수(IMF)와 오차항으로 분해한다. 분해된 IMF는 시간단위의 국지적 특성을 반영하며, ANN을 통해 개별적으로 예측된다. IMF는 원본데이터가 가진 비선형성, 비정상성, 진동 등의 복잡성을 완화하기 때문에, 원래의 통행속도에 비하여 더 정확하게 예측될 수 있다. 예측된 IMF들은 합산되어 예측 통행속도를 표현한다. 본 연구에서 제시된 방법을 검증하기 위하여 대구시의 DSRC로부터 구득된 통행속도 데이터가 활용된다. 성능평가는 도시부 링크 중 특히 예측이 어려운 지점에 대해 수행되었으며, 분석 결과 제시된 모형은 15분 후 예측에 대해 각각 평상시 10.41%, 와해상태시 25.35%의 오차율을 가지며, 단순 ANN 기법에 비하여 우수한 성능을 보이는 것으로 확인된다. 본 연구에서 개발된 모형은 도시교통관리체계의 신뢰성 있는 교통정보를 제공하는 데에 기여할 수 있을 것으로 기대된다.

• 한국지리정보학회지
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• 제9권4호
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• pp.105-118
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• 2006

도면과 대장정보를 데이터베이스화하여 구축한 기존의 시설물 관리시스템은 시설물의 2차원 또는 3차원 관리가 가능하도록 하였으나 현장에서 발생하는 다양한 정보를 실시간으로 수집할 수 없고 이를 종합적으로 운용하지 못하였다. 이러한 단점을 보완하기 위하여 유비쿼터스 기반의 시설물 관리시스템의 구축이 필요하다. 본 연구에서는 기존의 지형지물 전자식별자를 개량하여 시설물 관리에 적용 가능하도록 관리기관과 일련번호 부여방법이 개량된 UFID 체계를 제안하였다. 또한 제안한 UFID를 이용하여 유비쿼터스 기반의 시설물 관리를 위한 프로세스를 정립하였다. 본 연구의 적용성은 가로수를 대상으로 한 사례연구를 통하여 평가되었으며, 이는 유비쿼터스 기반의 다양한 시설물 관리에 적용될 수 있을 것으로 사료된다.

• Asian Journal of Atmospheric Environment
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• 제8권1호
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• pp.25-34
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• 2014

In winter 2013, extreme air pollution by fine particulate matter ($PM_{2.5}$) in China attracted much public attention. In order to simulate the $PM_{2.5}$ pollution, the Community Multiscale Air Quality model driven by the Weather Research and Forecasting model was applied to East Asia in a period from 1 January 2013 to 5 February 2013. The model generally reproduced $PM_{2.5}$ concentration in China with emission data in the year 2006. Therefore, the extreme $PM_{2.5}$ pollution seems to be mainly attributed to meteorological (weak wind and stable) conditions rather than emission increases in the past several years. The model well simulated temporal and spatial variations in $PM_{2.5}$ concentrations in Japan as well as China, indicating that the model well captured characteristics of the $PM_{2.5}$ pollutions in both areas on the windward and leeward sides in East Asia in the study period. In addition, contribution rates of four anthropogenic emission sectors (power generation, industrial, residential and transportation) in China to $PM_{2.5}$ concentration were estimated by conducting zero-out emission sensitivity runs. Among the four sectors, the residential sector had the highest contribution to $PM_{2.5}$ concentration. Therefore, the extreme $PM_{2.5}$ pollution may be also attributed to large emissions from combustion for heating in cold regions in China.

• The Plant Pathology Journal
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• 제31권4호
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• pp.350-362
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• 2015

A disease forecast model for bacterial grain rot (BGR) of rice, which is caused by Burkholderia glumae, was developed in this study. The model, which was named 'BGRcast', determined daily conduciveness of weather conditions to epidemic development of BGR and forecasted risk of BGR development. All data that were used to develop and validate the BGRcast model were collected from field observations on disease incidence at Naju, Korea during 1998-2004 and 2010. In this study, we have proposed the environmental conduciveness as a measure of conduciveness of weather conditions for population growth of B. glumae and panicle infection in the field. The BGRcast calculated daily environmental conduciveness, $C_i$, based on daily minimum temperature and daily average relative humidity. With regard to the developmental stages of rice plants, the epidemic development of BGR was divided into three phases, i.e., lag, inoculum build-up and infection phases. Daily average of $C_i$ was calculated for the inoculum build-up phase ($C_{inf}$) and the infection phase ($C_{inc}$). The $C_{inc}$ and $C_{inf}$ were considered environmental conduciveness for the periods of inoculum build-up in association with rice plants and panicle infection during the heading stage, respectively. The BGRcast model was able to forecast actual occurrence of BGR at the probability of 71.4% and its false alarm ratio was 47.6%. With the thresholds of $C_{inc}=0.3$ and $C_{inf}=0.5$, the model was able to provide advisories that could be used to make decisions on whether to spray bactericide at the preand post-heading stage.

• Biomolecules & Therapeutics
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• 제30권4호
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• pp.368-379
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• 2022

Hyaluronic acid (HA), a ligand of CD44, accumulates in some types of tumors and is responsible for tumor progression. The nuclear factor erythroid 2-like 2 (NRF2) regulates cytoprotective genes and drug transporters, which promotes therapy resistance in tumors. Previously, we showed that high levels of CD44 are associated with NRF2 activation in cancer stem like-cells. Herein, we demonstrate that HA production was increased in doxorubicin-resistant breast cancer MCF7 cells (MCF7-DR) via the upregulation of HA synthase-2 (HAS2). HA incubation increased NRF2, aldo-keto reductase 1C1 (AKR1C1), and multidrug resistance gene 1 (MDR1) levels. Silencing of HAS2 or CD44 suppressed NRF2 signaling in MCF7-DR, which was accompanied by increased doxorubicin sensitivity. The treatment with a HAS2 inhibitor, 4-methylumbelliferone (4-MU), decreased NRF2, AKR1C1, and MDR1 levels in MCF7-DR. Subsequently, 4-MU treatment inhibited sphere formation and doxorubicin resistance in MCF7-DR. The Cancer Genome Atlas (TCGA) data analysis across 32 types of tumors indicates the amplification of HAS2 gene is a common genetic alteration and is negatively correlated with the overall survival rate. In addition, high HAS2 mRNA levels are associated with increased NRF2 signaling and poor clinical outcome in breast cancer patients. Collectively, these indicate that HAS2 elevation contributes to chemoresistance and sphere formation capacity of drug-resistant MCF7 cells by activating CD44/NRF2 signaling, suggesting a potential benefit of HAS2 inhibition.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.