• 한국멀티미디어학회논문지
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• 제13권7호
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• pp.1076-1087
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• 2010

특정 응응 분야를 위해 개발된 임베디드 프로세서의 진화 및 새로운 출현과 더불어 이를 지원할 수 있는 소프트웨어 개발 환경에 관한 연구와 상용화 시도가 활성화되고 있다. 재목적성(retargetability)은 프로세서나 메모리에 대한 아키텍처 정보를 아키텍처 기술 언어(ADL)로 기술하여 컴파일러, 시뮬레이터, 어셈블러, 프로파일러, 디버거 등과 같은 소프트웨어 개발 도구를 생성하는데 이용된다. EXPRESSION ADL은 아키텍처 모델링, 소프트웨어 개발 도구 생성, 빠른 프로토타입핑, 아키텍처에 대한 설계 탐색과 SoC에 대한 기능적인 검증을 위해 개발된 ADL로서 프로세서 코어, 코프로세서, 메모리 등으로 구성된 소프트웨어적인 아키텍처를 구조와 동작 정보를 혼합하여 자연스럽게 모델링하였다. 이 논문에서는 EXPRESSION ADL을 기반으로 ADL의 작성 편리성, 확장성을 높이기 위해 클래스 기반 ADL을 설계하고 문법의 타당성을 검증하였다. 이를 위해, 6개의 핵심 클래스를 정의하고 MIPS R4000에 대한 ADL을 표현으로부터 EXPRESSION과 동일한 컴파일러, 시뮬레이터를 생성하였다.

• 한국동물위생학회지
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• 제40권4호
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• pp.253-258
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• 2017

The aim of this study is to develop an optimal analytical method for gonadorelin, progesterone, oxytocin and estradiol, the major components of hormones. A relatively simple and reproducible method using high performance liquid chromatography was developed and as a result of the measurement of specificity, linearity, repeatability, accuracy and intermediated precision, the validity of the developed method was verified with the result of meeting the verification criteria of analytical method validation. Using this newly developed method, 12 post-market veterinary products were tested and the ingredient content were 91.9~116.4%, which satisfied the 90~120% condition of the administrative measure standard. Therefore, if the newly developed method is used for the collection examination of hormone in veterinary medicine, it can be useful as an approved test method.

• Journal of Ecology and Environment
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• 제35권2호
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• pp.91-98
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• 2012

This study was performed in order to suggest the possibility of tree species classification using high-resolution QuickBird-2 images spectral characteristics comparison(digital numbers [DNs]) of tree species, tree species classification, and accuracy verification. In October 2010, the tree species of three conifers and eight broad-leaved trees were examined in the areas studied. The spectral characteristics of each species were observed, and the study area was classified by image classification. The results were as follows: Panchromatic and multi-spectral band 4 was found to be useful for tree species classification. DNs values of conifers were lower than broad-leaved trees. Vegetation indices such as normalized difference vegetation index (NDVI), soil brightness index (SBI), green vegetation index (GVI) and Biband showed similar patterns to band 4 and panchromatic (PAN); Tukey's multiple comparison test was significant among tree species. However, tree species within the same genus, such as $Pinus$ $densiflora-P.$ $rigida$ and $Quercus$ $mongolica-Q.$ $serrata$, showed similar DNs patterns and, therefore, supervised classification results were difficult to distinguish within the same genus; Random selection of validation pixels showed an overall classification accuracy of 74.1% and Kappa coefficient was 70.6%. The classification accuracy of $Pterocarya$ $stenoptera$, 89.5%, was found to be the highest. The classification accuracy of broad-leaved trees was lower than expected, ranging from 47.9% to 88.9%. $P.$ $densiflora-P.$ $rigida$ and $Q.$ $mongolica-Q.$ $serrata$ were classified as the same species because they did not show significant differences in terms of spectral patterns.

• 한국전자거래학회지
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• 제23권1호
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• pp.89-102
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• 2018

최근 4차 산업혁명의 도래로 인해 융합서비스 환경으로 변화함에 따라 융 복합적인 새로운 보안위협이 나타나고 있다. 이에 중소형 의료기관 또한 비즈니스 환경을 고려한 특화된 보안을 필요로 하고 있는 실정이다. 따라서 본 논문에서는 선행연구 분석을 통해 의료기관 보안 특성을 도출하고 중소형 의료기관의 현장조사를 통해 중소형 의료기관 보안 특성과 현황을 조사하였다. 이러한 중소형 의료기관 보안 특성을 기반으로 중소형 의료기관을 위한 보안관리 평가모형을 설계하고 검증하였다. 설계를 위해 현존하는 의료기관 관련 보안관리체계, 평가 인증 체계 비교분석을 수행하였고 본 논문에서 제안한 보안관리 평가 모형과 공유정도 또한 확인하였다. 또한 제안하는 중소형 의료기관을 위한 보안관리 평가모형의 통계적 검증을 위해 적합 타당성 검증을 수행하였고, AHP 분석을 통한 상대적 우선순위 분석을 수행하여 항목별 가중치를 도출하였다. 본 연구의 결과를 통해 중소형 의료기관이 실제 수행 가능한 보안관리 평가모형의 기준으로 활용될 수 있을 것으로 기대된다.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.589-598
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• 2018

To maintain safety and reliability of reactors, redundant sensors are usually used to measure critical variables and estimate their averaged time-dependency. Nonhealthy sensors can badly influence the estimation result of the process variable. Since online condition monitoring was introduced, the online cross-calibration method has been widely used to detect any anomaly of sensor readings among the redundant group. The cross-calibration method has four main averaging techniques: simple averaging, band averaging, weighted averaging, and parity space averaging (PSA). PSA is used to weigh redundant signals based on their error bounds and their band consistency. Using the consistency weighting factor (C), PSA assigns more weight to consistent signals that have shared bands, based on how many bands they share, and gives inconsistent signals of very low weight. In this article, three approaches are introduced for improving the PSA technique: the first is to add another consistency factor, so called trend consistency (TC), to include a consideration of the preserving of any characteristic edge that reflects the behavior of equipment/component measured by the process parameter; the second approach proposes replacing the error bound/accuracy based weighting factor ($W^a$) with a weighting factor based on the Euclidean distance ($W^d$), and the third approach proposes applying $W^d$, TC, and C, all together. Cold neutron source data sets of four redundant hydrogen pressure transmitters from a research reactor were used to perform the validation and verification. Results showed that the second and third modified approaches lead to reasonable improvement of the PSA technique. All approaches implemented in this study were similar in that they have the capability to (1) identify and isolate a drifted sensor that should undergo calibration, (2) identify a faulty sensor/s due to long and continuous missing data range, and (3) identify a healthy sensor.

• Food Science and Biotechnology
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• 제14권1호
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• pp.42-45
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• 2005

The present study was carried out to envisage the aerobic growth of Listeria monocytogenes and Yersinia enterocolitica on pork, which is one of the major meat sources in Korea. The results were compared with the previously developed predictive model systems for the verification of microbial growth in a real situation during pork processing. Pork loin samples (8.0 g, 5 mm thick) were aseptically prepared and inoculated with each pathogen by immersing into the respective inoculums for one min. Each of the samples were then wrapped with PE film and stored at 5, 10, and $15^{\circ}C$ up to 36 days to measure the growth profile of the respective pathogens. The growth parameters were calculated by using Gompertz equation and were compared with the previously reported data. The predicted generation time (GT) of L. monocytogenes at 5, 10 and $15^{\circ}C$ was 28.74, 7.85 and 4.02 hr, respectively, and for Y. enterocolitica was 10.29, 4.74 and 2.50 hr, at the same temperatures respectively. In this study, the GT values predicted on pork were slightly higher than the values predicted in other studies using liquid model systems. Unlike previous reports, both the pathogens were found to grow at $5^{\circ}C$ on pork. This finding recommends the necessity of controlling the growth of both the pathogens during the slaughtering process and distribution.

• 정보보호학회논문지
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• 제30권6호
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• pp.1167-1187
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• 2020

해킹, 말웨어, 자료 유출 및 도난 등의 사이버 위협은 생활 및 경제, 국가 안보의 중요한 문제가 될 정도로 일상과 밀접한 상황에 직면하고 있다. 국가·산업 차원의 안보 및 경제적 관점에서 사이버 위협 대응은 필수적인 방어책으로 인식하고 있으나, 사이버보안 분야에서의 산·학 기술격차 및 전문인력 부족 문제는 매우 심각한 상황이다. 본 논문에서는 이를 극복하는 방법으로 사이버보안 인력의 직무 역량 개념을 도입하여 사이버보안 기술적 대응 직무 역량(CtrJC) 프레임워크를 제안하고, 국가 및 조직 차원에서 시급히 요구되는 실시간 사이버 위협 대응 기술적 직무 역량(CtrJC-R) 모델을 구현하여 그 타당성을 확인한다.

• 한국IT서비스학회지
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• 제22권4호
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• 농업과학연구
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• 제48권4호
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• pp.891-897
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• 2021

There is a need for a technology that can quickly and accurately analyze soil carbon contents. Existing soil carbon analysis methods are cumbersome in terms of professional manpower requirements, time, and cost. It is against this background that the present study leverages the soil physical properties of color and water content levels to develop a model capable of predicting the carbon content of soil sample. To predict the total carbon content of soil, the RGB values, water content of the soil, and lux levels were analyzed and used as statistical data. However, when R, G, and B with high correlations were all included in a multiple regression analysis as independent variables, a high level of multicollinearity was noted and G was thus excluded from the model. The estimates showed that the estimation coefficients for all independent variables were statistically significant at a significance level of 1%. The elastic values of R and B for the soil carbon content, which are of major interest in this study, were -2.90 and 1.47, respectively, showing that a 1% increase in the R value was correlated with a 2.90% decrease in the carbon content, whereas a 1% increase in the B value tallied with a 1.47% increase in the carbon content. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) methods were used for regression verification, and calibration samples showed higher accuracy than the validation samples in terms of R² and MAPE.

• Nuclear Engineering and Technology
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• 제27권4호
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• pp.571-581
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• 1995

원자력 발전소의 비상운전절차서는 작성단계부터 난점이 많이 있고, 작성된 절차서를 착인 및 검증하는 데에도 많은 어려움이 따른다 캐나다의 원자력 규제기관인 AECB에서도 원자력발전소의 운전 허가를 받는 데 있어 발전소 비상운전 절차서를 하나의 요구조건으로 삼고 있는데, 그 요구조건은 발전소 내외의 비상상황에 대처하기 위한 상세한 계획 및 절차를 개발하는 것이다. 본 보고서에서는 미국의 원자력 발전소 비상운전 계획을 참고하고 있는 캐나다 원자력 발전소용 비상운전절차 표준지침서를 검토하고, 캐나다 비상운전 절차서 들의 현재 경향을 알아보았으며, 캔두형 발전소들의 비상운전 절차를 사고의 범위(개별적인 사고의 종류 지정), 사고진단 방법(사건 대응적, 징후 대응적, 또는 이 두 가지 방법의흔합형), 절차서 형식 등을 중점으로 비교 검토하고, 각 절차서의 내용을 포괄적으로 종합하여 현재 건설중인 월성 원자력 발전소 2, 3, 4호기용 비상운전절차서의 기본방향을 징후 대응적절차서(사고진단이 안될 경우 사용)와 논리도표와 문장이 혼합된 형식의 사건파악적절차서로 이루어진 비상운전절차서로 제시하였다.