• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.663-670
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• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.698-709
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• 2016

Career maturity and the acquisition of entrepreneurial knowledge are important in entrepreneurship education for youth, and the environments surrounding youths influence their mental state and academic achievement. This study aims to analyze the influences of an educational program and environment in entrepreneurship education for youths on the mindset and the entrepreneurial intention for proper career decisions according to the education climate and entrepreneurial knowledge. A total of 216 questionnaires were collected from students who participated in a creative and entrepreneurial tour of Incheon. After excluding 30 questionnaires with unreliable responses, 186 were empirically analyzed using SPSS 18.0 and Amos 18.0. The results show that the quality of education service and education environment decide the education climate, and the entrepreneurial knowledge has a positive influence on entrepreneurial intention. This research is significant because it strategically establishes directions for entrepreneurial education and as practical research for achieving outcomes of entrepreneurial education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1700-1709
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• 2019

Recently, many researches have been under way to develop systems (services) to support the safety navigation of ships, and in these studies, common difficulties have been encountered in assessing the usefulness and effectiveness of the developed system. To solve these problems, we propose the DEVS-based ship navigation modeling and simulation technique. Following the preceding study, we analyze the COLREG rules and reflected to officer and helmsman agent models for decision making. Also we propose estimation and interpolation techniques to adopt the motion characteristics of the actual vessel to simulation. In addition, we implement the navigation simulation system to reflect the designed proposed methods, and we present five-scenarios to verify the developed simulation system. And we conduct simulations according to each scenario and the results were reconstructed. The simulation results confirm that the components modelled in each scenario enable to operate according to the navigation relationships.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.853-860
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• 2024

In this study, a design method for a PWM control-based single input/single output buck DC-DC converter with multiple output voltages was studied which is recently required in industrial electronic devices. Based on the basic theory of the buck DC-DC converter, the design method of the buck converter was determined and a voltage control algorithm based on PWM control for a single-input, single-output buck converter that can selectively obtain multi-level output voltages was developed. To verify the effectiveness of the design methodology, a prototype was designed and made. Next, under a test environment based on the prototype DC-DC converter, the effectiveness of the PWM control-based voltage control method was verified by confirming that the multiple output voltages track within an error rate of 7% when the target voltages are selectively determined for the fixed and variable input voltages.

• Food Science of Animal Resources
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• v.31 no.1
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• pp.115-121
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• 2011

The objective of this study was to develop models to predict freshness factors (total viable counts (TVC), pH, volatile basic nitrogen (VBN), trimethylamine (TMA), and thiobarbituric acid (TBA) values) and the storage period in beef using a visible and near-infrared (NIR) spectroscopic technique. A total of 216 beef spectra were collected during the storage period from 0 to 14 d at a $10^{\circ}C$ storage. A spectrophotometer was used to measure reflectance spectra from beef samples, and beef freshness spectra were divided into a calibration set and a validation set. Multi-linear regression (MLR) models using the stepwise method were developed to predict the factors. The MLR results showed that beef freshness had a good correlation between the predicted and measured factors using the selected wavelength. The correlation of determination ($r^2$), standard error of prediction (SEP), and ratio of standard deviation to SEP (RPD) of the prediction set for TVC was 0.74, 0.64, and 2.75 Log CFU/$cm^2$, respectively. The $r^2$, SEP, and RPD values for pH were 0.43, 0.10, and 1.10; those for VBN were 0.73, 1.45, and 2.00 mg%; those for TMA were 0.70, 0.19, and 2.58 mg%; those for TBA values were 0.73, 0.13, and 2.77 mg MA/kg; and those for storage period were 0.77, 1.94, and 2.53 d, respectively. The results indicate that visible and NIR spectroscopy can predict beef freshness during storage.

• Journal of Applied Biological Chemistry
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• v.65 no.4
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• pp.343-348
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• 2022

This study was performed to establish an analytical method for the standardization of Angelicae Dahuricae Radix as a functional ingredient. We established six compounds including oxypeucedanin hydrate (1), byakangelcol (2), oxypeucedanin (3), imperatorin (4), phellopterin (5) and isoimperatorin (6) as marker compounds of Angelicae Dahuricae Radix. An analytical method using Ultra Performance Liquid Chromatography (UPLC) was established and validated for marker compounds of Angelicae Dahuricae Radix. The specificity was confirmed by the chromatogram from UPLC and the value of coefficient determination was also higher than 0.999, indicating high linearity. The relative standard deviation (RSD) and recovery of marker compounds were less than 5% and in the range of 90- 110%, respectively, which means that this method has high accuracy and precision. Therefore, this analytical method could be used as basic data for the development of functional ingredients for health functional food of Angelicae Dahuricae Radix.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.535-541
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• 2004

GPS는 직접적인 시통 및 거리에 대해 제한을 받는 일반 재래식 측량으로는 관측하기 힘든 중ㆍ장기선 관측의 경우 기선결정에 대한 정확도와 활용성이 우수하다고 알려져 있다. 그러나 현재 실질적으로 관측하여 취득하고자 하는 위치정보를 얻기 위해 지역의 대소에 관계없이 고가의 이주파 수신 장비를 사용하고 있으며, 최적의 기선해석과 망조정에 의한 성과를 얻기 위한 데이터 처리과정이 엄밀하게 확립되어 있지 않다 이러한 이유들로 인하여 고가의 이주파 수신기가 여러 분야에 적용되고 있으며, 성과물를 산출하고 있는 실정이다. 본 연구에서는 이주파 수신기를 사용하여 전국에 산재하고 있는 국가 기준삼각점을 대상으로 관측을 실시하여, 일주파만을 처리하는 L₁ Only 방식과 이주파를 처리하는 L₁&L₂ 방식과의 비교 분석하여 일주파에 대한 정확도와 적용성 그리고 기선의 거리에 따른 한계성에 대해 검증하고자 한다.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.76-81
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• 1996

CFMS(Critical Function Monitering System)는 원자력발전소에서 비상시 제어실 운전원에게 원전의 안전상태에 대한 정보를 제공하는 기능을 갖는데, 원전의 안전성을 위해 인간공학적 확인 및 검증(V&V)이 요구된다. 본 연구에서는 CFMS 화면설계의 인간공학적 평가를 위하여, 원전의 안전성에 대한 평가항목들의 상대적 중요도를 구하고 최종적으로 설계의 적합성을 평 가하는 방법론을 제시하고자 한다. 본 연구에서는 CFMS 화면설계의 평가항목 및 평가기준 확립, 원전에서 인적오류발생 가능성에 대한 평가항목의 상대적 중요도 결정, 분석하고자 하는 CFMS 화면에 대한 각 평가항목들의 인간공학적 설계의 적합성 평가로구성된다. CFMS의 인간공학적 평가를 위한 방법론으로 Fuzzy Set Theory와 AHP(Analytic Hierarchical Process) 기법을 적용 한 방법론을 제시하였다. 최종적으로는 전문가의 평가를 통하여 국내 건설되는 원전의 실제적인 문제에 적용하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.148-150
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• 2004

부정행위 탐지는 개별 사용자의 행동 기록과 그 사용자와 유사한 프로필을 갖고 있는 사용자들의 행동 기록을 바탕으로 행동 패턴 혹은 행동 규칙을 찾아내, 이 패턴/규칙과의 비교를 통해 현재 행위가 부정한 것인지를 결정하는 방법을 주로 사용한다. 그러나, 특정 사용자의 행위패턴이 급격하게 바뀌는 경우, 과거의 기록을 바탕으로 생성된 패턴의 유효성은 보장받을 수 없다. 더구나 기존 기록과 상이한 행위에 대한 새로운 패턴이 생성되기 위해서는 계속해서 그런 행위가 쌓여야만 하고, 그 쌓이는 양은 기존 패턴의 견고성에 비례된다. 또한 동일 사용자에게 털러 패턴을 적용시키는 방법 역시 패턴간의 충돌이 일어나는 등의 한계가 존재한다. 본 논문에서는 시간 논리(Temporal Logic)를 적용하여, 과거의 패턴의 유효성을 검증하고 신규패턴을 빠르게 찾아내는 방법을 제안하고자 한다.