• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.153-164
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• 2014

This study is to investigate the effects of intangible assets such as research & development, education & training and advertisement on firm values of high-technology firms and low-technology firms listed in the KOSDAQ market, and to analyze the value-relativeness between the audit quality of companies and the expenditure of intangible assets. The substitute measurement of firm values is Tobin's Q model. The sample period for positive analysis is from 2003 to 2008, and the samples, excepting for financial business, are manufacturing companies of closing accounts corporate on December, based on companies of KOSDAQ that are listed in security. Finally, data from about 305 companies are used in this analysis. Followings are the results of the analysis. First, research & development, education & training of high-technology firms have an effect on firm values, and education & training of low-technology have an effect on firm values. Second, we find that audit quality(BIG4) increases the value relevance of R&D expenditures of high-technology firms and audit quality(BIG4) increases the value relevance of education & training expenditures of low-technology firms. This paper is meaningful in that it verified the value-relativeness of cost of intangible assets compared with high-technology firms to low-technology firms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.29-36
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• 2017

In this study, acombined cogeneration power plant produced two types of thermal energy and electric or mechanical power in a single process. The performance of each component of the gas turbine-combined cogeneration system was expressed as a function of the fuel consumption of the entire system, and the heat and electricity performance of each component. The entire system consisted of two gas turbines in the upper system, and two heat recovery steam generators (HRSG), a steam turbine, and two district heat exchangers in the lower system. In the gas turbine combined cogeneration system, the performance test after 10,000 hours of operation time, which is subject to an ASME PTC 46 performance test, was carried out by the installation of various experimental facilities. The performance of the overall output and power plant efficiency was also analyzed. Based on the performance test data, the test results were compared to confirm the change in performance. This study performed thermodynamic system analysis of gas turbines, heat recovery steam generators, and steam turbines to obtain the theoretical results. A comparison was made between the theoretical and actual values of the total heat generation value of the entire system and the heat released to the atmosphere, as well as the theoretical and actual efficiencies of the electrical output and thermal output. The test results for the performance characteristics of the gas turbine combined cogeneration power plant were compared with the thermodynamic efficiency characteristics and an error of 0.3% was found.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.55.2-55.2
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• 2010

한국천문연구원은 과학기술위성 3호의 주탑재체인 다목적 적외선영상시스템(Multipurpose Infra-Red Imaging System, MIRIS)을 개발하고 있다. 이 연구개발 사업은 2007년 교육과학기술부의 과학위성 3호 사업 주탑재체 공모를 통하여 10여개의 후보 탑재체 제안서 중에서 최종적으로 채택되었고, 2011년 발사를 목표로, 3년 동안의 연구개발 기간을 거쳐 현재 비행모델 (FM, Flight Model) 개발이 진행 중이다. MIRIS는 한국천문연구원이 개발하여 2003년 발사에 성공한 과학위성 1호 주탑재체인 원자외선 영상분광기 (FIMS, Far ultra-violet IMaging Spectroscope)에 이어 국내에서 자체 개발되는 두 번째 우주망원경이다. MIRIS는 우주공간에서 0.9~2 micron 사이 적외선 영역의 파쉔 알파 방출선 (Paschen Alpha Emiision Line)과 광대역 I, H 파장영역을 관측할 예정이다. 주요 과학임무로는 아직까지 국제 천문학계에서 잘 알려지지 않은 우리은하 내부에 분포한 고온 플라즈마 (Warm Ionized Medium, WIM)의 기원 연구와 아울러 우리은하 성간난류(Interstellar Turbulence)의 특성 및 적외선 우주배경복사의 (Cosmic Infrared Background; CIB) 거대구조 등을 관측연구할 예정이다. 특히 MIRIS는 저온상태 (절대온도 77K, 약 $-200^{\circ}C$)에서 우주공간 관측을 수행할 예정이므로, 국내에서는 연구기반이 취약한 극저온 광학계 및 기계부 설계기술, 극저온 냉각기술 및 열해석 설계기술과 적외선 센서기술 및 자료처리 기술 등 관련기술을 개발하고 있으며 이러한 기반기술을 바탕으로, 아직까지 국내에서 시도된 바 없는 적외선우주망원경 개발을 통하여, 우리나라의 관련 우주기술 분야의 기초원천 기술로서 크게 활용될 것으로 기대하고 있다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.57-62
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• 2006

Busan New Port, under construction aiming for the hub of Northeast Asia and Partly in operation, had damaged up to 48 billion Won due to Typhoon 'maemi' in 2003. The present criteria of domestic harbor design only describes about the critical wave height with respect to the size of vessel for harbor tranquility. The berth operation ratio which represents the annual available berthing days is depending on the efficiency of cargo handling work and this depends on the motion of the moored vessel due to the wave action and the characteristics of cargo gears. The motion of moored vessel might be related not only to the wave height but also to wave period. Furthermore, the berth operation ratio relies on external forces such as currents and winds, including the characteristics of mooring system and the specification of the moored vessel. In this study we only deal with berth operation ratio in normal sea state, considering wave and current by measured data and numerical calculation. Especially we tried to evaluate the berth operation ratio for each berth adopting the variation of dredging and reclamation plan and the change of wave environment during the process of the new port construction. For better understanding and analysis of wave transformation process, we applied the steady state spectral wave model and extended mild-slope wave model to the related site. This study summarizes comparisons of harbor responses predicted by two numerical predictions obtained at Busan New port site. Field and numerical model analysis was conducted for the original port plan and the final corrected plan.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Journal of the Korean Institute of Gas
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• v.23 no.3
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• pp.20-26
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• 2019

Selective catalytic reduction(SCR) method is widely used among various methods for reducing nitrogen oxides in combustion devices of coal power plant. In the present study, the computational fluid dynamic analysis was accomplished to derive the optimal shape of ammonia-dilution air mixing device in a ammonia injection grid. The distribution characteristics of flow and $NH_3$ concentration had been elucidated for the reference shape of ammonia mixing device(Case 1). In the mixing device of Case 1, it could be seen that $NH_3$ distribution was shifted to the wall opposite to the inlet of the ammonia injection pipe. For the improvement of $NH_3$ distribution, the case(Case 2) with closing one upper injection hole and 4 side injection holes, the case(Case 3) with installing horizontal plate at the upper of ammonia injection pipe, the case(Case 4) with installing horizontal plate and horizontal arc plate at he upper of ammonia injection pipe were investigated by analyzing flow and $NH_3$ concentration distributions. From the present study, it was found that the % RMS of $NH_3$ for Case 4 was 4.92%, which was the smallest value among four cases, and the range of $R_{NH3}$ also has the optimally uniform distribution, -10.82~8.34%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.75-82
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• 2021

Low-temperature combustion (LTC) strategies, such as HCCI (Homogeneous Charge Compression Ignition), PCCI (Premixed Charge Compression Ignition), and RCCI (Reactivity Controlled Compression Ignition), have been developed to effectively reduce NOx and PM while increasing the thermal efficiency of diesel engines. Through numerical analysis, this study examined the effects of the injection timing and two-stage injection ratio of diesel fuel, a highly reactive fuel, on the performance and exhaust gas of RCCI engines using gasoline as the low reactive fuel and diesel as the highly reactive fuel. In the case of two-stage injection, combustion slows down if the first injection timing is too advanced. The combustion temperature decreases, resulting in lower combustion performance and an increase in HC and CO. The injection timing of approximately -60°ATDC is considered the optimal injection timing considering the combustion performance, exhaust gas, and maximum pressure rise rate. When the second injection timing was changed during the two-stage injection, considering the combustion performance, exhaust gas, and the maximum pressure increase rate, it was judged to be optimal around -30°ATDC. In the case of two-stage injection, the optimal result was obtained when the first injection amount was set to approximately 60%. Finally, a two-stage injection rather than a single injection was considered more effective on the combustion performance and exhaust gas.

• Journal of Practical Engineering Education
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• v.13 no.3
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• pp.461-471
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• 2021

This study investigated the application and satisfaction of the global industrial technology curriculum for foreign students at J University in Korea. In order to derive the global industrial technology curriculum, industry needs were analyzed, and the appropriateness of the curriculum was identified through the current status of the root industry. In order to investigate the satisfaction with the application of the global industrial technology curriculum, a questionnaire survey was conducted in the form of an in-person interview for two months from August to September 2021 for foreign students. The questionnaire surveys included general information of the subjects, the status of completion of the curriculum, questions about class satisfaction, the process of obtaining certifications, and whether or not they were employed. As a result of the study, the reasons for choosing the curriculum of the respondents were their interest in subjects related to the root industry (welding, machining, etc.) and the issuance of Korean employment and visas (E-7). The most preferred subject was welding practice at 36.8%, and in terms of subjects considered necessary, the subject of basic major terminology was the most at 29.2%. The difference in satisfaction between graduates and current students who applied the same curriculum was tested, and as a result of the analysis, it was confirmed that there was no difference in satisfaction between current students and graduates through the t test (significance level p=0.05). We believe that this study is meaningful in that it provides basic data for the domestic industrial technology curriculum for foreign students and suggests the direction of related research in a time when the existence of universities is threatened due to the decrease in the school-age population.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.