• 대한원격탐사학회지
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• 제37권1호
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• pp.41-55
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• 2021

To detect thunderstorms occurring in Korea, National Meteorological Satellite Center (NMSC) also introduced the rapid-development thunderstorm (RDT) algorithm developed by EUMETSAT. At NMCS, the H-RDT (HR) based on the Himawari-8 satellite and the K-RDT (KR) which combines the GK2A convection initiation output with the RDT were developed. In this study, we optimized the KR (KU) to improve the detection level of thunderstorms occurring in Korea. For this, we used all available data, such as GK2A/AMI, RADAR, lightning, and numerical model data from the recent two years (2019-2020). The machine learning of logistic regression and stepwise variable selection was used to optimize the KU algorithms. For considering the developing stages and duration time of thunderstorms, and data availability of GK2A/AMI, a total of 72 types of detection algorithms were developed. The level of detection of the KR, HR, and KU was evaluated qualitatively and quantitatively using lightning and RADAR data. Visual inspection using the lightning and RADAR data showed that all three algorithms detect thunderstorms that occurred in Korea well. However, the level of detection differs according to the lightning frequency and day/night, and the higher the frequency of lightning, the higher the detection level is. And the level of detection is generally higher at night than day. The quantitative verification of KU using lightning (RADAR) data showed that POD and FAR are 0.70 (0.34) and 0.57 (0.04), respectively. The verification results showed that the detection level of KU is slightly better than that of KR and HR.

• 대한건축학회논문집:구조계
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• 제36권5호
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• pp.177-185
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• 2020

The building energy performance indicator, called Energy Performance Index (EPI), has been used for the past decades in South Korea. It has a list of design variables assigned with weighting factors (a, b). Unfortunately, the current EPI method is not performance-based but very close to a prescriptive rating. With this in mind, this study aims to propose a new performance-based EPI method. For this purpose, a global sensitivity analysis method, Sobol, is employed. The Sobol method is suitable for complex nonlinear models and can decompose all the output variance due to every input. The Sobol sensitivity index of each variable is defined as 0 to 1 (0 to 100%), and the sum of all sensitivity indices is equal to 1 (100%). In this study, an office building was modeled using EnergyPlus and then the Latin Hypercube Sampling (LHS) was conducted to generate a surrogate model to EnergyPlus. The sensitivity index was suggested to replace weight (a) in the existing EPI. In addition, the discrete weight (b) in the existing EPI was replaced by a set of continuous regression functions. Due to the introduction of the sensitivity index and the continuous regression functions, the new proposed approach can provide far more accurate outcome than the existing EPI (R²: 0.83 vs. R²: 0.01 for cooling, R²: 0.66 vs. R²: 0.01 for total energy). The new proposed approach proves to be more rational, objective and performance-based than the existing EPI method.

• 상하수도학회지
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• 제35권1호
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• pp.93-100
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• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

• 한국정보통신학회논문지
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• 제26권5호
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• pp.641-648
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• 2022

본 논문은 3D 프린터 출력 방식 중 하나인, 전자빔용해법(EBM)의 공정 간에 발생하는 결함에 영향을 미치는 요인들을 데이터 분석을 통해 규명하는 연구이다. 선행 연구들을 기반으로 결함발생에 주요한 원인으로 지목되는 요소들을 참고하였으며, 공정 간 발생하는 로그파일 분석을 통해 결함 발생과 연관된 변수들을 추출하였다. 또한, 해당 데이터가 시계열 데이터라는 점에 착안하여 window의 개념을 도입하여, 현재 공정 층으로부터 총 3개 전 층까지의 데이터를 포함하여 분석에 사용 될 변수들을 구성하였다. 해당 연구의 종속변수는 결함발생유무이기에 이진분류를 통한 분석을 하였으며, 이때 결함 층의 비율이 낮다는(약 4%) 문제로 인해 SMOTE 기법을 적용하여 균형잡힌 훈련용 데이터를 만들었다. 분석을 위해 Gridsearch CV를 활용한 XGBoost를 사용하였고, 분류 성능은 혼동행렬을 기반으로 평가하였다. 마지막으로, SHAP값을 통한 변수 중요도 분석을 통해 연구의 결론을 내렸다.

• 아태비즈니스연구
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• 제13권2호
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• pp.39-50
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• 2022

Purpose - The purpose of this study is to examine how Korea's macroeconomic factors, such as GDP, CPI, Export, Import, Unemployment rate and USD/KRW exchange rate, are affected by the oil price shocks. Design/methodology/approach - This study used monthly and quarterly time-series data of each variable for the period 1983 to 2022, consisting of two sub-periods, to employ Granger causality test and GARCH method in order to identify the role of the oil price movement in macroeconomic factors in Korea. Findings - Korea's currency rate to the US dollar is negatively correlated with the price change of crude oil while the GDP change is positively correlated with the price change of crude oil with strong relationship between Export and Import in particular. The exchange rate and GDP growth are believed to be not correlated with the oil price change for the pre-GFC period. According to the Granger causality test, the price change in crude oil has a causal impact on CPI, Export and Import while other factors are relatively slightly affected. Transmission effect from the oil price to Export is found and there also exists volatility spillover from oil price to economic variables under examination. Comparing two sub-periods, CPI and Export volatility responds negatively to shocks in the oil price for the pre-GFC period while volatility of CPI and Unemployment reacts positively to the oil price shocks for the post-GFC period. Research implications or Originality - The findings of this study could be helpful for both domestic and international investors to build their portfolio for the risk management since rising WTI price can be interpreted as a result of global economic growth and ensuing increase in the worldwide demand of the crude oil. Consequently, the national output is expected to increase and the currency is also expected to be strong in the long run.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.1013-1016
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• 2005

Accounts of the development of a successful construction project often stress the importance of team relationship, project environment and senior management commitment. Numbers of studies carried out in the past decades indicate there needs to be a change of culture and attitude in the construction industry. In order for a turn around in the industry, relational contracting approaches have become more popular in recent years. However, not all relational contracting projects were successful. This paper details the fundamental principles of relational contracting. It further reports findings of a research currently taking place in Australia, how effective is relational contracting in practice. The problem addressed in this research is the implementation of relational contracting: • Throughout a range of projects • With a focus on client body staff The context within which the research was undertaken is: • Empowerment, regional development and promotion of a sustainable industry • The participating organisations have experience of partnering and alliancing • Success has been proven on large projects but performance is variable • Need has been identified to examine skill sets needed for successful partnering/alliancing The practical rationale behind this research is that: • Partnering and alliancing require a change of mind set - a culture change • The Client side must change along with contracting side • A fit is required between organisation structure and organisation culture Research Rationale: The rationale behind this project has been to conduct research within participating organisations, analyse, rationalise and generalise results and then move on to produce generic deliverables and "participating organisation specific" deliverables. This paper sets out the work so far, the links between the various elements and a plan for turning the research output into industry deliverables.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.584-588
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• 2021

음향시스템을 구성하는 스피커의 임피던스는 고정된 값으로 인식되고 있다. 그러나 스피커의 임피던스는 입력신호의 주파수 변화에 따라 계속 변화하고 그 변화량은 스피커의 공진 주파수 대역에서 매우 크다. 스피커의 음압 레벨은 스피커를 구성하는 코일에 흐르는 전류에 따라 결정되는데 스피커를 전압 구동 할 경우 변화하는 임피던스에 의해 음압 레벨의 왜곡이 발생한다. 스피커를 전류 구동 할 경우 이러한 문제는 해결되지만 저주파에서 공진의 영향으로 음압 레벨의 왜곡이 발생하는데 이는 음향시스템의 음질 저하를 가져올 수 있다. 본 논문에서는 전류구동 음향시스템의 음질 개선을 위해 DSP(Digital Signal Processing)를 이용하여 음압레벨의 왜곡을 보정하는 공진 보상회로를 제안한다. 본 논문은 스피커의 등가 모델을 이용한 음향 시스템의 전류 구동 모의실험을 통해 주파수 변화에 따른 음압 레벨 왜곡을 확인하고 이를 보정하는 회로를 제안하는 것으로 구성하였다. 제안한 회로는 상태변수필터를 이용하여 구성하였고 주파수 및 출력이 조절 가능하여 다양한 음향 시스템에 적용 가능 할 것으로 보인다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.297-299
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• 2021

치밀오일 미래 생산성 예측은 잔류오일 회수량 및 저류층 거동 분석을 위해 중요한 작업이다. 일반적으로 석유공학적 관점에서 감퇴곡선법을 이용하여 생산성 예측이 이루어지는데, 최근에는 데이터기반의 머신러닝 기법을 이용한 연구도 수행되고 있다. 본 연구에서는 딥러닝 기반 순환신경망과 LSTM, GRU 알고리즘을 이용하여 미래 생산량 예측을 위한 효과적인 모델을 제안하고자 한다. 입력변수로는 치밀오일 생산 시 산출되는 오일, 가스, 물과 이와 더불어 다양한 군집분석을 통해 산출된 표준곡선이 주요 매개변수이고, 출력변수는 월별 오일 생산량이다. 기존의 경험적 모델인 감퇴곡선법과 순환신경망 모델들을 비교하였으며, 모델의 예측성능을 향상시키기 위해 하이퍼파라미터 튜닝을 통해 최적 모델을 도출하였다.

• 한국정밀공학회지
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• 제12권11호
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• pp.27-35
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• 1995

The fluid coupling combined with a pump and a turbine have many merits compared with other couplings, their uses are increesing rapidly in various industrial fields at home and abroad in pursuit of high-speed more efficiency durability of various mechanic devices. The authorities concerned have recognized the improtance of the fluid coupling and supported its developement and now some trial products began to show up. As the structrue and characteristics of the fluid coupling have little similarity to other kinds of couplings and its fluid behavior is unique, so its characteristic analysis is expected to be difficult. Until now no satisfactory study on the characteristics of the fluid coupling seems to have been conducted at home, so a study on this field needs to be done urgently. The purpose of this research is to construct the experimental test set-ups and establish a series of performance test program for the domestically developed fluid couplings and to provide a software to store and utilize these experimental data which can be used to improve the performance of the fluid coupling and solve on the job problems confronted in operation. The performance test consists of taking measurment of torque, rpm and efficiency of the fluid coupling for three different amount of working fluid inside with various loads to the output shaft and finally infestigating the torque, rpm and efficiency characteristics of the fluid coupling with respect to these parameters. The results of this study can contribute valuable references to the development of variable speed fluid coupling and torque converter currently pursued by the domestic industry.

• Steel and Composite Structures
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• 제52권2호
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.