• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.591-601
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• 2021

This study examined the effect of branding capability on business performance moderated by learning capability. This study was conducted with small- and medium-sized enterprises (SMEs) of coffee cafes in the ex-Besuki region, East Java, Indonesia, covering four regencies located around coffee-producing areas with geographical indication (GI) certification. 150 managers of coffee cafe were sampled using the census technique. Data were collected by questionnaires distributed to the coffee cafe managers. The data were then analyzed by using simple regression analysis, Moderation Regression Analysis (MRA) and Moderated MultiGroup Analysis (MMA). The results showed that learning capability positively and significantly affect business performance, and learning capability moderated/enhanced the effect of branding capability on business performance. The findings of this study suggest that branding capability and learning capability play a crucial role in the performance of coffee cafe business especially in the dynamic environment. Coffee cafe managers need to take concrete steps to improve their branding capability and learning capability and they also need to improve their ability to interact with their environment and be committed in managing the coffee cafe. Therefore, it is imperative that the role of branding capability and learning capability be optimized in order to improve the business performance of the coffee cafe.

• 산경연구논집
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• 제13권1호
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• pp.23-32
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• 2022

Purpose: This study used ESG grade, but defined AESG, adjusted to the size of a company and examines whether it can be used as an investment strategy. Research design, data and methodology: The analysis sample in this study is a company that has given an ESG rating among companies listed on the Korea Stock Exchange. We examine the results through portfolio analysis and Fama-macbeth regression analysis. Results: As result of examining the long-only performance and the long-short performance by constructing quintile portfolios, it was observed that a significant positive return was shown. It was observed that there was an alpha that could not be explained in asset pricing models. Also, AESG had a return prediction effect in the result of a Fama-Macbeth regression that controlled corporate characteristic variables in individual stocks. Next, we confirmed AESG's usage through various portfolio composition. In the portfolio optimization, the Risk Efficient method was the most superior in terms of sharpe ratio and the construct multi-factor model with Value, Momentum and Low Vol showed statistically significant performance improvement. Conclusions: The results of this study suggest that it can be helpful in ESG investment to reflect the ESG rating of relatively small companies more through the scale adjustment of the ESG rating (i.e.AESG).

• 한국노년학
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• 제38권3호
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• pp.521-536
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• 2018

본 연구는 노인의 노화에 대한 태도가 이들의 삶의 만족에 미치는 영향을 확인하고, 이를 매개하는 요인으로써 부모-자녀 간의 결속감의 역할을 확인하는 것을 목적으로 한다. 이를 위하여 전국의 노인 2,067명을 대상으로 설문조사를 진행하였고, SPSS macro PROCESS v.2.16을 활용하여 다중매개효과를 검증한 결과는 다음과 같다. 첫째, 노인의 노화에 대한 태도는 삶의 만족에 유의미한 영향을 미치고 있었다. 둘째, 노인의 노화태도는 부모-자녀 간의 결속감의 다섯가지 하위차원에 있어 모두 유의미한 영향을 미치고 있었다. 특히 노화에 대하여 부정적인 태도를 가질수록 부모-자녀 간의 결속이 더욱 높아지는 결과를 보이고 있었다. 셋째, 부모-자녀 간 결속의 하위차원들 중 애정적 일치적 기능적 결속이 노인의 노화태도와 삶의 만족간의 관계를 매개하는 것으로 나타났으며, 이 중 애정적 결속이 가장 큰 효과를 보이는 것으로 나타났다. 이상의 결과를 바탕으로 노인의 삶의 만족을 위한 실천적 개입방안을 제안하였다.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Steel and Composite Structures
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• 제44권2호
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• 드라이브 ㆍ 컨트롤
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• 제21권2호
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• pp.44-52
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• 2024

In this study, we predicted PTO power requirements based on torque predicted by the discrete element method and the multi-body dynamics coupling method. Six different scenarios were simulated to predict PTO power requirements in different soil conditions. The first scenario was a tillage operation on cohesionless soil, and the field was modeled using the Hertz-Mindlin contact model. In the second through sixth scenarios, tillage operations were performed on viscous soils, and the field was represented by the Hertz-Mindlin + JKR model for cohesion. To check the influence of surface energy, a parameter to reproduce cohesion, on the power requirement, a simple regression analysis was performed. The significance and appropriateness of the regression model were checked and found to be acceptable. The study findings are expected to be used in design optimization studies of agricultural machinery by predicting power requirements using the discrete element method and the multi-body dynamics coupling method and analyzing the effect of soil cohesion on the power requirement.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2008년도 춘계학술대회 논문집
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• pp.142-145
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• 2008

Process simulation requires accurate and reliable data for a wide variety of material properties, ranging from thermal conductivity to flow stress curves. Traditionally such data are gathered from experimental sources, which has significant disadvantages in that not all of the required data is readily available, it may be from various sources that are themselves inconsistent, measurement of high temperature properties is expensive, and furthermore the properties can be sensitive to microstructure as well as to alloy composition. This article describes the development of a new multi-platform software program called JMatPro, which is based on CALPHAD methodology, for calculating the properties and behavior of multi-component alloys. A feature of the JMatPro is that the calculations are based on sound physical principles rather than purely statistical methods. Thus, many of the shortcomings of methods such as regression analysis can be overcome.

• 산업경영시스템학회지
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• 제44권2호
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• pp.1-14
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• 2021

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2690-2695
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• 2007

This work presents numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis. Reynolds-averaged Navier-Stokes (RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

• Journal of the Optical Society of Korea
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• 제13권2호
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• pp.279-285
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• 2009

Prediction of glucose concentration in the interstitial fluid (ISF) based on mid-infrared absorption spectroscopy was examined at the glucose fundamental absorption band of 1000 - 1500/cm (10 - 6.67 um) using multi-component analysis. Simulated ISF samples were prepared by including four major ISF components. Sodium lactate had absorption spectra that interfere with those of glucose. The rest NaCl, KCl and $CaCl_2$ did not have any signatures. A preliminary experiment based on Design of Experiment, an optimization method, proved that sodium lactate influenced the prediction accuracy of glucose. For the main experiment, 54 samples were prepared whose glucose and sodium lactate concentration varied independently. A partial least squares regression (PLSR) analysis was used to build calibration models. The prediction accuracy was dependent on spectrum preprocessing methods, and Mean Centering produced the best results. Depending on calibration sample sets whose sodium lactate had different concentration levels, the standard error prediction (SEP) of glucose ranged $17.19{\sim}21.02\;mg/dl$.