• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.57-58
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• 2022

Concrete mix design is used as essential data for the quality of concrete, analysis of structures, and stable use of sustainable structures. However, since most of the formulation design is established based on the experience of experts, there is a lack of data to base it on. are suffering Accordingly, in this study, the purpose of this study is to build a predictive model to use the concrete mixing factor as basic data for calculation using the DNN technique. As for the data set for DNN model learning, OPC and ternary concrete data were collected according to the presence or absence of admixture, respectively, and the model was separated for OPC and ternary concrete, and training was carried out. In addition, by varying the number of hidden layers of the DNN model, the prediction performance was evaluated according to the model structure. The higher the number of hidden layers in the model, the higher the predictive performance for the prediction of the mixing elements except for the compressive strength factor set as the output value, and the ternary concrete model showed higher performance than the OPC. This is expected because the data set used when training the model also affected the training.

• Journal of Korean Society for Quality Management
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• v.38 no.1
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• pp.85-95
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• 2010

In this paper we propose a product-service valuation model which is usable as a decision-making tool in order to attain a competitive advantage in service in the manufacturing industry. With this purpose, service quality, product quality and costs as valuation criteria are selected. Also, the paper utilizes an AHP model in order to differentiate a fuzzy theory and valuation factors to ensure objectivity in the evaluated results while excluding subjective factors in conducting the product-service valuation. Accordingly, the product-service valuation model and valuated results proposed in this paper are expected to be useful as a basic data for decision-making in order to draw competitive advantage strategies of service in the manufacturing industry.

• The Korean Journal of Health Service Management
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• v.10 no.4
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• pp.13-23
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• 2016

Objectives : This research used an exploratory approach to identify factors affecting business strategies due to changes in the healthcare market and customer loyalty factors. Methods : The research model was formulated using antecedents divided into diagnosis quality, employee attitudes, and servicescape. Moreover, differences in the structured model were analyzed according to hospital size. The data were gathered through surveys on clients, who has received care at participating hospitals. From the 200 that were distributed, 150 questionnaires were analyzed, to facilitate analysis of the research model. Results : The effects of diagnosis quality, employee attitudes, and servicescape, on customer loyalty were mediated by trust. We also found the differences between small and large hospitals. Conclusions : Customer loyalty in small hospitals was affected by servicescape, whereas that in large hospitals was affected by diagnosis quality and employee attitudes. The research results could be used to develop strategies to improve customer loyalty.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.340-345
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• 2020

In this study, we developed the quality control and assurance method of measurement data of SPAR (Soil-Plant-Atmosphere-Research) system, a climate change research facility, for the first time. It was found that the precise processing of CO2 flux data among many observations were sig nificantly important to increase the accuracy of canopy photosynthesis measurements in the SPAR system. The collected raw CO2 flux data should first be removed error and missing data and then replaced with estimated data according to photosynthetic lig ht response curve model. Comparing the correlation between cumulative net assimilation and soybean biomass, the quality control and assurance of the raw CO2 flux data showed an improved effect on canopy photosynthesis evaluation by increasing the coefficient of determination (R2) and lowering the root mean square error (RMSE). These data processing methods are expected to be usefully applied to the development of crop growth model using SPAR system.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.14 no.3
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• pp.340-350
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• 2007

Purpose: This study was done to construct a quality of life (QOL) model for recipients of a liver transplant. Method: In consideration of the main factors influencing QOL in recipient of liver transplants, a hypothetical model was constructed with 16 paths. A questionnaire was used to collect data from recipients of liver transplants who were being followed at one of 3 university hospitals. For the final analysis, there were 189 completed questionnaires and the hypothetical model was verified through covariance structure using LISREL program. Results: Overall fitness indices of hypothetical model were GFI= .99, AGFI= .97, NNFI= .96 and RMR=.020. After considering modification indices and paths that proved not to be significant and to improve model fitness, the hypothetical model was modified. In the final model, 3 paths from the hypothetical model were excluded. Overall fitness indices of the final model were GFI= .99, AGFI= .98, NNFI= .98 and RMR= .020. Eleven of fifteen paths proved to be significant. QOL was influenced by duration after transplantation, perceived health status, self-esteem, uncertainty, social support, self efficacy and depression and these variables explained 65% of the variance. Conclusion: This study presents a theoretical model for QOL for recipients of a liver transplant. Based on the results of this study and to improve QOL for recipients of a liver transplant, it is suggested that interventions to re-enforce self efficacy and self-help are needed.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.581-594
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• 2021

Purpose: The purpose of this study is to compare machine learning models for anomaly detection of mechanical facility equipment and suggest an anomaly detection system for mechanical facility equipment in subway stations. It helps to predict failures and plan the maintenance of facility. Ultimately it aims to improve the quality of facility equipment. Methods: The data collected from Daejeon Metropolitan Rapid Transit Corporation was used in this experiment. The experiment was performed using Python, Scikit-learn, tensorflow 2.0 for preprocessing and machine learning. Also it was conducted in two failure states of the equipment. We compared and analyzed five unsupervised machine learning models focused on model Long Short-Term Memory Variational Autoencoder(LSTM-VAE). Results: In both experiments, change in vibration and current data was observed when there is a defect. When the rotating body failure was happened, the magnitude of vibration has increased but current has decreased. In situation of axis alignment failure, both of vibration and current have increased. In addition, model LSTM-VAE showed superior accuracy than the other four base-line models. Conclusion: According to the results, model LSTM-VAE showed outstanding performance with more than 97% of accuracy in the experiments. Thus, the quality of mechanical facility equipment will be improved if the proposed anomaly detection system is established with this model used.

• Journal of Korea Water Resources Association
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• v.38 no.6 s.155
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• pp.505-515
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• 2005

Two representative river water quality models have been compared in this paper. The steady water quality model, QUAL2E, and the unsteady model, CE-QUAL-RIV1, have been chosen for comparative simulations. Under same reaction coefficients and boundary conditions, the water quality of the Geum river below the Daechung dam has been simulated using two different models, and the water quality equations are compared each other. Since basic model algorithm is very close, the input data required for model run is very similar. Upon the simulation under steady condition, the results of two models show very good agreement especially for BOD, DO, and $NH_3-N$, while the results of specific constituent such as dissolved P is quite different. As a result, dominant water quality parameters to compute each corresponding water quality variables are summarized and tablized through the sensitivity analysis.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.477-496
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• 2014

Purpose: This paper has examined the impact of open innovation business model in technology commercialization with the data from 30 companies of manufacturing firms in South Korea. Methods: The findings provide support for distinguishing five hypotheses relating to development time, IP management, sales, firm size and R&D intensity. To test the hypotheses, data were collected using via e-mail and fax. Small and medium-sized (less than 300 employees) and large industrial firms were chosen for this study. Results: The result shows that openness in its business model is positively associated with successful technology commercialization. Conclusion: The major findings and the implications are: First, as the business model gets more open, development period of technology will be more favorable which gets benefit from rising costs of innovation. Second, as the business model gets more open, large portion of sales are created from new products. Thus, the problem of shorter product life in the market which affects large portion of market revenue can be solved through an open business model. Third, in general, R&D intensity, firm size and the level of IP management affect determination of business model types. The findings also suggest that companies need to increasingly address their external technology exploitation process instead of focusing on their internal innovation processes.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.5
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• pp.35-42
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• 2011

This study is to check the applicability of SWAT-APEX (Soil and Water Assessment Tool-Agricultural Policy / Environmental eXtender) model as combined watershed and field models by applying the APEX to paddies in a watershed (465.1 $km^2$) including Yedang reservoir. Firstly, the SWAT were calibrated with 3 years (2000~2002) daily streamflow and monthly water quality (T-N and T-P) data, and validated for another 3 years (2003~2005) data. The average Nash-Sutcliffe model efficiency (ME) of streamflow during validation was 0.73, and the coefficient of determination ($R^2$) of T-N and T-P were 0.77 and 0.73 respectively. Next, running the SWAT-APEX model with the SWAT calibrated parameters for paddies, the $R^2$ of T-N and T-P were 0.80 and 0.76 respectively. The results showed that SWAT-APEX model was more correctly predicted for T-N and T-P loads than SWAT model. The difference results between watershed and field models was predicted to have substantial impact on NPS loads, especially on T-N and T-P loads. Therefore, to improve negative NPS load simulations should be considered the model characteristics as simulating mechanism to properly select the NPS model for agricultural watershed.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1023-1035
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• 2021

The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.