• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.573-579
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• 2019

The purpose of this study is predicted easily the entrance surface dose (ESD) in chest digital radiography. We used two detector type such as flat-panel detector (FP) and IP (Imaging plate detector). ESD was measured at each exposure condition combined tube voltage with tube current using dosimeter, after attaching on human phantom, it was repeated 3 times. Phantom images were evaluated independently by three chest radiologists after blinding image. Dose-area product (DAP) or exposure index (EI) was checked by Digital Imaging and Communications in Medicine (DICOM) header on phantom images. Statistical analysis was performed by the linear regression using SPSS ver. 19.0. ESD was significant difference between FP and IP($85.7{\mu}Gy$ vs. $124.6{\mu}Gy$, p=0.017). ESD was positively correlated with image quality in FP as well as IP. In FP, adjusted R square was 0.978 (97.8%) and linear regression model was $ESD=0.407+68.810{\times}DAP$. DAP was 4.781 by calculating the $DAP=0.021+0.014{\times}340{\mu}Gy$. In IP, adjusted R square was 0.645 (64.5%) and linear regression model was $ESD=-63.339+0.188{\times}EI$. EI was 1748.97 by calculating the $EI=565.431+3.481{\times}340{\mu}Gy$. In chest digital radiography, the ESD can be easily predicted by the DICOM header information.

• Clean Technology
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• v.28 no.1
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• pp.63-78
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• 2022

Electronics industrial wastewater treatment facilities release organic wastewaters containing high concentrations of organic pollutants and more than 20 toxic non-biodegradable pollutants. One of the major challenges of the fourth industrial revolution era for the electronics industry is how to treat electronics industrial wastewater efficiently. Therefore, it is necessary to develop an electronics industrial wastewater modeling technique that can evaluate the removal efficiency of organic pollutants, such as chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), and tetramethylammonium hydroxide (TMAH), by digital twinning an electronics industrial organic wastewater treatment facility in a cyber physical system (CPS). In this study, an electronics industrial wastewater activated sludge model (e-ASM) was developed based on the theoretical reaction rates for the removal mechanisms of electronics industrial wastewater considering the growth and decay of micro-organisms. The developed e-ASM can model complex biological removal mechanisms, such as the inhibition of nitrification micro-organisms by non-biodegradable organic pollutants including TMAH, as well as the oxidation, nitrification, and denitrification processes. The proposed e-ASM can be implemented as a Water Digital Twin for real electronics industrial wastewater treatment systems and be utilized for process modeling, effluent quality prediction, process selection, and design efficiency across varying influent characteristics on a CPS.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.147-157
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• 2022

This study tried to propose a smart farm technology strategy suitable for the domestic situation, focusing on the differentiation suitable for the domestic situation of ICT technology. In the case of advanced countries in the overseas agricultural industry, it was confirmed that they focused on the development of a specific stage that reflected the geographical characteristics of each country, the characteristics of the agricultural industry, and the characteristics of the people's demand. Confirmed that no enemy development is being performed. Therefore, in response to problems such as a rapid decrease in the domestic rural population, aging population, loss of agricultural price competitiveness, increase in fallow land, and decrease in use rate of arable land, this study aims to develop smart farm ICT technology in the future to create quality agricultural products and have price competitiveness. It was suggested that the smart farm should be promoted by paying attention to the excellent performance, ease of use due to the aging of the labor force, and economic feasibility suitable for a small business scale. First, in terms of economic feasibility, the ICT technology is configured by selecting only the functions necessary for the small farm household (primary) business environment, and the smooth communication system with these is applied to the ICT technology to gradually update the functions required by the actual farmhouse. suggested that it may contribute to the reduction. Second, in terms of performance, it is suggested that the operation accuracy can be increased if attention is paid to improving the communication function of ICT, such as adjusting the difficulty of big data suitable for the aging population in Korea, using a language suitable for them, and setting an algorithm that reflects their prediction tendencies. Third, the level of ease of use. Smart farms based on ICT technology for the development of the Industry6.0 (1.0(Agriculture, Forestry) + 2.0(Agricultural and Water & Water Processing) + 3.0 (Service, Rural Experience, SCM)) perform operations according to specific commands, finally suggested that ease of use can be promoted by presetting and standardizing devices based on big data configuration customized for each regional environment.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.127-144
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• 2021

The purpose of this study is to derive service factors based on the "Rail Statistical Yearbook" data of railroad service providers from 1990 to 2019, and to analyze the effect of the service factors on the operating profit ratio(OPR), a representative management performance variable of railroad transport service providers. In particular, it has academic significance in terms of empirical research to evaluate whether the management innovation of the KoRail has changed in line with the purpose of establishing the corporation by dividing the research period into the first period (1990-2003) and the latter (2004-2019). The contents of this study investigated previous studies on the quality of railway passenger transportation service and analyzed the contents of government presentation data related to the management performance evaluation of the KoRail. As an empirical analysis model, a research model was constructed using OPR as a dependent variable and service factor variables of infrastructure, economy, safety, connectivity, and business diversity as explanatory variables based on the operation and management activity information during the analysis period 30 years. On the results of research analysis, OPR is that the infrastructure factor is improved by structural reform or efficiency improvement. And economic factors are the fact that operating profit ratio improves by reducing costs. The safety factor did not reveal the significant explanatory power of the regression coefficient, but the sign of influence was the same as the prediction. Connectivity factor reveals a influence on differences between first period and latter, but OPR impact direction is changed from negative in before to positive in late. This is an evironment in which connectivity is actually realized in later period. On diversity factor, there is no effect of investment share in subsidiaries and government subsidies on OPR.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.48-62
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• 2023

As seen in the recent K-pop craze, the size and influence of the Korean music industry is growing even bigger. At least 6,000 songs are released a year in the Korean music market, but not many can be said to have been successful. Many studies and attempts are being made to identify the factors that make the hit music. Commercial factors such as media exposure and promotion as well as the quality of music play an important role in the commercial success of music. Recently, there have been many marketing campaigns using Internet memes in the pop music industry, and Internet memes are activities or trends that spread in various forms, such as images and videos, as cultural units that spread among people. Depending on the Internet environment and the characteristics of digital communication, contents are expanded and reproduced in the form of various memes, which causes a greater response to consumers. Previously, the phenomenon of Internet memes has occurred naturally, but artists who are aware of the marketing effects have recently used it as an element of marketing. In this paper, the mediated effect of Internet memes in relation to the success factors of popular music was analyzed, and a prediction model reflecting them was proposed. As a result of the analysis, the factors with the mediated effect of 'cover effect' and 'challenge effect' were the same. Among the internal success factors, there were mediated effects in "Singer Recognition," the genres of "POP, Dance, Ballad, Trot and Electronica," and among the external success factors, mediated effects in "Planning Company Capacity," "The Number of Music Broadcasting Programs," and "The Number of News Articles." Predictive models reflecting cover effects and challenge effects showed F1-score at 0.6889 and 0.7692, respectively. This study is meaningful in that it has collected and analyzed actual chart data and presented commercial directions that can be used in practice, and found that there are many success factors of popular music and the mediating effects of Internet memes.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.11-22
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• 2009

Long-term rainfall-runoff modeling is a key element in the Earth's hydrological cycle, and associated with many different aspects such as dam design, drought management, river management flow, reservoir management for water supply, water right permission or coordinate, water quality prediction. In this regard, hydrologists have used the hydrologic models for design criteria, water resources assessment, planning and management as a main tool. Most of rainfall-runoff studies, however, were not carefully performed in terms of considering reservoir effects. In particular, the downstream where is severely affected by reservoir was poorly dealt in modeling rainfall-runoff process. Moreover, the effects can considerably affect overall the rainfallrunoff process. An objective of this study, thus, is to evaluate the impact of reservoir operation on rainfall-runoff process. The proposed approach is applied to Anseong watershed, where is in a mixed rural/urban setting of the area and in Korea, and has been experienced by flood damage due to heavy rainfall. It has been greatly paid attention to the agricultural reservoirs in terms of flood protection in Korea. To further investigate the reservoir effects, a comprehensive assessment for the results are discussed. Results of simulations that included reservoir in the model showed the effect of storage appeared in spring and autumn when rainfall was not concentrated. In periods of heavy rainfall, however, downstream runoff increased in simulations that do not consider reservoir factor. Flow duration curve showed that changes in streamflow depending upon the presence or absence of reservoir factor were particularly noticeable in ninety-five day flow and low flow.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.