• The Journal of the Korea Contents Association
• /
• v.21 no.10
• /
• pp.444-456
• /
• 2021

Economic abundance and the development of medical technology led to an aging society with an average life expectancy of 100 years, but retiring from the labor market at the age of 65 has become more difficult. This study aims to identify the influence of entrepreneurship, social support, and entrepreneurship mentoring as an effective support method to increase the entrepreneurial intention in order to enhance the entrepreneurial intention as an adult's second career development. In this study, data were collected using questionnaires from 340 adults, but only 319 were selected because 21 were judged to be inappropriate. For statistical analysis, SPSS 18.0 was used, and reliability test, factor analysis, and multiple regression analysis were used for hypothesis testing. The research results are as follows. First, as a result of examining the effects of adult entrepreneurship factors on entrepreneurship, it was found that among entrepreneurship, innovation and initiative had a significant positive (+) effect on entrepreneurship. Second, as a result of examining the effect of social support on entrepreneurial intention, it was found that family support had a significant negative (-) effect on entrepreneurial intention. Third, as a result of examining the effect of entrepreneurship mentoring on entrepreneurial intentions, it was found that role models and mentors had a positive (+) effect on entrepreneurial intentions. Fourth, as for the mediating effect of entrepreneurial efficacy, there were significant mediating effects of innovativeness → entrepreneurial efficacy → entrepreneurial intention, role model → entrepreneurial efficacy → entrepreneurial intention, mentor → entrepreneurial efficacy → entrepreneurial intention.

• Journal of Korean Society of Disaster and Security
• /
• v.14 no.1
• /
• pp.9-21
• /
• 2021

Recently, the importance of eco-friendly power generation facility using renewable energy has newly appeared. Hydropower plant is a very important source of electricity generation and supply which is very important to secure safety because it is commonly connected with multi facility and operated on a large scale. In this study, a scenario-based analysis method was suggested to assess vulnerability of a penstock system caused by water hammer commonly occurred in the operation of hydropower plants. A hypothetical hydropower plant was used to demonstrate the applicability of a transient analysis model. In order to verify reliability of the model, the prediction of pressure behaviors were compared with the results of commercial model (SIMSEN) and measured data, then a real hydroelectric power plant was applied to develop all potential water hammer scenarios during the actual operation. The scenario-based simulation and vulnerability assessment for water hammer in the penstock system were performed with internal and external load conditions. The simulation results indicated that the vulnerability of a penstock system was varied with the operating conditions of hydropower facilities and significantly affected by load combination consisting of different load scenarios. The proposed numerical method could be an useful tool for the vulnerabilityty assessment of the hydropower plants due to water hammer.

• Journal of the Korea Convergence Society
• /
• v.12 no.8
• /
• pp.31-37
• /
• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.587-596
• /
• 2022

Sea levels are rising as a result of climate change, and low-lying areas along the coast are at risk of flooding. Therefore, we tried to investigate the relationship between sea level change and climate indices using satellite altimeter data (Topex/Poseidon, Jason-1/2/3) and southern oscillation index (SOI) and the Pacific decadal oscillation (PDO) data. If time domain data were converted to frequency domain, the original data can be analyzed in terms of the periodic components. Fourier transform and Wavelet transform are representative periodic analysis methods. Fourier transform can provide only the periodic signals, whereas wavelet transform can obtain both the periodic signals and their corresponding time location. The cross-wavelet transformation and the wavelet coherence are ideal for analyzing the common periods, correlation and phase difference for two time domain datasets. Our cross-wavelet transform analysis shows that two climate indices (SOI, PDO) and sea level height was a significant in 1-year period. PDO and sea level height were anti-phase. Also, our wavelet coherence analysis reveals when sea level height and climate indices were correlated in short (less than one year) and long periods, which did not appear in the cross wavelet transform. The two wavelet analyses provide the frequency domains of two different time domain datasets but also characterize the periodic components and relative phase difference. Therefore, our research results demonstrates that the wavelet analyses are useful to analyze the periodic component of climatic data and monitor the various oceanic phenomena that are difficult to find in time series analysis.

• Clean Technology
• /
• v.28 no.4
• /
• pp.323-330
• /
• 2022

Microfluidic paper-based analytical devices (μPADs) have recently been in the spotlight for their applicability in point-of-care diagnostics and environmental material detection. This study presents a double-sided printing method for fabricating 3D-μPADs, providing simple and cost effective metal ion detection. The design of the 3D-μPAD was made into an acryl stamp by laser cutting and then coating it with a thin layer of PDMS using the spin-coating method. This fabricated stamp was used to form the 3D structure of the hydrophobic barrier through a double-sided contact printing method. The fabrication of the 3D hydrophobic barrier within a single sheet was optimized by controlling the spin-coating rate, reagent ratio and contacting time. The optimal conditions were found by analyzing the area change of the PDMS hydrophobic barrier and hydrophilic channel using ink with chromatography paper. Using the fabricated 3D-μPAD under optimized conditions, Ni²⁺, Cu²⁺, Hg²⁺, and pH were detected at different concentrations and displayed with color intensity in grayscale for quantitative analysis using ImageJ. This study demonstrated that a 3D-μPAD biosensor can be applied to detect metal ions without special analysis equipment. This 3D-μPAD provides a highly portable and rapid on-site monitoring platform for detecting multiple heavy metal ions with extremely high repeatability, which is useful for resource-limited areas and developing countries.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.6
• /
• pp.803-814
• /
• 2022

The Lao government is continuously developing hydro-power dams in addition to the existing eight power plants in the Nam Ngum River basin and is expanding the power capacity of the existing power plants to meet the expected increase in electricity demand. Accordingly, the Lao government has requested an update on the existing reservoir operating rule curve in order to run the power plants efficiently. To this end, this study reviewed the current independent operating system as well as the joint operating system in order to maximize the annual power generation produced by a power plant by using CSUDP, general-purpose dynamic programming (DP) software. The appropriate operating regulation curve forms (URC/LRC, MRC) were extracted from the DP results, and the annual power generations were simulated by inputting them as the basic operating data of the reservoir operation set of the HEC-ResSim program. By synthesizing the amount of the annual power generation simulated, the existing operation regulation curve, the operational performance, and the opinion of the field operator, the optimal reservoir operation regulation curves that maximize the annual power generation of the target power plant were developed. Results revealed that a system operating in conjunction with the reservoir produces about 2.5 % more power generation than an independent reservoir due to the synergistic effect of the connection.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.3
• /
• pp.74-99
• /
• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.59-76
• /
• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of Industrial Convergence
• /
• v.21 no.5
• /
• pp.77-85
• /
• 2023

Suicide is the leading cause of death among Korean adolescents. There is a growing interest in the role of loneliness as a risk factor for suicide ideation and depressive symptoms. However, little is known in the Korean context. This study analyzed a total of 109,796 respondents from the Korea Youth Health Behavior Survey in 2020 and 2021. Multiple logistic regression models were implemented to test the association between loneliness and either of suicidal ideation and depressive mood. Covariates included demographic characteristics, school enrolled, household income, living arrangement, self-rated health, and the number of times treated for violence. Adjusted odd ratio (OR) and 95% confidence intervals (CI) were computed. 12.0% of adolescents reported to have felt lonely frequently and 3.0% always. 11.8% and 26.0% had suicidal ideation and depressive mood, respectively. The prevalence of suicidal ideation was higher in the always-lonely adolescents (52.6%) than in the frequently-lonely adolescents (35.1%). The always-lonely adolescents were nearly 30 times more likely to have suicidal ideation (OR=30.7; 95% CI, 27.1 - 34.8) and to feel depressed (OR=32.5; 95% CI, 29.2 - 36.4) than adolescents who felt never lonely. In conclusion, Loneliness was a major risk factor for suicidal ideation and depressive mood among Korean adolescents. Monitoring and addressing the condition of loneliness may help reduce suicidal ideation and depressive mood.

• Journal of the Korean Society of Radiology
• /
• v.17 no.3
• /
• pp.367-373
• /
• 2023

Aircrews and passengers are exposed to radiation from cosmic rays and secondary scattered rays generated by reactions with air or aircraft. For aircrews, radiation safety management is based on the exposure dose calculated using a space-weather environment simulation. However, the exposure dose varies depending on solar activity, altitude, flight path, etc., so measuring by route is more suggestive than the calculation. In this study, we developed an instrument to measure the cosmic radiation dose using a general-purpose Si sensor and a multichannel analyzer. The dose calculation applied the algorithm of CRaTER (Cosmic Ray Telescope for the Effects of Radiation), a space radiation measuring device of NASA. Energy and dose calibration was performed with Cs-137 662 keV gamma rays at a standard calibration facility, and good dose rate dependence was confirmed in the experimental range. Using the instrument, the dose was directly measured on the international line between Dubai and Incheon in May 2023, and it was similar to the result calculated by KREAM (Korean Radiation Exposure Assessment Model for Aviation Route Dose) within 12%. It was confirmed that the dose increased as the altitude and latitude increased, consistent with the calculation results by KREAM. Some limitations require more verification experiments. However, we confirmed it has sufficient utilization potential as a cost-effective measuring instrument for monitoring exposure dose inside or on personal aircraft.