• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.321-331
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• 2020

The purpose of this study is to explore the impact of home stay platform on guests' willingness to comment online under the Shared home stay business model. Shared platform of home stay facility in addition to providing a variety of support services, help the landlord to the tenant do offline accommodation services, implementation, trading, will need to take some measures to actively promote the tenant groups to the landlord, the evaluation is objective, effective and sufficient number in order to better promote the sharing credit ecological establishment of home stay facility. In this study, consumers who have used the Shared home stay platform are taken as the research objects. The survey method adopts network questionnaire survey and Likert seven subscales. The statistical software SPSS24.0 program is used to process the data. Firstly, descriptive statistical analysis was conducted, followed by validity analysis and reliability analysis. After the reliability and validity of the questionnaire were determined, correlation analysis and regression analysis were used to verify the proposed hypothesis. The research results of this study are summarized as follows :(1) the usability of platform comment function, guest satisfaction and platform reward have a positive impact on the guest online comment willingness; (2) The credit mechanism of the platform has a positive regulating effect on the process of tenant satisfaction influencing tenant comment intention.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.155-163
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• 2020

The purpose of this study was to verify the effect of job stress of caregivers on service quality for improve the quality of elderly long-term care services and the mediating effect of organizational commitment in the process. To this end, 30 long-term care facilities were randomly sampled out of 1,705 long-term care facilities in Gyeonggi-do, and as a result of a questionnaire survey of 500 caregiver for one month in May 2020, 443 samples were collected, and a total of 415 samples were finally analyzed. As for the analysis method, SPSS WIN 21.0 was used to verify the mediation model using frequency analysis, descriptive analysis, correlation analysis, and regression analysis. As a result of the study, first, job stress had a negative effect on service quality and organizational commitment. Second, organizational commitment has a static effect on service quality. Third, organizational commitment was verified as a perfect mediating in the relationship between job stress and service quality. This suggested the importance of the effect of organizational commitment in the process between job stress and service quality of caregivers and sought a policy ways to to improve the service quality of caregivers.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.196-207
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• 2021

The purpose of this study is to investigate the relationship between trust, satisfaction and cognitive performance of golf participants in golf device, focusing on the swing analyzer. A total of 328 questionnaires were collected. Collected data were analyzed by SPSSWIN and AMOS program and frequency analysis, confirmatory factor analysis, validity test, correlation analysis and structural equation model analysis were performed. The result of the study were as follows. First, the trust of golf participants in golf device data has a positive effect on satisfaction. Second, the trust of golf participants in golf device data does not affect Perceived performance. Third, the satisfaction of golf participants in golf device data does not affect Perceived performance. In conclusion, golf participants' trust and satisfaction of the golf swing analyzer are irrelevant to the perceived performance. In conclusion, it was found that golf participants trusted the data presented through the golf device and obtained satisfactory results. However, in that it did not affect the perceived performance, golf participants can think that golf devices should be used to play golf rather than thinking that golf devices enhance their performance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.469-482
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• 2022

The purpose of this study is to analyze regional variation factors of hypertension treatment rate in COVID-19 based on the analysis results based on ecological methodology. To this end, data suitable for ecological analysis were collected from the Korea Centers for Disease Control and Prevention's regional health statistics, local government COVID-19 confirmed cases, National Health Insurance Corporation, Health Insurance Review and Assessment Service's welfare statistics, and Korea Transport Institute's traffic access index. Descriptive statistics and correlation analysis were conducted using SPSS Statistics 23 for regional variation and related factors in hypertension treatment rate, and geographical weighted regression analysis was conducted using Arc GIS for regional variation factors. As a result of the study, the overall explanatory power of the calculated geo-weighted regression model was 27.6%, distributed from 23.1% to 33.4% by region. As factors affecting the treatment rate of hypertension, the higher the rate of basic living security medical benefits, diabetes treatment rate, and health institutions per 100,000 population, the higher the rate of hypertension treatment, the lower the number of COVID-19 confirmed patients, the lower the rate of physical activity, and the alcohol consumption. Percentage of alcohol consumption decreased due to COVID-19 pandemic. It was analyzed that the lower the ratio, the higher the treatment rate for hypertension. Based on these results, the analysis of regional variables in the treatment rate of hypertension in COVID-19 can be expected to be effective in managing the treatment rate of hypertension, and furthermore, it is expected to be used to establish community-centered health promotion policies.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.283-298
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• 2022

As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.481-494
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• 2022

This study proposed a evaluation of the monthly vulnerable period for groundwater level management in the Miho stream watershed and a technique for evaluating the vulnerable period for future groundwater level management using LSTM. Observation data from groundwater level and precipitation observation stations in the Miho stream watershed were collected, LSTM was constructed, predicted values for precipitation and groundwater levels from 2020 to 2022 were calculated, and future groundwater management was evaluated when vulnerable. In order to evaluate the vulnerable period of groundwater level management, the correlation between groundwater level and precipitation was considered, and weights were calculated to consider changes caused by climate change. As a result of the evaluation, the Miho stream watershed showed high vulnerability to underground water management in February, March, and June, and especially near the Cheonan Susin observation well, the vulnerability index for groundwater level management is expected to deteriorate in the future. The results of this study are expected to contribute to the evaluation of the vulnerable period of groundwater level management and the derivation of preemptive countermeasures to the problem of groundwater resources in the basin by presenting future prediction techniques using LSTM.

• Smart Media Journal
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• v.11 no.5
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• pp.9-16
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• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.349-357
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• 2021

Recently, drones are rapidly becoming common and expanding. There is a great need for diversity in whether drone flight data can be used as entertainment technology analysis data. In particular, it is necessary to check whether it is possible to analyze and utilize the flight and operation process of entertainment drones, which are developing through autonomous and intelligent methods, through data analysis and machine learning. In this paper, it was confirmed whether it can be used as a machine learning technology by using FC data in the evaluation of drones for entertainment. As a result, FC data from DJI and Parrot such as Mavic2 and Anafi were unable to analyze machine learning for entertainment. It is because data is collected at intervals of 0.1 second or more, so that it is impossible to find correlation with other data with GCS. On the other hand, it was found that machine learning technologies can be applied in the case of Fixhawk, which used an ARM processor and operates with the Nuttx OS. In the future, it is necessary to develop technologies capable of analyzing the characteristics of entertainment by dividing fixed-wing and rotary-wing flight information. For this, a model shoud be developed, and systematic big data collection and research should be conducted.

• Journal of Creative Information Culture
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• v.7 no.4
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• pp.187-196
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• 2021

This study was conducted as a study on the SW career education of students from underprivileged areas, which is a necessary condition for SW education to become a universal education or where research has not been conducted. Therefore, this study conducted a SW career selection model using structural equations for 2,231 students in grades 3 to 6 in 6 schools located in the marginalized areas of eup/myeon. As a result of the study, it was analyzed that the intrinsic SW learning motive of students from underprivileged areas did not affect their SW career choice, whereas the external SW learning motive was analyzed to affect their SW career choice. This is inferred that the intrinsic SW learning motive does not affect the SW career choice due to the lack of SW experience of students in underprivileged areas. The correlation between internal and external SW learning motivation was analyzed to be significant. In the future, as students from the underprivileged class have more SW education experiences, research should be conducted on how internal SW learning motives affect SW career choice and how external SW learning motives support internal SW learning motives.