• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.676-683
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• 2022

Due to the effects of rapid changes in the living environment since 2000 and the recent unforeseen pandemic, people are refraining from domestic and international traveling and movement, and outdoor activities for health and the public value of forest trails, called Dullegil Trail in Korea, have become more important. This study estimated the environmental value of the tentatively named "East-West Trail," which connects the forest trails crossing Chungcheong and Gyeongsang Provinces using CVM (Contingent Valuation Method). It surveyed visitors to the East-West Trail, and 725 questionnaires were used for analysis. The average characteristics of respondents were those who exercised 2-3 times per week, visited a forest trail not far from their residence with friends or family, and showed a tendency to spend 50 thousand Korean won or more per visit. Visitors to the Dullegil Trail felt that there was a shortage of information boards on the forest trail, and they preferred a shelter in appropriate locations. We used a double-bounded dichotomous choice (BDDC) logit model proposed by Hanemann to measure the conservation value of the East-West Trail. It was estimated that the environmental value that a visitor to the East-West Trail could obtain was 30,087 won per trip. The estimated environmental value of the East-West Trail can be converted to about 94 billion won total visitors annually based on the population belonging to the direct-use zone near the East-West Trail. As there has been no study on the environmental value of forest trails using CVM, the results of this study will be able to suggest the feasibility of the government policies on forest trails.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.11-21
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• 2023

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMY_normal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

• 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.26 no.4C
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• pp.247-254
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• 2006

Online testing method is one of the numerical experiment methods using experimental information for a numerical analysis directly. The method has an advantage in that analysis can be conducted without using an idealized mechanical model, because mechanical properties are updated from element test for a numerical analysis in real time. The online testing method has mainly been used for the geotechnical seismic engineering, whose major target is sand. A testing method that may be applied to a consolidation problem has recently been developed and laboratory and field verifications have been tried. Although related research thus far has mainly used a method to update average reaction for a numerical analysis by positioning an element tests at the center of a consolidation layer, a weakness that accuracy of the analysis can be impaired as the thickness of the consolidation layer becomes more thicker has been pointed out regarding the method. To clarify the effectiveness and possible analysis scope of the online testing method in relation to the consolidation problem, we need to review the results by applying experiment conditions that may completely exclude such a factor. This research reviewed the results of the online consolidation test in terms of reproduction of the consolidation settlement and the dissipation of excess pore water pressure of a clay specimen by comparing the results of an online consolidation test and a separated-type consolidation test carried out under the same conditions. As a result, the online consolidation test reproduced the change of compressibility according effective stress of clay without a huge contradiction. In terms of the dissipation rate of excess pore water pressure, however, the online consolidation test was a little faster. In conclusion, experiment procedure needs to improve in a direction that hydraulic conductivity can be updated in real time so as to more precisely predict the dissipation of excess pore water pressure. Further research or improvement should be carried out with regard to the consolidation settlement after the end of the dissipation of excess pore water pressure.

• 한국노년학
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• v.39 no.4
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• pp.663-677
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• 2019

This study was performed to identify the relationship and effects of nursing work environment and role conflict on job embeddedness among nurses working in long-term care hospitals. The data were collected from 200 nurses working in 10 long-term care hospitals from G - province from July to August 2018. Structured questionnaires assessing general characteristics and three major variables were distributed to the study participants and final 190 data set were analyzed using SPSS ver 25.0 program. Study results were as follows; mean score of job embeddedness was 2.98±0.46 out of 5 and the score of sub-domains were in order of fit, links, and sacrifice. The average score of the nursing work environment was 3.14 ± 0.42 and the leadership was the highest sub-domain followed by the working system, the relationship with peers, and the support of the institution. Overall role conflicts score was 3.43 ± 0.51, and environmental disorder, role ambiguity, lack of ability, lack of cooperation were reported in order as sub-domains. Job embeddedness of the study participants showed a statistically significant positive correlation with the nursing work environment and negative correlation with the role conflict. Factors affecting job embeddedness were nursing work environment, age, and role conflict, and the explanatory power of the model was 50.4%. The findings suggest that the overall level of job embeddedness of nurses working in long-term care hospitals is below middle level and efforts to improve job embeddedness through strategies related to nursing work environment and role conflict in organizational level. In addition, the relationship between age and job embeddedness needs to be studied further.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.455-476
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• 2023

This study focused on the characteristics of motiveless crimes that mainly originated from interpersonal problems and were acts of revenge against innocent third parties. This study confirmed the relationship between the experience of social exclusion and displaced aggression and examined the relationship between the two variables. We sought to confirm the role of related factors such as stress and social support. For this purpose, we established and tested hypotheses about the mediatingon effect of stress and the moderated mediatingon effect of social support on the effect of social exclusion experience on displaced aggression among 353 adult males aged between 19 and 49 years. The main results are that, first, social exclusion had a positive effect on displaced aggression. Second, stress was found to partially mediate the relationship between social exclusion and displaced aggression. Third, the hypothesis that social support would moderate the mediating effect of stress was not provedvaild, but the conditional direct effect of social support was confirmed in the mediation model. In other words, social support did not affect the indirect effect mediated by stress, but appeared to moderate the direct effect between social exclusion and displaced aggression. Social exclusion's prediction of displaced aggression was significant only in the average social support group (mean) and the high group (M+1SD), and appeared to increase as the group increased. This means that in groups with high social support, displaced aggression is used as a stress control strategy, which is a different result from previous studies that found that social support plays a role in lowerings aggression. People with low levels of social support showed unexpected results in that they used displaced aggression less frequently despite their experiencinge of social exclusion. In the discussion, the social implications of these results were interpreted, and additional research ideas were proposed to specify the relationship between social exclusion and displaced aggression.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.193-204
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• 2022

This study analyzed the degree of influence of measurement and improvement of customer satisfaction level targeting kiosk users. In modern times, due to the development of technology and the improvement of the online environment, the probability that simple labor tasks will disappear after 10 years is close to 90%. Even in domestic research, it is predicted that 'simple labor jobs' will disappear due to the influence of advanced technology with a probability of about 36%. there is. In particular, as the demand for non-face-to-face services increases due to the Corona 19 virus, which is recently spreading globally, the trend of introducing kiosks has accelerated, and the global market will grow to 83.5 billion won in 2021, showing an average annual growth rate of 8.9%. there is. However, due to the unmanned nature of these kiosks, some consumers still have difficulties in using them, and consumers who are not familiar with the use of these technologies have a negative attitude towards service co-producers due to rejection of non-face-to-face services and anxiety about service errors. Lack of understanding leads to role conflicts between sales clerks and consumers, or inequality is being created in terms of service provision and generations accustomed to using technology. In addition, since kiosk is a representative technology-based self-service industry, if the user feels uncomfortable or requires additional labor, the overall service value decreases and the growth of the kiosk industry itself can be suppressed. It is important. Therefore, interviews were conducted on the main points of direct use with actual users centered on display color scheme, text size, device design, device size, internal UI (interface), amount of information, recognition sensor (barcode, NFC, etc.), Display brightness, self-event, and reaction speed items were extracted. Afterwards, using the questionnaire, the Kano model quality attribute classification of each expected evaluation item was carried out, and Timko's customer satisfaction coefficient, which can be calculated with accurate numerical values The PCSI Index analysis was additionally performed to determine the improvement priorities by finally classifying the improvement impact of the kiosk expected evaluation items through research. As a result, the impact of improvement appears in the order of internal UI (interface), text size, recognition sensor (barcode, NFC, etc.), reaction speed, self-event, display brightness, amount of information, device size, device design, and display color scheme. Through this, we intend to contribute to a comprehensive comparison of kiosk-based research in each field and to set the direction for improvement in the venture industry.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.4
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• pp.29-39
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• 2023

In this study, an analysis of medical waste generation characteristics was conducted, differentiating between ordinary situation and the outbreaks of massive infectious diseases. During ordinary situation, prediction models for medical waste quantities by type, general medical waste(G-MW), hazardous medical waste(H-MW), infectious medical waste(I-MW), were established through regression analysis, with all significance values (p) being <0.0001, indicating statistical significance. The determination coefficient(R2) values for prediction models of each category were analyzed as follows : I-MW(R2=0.9943) > G-MW(R2=0.9817) > H-MW(R2=0.9310). Additionally, factors such as GDP(G-MW), the number of medical institutions (H-MW), and the elderly population ratio(I-MW), utilized as influencing factors and consistent with previous literature, showed high correlations. The total MW generation, evaluated by combining each model, had an MAE of 2,615 and RMSE of 3,353. This indicated accuracy levels similar to the medical waste models of H-MW(2,491, 2,890) and I-MW(2,291, 3,267). Due to limitations in accurately estimating the quantity of medical waste during the rapid and outbreaks of massive infectious diseases, the generation unit of I-MW was derived to analyze its characteristics. During the early unstable stage of infectious disease outbreaks, the generation unit was 8.74 kg/capita·day, 2.69 kg/capita·day during the stable stage, and an average of 0.08 kg/capita·day during the reduction stage. Correlation analysis between generation unit of I-MW and lethality rates showed +0.99 in the unstable stage, +0.52 in the stable stage, and +0.96 in the reduction period, demonstrating a very high positive correlation of +0.95 or higher throughout the entire outbreaks of massive infectious diseases. The results derived from this study are expected to play a useful role in establishing an effective medical waste management system in the field of health care.