• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.404-414
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• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.44-56
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• 2023

This research explores the enhancement of park operation and management by analyzing the changing demands of park users. While traditional methods depended on surveys, there has been a recent shift towards utilizing social media data to understand park usage trends. Notably, most research has focused on text data from social media, overlooking the valuable insights from image data. Addressing this gap, our study introduces a novel method of assessing park usage using social media image data and then applies it to actual city park evaluations. A unique image analysis tool, built on Visual Question Answering (VQA) deep learning technology, was developed. This tool revealed specific city park details such as user demographics, behaviors, and locations. Our findings highlight three main points: (1) The VQA-based image analysis tool's validity was proven by matching its results with traditional text analysis outcomes. (2) VQA deep learning technology offers insights like gender, age, and usage time, which aren't accessible from text analysis alone. (3) Using VQA, we derived operational and management strategies for city parks. In conclusion, our VQA-based method offers significant methodological advancements for future park usage studies.

• Korean Journal of Community Nutrition
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• v.29 no.1
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• pp.65-80
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• 2024

Objectives: This study aims to compare changes in physical factors, health behaviors, eating habits, and nutritional intake among Korean male and female adults over a period of three years (2019-2021) before and after the outbreak of coronavirus disease 2019 (COVID-19). Methods: This study utilized raw data from the 8th Korea National Health and Nutrition Examination Survey (2019-2021). The participants in this study included 6,235 individuals in 2019, 5,865 individuals in 2020, and 5,635 individuals in 2021. Individuals whose daily energy intake was less than 500 kcal or exceeded 5,000 kcal were excluded from the study. Results: In comparison to 2019, overweight/obesity rates, weight, waist circumference, weekend sleep hours, and resistance exercise days/week increased in both male and female during the COVID-19 pandemic. Regarding eating habits, the proportions of people skipping breakfast, not eating out, consuming health supplements, and recognizing nutritional labels increased in 2020 and 2021, whereas the rate of skipping dinner decreased. Total energy intake has continued to decrease for the two years since 2019. A comparison of nutrient intake per 1,000 kcal before and after the outbreak of COVID-19 revealed that intake of nutrients, including protein, phosphorus, iron, vitamin A, riboflavin, and niacin increased, while folic acid intake decreased. In male, calcium, phosphorus, riboflavin, and niacin intakes increased, whereas iron, vitamin C, and folic acid intakes decreased. In female, phosphorus, iron, vitamin A, and riboflavin intake increased significantly, while protein and niacin intake decreased significantly. Conclusions: After COVID-19, the obesity rate, breakfast skipping rate, health supplement intake, and nutritional label use increased, while the frequency of eating out, dinner skipping rate, and total energy intake decreased. These environmental changes and social factors highlight the need for nutritional education and management to ensure proper nutritional intake and reduce obesity rates in the post-COVID-19 era.

• Resources Recycling
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• v.33 no.1
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• pp.58-68
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• 2024

Critical minerals such as nickel, cobalt and lithium, are known as materials for cathodic active materials of lithium ion batteries. The consumption of the minerals is expected to grow with increasing the demands of electric vehicles, resulting from carbon neutrality. Especially, the demand for LIB (lithium ion battery) recycling is expected to increase to meet the supply of nickel, cobalt and lithium for LIB. The recycling of EOL (end-of-life) LIB can be achieved by leaching EOL LIB using inorganic acid such as HCl, HNO₃ and H₂SO₄, which are regarded as hazardous materials. In the present study, the potential use of MSA (Methanesulfonic acid), as an alternative lixiviant replacing sulfuric acid was investigated. In addition, leaching behaviors of NCM black mass leaching with MSA was also investigated by studying various leaching factors such as chemical concentration, leaching time, pulp density (P/D) and temperatures. The leaching efficiency of nickel (Ni), cobalt (Co), lithium (Li), and manganese (Mn) from LIB was enhanced by increasing concentration of lixiviant and reductant, leaching time and temperature. The maximum leaching of the metals was above 99% at 80℃. In addition, MSA can replace sulfuric acid to recover Ni, Co, Li, Mn from NCM black mass.

• Information Systems Review
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• v.23 no.4
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• pp.23-43
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• 2021

Due to the recent transition to a non-face-to-face society, many organizations are quickly adapting to foster a smart work environment. The introduction of smart work does not simply end with incorporating ICT systems or solutions into business models since fundamental factors such as forms of employment and work styles need to be in line with the progression of technological advances. However, previous studies regarding smart work focus on improvements in productivity and efficiency from a technology acceptance perspective. Therefore, there is a lack of discussion on innovation resistance from employees and management control when ICT systems are introduced into the workplace. This study empirically analyzes the moderating effects of the organizational control method for employees and innovation resistance within a smart work environment. Additionally, this study aims to identify the structural characteristics that employees resist from an innovation resistance perspective when organizational innovation occurs. The empirical analysis of this study suggests that when smart work such as ICT technology is introduced into the workplace the level of innovation resistance decreases when there is a high level of relative advantage and self-efficacy, whereas the level of innovation resistance increases when there is a high level of use complexity. Moreover, this study revealed that the level of innovation resistance increases when the employees' behaviors were controlled. The results of this study intend to contribute to improving business management by suggesting factors worth considering when incorporating smart work into work places through a thorough case analysis.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Nutrition and Health
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• v.51 no.4
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• pp.295-306
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• 2018

Purpose: Koreans' internet and smartphone usage hours are steadily increasing and the dependence of young people on smartphones is causing social problems. Therefore, this study was conducted to examine health and dietary habits, as well as hair mineral contents according to the level of dependence of smartphone use among adolescents to clarify the interrelation of smartphone dependence, lifestyle, dietary behavior, and mineral nutrition status. Methods: A total of 80 smartphone-addicted adolescents participated in this study and were divided into three groups (general, potential and danger group) according to smartphone dependence. The subjects' lifestyles and dietary behaviors were then surveyed, and hair mineral contents were analyzed. Results: Higher smartphone dependence was associated with lower average weekly sleeping time and later first smoking age. In the danger group, the rate of eating fast and the rate of snacking twice a day was also relatively high. Parents (45.0%) and mobile (30.0%) were the factors having the greatest influence on an individual's dietary behavior. In the hair mineral analysis, all subjects had lower selenium concentrations and higher lead concentrations than normal. In addition, the levels of aluminum in the danger group were higher than in the normal range and the highest among the three groups. Conclusions: It is necessary to guide adolescents to use smartphones correctly and manage dietary habits. In addition, careful attention is needed the mineral nutritional status of smartphone-addicted adolescents.

• Cartoon and Animation Studies
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• s.51
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• pp.129-158
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• 2018

China has had many excellent Chinese-style animation since the 1950s. These animations are of distinctive Chinese national characteristics. They have won many awards both at home and abroad, such as "Feelings of Mountains and Waters", "Uproar In Heaven", "Why is the Crow Black-Coated" and so on. But nowadays, Chinese animations that mimics Japanese and American animation are very often, and there are few animation works with rich traditional Chinese culture. There are some works in the name of Chinese style, but they have not been fully accepted by the audiences. If one wants to create animated works of Chinese style, the author must have an in-depth understanding of Chinese traditional and folk art. Animation can not be designed only on the surface. This paper mainly studies the traditional five color concept in China and its application in animation. The purpose is to provide some references to differentiate Chinese animation from other countries in terms of style and color. The main content of this paper is to understand the concept and history of Chinese traditional five color views, and to know that this color system has reflected the ancestors' concept of nature and society. On the basis of five monochromatic colors, red, yellow, green, white and black, it is a kind of complex color concept that has been developed and perfected continuously after a long period of accumulation and precipitation in the practice of life. It is the theoretical basis of Chinese traditional color system and a complete set of historical, cultural, philosophical and religious theories. Finally, this paper analyzes the colors and their symbolic meanings of the main roles in "Uproar In Heaven", a color long animation produced by Shanghai Animation Film Studio, including Sun Wukong, the Jade Emperor and Na Zha. Color is the first visual language. The use of color symbols to express the inner feelings, status, good and evil of the characters will affect the audience's emotions, behaviors and opinions imperceptibly. The traditional Chinese five color concept has gone through such a long history, and its symbolic meaning has a more profound impact on Chinese people. Applying the color concept and symbolic meaning of Chinese traditional five color concept will further highlight the personalities and emotions of the roles in Chinese style animations. This paper takes the five-color view as the theoretical basis, and through the analysis of cartoons with traditional Chinese color, the author finds ways to flexibly use traditional Chinese culture.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.11 no.1
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• pp.27-41
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• 2000

Objects：This study was designed for studying of current Korean adolescents computer game playing habits and exploring associations with parental rearing patterns and individual psychopathology. Methods：One hundred twenty four adolescents(age 13-15) who reside in urban area completed self-report questionnaires containing Questionnaires designed by authors, Symptom Checklist-90-Revision of Korean Version(SCL-90-R) and Parental Bonding Instrument(PBI). Results：1) Computer game playing appears to be one of the social and leisure phenomena in these days. Although Adolescents spend a lot of times on computer game, Many of them perceive not problematic. 2) Compared with females, Male play computer games more regularly, more longer, spent more times in gamebang. 3) There was positive relationship between anxiety subscale in SCL-90-R and detrimental effects of computer game. 4) There was positive relationship between game frequency and maternal overprotection. 5) The main reasons for playing are 'for an avoidance of stressful life events', academic burden was the most troublesome issues in korean adolescents. 6) Many adolescents use gamebang as a social place, but they thought that gamebang is not good places to have a good time. Conclusion：This papers shows that computer game playing is a popular social leisure activity in Korean adolescents. And, Most of korean adolescents reported that they are suffered from pressure of academic achievements. They use computer game mainly by means of relieving academic pressures. In a heavy game users who have many conflicts with parents, teacher and who has frequent truancy and social withdrawal show significant anxiety. Maternal overprotection was observed in heavy game users. Authors recommended that clinicians should be careful in examining heavy computer game behaviors. Both underlying affective states and environmental influences, including family situations should be vicariously examined.