• Korean Journal of Culture and Social Issue
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• v.12 no.5_spc
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• pp.81-98
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• 2006

The objective of this research is to do a cultural comparison on the daily lives of the children of Korea, Japan and China. To achieve this objective, the questionnares were distributed to the 2940 mothers of children from the ages of 3 to 6 in the countries of Korea, Japan and China. The target audience consisted of 941 mothers living in Seoul and Kyunggi area for Korea, 1007 mothers living in Tokyo for Japan, and 992 mothers living in Beijing for China. As a result of the research, we found out that firstly, although children in general got up anytime between 7:00am to 9:00am and went to bed between 8:00pm and 11:00pm, 61.5% of the Korean children went to bed after 10pm and 16.8% after 11pm. Besides that, we found that compared to 3.51% of Korean children who got up before 6am, 13.41% of Japanese children and 17.24% of Chinese children got up before 6:00am. So we could see that the Korean children got up later and went to bed later than their Japanese and Chinese counterpart. This pattern could also be seen in the average rising time and bed time. Korean children went to bed at 10:00pm and woke up at 7:75am whereas the Japanese children went to bed at 9:28pm and woke up at 7:39am, and the Chinese children went to bed at 9:05pm and woke up at 7:05am. The average sleeping hours for Japanese children was 10.12 hours, 9.50 hours for the Chinese and 9.75 hours for the Korean. As a result, we could see that the Korean children went to bed later, got up later and slept fewer hours than their Japanese and Chinese counterparts. Also, since the rising time and bedtime of the Korean children was later than those of the Chinese and Japanese counterparts, the former s' breakfast and dinner time was also much later. Secondly, we looked at the time children went off to and came back from institutes such as kindergarten and child care centers. The Chinese were earliest at going with average attendance at 7:83am, the Japanese came next at 8:59am and the Korean children were last at 8:90am, whereas the Japanese came first in coming back home at 3:36pm, Korean next at 3:91pm and the Chinese last at 5:46pm. Next when we looked at the hours spent at the kindergartens and child care centers, Japan spent 6.76 hours, Korea 7.01 hours and China spent the longest hours with 9.63 hours. Excluding China where all preschool institutes are centralized into kindergartens, we nest looked at time children went to and came back from the institutes as well as the time spent there. In the case of kindergarten, there was not much difference but in the case of child care centers, the Japanese children went to the child care centers mach earlier and came home later than the Korean children. Also, the time spent at the child care center was much longer for the Japanese than the Korean children. This fact coincides with the Korean mothers' number one wish to the kindergartens and child care centers i.e. for the institutes to prolong their school hours. Thus, the time spent at child care centers for Korea was 7.75 hours, 9.39 hours for Japan and 9.63 hours for China. The time for Korea was comparatively much shorter than that of Japan and China but if we consider the fact that 50% of the target audience was working mothers, we could easily presume that the working parents who usually use the child care centers would want the child care centers to prolong the hours looked after their children. Besides this, the next most wanted wish mothers have towards the child care centers and kindergartens was for those institutes to "look after their children when sick". This item showed high marks in all three countries, and the marks in Korea was especially higher when compared to Japan and China. Thirdly, we looked at the private extracurricular activities of the children. We found that 72.6% of the Korean children, 61.7% of the Japanese children, and 64.6% of the Chinese children were doing private extracurricular activities after attending kindergarten or day care centers. Amongst the private extracurricular activities done by Korean children, the most popular one was worksheet with 51.9% of the children doing it. Drawing (15.20%) and English (11.6%) came next. Swimming (21.95%) was the most popular activity for Japan, with English (17.48%), music (15,79%) and sports (14.70%) coming next. For China, art (30.95%) was first with English (22.08%) and music (19.96%) following next. All three countries had English as the most popular activity related to art and physical activities after school hours, but the rate for worksheet studies was much higher for Korea compared to Japan China. The reason Koreans universally use worksheet in because the parents who buy the worksheet are mothers who have easy access to advertisement or salespeople selling those products. The price is also relatively cheap, the worksheet helps the children to grow the basic learning ability in preparation for elementary school, and it is thought to help the children to build the habit of studying everyday. Not only that but it is estimated that the worksheet education is being conducted because parents can share the responsibility of the children's learning with the worksheet-teacher who make home visits. Looking at the expenses spent on private extracurricular activities as compared to income, we found that China spent 5% of income for activities outside of regular education, Korea 3% and Japan 2%. Fourthly, we looked at the amount of time children spent on using multimedia. The majority of the children in Korea, Japan and China watch television almost every day. In terms of video games, the Japanese children played the games the most, with Korea and China following next. The Korean children used the computer the most, with Japan and China next. The Korean children used about 21.17% of their daily time on computers which is much more than the Japanese who used 20.62% of their time 3 or 4 times a week, or the Chinese. The Chinese children were found to use considerably less time on multimedia compared to the Korean of Japanese.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.4
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• pp.610-618
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• 2015

The purpose of this study was to analyze differences in emotional labor strategies, burnout, and work engagement according to general characteristics of school foodservice employees as well as verify differential effects of two emotional labor strategies on burnout and work engagement. Our survey was administered to 400 school foodservice employees in Gyeongbuk from March 3 to April 25, 2014. A total of 358 completed questionnaires were returned, and 350 questionnaires were used for final analysis. For verification of mean differences, the mean scores for surface acting, deep acting, burnout, and work engagement were shown to be 2.38/5.00, 3.46, 2.67, and 3.41, respectively. The mean surface acting was significantly different according to cooking certification (P<0.001), turnover number (P<0.001), salary (P<0.001), and school level (P<0.01). The mean deep acting was significantly different according to educational background (P<0.001), cooking certification (P<0.001), employment status (P<0.001), salary (P<0.001), school level (P<0.01), and meal service time (P<0.05). The mean burnout was significantly different according to educational background (P<0.01), cooking certification (P<0.05), employment status (P<0.001), school level (P<0.001), and meal service time (P<0.001). The mean work engagement was significantly different according to cooking certification (P<0.001), employment satus (P<0.001), salary (P<0.001), school level (P<0.01), and meal service time (P<0.05). Verification of causal models found that surface acting and deep acting increased burnout and deep acting, respectively (research model). Additionally, surface acting did not influence work engagement, and deep acting did not influence burnout (alternative models). In other words, we identified that emotional labor strategies have differential influences on burnout and work engagement. Finally, implications and limitations of this study are discussed.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.71-88
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• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of the Korean Geographical Society
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• v.29 no.2
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• pp.166-182
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• 1994

Kyonggi Province in the late Chosun dynasty was a center of superior government offices including 'Han' River water-road transportation and was located in the middle of an 'X'-shaped arterial road network. Because of these reasons, Kyonggi Province had a faster inflow of commodities, informations and technics compared with the other province. At this period of time, every local 'Eup' (name of administrative district) had not been affected by their above administrative districts and had their own autonomy. For this reason, every 'Eup' could be developed as a town, even if its size was small when it had sufficient internal growing conditions. Moreover, the markets ('Si-Jon') in big towns and periodical markets which were spread over the Kyonggi Province played role of commercial functions of town. And because military bases for the defence of the royal capital in Kyonggi Province also took parts of a non-agricultural city role, Xyonggi Provinc had much more possibilities of growing as a town rather than the other provinces. The towns of the late Chosun Dynasty were, except the capital and superior administrative districts which were governed by the 'You-Su', small towns which had only about 3, 000-5, 000 people. Most of the town dewellers were local officials, nobles, merchants, craftmen and slaves. And the farmers who lived near town became a pseudo-towner through suburb agriculture. Among these people, the merchants were leaders of townization. The downtowns were affected by the landform and traffic roads. The most fundamental function of towns were administrative. The opcial's grade, which was dispatched to the local administrative district ('Kun' or 'Hyun'), was decided by the size of population and agricultural land of each county. Large county which was governed by a high ranking opcial had more possibilities to develop as a large town. Because they supervised other opcials of lower rank and obtained more land and population for the town. The phonomena of farm abandonment after the Japanese Invasion of Korea in 1592-1598 stimulated the development of towns for commercial function. The commercial functions of towns were evident in the Si-Jon or Nan-Jon (names of markets) in the big cities such as Hansung and Kaesung, meanffwhile in the local areas it was emerged in the shape of periodical market networks as allied with near markets (which were called as Jang-Si) or permanent markets which were grown up from periodical markets. These facts of commercial development induced the birth of commercial town. Kyonggi Province showed the weak points of its defense system during both wars (Japanese Invasion in 1592 and Manchu's Invasion in 1636). The government reinforced its defense system by adding 4 'You-Su-Bus' and several military bases. Each local districts ('Eup'), where Geo-Jins were established, were stimulated to be a town while Jin-Kwan system were, adjusted and enforced. Among Dok-Jins(name of solitary military bases), Youngjongjin was grown up as a large garrison town which only played a role of defense. The number of towns that took roles of non-agricultural functions in Kyonggi Province was 52. Among these towns, 29 were developed as big towns which had above 3, 000 people and most of these towns were located on the northwest-southeast axes of 'X'-shaped arterial trafic network in the Chosn Dynasty, This fact points out that the traffic road is one of the important causes of the development of towns. When we make hierarchy of the towns of Kyonggi Province according to its population and how many functions it had, we can make it as 6 grades. The virst grade town 'Hansung' was the biggest central town of administration, commerce and defdnse. The 2nd grade town includes 'Kaesung' which had historical inertia that it had been the capital of the Koryo Dynesty. The 3rd grade towns include some 'You- Su-Bus' such as Soowon, Kanghwa, Kwangju and also include Mapo, Yongsan and from this we can imagine that the commercial development in the late Chosun Dynasty extremely affected the townization. The 4th-6th grade towns had smiliar population but it can be discriminated by how many town functions it had. So the 4th grade towns were the core of administration, commerce and defense function. 5th grade towns had administrative functions and one of commercial and defense functions. 6th grade towns had only one of these functions. When we research and town conditions of each grades as the ratio of non-agricultural population, we can find out that the towns from the 1st grade to 4th grade show difference by degree of townization but from the 4th grade to 6th grade towns do not show big difference in general.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.