• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.406-418
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• 2021

This study was conducted to evaluate optimal storage techniques for bare root plants and container seedlings of Quercus variabilis and Zelkova serrata in order to maintain high quality of seedlings until planting. Refrigerated storage treatments were given at two temperatures (-2℃ [freezing] and 2℃ [cooling]) for nine different durations (0, 15, 30, 60, 120, 180, 240, 300, and 360 days after storage). We analyzed total nonstructural carbohydrate (TNC) content and measured shoot moisture content (SMC) during the storage stage and survival rate (SR) and dry weight during the planting stage of seedlings. The TNC content and SMC of the seedlings of the two species decreased with an increase in storage duration. The TNC content of seedlings rapidly decreased after 180~240 days of storage. The TNC reduction rate in the freezing treatment was lower than that in the cooling treatment. Also, with an increase in the storage duration of the two species, the SMC reduction rate in the cooling treatment increased in comparison with that in the freezing treatment. In both the species, the SR after planting decreased rapidly after 60 days of cooling storage and 180 days of freezing storage, respectively. The SR after planting was less than 60% when the TNC content for both the species dropped below 20 mg g^-1. In addition, the SR was lower than 80% when SMC measured before storage decreased by approximately 30% and 20% for Q. variabilis and Z. serrata, respectively. Our results suggest that cooling (1~2℃) storage is recommended for a short-term period (2 months or less), whereas freezing (-2~-4℃) storage is suitable for longer periods (2~6 months). These optimal storage techniques, allied with seedling harvesting and handling systems, will improve the quality of seedling production in nursery stages and increase seedling growth performances in plantations.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• 기호학연구
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• no.56
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• pp.45-70
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• 2018

Criticism of the postmodern philosophy of the system of representation, which has continued since the Renaissance, is based on a critique of the dichotomy that separates the subjects and objects and the environment from the human being. Interactivity, highlighted in a series of works emerging as postmodern trends in the 1960s, was transmitted to an interactive aspect of digital art in the late 1990s. The key feature of digital art is the possibility of infinite variations reflecting unpredictable changes based on public participation on the spot. In this process, the importance of computer programs is highlighted. Instead of using the existing program as it is, more and more artists are creating and programming their own algorithms or creating unique algorithms through collaborations with programmers. We live in an era of paradigm shift in which programming itself must be considered as a creative act. Simulation technology and VR technology draw attention as a technique to represent the meaning of reality. Simulation technology helps artists create experimental works. In fact, Baudrillard's concept of Simulation defines the other reality that has nothing to do with our reality, rather than a reality that is extremely representative of our reality. His book Simulacra and Simulation refers to the existence of a reality entirely different from the traditional concept of reality. His argument does not concern the problems of right and wrong. There is no metaphysical meaning. Applying the concept of simulation to algorithmic art, the artist models the complex attributes of reality in the digital system. And it aims to build and integrate internal laws that structure and activate the world (specific or individual), that is to say, simulate the world. If the images of the traditional order correspond to the reproduction of the real world, the synthesized images of algorithmic art and simulated space-time are the forms of art that facilitate the experience. The moment of seeing and listening to the work of Ian Cheng presented in this article is a moment of personal experience and the perception is made at that time. It is not a complete and closed process, but a continuous and changing process. It is this active and situational awareness that is required to the audience for the comprehension of post-representation's forms.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.139-149
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• 2019

The purpose of this study is to contribute to the minimization of the social cost due to the insolvency by improving the success rate of the startups by providing useful information to the founders and the start-up support institutions through analysis of non-financial information affecting the failure of the startups. This study is aimed at entrepreneurs. The entrepreneurs that are defined by the credit guarantee institutions generally refer to entrepreneurs within 5 years of establishment. The data used in the study are sampled from the companies that were supported by the start-up guarantee from January 2014 to December 2013 as the end of December 2017. The total number of sampled firms is 2,826, 2,267 companies (80.2%), and 559 non-performing companies (19.8%). The non-financial information of the entrepreneur was divided into the entrepreneur characteristics information, the entrepreneur characteristics information, the entrepreneur asset information and the entrepreneur 's credit information, and cross-tabulations and logistic regression analysis were conducted. As a result of cross-tabulations, univariate analysis showed that personal credit rating, presence in the industry, presence of residential housing, presence of employees, and presence of financial statements were selected as significant variables. As a result of the logistic regression analysis, three variables such as personal credit rating, occupation in the industry, and presence of residential house were found to be important factors affecting the failure of founding companies. This result shows the importance of entrepreneur 's personal credibility and experience and entrepreneur' s assets in business management. The start-up support institutions should reflect these results in the entrepreneur 's credit evaluation system, and the entrepreneurs need training on the importance of the personal credit and the management plan in the entrepreneurial education. The results of this analysis will contribute to the minimization of the incapacity of startups by providing useful non-financial information to founders and start-up support organizations.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.208-220
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• 2019

The seasonality of leaf fall has important implications for understanding the response of trees' phenology to climate change. In this study, we quantified the leaf fall pattern with a model to estimate the timing and speed of leaf litter according to species and considered the nutrient use strategy of canopy species. In the autumns of 2015 and 2016, leaf litter was collected periodically using 36 litter-traps from the deciduous forests in Gwangneung and sorted by species. The seasonal leaf fall pattern was estimated using the non-linear regression model of Dixon. Additionally, the resorption rate was calculated by analyzing the nitrogen concentration of the leaf litter at each collection time. The leaf litter generally began in early October and ended in mid-November depending on the species. At the peak time (T50) of leaf fall, on average, Carpinus laxiflora was first, and Quercus serrata was last. The rate of leaf fall was fastest (18.6 days) for Sorbus alnifolia in 2016 and slowest (40.8 days) for C. cordata in 2015. The nitrogen resorption rates at T50 were 0.45% for Q. serrata and 0.48% for C. laxiflora, and the resorption rate in 2015 with less precipitation was higher than in 2016. Since falling of leaf litter is affected by environmental factors such as temperature, precipitation, photoperiod, and $CO_2$ during the period attached foliage, the leaf fall pattern and nitrogen resorption differed year by year depending on the species. If we quantify the fall phenomena of deciduous trees and analyze them according to various conditions, we can predict whether the changes in leaf fall timing and speed due to climate change will prolong or shorten the growth period of trees. In addition, it may be possible to consider how this affects their nutrient use strategy.

• The Korean Journal of Archival Studies
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• no.48
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• pp.93-139
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• 2016

Aside from serving as a body that monitors and criticizes the government through reviews and comments on public issues, newspapers can also form and spread public opinion. Metadata contains certain picture records and, in the case of local newspapers, the former is an important means of obtaining locality. Furthermore, advertising in newspapers and the way of editing in newspapers can be viewed as a representation of the times. For the value of archiving in newspapers when a documentation strategy is established, the newspaper is considered as a top priority that should be collected. A newspaper archive that will handle preservation and management carries huge significance in many ways. Journalists use them to write articles while scholars can use a newspaper archive for academic purposes. Also, the NIE is a type of a practical usage of such an archive. In the digital age, the newspaper archive has an important position because it is located in the core of MAM, which integrates and manages the media asset. With this, there are prospects that an online archive will perform a new role in the production of newspapers and the management of publishing companies. Korea Integrated News Database System (KINDS), an integrated article database, began its service in 1991, whereas Naver operates an online newspaper archive called "News Library." Initially, KINDS received an enthusiastic response, but nowadays, the utilization ratio continues to decrease because of the omission of some major newspapers, such as Chosun Ilbo and JoongAng Ilbo, and the numerous user interface problems it poses. Despite these, however, the system still presents several advantages. For example, it is easy to access freely because there is a set budget for the public, and accessibility to local papers is simple. A national library consistently carries out the digitalization of time-honored newspapers. In addition, individual newspaper companies have also started the service, but it is not enough for such to be labeled an archive. In the United States (US), "Chronicling America"-led by the Library of Congress with funding from the National Endowment for the Humanities-is in the process of digitalizing historic newspapers. The universities of each state and historical association provide funds to their public library for the digitalization of local papers. In the United Kingdom, the British Library is constructing an online newspaper archive called "The British Newspaper Archive," but unlike the one in the US, this service charges a usage fee. The Joint Information Systems Committee has also invested in "The British Newspaper Archive," and its construction is still ongoing. ProQuest Archiver and Gale NewsVault are the representative platforms because of their efficiency and how they have established the standardization of newspapers. Now, it is time to change the way we understand things, and a drastic investment is required to improve the domestic and international online newspaper archive.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.1-9
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• 2021

This study was conducted to examine the changes of photosynthesis, growth, chlorophyll contents and functional material contents in light intensity and EC concentration of wild baby leaf vegetable, Indian lettuce (Lactuca indica L. cv. 'Sunhyang') in DFT hydroponics. The cultivation environment was 25±1℃ of temperature and 60±5% of relative humidity in growth system. At 14 days after sowing, combination effect of light intensity (Photosynthetic Photon Flux Density (PPFD 100, 250, 500 µmol·m-2·s-1) and EC level (EC 0.8, 1.4, 2.0 dS·m-1) of nutrient solution was determined at the baby leaf stage. The photosynthesis rate, stomatal conductance, transpiration rate and water use efficiency of Indian lettuce increased as the light intensity increased. The photosynthesis rate and water use efficiency were highest in PPFD 500-EC 1.4 and PPFD 500-EC 2.0 treatment. The chlorophyll content decreased as the light intensity increased, but chlorophyll a/b ratio increased. Leaf water content and specific leaf area decreased as light intensity increased and a negative correlation (p < 0.001) was recognized. Plant height was the longest in PPFD 100-EC 0.8 and leaf number, fresh weight and dry weight were the highest in PPFD 500-EC 2.0. Anthocyanin and total phenolic compounds were the highest in PPFD 500-EC 1.4 and 2.0 treatment, and antioxidant scavenging ability (DPPH) was high in PPFD 250 and 500 treatments. Considering the growth and functional material contents, the proper light intensity and EC level for hydroponic cultivation of Indian lettuce is PPFD 500-EC 2.0, and PPFD 100 and 250, which are low light conditions, EC 0.8 is suitable for growth.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.129-149
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• 2021

Recently, interest in social robots that can socially interact with humans is increasing. Thanks to the development of ICT technology, social robots have become easier to provide personalized services and emotional connection to individuals, and the role of social robots is drawing attention as a means to solve modern social problems and the resulting decline in the quality of individual lives. Along with the interest in social robots, the spread of social robots is also increasing significantly. Many companies are introducing robot products to the market to target various target markets, but so far there is no clear trend leading the market. Accordingly, there are more and more attempts to differentiate robots through the design of social robots. In particular, anthropomorphism has been studied importantly in social robot design, and many approaches have been attempted to anthropomorphize social robots to produce positive effects. However, there is a lack of research that systematically describes the mechanism by which anthropomorphism for social robots is formed. Most of the existing studies have focused on verifying the positive effects of the anthropomorphism of social robots on consumers. In addition, the formation of anthropomorphism of social robots may vary depending on the individual's motivation or temperament, but there are not many studies examining this. A vague understanding of anthropomorphism makes it difficult to derive design optimal points for shaping the anthropomorphism of social robots. The purpose of this study is to verify the mechanism by which the anthropomorphism of social robots is formed. This study confirmed the effect of the human-likeness of social robots(Within-subjects) and the construal level of consumers(Between-subjects) on the formation of anthropomorphism through an experimental study of 3×2 mixed design. Research hypotheses on the mechanism by which anthropomorphism is formed were presented, and the hypotheses were verified by analyzing data from a sample of 206 people. The first hypothesis in this study is that the higher the human-likeness of the robot, the higher the level of anthropomorphism for the robot. Hypothesis 1 was supported by a one-way repeated measures ANOVA and a post hoc test. The second hypothesis in this study is that depending on the construal level of consumers, the effect of human-likeness on the level of anthropomorphism will be different. First, this study predicts that the difference in the level of anthropomorphism as human-likeness increases will be greater under high construal condition than under low construal condition.Second, If the robot has no human-likeness, there will be no difference in the level of anthropomorphism according to the construal level. Thirdly,If the robot has low human-likeness, the low construal level condition will make the robot more anthropomorphic than the high construal level condition. Finally, If the robot has high human-likeness, the high construal levelcondition will make the robot more anthropomorphic than the low construal level condition. We performed two-way repeated measures ANOVA to test these hypotheses, and confirmed that the interaction effect of human-likeness and construal level was significant. Further analysis to specifically confirm interaction effect has also provided results in support of our hypotheses. The analysis shows that the human-likeness of the robot increases the level of anthropomorphism of social robots, and the effect of human-likeness on anthropomorphism varies depending on the construal level of consumers. This study has implications in that it explains the mechanism by which anthropomorphism is formed by considering the human-likeness, which is the design attribute of social robots, and the construal level of consumers, which is the way of thinking of individuals. We expect to use the findings of this study as the basis for design optimization for the formation of anthropomorphism in social robots.