• Journal of Bio-Environment Control
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• v.11 no.4
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• pp.151-156
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• 2002

Root zone cooling, such as soil or nutrient solution cooling, is less expensive than air cooling in the whole greenhouse and is effective in promoting root activity, improving water absorption rate, decreasing plant temperature, and reducing high temperature stress. The heat transfer of a soil cooling system in a plastic greenhouse was analyzed to estimate cooling loads. The thermal conductivity of soil, calculated by measured heat fluxes in the soil, showed the positive correlation with the soil water content. It ranged from 0.83 to 0.96 W.m$^{[-10]}$ .$^{\circ}C$$^{[-10]}$ at 19 to 36％ of soil water contents. As the indoor solar radiation increased, the temperature difference between soil surface and indoor air linearly increased. At 300 to 800 W.m$^{-2}$ of indoor solar radiations, the soil surface temperature rose from 3.5 to 7.$0^{\circ}C$ in bare ground and 1.0 to 2.5$^{\circ}C$ under the canopy. Cooling loads in the root zone soil were estimated with solar radiation, soil water content, and temperature difference between air and soil. At 300 to 600 W.m$^{-2}$ of indoor solar radiations and 20 to 40％ of soil water contents,46 to 59 W.m$^{-2}$ of soil cooling loads are required to maintain the temperature difference of 1$0^{\circ}C$ between indoor air and root zone soil.

• Food Science and Preservation
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• v.21 no.2
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• pp.239-245
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• 2014

This study was performed to provide fundamental information regarding the quality change of buckwheat soksungjang (BWS) during its storage. BWS was divided into three different containers (pot, plastic, and glass) and was stored at three different temperatures (5, 15, and $25^{\circ}C$), and the changes in pH, acidity, amino-type nitrogen, total bacterial count, and chromaticity were examined during the storage period. The pH (0 day, pH 4.37) and acidity (0 day, 2.93% acidity) of the samples, except at the 15 and $25^{\circ}C$ pots, did not show any significant change during storage, but 98 days after storage, the pH values of the 15 and $25^{\circ}C$ pots were pH 5.6 and 7.4, and their acidity values were 1.85 and 0.71%, respectively. At 98 days, the amino-type nitrogen of the $25^{\circ}C$ plastic sample had slightly increased to $0.75{\pm}0.01%$, and that of the $25^{\circ}C$ pot had drastically risen to $0.92{\pm}0.01%$. It was also shown that little change in the total bacterial count was found during the experiment period in every sample. The chromaticity results confirmed that the L (lightness), a (redness), and b (yellowness) values of the $25^{\circ}C$ pot sample showed relatively large changes during storage compared to the other samples. These results suggest that the desirable storage temperature of BWS is in the range of $5-15^{\circ}C$, and that a glass container is the most suitable container for BWS as it can reduce the quality alteration during storage.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.8
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• pp.1121-1127
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• 2011

We evaluated the quality characteristics of adzuki bean sediment according to variety. The moisture, crude protein, and crude ash contents of the various adzuki bean varieties were 8.2~11.1, 15.4~20.6 and 3.3~3.6 g/100 g, respectively. The potassium contents of Chilbo-pat (CB) and Hongeon-pat (HE) were 875.1 and 873.1 mg/100 g, respectively. The calcium contents of Jungbu-pat (JB) and Kumsil-pat (KS)were 73.6 and 73.2 mg/100 g, respectively. A high level of magnesium (131.4 mg/100 g) was found in Yeonkeum-pat (YK). The yields of adzuki bean sediment according to variety were no different either wet (188.3~204.7%) or dry (62.1~66.0%). The L-values on sediment of YK and KS were 67.0 and 68.0, respectively; however, the CB L-value was low at 54.0. A high level of a- (6.6) and b-value (12.8) was found in YK; however, the values for CB were much lower at 3.8 and 5.9, respectively. There was no difference in particle-size distribution, water binding capacity, and solubility of adzuki bean sediment according to variety. High levels of peak (3.79 RVU), trough (3.75 RVU), final (7.33 RVU), and setback viscosity (3.54 RVU) were found in JB. The sensory properties of products in food processing are important, and the variety of adzuki bean sediment should be chosen depending on desired product characteristics.

• The Journal of Natural Sciences
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• v.10 no.1
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• pp.209-216
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• 1998

The results of the study of diving the 21st century's Korea fashion market into consumer market, fashion market, and a new marketing strategy are as follows. The 21st consumer market is First, a fashion democracy phenomenon. As many people try to leave unconditional fashion following, consumer show a phenomenon to choose and create their own fashion by subjective judgements. Second, a phenomenon of total fashion pursuit. Consumer in the future are likely to put their goals not in differentiating small item products, but considering various fashion elements based on their individuality and sense of value. Third, world quality-oriented. With the improvement of life level, it accomplishes to emphasize consumers' fashion mind on the world wide popular use of materials, quality, design and brand image. Fourth, with the entrance of neo-rationalism, consumers show increasing trends to emphasize wisdom, solidity in goods strategy pursuing high quality fashion and to demand resonable prices. Fifth, concept-oriented. Consumers are changing into pursuing concept appropriate to individual life scene. Prospecting the composition of the 21st century's fashion market, First, sportive casual zone will draw attention more than any other zone. This is because interest in sports will grow according to the increase of leisure time and the expasion of time and space in the 21st century, and also ecology will become the important issue of sports sense because of human beings's natural habit toward nature. Second, the down aging phenomenon will accelerate its speed as a big trend. Third, a retro phenomenon, a concept contrary to digital and high-tech, will become another big trend for its remake, antique, and classic concept in fashion market with ecology trend. New marketing strategy to cope with changing fashion market is as follows. First, with the trend of borderless concept, borders between apparels are becoming vague, for example, they offer custom-made products to consumers. Second, as more enterprises take the way of gorilla and guerrilla where guerrillas who aim at niche market show up will develop. Basically, they think highly of individual creative study, and pursue the scene adherence with high sensitiveness. However this polarization becomes mutually-supplementing relationship showing gorilla's guerilla movement, and guerilla's gorilla high-tech. Third with the development of value retailing, enterprises pursuing mass merchandising of groups called category killers are expanded and amplified to new product fields, and expand business' share. Fourth, using outsourcing, the trend to use exterior function leaving each enterprise's strength by inspecting its own work is gradually strong. Fifth, with the expansion of none store sale, the entrance of the internet and the CD-ROM sales added to communication sales such as catalogues are specified. An eminent American think tank expect that 5-5% of the total sale of clothes and home goods in 2010 will be done by none store sale. Accordingly, to overcome the problems, First international, global level marketing, Second, the improvement of technology, Third, knowledge-creating marketing are needed.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.29-45
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• 2015

Systematic theory, concepts, and methodology for the biological evolution have been developed while patterns and principles of the evolution have been actively studied in the past 200 years. Furthermore, they are applied to various fields such as evolutionary economics, evolutionary psychology, evolutionary linguistics, making significant progress in research. In addition, existing studies have applied main biological evolutionary models to artifacts although such methods do not fit to them. These models are also limited to generalize evolutionary patterns of artifacts because they are designed in terms of a subjective point of view of experts who know well about the artifacts. Unlike biological organisms, because artifacts are likely to reflect the imagination of the human will, it is known that the theory of biological evolution cannot be directly applied to artifacts. In this paper, beyond the individual's subjective, the aim of our research is to present evolutionary patterns of a given artifact based on peeping the idea of the public. For this, we propose a text mining approach that presents a systematic framework that can find out the evolutionary patterns of a given artifact and then visualize effectively. In particular, based on our proposal, we focus mainly on a case study of mobile phone that has emerged as an icon of innovation in recent years. We collect and analyze review posts on mobile phone available in the domestic market over the past decade, and discuss the detailed results about evolutionary patterns of the mobile phone. Moreover, this kind of task is a tedious work over a long period of time because a small number of experts carry out an extensive literature survey and summarize a huge number of materials to finally draw a diagram of evolutionary patterns of the mobile phone. However, in this work, to minimize the human efforts, we present a semi-automatic mining algorithm, and through this research we can understand how human creativity and imagination are implemented. In addition, it is a big help to predict the future trend of mobile phone in business and industries.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of Korean Home Economics Education Association
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• v.33 no.2
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• pp.95-113
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• 2021

This study examined the food group and dietary nutrient intakes by sugar-sweetened beverage(SSB) intake level in high school students aged 15~18 years(n=2,377) using the 2007~2015 Korea National Health and Nutrition Examination Survey. Subjects were classified into three groups by SSB(included carbonated drinks, sports drinks, and caffeinated drinks that contained added sugars) intake level obtained from 24-hour recall method: SSB 1(SSB intake 0 g/d), SSB 2(0 g/d < SSB intake < 50th percentile) and SSB 3(SSB intake ≥ 50th percentile). Result of daily intakes of SSB were 160.6±10.5 g/d for boys and 98.6±7.1 g/d for girls and it increased for boys(p<0.0001) and girls(p=0.0280) by year. The highest intakes were carbonated drinks followed by fruit juices for boys and girls. Intakes of carbonated drinks increased as 2.7 times for boys(p<0.0001) and 1.6 times for girls between 2007 and 2015 year. Daily intakes of vegetables were the lowest in SSB 3 of three groups for boys and girls(p<0.0001), and those of fruits were lower in SSB 2 and SSB 3 than SSB 1 for boys(p=0.0013). Daily intakes of milk & milk products decreased toward SSB 3 group for boys(p<0.0001) while those were the lowest in SSB 3 of three groups for girls. Daily intakes of dietary fiber(21.3~25.3%) and calcium(49.6~59.8%) were very low compared to the dietary reference intakes. Percentage of daily intakes compared to the dietary reference intakes increased for energy for boys and girls(p<0.0001) while decreased for vitamin C toward SSB 3 group for boys(p<0.0001) and girls(p=0.0382). Those of calcium were the lowest in SSB 3 of three groups for boys(p<0.0001) and girls(p=0.0008). Ratio of excess intakes of energy/fat increased toward SSB 3 group for boys and girls(p=0.0002). Ratio of calcium deficiency was not different among groups but that was very high(85.9~92.5%). Therefore, it should be emphasized to reduce SSB intakes in order to improve diversity in food group and dietary nutrient intakes among high school students through dietary education and government support.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Korea Science and Art Forum
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• v.20
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• pp.147-158
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• 2015

This study made an approach to the industrial exhibition space, which is a medium of marketing communication, from the position of an enterprise and consumers through the output of Space Design, and conducted it with focus on B2B transactions among specialized exhibitions. In addition, this study inquired into what factors should be considered along with space design by interpreting the purpose of participating in the exhibition and space design of the enterprise which supply capital goods, elements, related technologies and materials, etc. This study aimed at drawing the direct/indirect effect, produced by space design, on the marketing by analyzing correlation between space design and participating enterprises' marketing. Despite the marketing effect of the exhibition, which was proved by preceding research results, the reality is that exhibition-participating expenses work as considerable burden on enterprises. Particularly, booth design, which is forming the most proportion among the participating expenses, was found to have insufficient influence on visitors due to the decline in its importance among diverse factors influencing visitor's decision to visit a booth. Regardless of the business category of participating enterprises in the exhibition, the standard of exhibits was ranked as the most important consideration factor in visiting a booth. Even by business category, the standard of booth design rarely had an influence on booth visit. Booth design had an affirmative influence on participating enterprise's preference, but its influence on product purchase or business talk & contact with a participating enterprise or price was found to be extremely low. It's difficult to judge marketing success or failure of an exhibition by the form and standard of booth design. Preferably, this study infers that it's necessary to put much weight on qualitative excellence of an exhibition, which consists of participation of an enterprise in possession of excellent technologies, exhibits with higher standards and high-quality visitors with purchasing power. This study suggests that it's more effective to set up the plan for expansion of participation in exhibition by optimally regulating the proportion of space design in participating expense to increase marketing effectiveness of an exhibition. The limitations of this study, analysis of which based on the visitors to an exhibition only, requires supplementation through the follow-up research work on participating enterprises in the exhibition.