• Cartoon and Animation Studies
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• s.37
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• pp.107-142
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• 2014

The purpose of this study is to examine the effects of the teaching English factors on student's communicative competence and motivation by using animation at the College. To achieve this purpose, this study presented an effective integrative teaching model to develop students communicative competence. The study created animation based teaching English model by using the animation of Frozen and applied it to lectures. Using animation in the classroom was a creative English teaching technique involving authentic activities like English dram, English guide contest, and various communicative activities A case study on the use of the animation in English classes at was examined and the language teaching syllabus were provided. In order to investigate the motivation and proficiency of learners, the writer chose 79 students who took the lecture. The study discovered the students' motivation and proficiency in English improved significantly. The results of experiment are as follows: First, using animation in the English class was found to have meaningful influence student's intrinsic motivation to learn English. Second, using animation in the English class was found to be effective for developing student's English proficiency. Third, appropriate materials should be selected and applied it to the real classroom activities. In conclusion, one of disadvantages of learning is less communication and the authentic interaction in a real life, so that the integrative teaching methodology which is combined English content and English animation content is also the effective method to improve student's intrinsic motivations in the age of global village.

• Korean Journal of Plant Resources
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• v.24 no.1
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• pp.76-88
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• 2011

The flora of vascular plants in the mountains located in the Uiryeong-gun area, in the South province of the Korean Peninsula, such as Jagul-san(897.1 m), 676 Highland(676 m), Byeokhwa-san(522 m), and the Bangeo-san(530.4 m), was investigated between April 2008 and August 2009. These investigations found 580 taxa consisting of 496 species, 1 subspecies, 77 varieties, and 6 forms, found within 319 genera under 103 families. The count totaled at 744 taxa(16.2% of all vascular plant taxa in Korea), which was made up of 648 species, 3 subspecies, 81 varieties, and 12 forms, found within of 362 genera under 109 families, when voucher specimens from the previous research studies were added. Forests of the investigated areas were generally composed of mixed Pinus densiflora and Quercus sp. The areas with comparatively excellent vegetation were the valley neighboring Baekun-sa(temple) (in the eastern slope of Mt. Jagul-san), the southwest slope of Mt. 676 Highland, the eastern slope of Mt. Byeokhwa-san, and the northern slope of Mt. Bangeo-san. 10 families were collected in abundance: Compositae, Graminae, Leguminosae, Liliaceae, Rosaceae, Cyperaceae, Labiatae, Polygonaceae, Ranunculaceae, and Violaceae these families made up 50% of all collected taxa. 19 taxa were endemic to the area, including Salix hallaisanensis H.Lev, S. koriyanagi Kimura, Aconitum austrokoreense Koidz, A. pseudolaeve Nakai, Clematis trichotoma Nakai, Thalictrum uchiyamai Nakai, Stewartia pseudocamellia Maxim, Philadelphus schrenkii Rupr., Lespedeza ${\times}$ robusta Nakai, Vicia chosenensis Ohwi, Euonymus trapococca Nakai, and Angelica cartilagino-marginata var. distans(Nakai) Kitag. Eight of the taxa were rare and endangered plants, as designated by the Korea Forest Service, including Jeffersonia dubia(Maxim.) Baker & S. Moore and Viola diamantiaca Nakai. 38 taxa of alien plants were found. Vegetation of the surveyed areas falls in the South province of the Korean Peninsula. Of all the taxa collected, 463 taxa(10.06% of all vascular plants in Korea) are considered useful plants, 231 taxa are edible, 193 taxa have medicinal uses, 65 taxa are used ornamentally, 234 taxa are important forage, 3 taxa are used as an industrial raw material, 17 taxa are used for timber, 18 taxa contain useful dyes, and 7 taxa are used for fiber.

• Korean Journal of Plant Resources
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• v.22 no.5
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• pp.365-380
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• 2009

Flora of vascular plants in mountains located at Wonju-si and Hoengseong-gun areas such as Chiak-san(1,288m), Taegi-san(1,261.4m), Obong-san(1,126.2m), Eungbong-san(1,094.9m), Eodab-san(789.4m), Deokgo-san(521.2m), Deokga-san(700.5m) and Seongji-bong(791m) was investigated from April, 2008 to May, 2009, and results from the previous researches in which voucher specimens had been presented from Balgyo-san(998.4m), Oeum-san(930.4m), Baekun-san(1,037.1m) and Chiak-san(1,288m) were included in the list of vascular plants from these areas. This investigations resulted in 804 taxa consisted of 680 species, 1 subspecies, 111 varieties, and 12 forms of 383 genera under 97 families, and totally in 973 taxa (21.14% of all vascular plants in Korea) of 818 species, 1 subspecies, 138 varieties and 16 forms of 418 genera under 105 families on addition of voucher specimens in the previous researches. Forests of the investigated areas were generally mixed of Pinus densiflora and deciduous trees. The areas with comparatively excellent vegetation were valley from Guryong-sa (temple) to Biro-bong (summit) via Seryeom-pokpo (fall) in Mt. Chiak-san, and Keunseong-gol (valley) and eastern slope from Taegibungyo-teo to Naksu-dae (fall) in Mt. Taegi-san. 10 families with abundantly collected species were Compositae, Graminae, Rosaceae, Ranunculaceae, Leguminosae, Cyperaceae, Liliaceae, Saxifragaceae, Umbelliferae and Labiatae in order, and they occuied 49.12% of all collected taxa. Endemic plants found in these areas were 38 taxa including Hanabusaya asiatica, Megaleranthis saniculifolia, and Pyrus ussuriensis var. diamantica, and rare and endangered ones were 24 taxa including Hanabusaya asiatica, Viola websteri, Viola diamantica, and Patrina saniculaefolia. Specially designated plants by the Ministry of Environment were 88 taxa including 12 taxa of 5th degree such as Woodsia intermedia, Hanabusaya asiatica, Equisetum pratense, Iris koreana, Lilium cernum, Trillium tschonoskii, Magnolia kobus(cultivated), Gastrodia elata, Polypodium virginianum, Cimicifuga heracleifolia, Megaleranthis saniculifolia and Viola websteri. 47 taxa of alien plants were found. As to 609 taxa (13.23% of all vascular plants in Korea) of useful plants, 334 taxa for the edible, 269 taxa for the medicinal, 127 taxa for the ornamental, 332 taxa for the forage, 3 taxa for the industrial raw material, 31 taxa for the timber and 13 taxa for the fiber were classified, respectively.

• The Korean Journal of Archival Studies
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• no.28
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• pp.35-81
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• 2011

Manhwa is a mass media (to expose all faces of an era such as politics, society, cultures, etc with the methodology of irony, parody, etc). Since the Manhwa records is primary culture infrastructure, it can create the high value-added industry by connecting with fancy, character, game, movie, drama, theme park, advertising business. However, due to lack of active and systematic aquisition system, as precious Manhwa manuscript is being lost every year and the contents hard to preserve such as Manhwa content in the form of electronic records are increasing, the countermeasure of Manhwa contents management is needed desperately. In this study, based on these perceptions, the need of Manhwa records management is examined, and the characteristics and the components of Manhwa records were analyzed. And at the same time, the functions of record management process reflecting the characteristics of Manhwa records were extracted by analyzing various cases of overseas Cartoon Archives. And then, the framework of record-keeping regime was segmented into each of acquisition management service areas and the general Manhwa records archiving strategy, which manages the Manhwa contents records, was established and suggested. The acquired Manhwa content records will secure the context among records and warrant the preservation of records and provide diverse access points by reflecting multi classification and multi-level descriptive element. The Manhwa records completed the intellectual arrangement will be preserved after the conservation in an environment equipped with preservation facilities or preserved using digital format in case of electronic records or when there is potential risk of damaging the records. Since the purpose of the Manhwa records is to use them, the information may be provided to diverse classes of users through the exhibition, the distribution, and the development of archival information content. Since the term of "Manhwa records" is unfamiliar yet and almost no study has been conducted in the perspective of records management, it will be the limit of this study only presenting acquisition strategy, management and service strategy of Manhwa contents and suggesting simple examples. However, if Manhwa records management strategy are possibly introduced practically to Manhwa manuscript repositories through archival approach, it will allow systematic acquisition, preservation, arrangement of Manhwa records and will contribute greatly to form a foundation for future Korean culture contents management.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.