• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.1
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• pp.83-96
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• 2016

Through the research and analysis on the vegetation environment, flora of habitats through documentary and field studies over 14 habitats of Euryale ferox Salisbury within Jeollabukdo, with the objective of acquiring the basic data for forming an environment based on plantation of reservoirs that are composed with Euryale ferox, the following results were obtained. 1. The entire flora of the 14 habitats appeared to be 79 families, 211 genus, 298 species, two subspecies, 30 varieties and six forma, thus, a total of 336 taxa was confirmed. Among these, emergent water plants appeared to compose 17 taxa, floating-leaved plants to compose seven taxa including Euryale ferox floating plants to compose five taxa and submerged water plants to compose two taxa. As a result of analyzing the similarity only over the water plants. The lowest similarity rate appeared between Gamdong Reservoir and Aedang Reservoir, as the similarity rate between the two regions appeared to be 0% as a result of the analysis. Floating-leaved plants, lotuses and caltrops, appeared to be equally inhabiting in Hanseongji at Jeongeup and Seoknam Reservoir at Gochang, which showed the highest similarity rate, in addition to Euryale ferox. 2. When examining the appearance frequency of aquatic plants per growth type, Actinostemma lobatum and Phragmites communis, in addition to Euryale ferox each appeared 11 times, showing a high frequency of 78.6% and Trapa japonica, which is a floating-leaved water plant, appeared ten times(71.4%) and Zizania latifolia appeared eight times(57.1%). In addition, the appearance rate appeared to be high in the order of Persicaria thunbergii, Leersia sayanuka, Ceratophyllum demersum, Echinochloa crusgalli var. oryzicola, Scirpus maritimus, and Nelumbo nucifera. 3. The rare plants discovered in the Euryale ferox habitats pursuant to the IUCN evaluation standards was confirmed to be composed of five taxa, with three taxa including the least concerned species(LC), Melothria japonica at Yanggok Reservoir, Hydrocharis dubia at Myeongdeokji and Ottelia alismoides at Daewi Reservoir, in addition to vulnerable species(VU), Utricularia vulgaris at Sangpyeong Reservoir, along with Euryale ferox. 4. Most of the group or community types of the natural habitats of Euryale ferox appeared to be the Euryale ferix community' and the Daewi Reservoir of Gunsan was defined as caltrop + Euryale ferox + Nymphoides indica community. The green coverage ratio of Euryale ferox per natural habitats showed a considerably huge deviation from 0.03 to 36.50 and as the average green coverage ratio was appropriated as 9.8, it can be considered that maintaining the green coverage ratio of Euryale ferox in a 10% level would be advisable when forming a reservoir with Euryale ferox as the key composition species. 5. The vegetation community nearby the natural habitats of Euryale ferox per research subject area appeared to be composed of three Leersia japonica communities, two communities each for Zizania latifolia community and Trapa japonica community and one community each for Nelumbo nucifera community, Nymphoides peltata + Typha orientalis community, Trapa japonica + Nelumbo nucifera community, Hydrocharis dubia community, Leersia japnica + Paspalum distichum var. indutum community and Euryale ferox + Trapa japonica community, showing a slight difference depending on the location conditions of each reservoir. Thus, this result may be suggested as a guideline to apply when allocating the vegetation ratio and the types of floating-leaved plants upon planting plants in reservoirs with Euryale ferox as the main companion species.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.