• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.6
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• pp.101-110
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• 2014

This study analyzes the validity of the type classification of the type and design changes of apartment landscaping planting construction design changes that were completed in the private sector, efficiently manages the design changes that are displayed over landscaping planting work in general in the future, and performs research by placing the object underlying the presentation. The results are as follows. First, the percentage that occurred in the planting construction of design changes that have occurred in the apartment landscaping construction was carried out in the private sector and accounted for 61.8%. This indicates that part of the planting is a major design change. Second, as the cause of such a design change to be those associated with the field conditions such as lack of main construction period. In particular, due to a change in oral, appeared 7-48 times design changes of one review design change approval is complex, design changes of planting construction had shown a feature that occurs in multiple simultaneous. Third, the 7 types of Design Changes in planting design were delineated as 'design changes for consideration of the user', 'design changes for image improvement', 'design changes for ease of maintenance', 'design changes due to the mismatch of design statement', 'design changes due to the relationship with the engineering species of other', 'design changes due to lack of field study', and 'design changes due to the consideration of feasibility.' Fourth, 'design changes for consideration of the user' and 'design changes for image improvement' were found in more than half of the frequency of the overall changes. This differed from the results shown in public corporations. Fifth, if planting construction design change process, private companies, it was found that is showing the approval of the practice after the previous construction of the construction cost savings due to construction time. However, in the case of a public corporation, these exhibited a different aspect from the private sector and show a design change procedure that reflects the changes after the design change events in the field have occurred. The above results, the type of landscaping works in planting design change of public enterprises, regardless of the private sector, is the same in the seven types, the main reason of and procedures for design changes, indicating that there are other respects. In design change, it may be desirable to apply becomes liquidity rationality and efficiency of the dimension, depending on the nature of the landscape construction.

• Korean Journal of Heritage: History & Science
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• v.38
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• pp.129-156
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• 2005

Increasing discovery cases of underground storage facilities made of earth, wood, or stone are being reported from the recent excavation survey of the Baekje relics. Accordingly, the purpose of this study is to examine the structure and function of the underground storage facilities of Baekje following a classification made by the type and building method as follows: plask shape, wooden box shape, and stone box shape. The plask shape storage is the most representative underground storage of Baekje that has been found in numerous relics more than 600 sets around Hangang(Han River) and Geumgang(Geum River) from the Hansung period to Sabi period in Baekje Dynasty. It is a historical artefact as a part of the unique storage culture of Baekje around Hangang and Geumgang from the 3rd to 7th Century. Considering its structure and the example of Chinese one, it might had been used for a long-term storage of grains and various other items including earth wares. The storage facility in wooden box shape and stone box shape are found mostly in the relics Of Sabi period. Thus it might had taken some functions of the storage in traditional pouch shape which had decreased after the 6th Century. In particular, the wooden box shape and stone box shape storage required enormous labor force to build owing to their structure and building method. Thus, they were considered to had been used for official purposes in province fortress and citadel artefact. The wooden box shape storage facility is classified into flat rectangular type and square type based on the structure, and into Gagu type(架構式) and Juheol type(柱穴式) based on the building method. It might had been decided according to the geography and geological feature of the place where the storage was to be built. Considering the examples of Gwanbuk-ri relics and Weolpyong-dong relics, the wooden box shape storage facility might had been used for various items depending on the needs, including foods such as fruits and essential provisions at the military base. Considering the long-term food storage, the examples in Japan, and the functional characteristics of the underground storage facility, there is a possibility that the wooden and stone box shape storage facilities had been built so as to safely store important items in case of fire. This study is only a rudimentary examination for the storage facility in Baekje. Thus further studies are to be made specifically and comprehensively on the comparison with other regions, distribution pattern, discovered relics and artefacts, and functions.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• 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 the Korean Society of Food Culture
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• v.1 no.2
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• pp.116-126
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• 1986

This study was designed to be a link in the chain of the investigation on daily life and consciousness of longevous people in Korea, and to investigate the regional feature of longevity areas. The daily life and consciousness were investigated on 379 subjects(male 121, female 258) of the aged who were above 80 years of age, from June to November in 1985. This paper is to report the results investigated the longevity rate, distribution, classification and weather of longevity districts, and also the actual conditions such as the functions of daily life and educational degree of longevous people. 1. The number of longevous people in Korea was 171,449 (male 42,842, female 128,607), and the average longevity rate was 0.46% against total population in Korea(male 0.23%, female 0.69%). 2. Of the longevity rates of shi and/or do in Korea, Cheju(1.03%) was the highest among these districts, and decreased in the order of Chonnam(0.79%), Chonbuk(0.66%), Kyongbuk(0.65%) and Kyongnam(0.61%), whereas the large cities such as Inchon(0.22%), Seoul(0.23%), Pusan(0.23%) and Taegu(0.28%) were remarkably lower than districts in seasides and mountains. 3. The districts above 1.0% of longevity rate in Korea showed 17-guns, and the distribution of these districts was 10-guns of Chonnam, 2-guns of Kyongbuk and Kyongnam, and 1-gun of Kyonggi, Cho-nbuk and Cheju, respectively. 4. Of these districts, Pukcheju(1.65%) was the highest, and decreased in the order of Namhae(1.56%), Sungju(1.24%), Posong(1.22%) and Koksong(1.20%). The highest figure(male 0.71%, female 2.51%) was observed in Pukcheju as contrasted with 0.23%(male) and 0.69%(female) of the average longevity rate in Korea. 5. The sex ratio of longevous people in Korea showed the female/male ratio of 3.0. It is, therefore, believed that the longevity rate of female was 3 times higher than that of male. 6. The longevity districts were classified into seven districts in seasides, three districts in isolated islands, and seven rural districts in mountains. 7. The situation of weather in longevity districts was in the range of 11.2 to $14.8^{\circ}C$ at annual average temperature, and 878.5 to 1585.9mm at annual average rainfall. 8. Of the educational degree of longevous people, uneducated(71.5%) was the highest, and followed by the order of village school(15.8%) and above elementary school(4.8%). 9. In the functions of daily life, the aged moving actively(53.0%) was the highest among these longevous people, followed by the aged moving a little(23.5%). Therefore, it is believed that health degree of these longevous peoples by the functions of daily life was very gratifying.