• Journal of Korean Society of Forest Science
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• v.97 no.1
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• pp.71-76
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• 2008

The objective of this study was to determine the effective land units of communal forests for evaluating forest functions using GIS. As the candidates of the effective land units, we chosen the administrative units of parcels and 3 different scales of watersheds; small, medium and large. However, the administrative units of parcels were discarded because of the extreme complexity of ownerships and the highly petty scales of parcels. Instead, the 3 different levels of watershed units were applied to evaluating forest functions and the effectiveness of the resultant functional maps were compared each other in terms of the appropriateness or adequacy for field applications. According to the results of this study, it was found that the aggregated area by watersheds provides the appropriate scales of land area for forest functions and the small watershed is the most adequate one as the effective land units for forest functions in communal forests.

• Journal of Sericultural and Entomological Science
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• v.29 no.1
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• pp.39-47
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• 1987

The incidence of mulberry dwarf disease was surveyed on seventy two cocoon producing areas throughout the country from 1983 to 1984. There was a low tendency of incidence in Gyeonggi, Gangwon and Chungbuk, the northern parts of 37 degrees north latitude. However, in Chungnam and Gyeongnam, was a relatively high incidence observed. The most severe incidence areas of mulberry dwarf disease were found in Jeonnam, Jeonbuk and Gyeongbuk.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• 한국디지털정책학회:학술대회논문집
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• 2005.11a
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• pp.163-173
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• 2005

금융기관들은 개인들의 다양한 욕구를 충족시키기 위해서 경쟁적으로 수많은 금융상품을 개발하여 판매하기 시작하였으며, 이러한 상품들은 나름대로 독특한 특성을 지니게 되어, 개인들은 이에 대한 수많은 정보를 수집하고 분석할 필요성을 가지게 되었다. 금융상품정보의 정보원 선택 선호요인으로는 정보원 자체의 특정 이외에도 개인이 가지는 성격적 특징과 환경적 요인들을 들 수 있다. 따라서 개인의 성격유형에 따라 선호하는 정보원의 유사점과 차이점을 알아보았다. 본 연구에서는 금융상품정보를 취득하는 정보원에 대한 선호요인인 개인의 성격유형을 파악하기 위하여 MBTI(Myers Briggs Type Indicator)를 근거로 작성된 성격특성평정척도(Personality Trait Ration Scales: PTRS)를 이용하여 파악된 성격유형들이 금융상품 정보원 선택에 미치는 영향을 분석하였다. 성격유형변수를 4개의 군집으로 집단화하고 성격요인과 차이분석을 실시한 결과 감정 직관적인 성격이 강한 주관적 감정형, 외향 직관적인 성격이 강한 사교적활동형, 내향 사고적인 성격이 강한 수동적개인형, 인식 판단적인 성격이 강한 합리적이성형의 4개 군집으로 나누어졌다. 8개의 성격요인 중 감각을 제외한 7개 성격요인 모두가 p<. 05에서 4개의 성격유형군집과 유의적인 차이가 있는 것으로 나타났다. 사교적활동형은 여러 정보원 중 'TV'를, 합리적이성형은 '잡지', '금융상품팜플렛', '재테크서적'을, 주관적감정형은 '은행창구직원', '친구나친지'를 선호하였다. 그러나 수동적개인형은 어떠한 금융상품정보원도 선호하지 않았다. 특히, 합리적이성형은 전문금융정보를 원하는 것으로 나타났고, 주관적감정형은 인간적인 면을 더 선호하는 것으로 나타났다. 본 연구가 가지는 의의는 각 성격유형별로 선호하는 금융상품정보원의 차이를 분석함으로써 개인의 정보욕구를 보다 더 만족시킬 수 있는 하나의 요인으로 성격요인과 정보원의 차이에 관한 정보를 제공하며, 금융정보를 제공하는 주체들에게 각 정보원에 적합하도록 정보의 성격에 관한 특성요인과 고객선호정보원을 살펴볼 수 있는 정보취득방안에 대한 연구의 필요성을 제시하는데 있다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.145-151
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• 2006

In conjunction with the rapid progress of IT(information technology) an increasing amount of data are being generated. The amount of new data is so big and the type of new data generated from clients or sensor devices needed to be in ubiquitous environment is so various that it is hard to manage and control them. Especially data to be occurred in ubiquitous environment are generated through PDA(personal digital assistant) smart phone, mobile device or sensor units etc. Therefore, to manage and control them generated from ubiquitous devices for decision making, we can use data warehouse as an integrated storage. A data warehouse integrates and aggregates data of several different DBMS into one DBMS. Also the updated data from source data have to be effectively propagated to the data warehouse. Therefore, in this paper, we proposed a mode) for an exact and efficient data management methodology in new IT paradigm environment, ubiquitous computing environment, to apply updated data on the warehouse to make decision. We also show brief result compared to conventional methodology.

• Journal of Korean Society of Forest Science
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• v.102 no.2
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• pp.292-299
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• 2013

The purpose of this study is to identify the regional specialization of Fruit trees in Korea and to provide direction for the activation. As a result of the analysis, In the case of chestnut, Gongjun is adjacent to Cheongyang and Buyeo. These region are classified as deeply specialized regions. Walnut trees are grown in large amounts in Gimcheon-si, Yeongdong-gun and Muju-gun, which are all regions classified as deeply specialized regions. Jujube trees are grown mostly Gyeongsan-si, Gunwi-gun, Cheongdo-gun and Yeongcheonsi are geographically close to each other. The production quantity of these areas also showed steady growth. These region are classified as deeply specialized regions. Astringent persimmons are Sangju-si and Yeoungdonggun are close to each other. Other regions are scattered over the country.

• Spatial Information Research
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• v.9 no.2
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• pp.341-352
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• 2001

This study was carried out to developed that appraisement model of Potential Productivity Index(PPI). PPI model was used Farmland Productivity Index(FPI) and Labor Productivity Index(LPI) by GIS, and PPI model applied to farm land consolidation region which has been completed recently. FPI was determined by overlapping Poly Grid of the soil properties at the analyzed project region. LPI was estimated by addition productive wages ratio of total direct productive cost. Addition productive wages determined by GIS Network analysis GIS Network analysis of working distance between farm house to paddy and each paddy. PPI variation of each the analyzed paddy of Masu and Weoncheon region was 0.967~0.780 respectively, and could be showed relative largely PPI value. PPI will provide basic element for large scaling and gathering of farm land and a substitute lot of farm land consolidation, and will be maximize productivity of paddy.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.31-35
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• 1997

In this paper, we propose a variable vocabulary word recognizer that is able to recognize new words not exist in training data. For the variable vocabulary word recognizer, we must have an on-line lexicon generator to transform new candidate words to the corresponding pronunciation sequences of phones without any large lexicon table. And, we also must make outputs. In order to model the phones and allophones reliably, we define Korean allophones by triphone clustering based on phonetic knowledge of preceding and succeeding phones of each phone. Using the clustering method, we generated 1,548 allophones with POW (Phonetically Optimized Words) 3,848 word DB. We evaluated the proposed word recognizer with POW 3,848 DB, PBW (Phonetically Balanced Words) 445 DB, and 244 word DB in hotel reservation task. Experimental results showed word recognition accuracy of 79.6% for the POW DB corresponding to vocabulary-dependent case, 79.4% in case of 445 word lexicon and 88.9% in case of 100 word lexicon for the PBW DB, and 71.4% for the hotel reservation DB corresponding to vocabulary-independent case.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1665-1680
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• 1997

The Entity-Relationship (E-R) model is widely used not only to increase understanding between user and designer, but also to model the relationship of real world data appropriately when designing database system in many application areas. It should be then transformed into an Object-Oriented database model which gives good merits to represent and manipulate data efficiently. Therefore, a method of transforming an E-R model into an Object-Oriented database model should be studied, but without losing any semantics of concept for the E-R model. This paper not only deals with transformation rules taking as input the elements of E-R model and delivering the elements of an Object-Oriented database model, but also improves the concept of generalization and aggregation inheritance. The paper also presents a method of transformation of relationship depending on these rules. The proposed method that obtains Object-Oriented database schema from an E-R model with preserving the properties of the E-R model is shown with examples. The method presented is able to be used to the logical database design.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1591-1601
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• 2004

Clustering, a technique for the analysis of the genes, organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster or analyzing the functions of unknown gones. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to a group. In this paper, a Bayesian validation method is proposed to evaluate the fuzzy partitions effectively. Bayesian validation method is a probability-based approach, selecting a fuzzy partition with the largest posterior probability given the dataset. At first, the proposed Bayesian validation method is compared to the 4 representative conventional fuzzy cluster validity measures in 4 well-known datasets where foray c-means algorithm is used. Then, we have analyzed the results of Saccharomyces cell cycle expression data evaluated by the proposed method.