• Korean Journal of Environmental Biology
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• v.29 no.1
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• pp.31-45
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• 2011

To understand the marine algal flora on Goheung coast, Korea, marine algae at 8 points were collected from November 2008 to February 2009 and from April to June 2009. Thirty-seven species (2 species of angiosperms, 5 chlorophytes, 12 phaeophytes, and 18 rhodophytes) occurred from fall to winter and 52 species (2 species of angiosperms, 9 chlorophytes, 18 phaeophytes, and 23 rhodophytes) occurred from spring to summer. Commonly occurring species were Ulva pertusa, Sargassum thunbergii, Hizikia fuziformis, and Gelidium amansii, and dominant species at most points were Ulva pertusa, Sargassum thunbergii, and Gelidium amansii. The average of the ratio of total rhodophytes and chlorophytes to phaeophytes ((R+C)/P) was 1.61 in fall to winter and 1.69 in spring to summer, and the average Laminariales/Fucales/Dictyotales (LFD) ratio was 1.14 in fall to winter and 1.18 in spring to summer. These results show that the marine algal flora of Goheung could be considered as temperate. The LFD ratio was fit for showing a feature of algal flora of Goheung. Species diversity index was high at Points 4~6 while low at Points 1 and 8. Detrended correspondence analysis (DCA) showed that the similarity of occurring species at Points 3 and 4 was higher than the other points from fall to winter, whereas the occurred species at Points 1~4 were similar from spring to summer. The average values of ecological evaluation index (EEI) of the investigation points were 6.8 from fall to winter and 6.3 from spring to summer, which means that the ecological environment of the investigation points were middle class and the EEI values of outer sea points were higher than the inner bay points.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.8 no.2
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• pp.45-51
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• 1990

DEM must have a high accuracy against the actual topographic model. A model which can compute heights responding to random plane position by using of the topographic data and interpolation must be constructed. Interpolation affected by the accuraccy of the observations included noise, which affected by the slop and curvature weight. Data smoothing is a method to reduce the noise. Average declination and area ratio are variable which result similarity in according to slope. But in local area, area ratio well shows a local change. This study try to classify the terrain by the declination to analysis the effects of the declination and curvature weights, and then to represent the most probable model. The result are following : In terrain classification by the slop, p16 and p24 were fitted in the plane surface fit p16 and S in the varying surface, and S and p24 in the irregular surface in classification by curvature, p24 and S were fitted in the plane or varying surface, and p16 in the irregular surface In case of hybrid, p16, p24 and S are fitted in the plane, varying and irregular surface respectively. Smoothing is the most effective in case of slope of 50 persentage and of curvature weight of 0.0015.

• Smart Media Journal
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• v.6 no.1
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• pp.16-21
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• 2017

Long time has passed since computers which used to be a means of research were commercialized and available for the general public. People used writing instruments to write before computer was commercialized. However, today a growing number of them are using computers to write instead. Computerized word processing helps write faster and reduces fatigue of hands than writing instruments, making it better fit to making long texts. However, word processing programs are more likely to cause spelling errors by the mistake of users. Spelling errors distort the shape of words, making it easy for the writer to find and correct directly, but those caused due to users' lack of knowledge or those hard to find may make it almost impossible to produce a document free of spelling errors. However, spelling errors in important documents such as theses or business proposals may lead to falling reliability. Consequently, it is necessary to conduct research on high-level spelling error correction programs for the general public. This study was designed to produce a system to correct sentence-level spelling errors to normal words with Korean alphabet similarity algorithm. On the basis of findings reported in related literatures that corrected words are significantly similar to misspelled words in form, spelling errors were extracted from a corpus. Extracted corrected words were replaced with misspelled ones to correct spelling errors with spelling error detection algorithm.

• Asia Marketing Journal
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• v.9 no.3
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• pp.33-73
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• 2007

This paper explores major factors that drive relationship quality and strength in business-to-business transactions. Three major factors, including offering characteristics, managers' interpersonal factors (similarity, expertise), and relational factors (relationship length, cooperation, and dependence), were proposed to affect relationship quality, and indirectly affect relationship strength. In addition, both economic/instrumental dimension (offering characteristics) and affective, relational dimensions (trust and commitment) are also expected to influence relationship strength. In the empirical study which used the textile-dyeing company managers' ratings of local exporters, structural equation modeling presented a well-fit evidence that relationship quality variables and strength are influenced by their proposed antecedents. Specifically, it was found that all characteristics (except relationship length) have direct effect on their relationship quality with local exporters, and indirectly impact on relationship strength which was measured along dimensions of intention to continue the business relationship in the future and the current share of business given to a local exporter. Together with the minor influence that instrumental dimension (offering characteristics) has on relationship strength, this study suggests that the willingness to remain in business relationship or current proportion of business shared is influenced more by affective assessment like relationship quality than by calculative motivation.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Fashion Business
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• v.3 no.4
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• pp.147-160
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• 1999

This paper is intended to compare and analyze the fashion trends that were introduced in the recent shows, held abroad and in Korea, so as to investigate how well the designers in Taegu and Kyungbuk (TK) area are keeping up with the international vogue. The research has done, analyzing Pret-a-Porter in Paris and the three events held in the TK area in 1997 -The Taegu Collection, Kyungbuk Fashion Festival and Textile & Apparel Fair and using reference pictures and documentary records. In order to investigate the trends the research is divided by four groups which are the silhouette, the detail, the color and the Fashion image and has done with the help of three postgraduate students. The results are as follows. 1. The Silhouette The slim-line has the greatest importance in the silhouette analysis of the recent collections. Like Elongated and Fit & Flare, tight-fitting and female-line were also appeared quite a lot. Compared with foreign collection, Korean collections put the bigger importance on the slim-line. 2. The Detail The printings, using paintings and plant-logos had the large portion of the accessories in both foreign and Korean collections. Draw string and wrap style were also presented a lot. Especially, at the Korean collections, layerd, corsage, and craft accent were emphasized, too. As for the necklines the similarity was found over the four events considering. Camisole neckline and halter neck were presented the most, and bared top, Vneckline, boat and low-neck which can highlight the feminity were often appeared as well. Considering collars, tailored and peaked collars which are frequently used for the jackets, were usually shown at the collections. Like convertable, shirts, wing and Italian collar, the collars that can be applied for the sports wears were presented a lot. Virtually no variation of design was found in the sleeve analysis. While set-in-sleeve and sleeveless were found commonly, not so many ornaments were added to the sleeves. The ankle and knee length for the pants and skirts were common. Furthermore, including the micro-mini, showing extremely feminine style the mini-style had the 20% portion of the skirt-length. Unbalanced lengths, using bias-cut were presented quite a lot on the runways. Deep slit skirts, wide pants and irregular hem skirts were in vogue. On the runways of Paris, more than 21% of the design was the burmuda pants. 3. The Color Red and Blue were in vogue in the four collections considering. Sometimes, yellowish was combined in Korean collections. Black and pale tone were appeared to be in fashion also with light grayish, moderate and deep tone. 4. The Fashion image As for the fashion image, feminine-decorative trend amounted to the large percentage in korean collections. At the foreign collection feminine-decorative trend and feminine trend were predominant, then mannish trend and simple trend were apeared equally. The research shows that TK area and foreign collections are fairy similar, which means that the designers in TK area have been making their efforts to satisfy the clients who have the international minds. However, compared with foreign collections, TK collections were apprered to be strongly inclined to only a few trends. Consequently the season trends are not as diverse as the foreign trends, which cannot satisfy the fashion taste of the clients in TK area. The local designers should know the tendency and the taste of the clients and make the more efforts to read local clients' mind.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.