• Journal of Radiation Protection and Research
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• v.26 no.2
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• pp.59-66
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• 2001

It is a hot issue to determine the spatial location and shape of tumor boundary in fractionated stereotactic radiotherapy (FSRT). We could get consecutive transaxial plane images from the phantom (paraffin) and 4 patients with brain tumor using helical computed tomography(HCT). K-means classification algorithm was adjusted to change raw data pixel value in CT images into classified average pixel value. The classified images consists of 5 regions that ate tumor region (TR), normal region (NR), combination region (CR), uncommitted region (UR) and artifact region (AR). The major concern was how to separate the normal region from tumor region in the combination area. Relative average deviation analysis was adjusted to alter average pixel values of 5 regions into 2 regions of normal and tumor region to define maximum point among average deviation pixel values. And then we drawn gross tumor volume (GTV) boundary by connecting maximum points in images using semi-automatic contour method by IDL(Interactive Data Language) program. The error limit of the ROI boundary in homogeneous phantom is estimated within ${\pm}1%$. In case of 4 patients, we could confirm that the tumor lesions described by physician and the lesions described automatically by the K-mean classification algorithm and relative average deviation analyses were similar. These methods can make uncertain boundary between normal and tumor region into clear boundary. Therefore it will be useful in the CT images-based treatment planning especially to use above procedure apply prescribed method when CT images intermittently fail to visualize tumor volume comparing to MRI images.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.2
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• pp.282-290
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• 1997

Hepatocytes of Anguilla japonica have been prepared using a collagenase perfusion technique. The isolated cells attached efficiently to fibronectin-coated culture dishes and subsequently formed monolayers in serum-free medium. These cultures maintained in appropriate medium at least for 10 days with minimal cell loss. The effects of estradiol and pituitary hormones on vitellogenin (Vg) synthesis were examined in primary hepatocyte culture of the immature eels. In fish, as in other oviparous vertebrates, estrogen is a major inducer of Vg synthesis. However, $estradiol-17\beta(E_2)$ alone was insufficient to induce Vg synthesis in cultures of eel hepatocytes. Combination of $E_2$ with growth hormone (GH) and/or prolactin (PRL) markedly stimulated Vg synthesis. Even in cultures exposed to $E_2$ or precultured without hormones for 8 days, $E_2$ alone could not fully induce Vg synthesis. The synthesis of Vg was dramatically increased when hepatocytes were cultured in medium supplemented with $E_{2}+GH+PRL$ for 6 days. At this point, even though GH and/or PRL were eliminated from the medium, Vg synthesis was not influenced by these factors during culture of further 3 days. These results indicate that pituitary hormones, in particular GH and PRL, play important roles in the regulation of Vg synthesis in primary cultures of eel hepatocytes.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.3
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• pp.154-162
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• 2011

Seawater quality was investigated each month at 30 stations near Tongyeong, South Korea, to provide data for the effective use of coastal fisheries and the reduction of economic damage to marine products. Water temperature was lowest in January and highest at the end of August. Neither extremely low water temperature below $4^{\circ}C$ nor fish damage caused by low water temperature was observed. Salinity ranged from 24.04 to 34.39 psu in the surface layer and from 29.92 to 34.39 psu in the bottom layer. The minimum salinity, attributable to rainfall events, was observed in July; salinity increased to high of about 34 psu in November. Low dissolved oxygen (DO), below 4 mg/L, was observed at Wenmun and Buksin Bays during May to October. Concentrations of $NO_2$-N, $NO_3$-N, and $PO_4$-P were low from March to September and high from October to February. Transparency was 6 m on average and was high in Wenmun Bay. Chemical oxygen demand (COD) and chlorophyll a (Chl. a) were high during summer, when the water temperature was high. With cluster analysis based on environment factors related to water quality, the study area could be divided into three main sea areas: Buksin Bay, coastal seawater, and offshore seawater. Buksin Bay was characterized by low salinity, high DO and Chl. a, and high transparency in the surface layer and by low DO and high $NH_4$-N in the bottom layer. Offshore seawater had high salinity and $NO_3$-N and low Chl. a concentration. In summer season that oyster need lots of phytoplankton, $NO_3$-N and Chl. a concentrations at this study area were low compare to Gwangy-ang and Gamak Bays. In winter, a sea squirt swallow much more than other season, the Chl. a concentrations were also low than Gwangyang and Gamak Bays.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.1040-1046
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• 2010

This research is composed of a series of survey of existing plants species by classifying biotope type of agro-ecosystem of Guksoo village area of Yangpyeong County, to collect and analyze basic data of vegetation analysis for indicators development of agro-ecosystem habitat quality. From the observation area, we found total 141 kinds of tracheophytes (53 Family 114 Genus 124 Species 16 Variety 1 Breed) and they are 3.36% of total Korean tracheophytes (4,191 kinds). Among those 141 tracheophytes, there are 23 kinds of naturalized plants (11 Family 20 Genus 20 Species 2 Variety) and they are 8.61% of total Korean naturalized plants (267 kinds). Among those 141 tracheophytes, they include 0.71% of pteridophyte, 0.71% of gymnosperm, 98.58% of angiosperm. So, most of them are angiosperm. When we classify them according to plant life form characteristics, dormant/diapause type plants include 45 species (31.91%) of annual plant (Th), 19 species (13.48%) of Th (w), 17species (12.06%) of hemicryptophyte (H). Regarding propagation type, as for the Radicoid form, there are 99 species (70.21%) of crumb structure plant, 13 species (9.22%) of $R_4$, 12 species (8.51%) of $R_{2.3}$ are the crumb structure does not make any connection on the ground or under ground. As for the Disseminule form of propagation type, there are 62 species (43.97%) of Gravity dispersal type $D_4$), 23 species (16.31%) of Wind dispersal type ($D_1$), 21 species (14.89%) of $D_{1.4}$. According to this survey of plant distribution rate by plant life form characteristics, we may acquire many knowledge about species composition of sociability, cluster's reaction against environmental elements, space usage and possible species competition in community. It may be very useful basic data for habitat preservation to keep and promote biological diversity.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.