• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.67-88
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• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.301-311
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• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.

• Childhood Kidney Diseases
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• v.17 no.2
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• pp.49-56
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• 2013

Purpose: IgA nephropathy (IgAN) is one of the major causes of end-stage renal disease. Mass school urine screening (SUS) has been performed to enable early detection of chronic renal diseases, including IgAN. We wanted to evaluate the patients with IgAN, including those diagnosed through SUS. Methods: Between 1998 and 2010, 64 children were diagnosed with IgAN based on renal biopsy results obtained at the Pediatric Nephrology Department, ${\bigcirc\bigcirc}$ University Hospital. We divided these patients into the SUS group (37 cases), diagnosed through SUS, and the symptomatic (Sx) group (27 cases), diagnosed clinically. The medical records of both groups were analyzed retrospectively. Results: The mean age of the SUS and Sx groups was $10.8{\pm}2.7$ and $9.5{\pm}3.4$ years (P >0.05), respectively. Both groups had a higher proportion of male patients. The time from the notification of an abnormal urinary finding to a hospital visit or renal biopsy was shorter in the Sx group than in the SUS group. Regarding clinical manifestations, there were fewer cases with gross hematuria (P <0.001) and edema (P =0.008) in the SUS group, but there were no differences in terms of the therapeutic regimen and treatment duration. Regarding laboratory parameters, the Sx group had a higher white blood cell count (P =0.007) and lower hemoglobin (P =0.007) and albumin (P =0.000) levels. There were no differences in the renal biopsy findings in both groups, based on the history of gross hematuria or the severity of proteinuria. However, in all 64 patients with IgAN, the light microscopy findings (Hass classification) were related to a history of gross hematuria or the severity of proteinuria. Conclusion: There were no significant clinical and histological differences between the groups, as both had early stage IgAN. Although SUS facilitates the early detection of IgAN, long-term, large-scale prospective controlled studies are needed to assess the benefits of early diagnosis and treatment in chronic renal disease progression.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.76-82
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• 2017

Purpose Whole body bone scan is one of the most frequently performed in nuclear medicine. Basically, both the anterior and posterior views are acquired simultaneously. Occasionally, it is difficult to distinguish the lesion by only the anterior view and the posterior view. In this case, accurate location of the lesion through SPECT / CT or additional static scan images are important. Therefore, in this study, various improvement activities have been carried out in order to enhance the work capacity of technologists. In this study, we investigate the effect of technologist training and standardized work process processes on bone scan error reduction. Materials and Methods Several systems have been introduced in sequence for the application of new processes. The first is the implementation of education and testing with physicians, the second is the classification of patients who are expected to undergo further scanning, introducing a pre-filtration system that allows technologists to check in advance, and finally, The communication system called NMQA is applied. From January, 2014 to December, 2016, we examined the whole body bone scan patients who visited the Department of Nuclear Medicine, Asan Medical Center, Seoul, Korea Results We investigated errors based on the Bone Scan NMQA sent from January 2014 to December 2016. The number of tests in which NMQA was transmitted over the entire bone scan during the survey period was calculated as a percentage. The annual output is 141 cases in 2014, 88 cases in 2015, and 86 cases in 2016. The rate of NMQA has decreased to 0.88% in 2014, 0.53% in 2015 and 0.45% in 2016. Conclusion The incidence of NMQA has decreased since 2014 when the new process was applied. However, we believe that it will be necessary to accumulate data continuously in the future because of insufficient data until statistically confirming its usefulness. This study confirmed the necessity of standardized work and education to improve the quality of Bone Scan image, and it is thought that update is needed for continuous research and interest in the future.

• Korean Journal of Food Science and Technology
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• v.45 no.1
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• pp.1-7
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• 2013

Niphon spinosus, Epinephelus bruneus, and Epinephelus septemfasciatus are involved in the Perciformes Order and Serranidae Family. When E. bruneus and E. septemfasciatus are fully grown, the striped pattern on the body gradually disappears. Therefore, morphological classification of adult fishes is quite difficult to identify the differences to N. spinosus. In this study, we investigate the method to differentiate those using PCR. To design the primers, 16S rRNA region of N. spinosus, E. bruneus, and E. septemfasciatus registered in the GeneBank (www.ncbi.nlm.nih.gov) have been used and for the analysis, Bio Edit ver. 7.0.9.0 was used. As a result, it was design NS-003-F/NS-005-R (136 bp), EB-001-F/EB-002-R (181 bp), and ES-001-F/ES-001-R (123 bp) primers for the differentiation of each 3 different fishes. Therefore, the species-specific primer sets would be a useful tool for scientific and speedy differentiation against the illegal distribution for consumer protection.

• Horticultural Science & Technology
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• v.33 no.1
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• pp.93-105
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• 2015

This study was performed to analyze genetic relationships of the four major cucurbitaceous crops including watermelon, melon, cucumber, and squash/pumpkin. Among 120 EST-SSR primer sets selected from the International Cucurbit Genomics Initiative (ICuGI) database, PCR was successful for 51 (49.17%) primer sets and 49 (40.8%) primer sets showed polymorphisms among eight Cucurbitaceae accessions. A total of 382 allele-specific PCR bands were produced by 49 EST-SSR primers from 24 Cucurbitaceae accessions and used for analysis of pairwise similarity and dendrogram construction. Assessment of the genetic relationships resulted in similarity indexes ranging from 0.01 to 0.85. In the dendrogram, 24 Cucurbitaceae accessions were classified into two major groups (Clade I and II) and 8 subgroups. Clade I comprised two subgroups, Clade I-1 for watermelon accessions [I-1a and I-1b-2: three wild-type watermelons (Citrullus lanatus var. citroides Mats. & Nakai), I-1b-1: six watermelon cultivars (Citrullus lanatus var. vulgaris S chrad.)] a nd C lade I -2 for melon and cucumber accessions [I-2a-1 : 4 melon cultivars(Cucumis melo var. cantalupensis Naudin.), I-2a-2: oriental melon cultivars (Cucumis melo var. conomon Makino.), and I-2b: five cucumber cultivars (Cucumis sativus L.)]. Squash and pumpkin accessions composed Clade II {II-1: two squash/ pumpkin cultivars [Cucurbita moschata (Duch. ex Lam.)/Duch. & Poir. and Cucurbita maxima Duch.] and II-2: two squash/pumpkin cultivars, Cucurbita pepo L./Cucurbita ficifolia Bouche.}. These results were in accordance with previously reported classification of Cucurbitaceae species, indicating that watermelon EST-SSRs show a high level of marker transferability and should be useful for genetic study in other cucurbit crops.