• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.215-224
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• 2009

A review was given of the role of traditional morphometrics in plant morphological studies using 54 published studies in three major journals and others in Korea, such as Journal of Korean Forestry Society, Korean Journal of Plant Taxonomy, Korean Journal of Breeding, Korean Journal of Apiculture, Journal of Life Science, and Korean Journal of Plant Resources from 1997 to 2008. The two most commonly used techniques of data analysis, cluster analysis (CA) and principal components analysis (PCA) with other statistical tests were discussed. The common problem of PCA is the underlying assumptions of methods, like random sampling and multivariate normal distribution of data. The procedure was intended mainly for continuous data and was not efficient for data which were not well summarized by variances or covariances. Likewise CA was most appropriate for categorical rather than continuous data. Also, the CA produced clusters whether or not natural groupings existed, and the results depended on both the similarity measure chosen and the algorithm used for clustering. An additional problems of the PCA and the CA arised with both qualitative and quantitative data with a limited number of variables and/or too few numbers of samples. Some of these problems may be avoided if a certain number of variables (more than 20 at least) and sufficient samples (40-50 at least) are considered for morphometric analyses, but we do not think that the methods are all mighty tools for data analysts. Instead, we do believe that reasonable applications combined with focus on objectives and limitations of each procedure would be a step forward.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.635-646
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• 2018

This study sought to identify the characteristics of seasonal concentration differences of particulate matter influenced by land cover types associated with particulate matter emission and reductions, namely forest and urbanized regions. PM10 and PM2.5 was measured with quantitative concentration in 2016 on 23 urban air monitoring stations in Seoul, classified the stations into 3 groups based on the ratio of urbanized and forest land covers within a range of 3km around station, and analysed the differences in particulate matter concentration by season. The center values for the urbanized and forest land covers by group were 53.4% and 34.6% in Group A, 61.8% and 16.5% in Group B, and 76.3% and 6.7% in Group C. The group-specific concentration of PM10 and PM2.5 by season indicated that the concentration of Group A, with high ratio of forests, was the lowest in all seasons, and the concentration of Group C, with high ratio of urbanized regions, had the highest concentration from spring to autumn. These inter-group differences were statistically significant. The concentration of Group C was lower than Group B in the winter; however, the differences between Groups B to C in the winter were not statistically significant. Group A concentration compared to the high-concentration groups by season was lower by 8.5%, 11.2%, 8.0%, 6.8% for PM10 in the order of spring, summer, autumn and winter, and 3.5%, 10.0%, 4.1% and 3.3% for PM2.5. The inter-group concentration differences for both PM10 and PM2.5 were the highest in the summer and grew smaller in the winter, this was thought to be because the forests' ability to reduce particulate matter emissions was the most pronounced during the summer and the least pronounced during the winter. The influence of urbanized areas on particulate matter concentration was lower compared to the influence of forests. This study provided evidence that the particulate matter concentration was lower for regions with higher ratios of forests, and subsequent studies are required to identify the role of green space to manage particulate matter concentration in cities.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Korean Journal of Environment and Ecology
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• v.22 no.5
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• pp.521-529
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• 2008

This research conducted a survey on waterbirds visiting this area four times by season from February to October in 2007 to look into the habitat use of waterbirds, to do a value and grade testing of the tidal flat by dividing the foreshore on Muan Bay located in Jeollanam-do into four areas (Dongam, Guro, Bokryong and Wangsan tidal flats). The survey results revealed that there existed a total of 15,755 individuals of 54 species including 2 species of grebes, 7 species of herons, 7 species of dabbling ducks, 6 species of diving ducks, 20 species of waders, 3 species of gulls and 9 other species and this survey also observed 9,291 individuals of the wading birds as a dominant group on Muan Bay. In these classified groups, the gulls and waders were observed to mostly use Dongam tidal flat as their habitat, while the group using Guro tidal flat as their habitat was mostly grebes, dabbling and diving ducks. As a result of UPGMA clustering analysis in consideration of the species and number of individuals, there appear the close similarities between Dongam and Bokryong tidal flats and so do Guro and Wangsan tidal flats. Taking a look at the grading of tidal flats by setting up ecological indexes, such as diversity index, abundance index, and dominance index, etc. legally reserved species and maximum number of individuals as a standard, the rank for the value and importance degree of Bokryong tidal flat appeared higher than that of the other three tidal flats. Like this, the gradation of tidal flats according to waterbirds are judged to able to suggest objective data on the issue of proper judgment and designation of valuable tidal flat areas and its subsequent effective preservation and management.

• Korean Journal of Environmental Biology
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• v.31 no.1
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• pp.19-36
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• 2013

Physiochemical characters of sea waters during summer flood- and winter dry-seasons and their spatial variations were investigated along the coastal area off the eastern South Sea, Korea. Using the hierarchical clustering method, in this study, we present comprehensive analyses of coastal waters masses and their seasonal variations. The results revealed that the coastal water of the study area was classified into six water masses (A to F). During summer season, the surface water was mainly occupied by the coastal pseudo-estuarine water (water mass B) with low salinity and high nutrients and the river-dominated coastal water (water mass C) with low nutrients, respectively. The bottom water was dominated by cold water (water mass D) with very low temperature, high salinity and high nutrients, compared to masses of surface water. Notably, the water mass B, with high concentrations of nutrients (silicate and nitrogen) and low salinity, which is strongly controlled by the water quality of river freshwater, seems to play an important role in controlling the water quality and further regulating physical processes on ecosystem in the eastern coastal area of South Sea. The water mass D (bottom cold water) coupled with a strong thermocline, which exists in near-bottom layer along the western margin of Korea Strait, has a low temperature, pH and DO, but abundant nutrients. This water mass disappears in winter owing to strong vertical mixing, and subsequently may act as a pool for nutrients during winter dry-season. On the other hand, vertically well-mixed water column during the winter season was typically occupied by the Tsushima (water mass E) and the coastal water (water mass F) with a development of coastal front formed in a transition zone between them. These winter water masses were characterized by low nutrient concentration and balance in N/P ratio, compared with summer season with high nutrient concentrations and strong N-limitation. Accordingly, the analysis of water masses will help one to better chemical and biological processes in coastal area. In most of the study area, characteristically, the growth of phytoplankton community is limited by nitrogen, which is clearly different with coastal environment of West Sea of Korea, with a relative lack of phosphorus. It showed the western and the southern coasts in Korea are substantially different from each other in environmental and ecological characteristics.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Korean Journal of Weed Science
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• v.9 no.1
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• pp.1-15
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• 1989

The seventeen barnyardgrass [Echinochloa crus-galli (L.) Beauv.] accessions, which were collected national-widely in 1986 and selected two times through 1987, were experimented at 1988. To identify the ecological properties of the collected accessions of native barnyardgrass species as a weed, the experiment was conducted with Wagner pots in size of I/500a and under PE film house. 1. Accessions were classified into 5 specific groups in plant type properties by use of data from plant height, number of maximum tillers, erectness, culm length and panicle type, among others. 2. As for species identification, they were clustered into 3 similar groups and 2 individual species by use of data from color, first-glumer type, and erectness. 3. Four groups were identified for elongational properties by plant height of 22 days old seedling, length of culm, panical, leaf length and width, and inter-node and spikelet, among others. 4. Properties on quanititative growth were classified into 4 groups and 1 individual accession corresponding to differential plant height of 22 days old seedling, length of culm, panical, inter-node, leaf-sheath, spikelet, first-glumes length, grain, number of tillers, spike, and grain weight. 5. Due to different daily increasing rate in seedling height, dry weight, number of tillers and ratio in dry weight to plant height, the growth rate properties were clustered into 4 groups and one individual accession. 6. Properties on seedling growth were classified into 4 groups by use of differential date in length and width of first-leaf, plant height, number of tillers, and dry weight of young and medium aged seedling. 7. Responding to heading date, the accessions were classified into 3 groups : temperative sensitive, medium, and short-day length sensitive types, respectively. 8. By integrating of all quanititative and attributable characters, the seventeen accessions were clustered into 4 groups and 2 individual accessions.

• 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.

• Cartoon and Animation Studies
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• s.29
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• pp.129-149
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• 2012

The purpose of this study was to investigate the clustering and Big Five Personality domains in discriminating groups by level of school-related adjustment, as experienced by Social Sims game users. Social Games are based on web that has simple rules to play in fictional time and space background. This paper is to analyze the relationships between social networks and user behaviors through the social games . In general, characteristics of social games are simple, fun and easy to play, popular to the public, and based on personal connections in reality. These features of social games make themselves different from video games with one player or MMORPG with many unspecific players. Especially Social Game show a noticeable characteristic related to social learning. The object of this research is to provide a possibility that game that its social perspective can be strengthened in social game environment and analyze whether it actually influences on problem solving of real life problems, therefore suggesting its direction of alternative play means and positive simulation game. Data was collected by administering 4 questionnaires (the short version of BFI, Satisfaction with life, Career Decision-.Making Self-.Efficacy, Depression) to the participants who were 20 people in Seoul and Daejeon. For the purposes of the data analysis, both Stepwise Discriminant analysis and Cluster analysis was employed. Neuroticism, Openness, Conscientiousness within the Big Five Personality domains were seen to be significant variables when it came to discriminating the groups. These findings indicated that the short version of the BFI may be useful in understanding for game user behaviors When it comes to cultural research, digital game takes up a significant role. We can see that from the fact that game, which has only been considered as a leisure activity or commercial means, is being actively research for its methodological, social role and function. Among digital game's several meanings, one of the most noticeable ones is the research on its critical, social participating function. According to Jame Paul gee, the most important merit of game is 'projected identity'. This means that experiences from various perspectives is possible.[1] In his recent autobiography , he described gamer as an active problem solver. In addition, Gonzalo Francesca also suggested an alternative game developing method through 'game that conveys critical messages by strengthening critical reasons'. [2] They all provided evidences showing game can be a strong academic tool. Not only does a genre called social game exist in the field of media and Social Network Game, but there are also some efforts to positively evaluate its value Through these kinds of researches, we can study how game can give positive influence along with the change in its general perception, which would eventually lead to spreading healthy game culture and enabling fresh life experience. This would better bring out the educative side of the game and become a social communicative tool. The object of this game is to provide a possibility that the social aspect can be strengthened within the game environment and analyze whether it actually influences the problem solving of real life problems. Therefore suggesting it's direction of alternative play means positive game simulation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.3
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• pp.203-213
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• 2017

This study investigated the changes in yields, antioxidant compounds, and antioxidant activities among 11 adzuki bean cultivars cultivated in a paddy field with somewhat poorly drained soil. The adzuki bean cultivars were cultivated in the paddy field from 2015 to 2016 in Milyang, Gyeongsangnam, Korea. Average soil moisture content was 16.5% in 2015 and 31.0% in 2016 at the experimental site during adzuki bean cultivation. As the soil moisture content increased, most of the adzuki bean cultivars showed deceases in stem height, first setting pod node, number of pods, 100 seed weight, and yield. Chungju-pat produced the greatest yields among the 11 cultivars in 2015 and 2016, whereas Hongeon had the smallest yields. Adzuki bean yields after paddy field cultivation was lower for all cultivars than for adzuki bean yields from the same cultivars after upland field cultivation. Chungju-pat and Chilbo-pat showed the smallest decreases in yields after paddy field cultivation, whereas Hongeon and Kumsil showed the greatest yield reductions. There were no significant differences in proximate composition. Some mineral components ($P_2O_5$, Ca, and Mg) were statistically different across cultivars. Chungju-pat had the highest Ca and Mg contents, but antioxidant components (polyphenol and flavonoids) and antioxidant activities (ABTS and DPPH) were highest in Saegil and lowest in Jungbu-pat. PCA and clustering analyses, based on the growth, yield, and antioxidant component measurements, performed to identify which variables contributed the most to separating adzuki bean cultivars or to grouping cultivars with similar characteristics. These analyses showed that the antioxidant components and antioxidant activities had the most influence on grouping cultivars together. Among the 11 cultivars, Saegil was statistically different from the other cultivars, but the other 10 cultivars were not significantly different under paddy field cultivation. Soil moisture content affected adzuki bean yield and antioxidant component contents. An increase in soil moisture led to a decrease in yield, but an increase in antioxidant components. These results provide information that will improve the selection of an appropriate adzuki bean cultivar for use in paddy fields.