• Journal of the Korean earth science society
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• v.38 no.7
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• pp.511-521
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• 2017

This study analyzes Two-dimensional video disdrometer (2DVD) data while summer 2011-2012 in Daegu region and compares with Marshall and Palmer (MP) distribution to find out statistical characteristics and characteristics variability about drop size distribution (DSD) of Daegu region. As the characterize DSD of Daegu region, this study uses single moment parameters such as rainfall intensity (R), reflectivity factor (Z) and double moment parameters such as generalized characteristics number concentration ($N{_0}^{\prime}$) and generalized characteristics diameter ($D{_m}^{\prime}$). Also, this study makes an assumption that DSD function can be expressed as general gamma distribution. The results of analysis show that DSD of Daegu region has ${\log}_{10}N{_0}^{\prime}=2.37$, $D{_m}^{\prime}=1.04mm$, and c =2.37, ${\mu}=0.39$ on average. When the assumption of MP distribution is used, these figures then end up with the different characteristics; ${\log}_{10}N{_0}^{\prime}=2.27$, $D{_m}^{\prime}=0.9mm$, c =1, ${\mu}=1$ on average. The differences indicate liquid water content (LWC) of Daegu distribution is generally larger than MP distribution at equal Z. Second, DSD shape of Daegu distribution is concave upward. Other important facts are the characteristics of Daegu distribution change when Z changes. DSD shape of Daegu region changes concave downward (c =2.05~2.55, ${\mu}=0.33{\sim}0.77$) to cubic function-like shape (c =3.0, ${\mu}=-0.13{\sim}-0.33$) at Z > 45 dBZ. 35 dBZ ${\leq}$ Z > 45 dBZ group of Daegu distribution has characteristics similar to maritime cluster of diverse climate DSD study. However, Z > 45 dBZ group of Daegu distribution has a difference from the cluster.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of Global Scholars of Marketing Science
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• v.19 no.2
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• pp.53-67
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• 2009

For the last decades, service quality has been studied as one of the most important tools for a service company to compete with the other companies. Based on these past researches, it has been agreed that the service quality is a basic and powerful tool to create the competitive advantage. Due to similar reason, many service marketing practitioners have been also focused on the service quality to retain the existing consumers and collect the new consumers. However, service quality is subjectively perceived by individual consumers. Consumer evaluation of service quality can be different from each other. Especially consumers with one life-style may evaluate the service quality differently from the consumers with the other life-styles. Therefore we need to know whether there are differences in service quality perception on the categories of life-style. Life-style refers to a distinctive mode of living in its aggregate and broadest sense. It embodies the patterns that were developed and emerged from the dynamics of living in a society. Since the concept of life-style and its relationship to marketing was introduced in 1963 by William Lazer, methods of measuring the life-style and their application have been developed. Life-style has been usually used to segment the marketplace because it offers marketers a unique and important view of the market. When Life-style is combined with clustering methods, life-style segmentation can generate identifiable whole persons rather than isolated fragment. Life-style segmentation begins with people instead of products and classifies them into different life-style types, each characterized by a unique style of living based on a wide range of activities, interests, and opinions(Plummer, 1974). In this study we applies the life-style segmentation based on the AIO(Activities, Interests, and Opinions) to the consumers of the large discount stores. In Korea, the large discount store market has entered into maturity stage so that the market differentiation strategy is becoming a more critical issue to the marketing practitioners. One of the most important tools to differentiate from the competitors in large discount store market is continuously to provide service of better quality than competitors. This study tries to find answers about the following questions: 1) How can we categorize the consumer life-styles in the large discount store? 2) What are the characteristics of the categorized groups? 3) Are there any differences in service quality perception among the consumers with different life-styles 4) Are there any differences in consumer behavior among them in the large discount store? For the purpose, we collected survey data from consumers and analyzed the data with the SPSS package where we had $X^2$-test, factor analysis, ANOVA, MANOVA, and cluster analysis. The survey was made during one month in the April of 2008. Among the collected 306 copies of questionnaires, 281 copies were chosen as the effective samples for empirical analysis except 25 copies with wrong responses. To identify the life-style patterns, we used the measures employed by Kim and Kwon(1999), where 44 items on a seven-point scale were used to measure factors of the life-style patterns. The Principal Component Method was used for factor extraction, and the VARIMAX orthogonal factor rotation was employed. The 7 items showing low factor loading were eliminated. The results of the factor analysis suggested that nine factors of the life-style patterns were identified as follows: 1) the equality-of-sexes and pursuit-of-independence tendency 2) self-management tendency 3) sociable tendency 4) self-display tendency 5) degree of a dilettante life 6) pursuit-of-information tendency 7) bargain hunter tendency 8) TV preference tendency 9) pursuit-of-leisure tendency. Next, after the K-means cluster analysis was performed with nine factors of the life-style patterns, the life-styles of the respondents were classified into four groups which are named as the 'progressive practicality-oriented group', 'positive success-oriented group', 'sociable ostentation-oriented group', 'stable conservation-oriented group'. The analysis results for usage behavior between the market segments showed statistically significant differences in the frequency of usage, duration time in the store, consumer satisfaction, and loyalty. Also, we tried to investigate whether the large discount store consumers differently perceive the quality of service based upon the types of life-style. To measure the service quality of large discount store, we adapted several measurement models measuring the service quality such as SERVPERF, BCP, R-SERVPERF, R-BCP. MANOVA and One-Way ANOVA were performed to confirm the difference in service quality perception based on the market segments. The results have also shown significant differences between life-style types in service quality perception. These findings show that the large discount store marketers should consider consumer life-style as one of the most important market segments for marketing and understand the difference in service quality perception between life-style types. Our findings give important implications to marketers of large discount stores as well as life-style researchers. First, this study showed there were significant differences in consumer's service quality perception and usage behavior between the types of life-style. It provides evidence that the life-style approach can be a important basis in segmenting the large discount store market and will make consumers perceive the service quality high. Second, most previous researches on service quality have been in aggregate level. However, our results imply that the future research on service quality have to focus on segment level.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.183-193
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• 2020

This study was conducted to evaluate the effect of environment factors on the growth of autumn season cultivation of Kimchi cabbage using the big data in terms of public open data(weather, soil information, and growth of crop, etc.). The growth data and the environment data such as temperature, daylength, and rainfall from 2010 to 2019 were collected. As a result of composing the correlation matrix, the height and leaf number showed high correlation in growing degree days(GDDs) and daylength, and the yield showed negative correlation in growing degree days and the concentration of clay. GDDs and daylength explained about 89% and 84% of variation in height, respectively. These two environmental factors also explained about 85% and 79% of variation in leaf numbers, respectively. In contrast, the coefficient of determination was low for yield when GDDs and concentration of clay was used. The outcome of regional statistical analysis indicated that relationship between yield and sum of sand and silt were high in Haenam and Jindo areas. Hierarchical cluster analysis, which was performed to verify the association of yield, GDDs, and concentration of clay, showed that Haenam and Jindo were clustered together. Although GDDs and yield vary by year and region, and there are regions with similar concentration of clays, observation data are grouped as the result. These suggests that GDDs and soil texture are expected to be related to yield. The cluster analysis results can be used for further data analysis and agricultural policy establishment.

• Restorative Dentistry and Endodontics
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• v.32 no.6
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• pp.498-513
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• 2007

Since it was reported that incipient enamel caries can be recovered, previous studies have quantitatively evaluated that enamel artificial caries have been, remineralized with fluoride showing simultaneously the increase of width of surface layer and the decrease of width of the body of legion. There is, however, little report which showed that remineralization could occur without fluoride. In addition, the observations on the change of hydroxyapatite crystals also have been scarcely seen. In this study, enamel caries in intact premolars or molars was induced by using lactic acidulated buffering solutions over 2 days. Then decalcified specimens were remineralized by seven groups of solutions using different degree of saturation(0.212, 0.239, 0.301, 0.355) and different pH(5.0, 5.5, 6.0) over 10 days. A qualitative comparison to changes of hydroxyapatite crystals after fracturing teeth was made under SEM(scanning electron microscopy) and AFM(atomic force microscopy). The results were as follows: 1. The size of hydroxyapatite crystals in demineralized area was smaller than the normal ones. While the space among crystals was expanded, it was observed that crystals are arranged irregularly. 2. In remineralized enamel area, the enlarged crystals with various shape were observed when the crystals were fused and new small crystals in intercrystalline spaces were deposited. 3. Group 3 and 4 with higher degree of saturation at same pH showed the formation of large clusters by aggregation of small crystals from the surface layer to the lesion body than group 1 and 2 with relatively low degree of saturation at same pH did. Especially group 4 showed complete remineralization to the body of lesions. Group 5 and 6 with lower pH at similar degree of saturation showed remineralization to the body of lesions while group 7 didn't show it. Unlike in Group 3 and 4, Group 5 and 6 showed that each particle was densely distributed with clear appearance rather than crystals form clusters together.

• 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 Wetlands Research
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• v.17 no.2
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• pp.124-129
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• 2015

In order to get as ecological basic data for river restoration, vegetation investigation was conducted in natural river and analysed it synecological methods, such as ordination cluster. 29 plant communities units were identified and the major dominant plant communites were Quercus mongolica community, Pinus densiflora community, Populus davidiana community, Q. variabilis community and Prunus sargentii community. River vegetations were classified into ravine and gorge forest type and riverine softwood forest type. Ravine and gorge forest was dominanted by hardwood which located in steep slope and in high elevation, and riverine softwood forest by softwood, salix spp. Naturality was an important criterion for the selection of rivers, so many of the selected rivers are located in the upper stream and mid stream rather than the lower stream, where more human intervention is involved. Plant communities were consisted of hardwood forest(44 plots, 92%) and softwood forest(4 plot, 8%), respectively. PCA with total layer data showed 5 groups of communities: Q. mongolica community group, Prunus sargentii community group, Pinus densiflora community group, Prunus sargentii community - Pinus densiflora community group and the rest communities group. PCA with tree layer showed 3 groups: Q. mongolica community group, Prunus sargentii community group, and the rest community group. Cluster analysis also a showed a similar communities group to PCA ordination, but Magnolia sieboldii community and Prunus sargentii community were distinguished from the PCA result. From the result, it can be concluded that the plant communities of riparian be divided into hardwood and softwood forest by statistical techniques. It was appropriate to plant species such as Quercus mongolica, Pinus densiflora, Populus davidiana, Quercus variabilis and Prunus sargentii, at levee zone and high water level. And Sliax spp. were appropriate for planted plants at waterfront and low water level. The herb species to be planted on the floodplain were recommanded in the species composition co-occurred with the woody species.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.525-538
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• 2019

The purpose of this study was to analyze the habitat status and species diversity of benthic diatoms and estimate the applicability of TDI (Trophic Diatom Index) to obtain the basic data for the identification and management of created wetlands in the Nakdong River. We observed a total of 38 families and 173 species of benthic diatom during the survey period, and spring and autumn showed a similar number of species of 156 and 154, respectively. The result of the SOM (Self-Organizing Map) analysis showed that the distribution of benthic diatom was sensitive to environmental factors such as nutrient concentration and rainfall in each wetland. The cluster 1 was characterized by the survey sites of autumn mostly and consisted of points of high TDI, although the nutrients such as total phosphorus and total nitrogen were low, and the species number and abundance of diatoms were low. Conversely, cluster 4 was characterized by the survey sites of spring mostly and consisted of points of low TDI, even though total nitrogen was high. Considering that most of the created wetlands had the reduced inflow and outflow, the increased flow rate in the summer lowers nutrient values in autumn, and the species number and abundance of benthic diatom decreases due to the increase of turbidity, which reduces the light penetrations to the substrates. On the contrary, the TDI value is low in spring because the low water level causes insufficient substrate surface to the benthic diatoms, and it is too early for the establishment and development of saprophilous species. Although various studies have used TDI as an indicator for evaluating the habitat environment and water quality, it is not a good evaluation indicator in this study since the nutrient concentration in the wetlands mostly high as they have a low flow rate and are close to the stagnant area. Nevertheless, additional periodic surveys that comprehensively reflect the fact that the summer rainfall and inflow/outflow regulating function might affect the species diversity and distribution of benthic diatoms are necessary.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.