• Journal of Korea Trade
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• v.27 no.1
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• pp.119-138
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• 2023

Purpose - This research is to empirically explore the differences in apparel consumption among male and female teenagers and college students in Korea and China. By conducting a survey to understand customers' needs and behaviors, fashion businesses will be able to improve their customer satisfaction and avoid redundancy, inventory, and the waste of resources, effort and money. Design/methodology - The research design considers the consumption patterns of male and female high school and college students in Korea and China. To analyze the data, the study employs decision trees, a type of machine learning algorithm. A decision tree model was developed to examine the relationship between the explanatory and response variables, which can be either quantitative or qualitative in nature. Findings - The main findings of this study indicate that there are differences in shopping behavior among different customer segments. The results show that men have a simpler shopping behavior compared to women. Additionally, cultural factors and the difference in fashion needs between students and non-students have a significant impact on the shopping choices of Chinese and Korean individuals. Originality/value - Existing studies often assume that the shopping behavior of high school and university students is similar and that there are no significant differences in clothing purchases between men and women across countries. The results provide valuable insights into the unique shopping behavior of different customer segments, and can inform fashion businesses in their efforts to meet the needs of their customers.

• Korean Journal of Environment and Ecology
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• v.21 no.6
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• pp.554-558
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• 2007

Current biomass stock of forest has been calculated by using biomass expansion factors (BEFs) that convert timber volumes to dry weight and stem density. The objective of this study was to estimate stem density values and to develop BEFs that are dependent on tree age classes for Quercus acuta stands in Jeonnam Wando-gun. Sample trees on the three different age classes were harvested to obtain each components biomass with stem analysis. Stem density values as tree age classes were ranged from 0.557 to 0.636. Aboveground BEFs were ranged from 1.168 to 1.324. BEFs were increased with increasing age classes. There was a significant difference between BEFs and stem density values with tree age classes. These results suggest that the reliability of the national carbon stock inventory could be improved by applying age classes BEFs, which are formulated on the basis of representative for Quercus acuta.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.543-549
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• 2013

This study was conducted to evaluate age-specific and generalized allometric equations and biomass expansion factors (BEFs) for each tree component across three age-sequence stands (35-year-old, 51-year-old, 62-year-old) of black pine (Pinus thunbergii Parl.) in Jinju, located in the western part of Gyeongnam province, Korea. Biomass in each tree component, i.e. foliage, branch, and stem, was quantified by destructive tree harvesting. Allometric regression equations were significant (P<0.05) with diameter at breast height (DBH) or combination of DBH and height ($DBH^2H$) accounting for 55-98% of the variation (as indicated by coefficients of determination, $R^2$) in aboveground biomass except for foliage biomass of the 62-year-old stand. Generalized allometric equations can be used to estimate the biomass of black pine stands because the slopes of age-specific equations over 35-year-old stands were not significantly different by the age-sequence. The stem density and biomass expansion factor (BEFs) were not significantly different (P>0.05) from different stand ages and ranged from 0.45 to $0.51gcm^{-3}$, and from 1.32 to 1.38, respectively. The results indicate that allometric equations, stem density and aboveground BEFs in the matured black pine over 35-year-old are little influenced by different stand ages.

• Journal of Korean Society of Forest Science
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• v.99 no.1
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• pp.62-67
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• 2010

This study was carried out to develop local allometric biomass regression equations and to estimate aboveground biomass of red pine (Pinus densiflora S. et Z.) stands among three regions (Hadong, Hamyang, Sancheong) from the western regions of Gyeongnam province. We selected three natural red pine stands with similar stand ages (about 40-year-old) from each region. The allometric regression equations were significant in all tree components (P<0.05) and the determination of coefficient ($R^2$) ranged 0.87 from 0.99. There was a significant difference (P<0.05) in the biomass of tree components among three regions. The biomass was 173.3 Mg/ha in Hadong, 131.0 Mg/ha in Sancheong, and 66.5 Mg/ha in Hamyang. The proportion of biomass was 70.4-77.1% in stemwood, 10.9-15.2% in branch, 8.9-10.4% in stembark, and 3.1-4.4% in needle. The results indicated that red pine stands in the western Gyeongnam regions showed the significant difference of aboveground biomass which was attributed to site quality and stand density.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.441-445
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• 2005

Researches on estimating national-scaled forest biomass are being carried out to quantify the carbon stock of forests with the Kyoto Protocol. In general, estimates of national-scaled forest biomass are based on forest inventory data which provides estimates of forest area, stem volume, and growth of stem by age classes. Estimates of forest biomass are, however, obtained by converting stem volumes to dry weight with stem density and thereafter to whole tree biomass with biomass expansion factors (ratios of whole tree dry weight to stem dry weight). Pinus densiflora is widely distributed and one of the most economically important timber species in Korea. The species are largely grouped into two ecotypes of Geumgang and Jungbu. Stems of Geumgang type trees are straight and high compared to those of Jungbu type trees. The objective of this study was to determine and compare stem density and biomass expansion factors fore two ecotypes of Pinus densiflora according to stand age. Stem density of both ecotypes of Pinus densora increased and biomass expansion factors of them decreased with increasing tree age. In he same age class, stem density and biomass expansion factor of Geungang type Pinus densiflora were lower than those of Jungbu type Pinus densiflora. There were statistically significant differences in stem density and biomass expansion factors between Geumgang type and Jungbu type Pinus densiflora in 0-20-year-old stands and 40-60-year-old stands. Our results suggested that the reliability of the national forest biomass inventory could be improved by applying the ecotype- and age-dependent stem density and biomass expansion factors.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.450-456
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• 2017

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.929-933
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• 2010

According to the IPCC guideline (2006), uncertainty assessment is very important in terms of the greenhouse gas inventory. Therefore, the purpose of this study is to estimate the basic wood density (BWD) and its uncertainty for Pinus densiflora in Korea. In this study, Pinus densiflora forests were divided into two ecotypes which were Gangwon and Jungbu regions. A total of 33 representative sampling plots was selected to collect sample trees after considering the tree ages and DBH distributions. The BWD showed statistically no difference between age classes based on IPCC's classification. While, it showed statistically difference(pvalue=0.0017) between eco-types. The BWD and uncertainty was 0.396(g/$cm^3$) and 12.9(%) for Pinus densiflora in Gangwon, while it was 0.470(g/$cm^3$) and 3.8(%) for Pinus densiflora in Jungbu. The values of the BWD uncertainty for Pinus densiflora were more precised than the values given by the IPCC guideline.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.267-273
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• 2017

This study was conducted to construct a empirical yield table for Pinus densiflora in real forest. Since existing normal yield tables have been derived by studying and analyzing communities in ideal environment for tree growth, those tables provide more over-estimated values than ones from real forest. Because of this, there are some difficulties to apply the tables to empirical forest except for normal forest. In this study, therefore, we estimated stand growth for real forest on P. densiflora as the representative species of conifers. We used 1,957 sample plot data of P. densiflora in central Korea from National Forest Inventory (NFI) system, and analyzed through estimation, recovery and prediction in order by using Weibull function as a diameter distribution model. Weilbull and Schumacher models were applied for estimating mean DBH and mean basel area and it was found that the site index for P. densiflora in central Korea ranges from 8 to 14 at reference age 30. According to site 12 in the stand yield table, the Mean Annual Increment (MAI) of P. densiflora was $4.42m^3/ha$ at 30 years of age. Compared to existing volume table constructed before, it is showed that MAI of this study were lower. According to the paired t-test that is conducted with the gap of volume values between normal forest and real forest by site index and age, the P-value was less than 0.001 which is recognized to have a statistically significant difference. Based on the results in this study, it is considered to be helpful for practical management and management policy on P. densiflora in central Korea.