• Korean Journal of Ecology and Environment
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• v.56 no.1
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• pp.45-56
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• 2023

In stream ecosystem assessment, RIVPACS, which makes a simple but clear evaluation based on macroinvertebrate community, is widely used. In this study, a preliminary study was conducted to develop a RIVPACS-type model suitable for Korean streams nationwide. Reference streams were classified into two types(upstream and downstream), and a prediction model for macroinvertebrates was developed based on each family. A model for upstream was divided into 7 (train): 3 (test), and that for downstream was made using a leave-one-out method. Variables for the models were selected by non-metric multidimensional scaling, and seven variables were chosen, including elevation, slope, annual average temperature, stream width, forest ratio in land use, riffle ratio in hydrological characteristics, and boulder ratio in substrate composition. Stream order classified 3,224 sites as upstream and downstream, and community compositions of sites were predicted. The prediction was conducted for 30 macroinvertebrate families. Expected (E) and observed fauna (O) were compared using an ASPT biotic index, which is computed by dividing the BMWPK score into the number of families in a community. EQR values (i.e. O/E) for ASPT were used to assess stream condition. Lastly, we compared EQR to BMI, an index that is commonly used in the assessment. In the results, the average observed ASPT was 4.82 (±2.04 SD) and the expected one was 6.30 (±0.79 SD), and the expected ASPT was higher than the observed one. In the comparison between EQR and BMI index, EQR generally showed a higher value than the BMI index.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.1
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• pp.35-46
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• 2023

This study investigated and analyzed ancient records on the type, planting background, and construction process of Gwanbang forest(關防林) planned for military defense during the Joseon Dynasty to find out the purpose, location, and planting species of Gwanbang forest. The research results were as follows. During the Joseon Dynasty, Gwanbang forests were created around various government facilities(關防施設), such as Eupseong(邑城), major government offices, camps, and fortifications, for the purpose of defending against enemies. Gwanbang forest includes Yeongaeglim(嶺阨林), which was created on the crest of a strategically important hill, and Military Forest created for military purposes. Most of the spirit forest was designated as Geumsan(禁山) and protected and managed, and the Gwanbang forest was created for various purposes such as shielding, flood damage and river bank erosion prevention as well as external defense. In addition, in order to continuously and efficiently produce wood, which is a material for ships, buildings, and agricultural tools, in most cases, large areas were created as mixed forests. As for the species constituting the Gwanbang forest, there are records of tangerine tree, which is effective for defense because it has thorns, and deciduous broad-leaved trees such as zelkova, elm, willow, david hemiptelea, and oak appear. In the case of Ganghwa island, which served as the defense of the capital and the royal family during the Joseon Dynasty, several records have confirmed that a forest densely planted with trifoliate orange was created for the purpose of Gwanbang forest to reinforce the defense of the outer fortress. Based on historical research in the literature, assuming that the natural monument 'Gapgotri tangerine tree in Ganghwa Island' was planted in the 30th year of King Sukjong(1704), the first record of planting trifoliate orange in Ganghwa Island, the maximum age is estimated to be more than 319 years.

• 한국노년학
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• v.31 no.1
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• pp.129-141
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• 2011

This study aims to find out effects of a tailored occupational activity program(TOAP) on the activities of daily living(ADL), cognitive function, depressive mood, and caregiver burden, who live in the community. Method : From October 2009 to May 2010, the TOAP was applied to 15 dementia patients and 15 of their caregivers, who was visitors of the Y-city Center for Managing Dementia in Gyunggi-do. The TOAP was designed for habituating patients and caregivers to the techniques acquired through goal activities and task and making it capable of being routinized regularly. The TOAP was applied to dementia patients and their cvaregivers twice a week for 7 weeks(one-time home visit, one-time phone inspection), a total of 14 times. Results: Significant differences among pre-test and post-test were found in the AMPS motor skills(1.10±1.14 and 1.34±1.2 respectively) scores, AMPS process skills(0.32±0.55 and 0.77±0.66 respectively) scores, ACL(3.86±0.65 and 4.17±0.64 respectively) scores, MMSE-KC(17.33±4.6 and 19.33±4.97 respectively) scores, GDS(11.73±6.87 and 8.53±7.09 respectively) scores, and caregiver burden(31.80±20.06 and 26.13±18.07 respectively) scores(p<0.05). A significant effect was confirmed from the TOAP which ADL, cognitive function, reduced patient's depression and caregiver burden(p<0.05). Conclusion: From the above results that a TOAP has an effect on the improvement of the ability to ADL, cognitive function and reduced depression and caregiver burden of dementia patients living in community. The present author hopes that, in the future, more diverse community based on tailored occupational activity programs will be developed to improve the functions of dementia patients living in community.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.869-881
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• 2023

Purpose: This study aims to come up with improvement measures to improve the effectiveness of child safety measures. Method: The current status of child safety accidents was investigated and implications were deduced by analyzing major child safety measures by government department in the past. In addition, a perception survey was conducted on 1,000 people including children, guardians, and children's facility workers who are subject to child safety policies. Result: Regarding the safety of children's living space(environment), 35.3% of guardians answered that more than 1/3 of them were not safe. Both guardians(95.3%) and children's facility workers(89%) answered that there was the highest risk of 'traffic accidents', and the second risk factor was parents(carelessness of workers at children's facilities) and children's facility workers(careless of guardians at home). Looking at the risks by place, "road and sidewalk" was the most dangerous place and for child safety, guardians(64.3%) and workers (78.3%) both said that the role of "parent" is the most important. For improvements to prevent child safety accidents, the response rate of "strengthening safety management of road traffic facilities" is the most necessary with 75.8% for guardians and 65% for child use facilities. Conclusion: The reinforcement measures to strengthen the effectiveness of child safety measures are as follows. First, in order to ensure the continuity of child safety measures, it should be operated effectively so that those subject to the establishment of the Comprehensive Plan for Child Safety, which took effect in August 2022, can feel it. Second, in order to improve the sensitivity of children's policy targets, promotion measures that take into account the characteristics of each child safety field should be continuously strengthened. Third, it is necessary to expand safety infrastructure for each field to secure child safety. Fourth, it is necessary to strengthen safety education that can ensure safety for children themselves and to come up with detailed measures to make safety education for parents(guardians) mandatory.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.8
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• pp.995-1009
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• 2007

The purpose of this study was to investigate the dietary habits, behaviors, and food consumption frequency according to gender and obesity level among middle school students in the Jeonju area. Subjects for the questionnaire were 450 middle school students (male 255, female 195) and were classified as either obese students (n=150 or non-obese students (n=299) by the obesity assessment method. The results were analyzed with SAS program (Version 9.1), and were as follows. 1. Dietary behaviors were significantly different in the rate of 'Skipping breakfast (p<0.05)', 'Duration of meal time (min) (p<0.05)' and 'Unbalanced diet (p<0.01)' between males and females. Dietary habits and behaviors also differed significantly for the rate of ‘Taste preferences (p<0.05)’, and 'Unbalanced diet (p<0.01)' between obese students and non-obese students. 2. Food consumption frequency per week was as follows. First, males were significantly higher than females in 'Instant noodle (p<0.05)', 'Milk (p<0.01)', and 'Soda pop (p<0.01)'; on the other hand females were significantly higher than males in 'Chocolate, Candy (p<0.01)'. Second, non-obese students were significantly higher than obese students in 'Instant noodle (p<0.05)', 'Hamburger, Pizza (p<0.05)', and 'Chocolate, Candy (p<001)'. Especially, non-obese male students were higher in 'Instant noodle (p<0.05)' and 'Hamburger, Pizza (p<0.05)'; non-obese female students were higher in 'Chocolate, Candy (p<0.01)'. In conclusion, an action program is needed to encourage healthful dietary behaviors, increased physical activity, and forming good lifelong habits.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.