• Food Science of Animal Resources
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• v.25 no.4
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• pp.365-372
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• 2005

This study was carried out to investigate the Physiochemical Properties of loin and rump in the native horse meat from Jeju. In the analysis of chemical composition of loin and rump, the result showed $72.2\%\;and\;73.8\%$ in moisture content $20.1\%\;and\;21.2\%$ in crude protein, $2.42\%\;and\;3.08\%$ in crude Int and $0.13\%\;and\;0.14\%$ in crude ash respectively. Glutamic acid was 3,275mg/100g and 3,577mg/100g in loin and rump each and it had highest result in amino acid analysis. K content was 388.0mg/100g which showed highest result in mineral analysis and next contents were P>Na>Mg>Ca. Oleic acid had highest result in fatty acid composition which were $62.64\%\;and\;63.77\%$ in loin and rump respectively. Cholesterol content of loin and rump were 43.25 and 43.57 mg/100g but showed no significant differences to the part. pH of loin and rump were 5.60 and 5.75 which had no significant differences. Loin had Higher result than that of rump with no significant differences in WHC and springiness of texture analysis. Redness of rump was higher than that of loin. In the sensory evaluation, there were significant differences in the color and odor. Loin had higher result than that of rump in the overall palatability but showed no significant differences. With the result of this experiment native horse meat from Jeju could be understood as good meat resources.

• 한국노년학
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• v.38 no.4
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• pp.1057-1081
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• 2018

This study analyzed the case of elderly care cluster in Gwangjin-gu to explore the possibilities of social economy as a provider of community-based social services. Community-based means the approach by which community organizations build a voluntary and collaborative network to enhance collective problem-solving abilities. Therefore, it is very likely that the social economy that emphasizes people, labor, community, and democratic principles can contribute to community-based social services. This study analyzed social economic network by using four characteristics of social economy suggested by OECD community economy and employment program as an analysis framework. The results of this study are as follows: First, it is found that social economy would hardly supply community-based social services through network cooperation because of a large variation in community identity, investment to new product, and labor protection. Second, community users are not the consumers of the social economy and the products of the social economy stay in market products only for the organizations in social economy. In order to create good services that meet the needs of residents, community development approaches are required at the same time. The importance of community space where local residents and social economy meet is derived. Third, public support such as purchasing support has weakened the ecosystem of social economy by making the distinction between public economy and social economy more obscure. On the other hand, public investment in community infrastructure is an indirect aid to social economy to communicate with residents and to promote good supply and consumption. In the end, community-based social services need a platform where the social economy and the people meet. This type of public investment can create the ecosystem of the social economy.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.6
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• pp.1-15
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• 2020

The purpose of this study is to propose a direction for designing and managing a smart park for realizing a smart green city and to present measures in the landscape field to foster related industries. The research process is as follows. First, the concept of a smart park was operationally defined through a literature review, and three principles to be considered in the process of creation and management were established. Second, in terms of the three principles, problems and implications for improvement were derived through an analysis of established cases of smart parks in new and pre-existing cities. Third, a pool of designs and management standards for each spatial component of a smart park was prepared through literature and case studies, and then further refined through brainstorming with experts in related fields. Fourth, measures were suggested to the government, local governments, and the landscape field to promote smart park creation and management. The main findings are as follows. First, the concept of a smart park is defined as "a park that contributes to securing the social, economic, and environmental sustainability of cities and local communities by supporting citizens' safe and pleasant use of parks and improving the management and operational efficiency by utilizing the digital, environment, and material technologies." Second, the three principles of smart parks are to improve the intrinsic value of parks, to improve the innovative functions of parks to solve urban problems, and to make the design, construction, and management process smart. Third, improvement implications were derived through the analysis of cases of smart parks creation in new and pre-existing cities. Fourth, the directions for smart park design and management were suggested in five aspects: green area, hydroponic facility area, road and plaza area, landscape facilities area, and park design method. Fifth, as for policy implications for revitalizing the construction and management of smart parks, the development of smart park policy business models by city growth stage, and park type, the promotion of pilot projects, the promotion of smart park projects in connection with the Korean New Deal policy, and smart park policies led by landscape experts were presented.

• Knowledge Management Research
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• v.22 no.4
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• pp.103-118
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• 2021

Gross Domestic Product(GDP) is affected by the economic power of each industry. Therefore, using statistical data related to the food and drug industry, we tried to determine the proportion of GDP and analyzed the impact of the food, medical & drug industry on the domestic economy through comparison with other industries. The food, medical & drug industry has a wide range of industries among domestic industries and is closely related to the lives of the people. In addition, human lifespan is increasing, and recently, due to the spread of an infectious disease called COVID-19, the bio sector belonging to the food, medical & drug industry is in the spotlight. Attention is needed to the industry as the competitiveness of the food, medical & drug industry is expected to increase. The Ministry of Food and Drug Safety provides statistics on the food, medical & drug industry, but does not provide a systematic share of GDP. Since it is difficult to determine how influential the industry is compared to other industries, this study attempts to obtain the share of GDP in the food, medical & drug industry and compare it with other industries. In the process of obtaining GDP in the food, medical & drug industry sector, there was a difficulty in that the figures in statistical data were not unified by time point. In order to overcome the limitations, statistical data as a standard are determined. The GDP of the Food, Medical & Drug Industry was estimated using total added value, production, sales, and added value by industry. Compared to other industries, the Food, Medical & Drug Industry's GDP ranked second after the GDP of the manufacturing industry. As a result, it suggests that the food, medical & drug industry has a great influence on the national economic power among domestic industries.

• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.118-138
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• 2022

This study was conducted to identify the vegetation structure of major vegetation by region and elevation in the subalpine zone of Seoraksan National Park and prepare an inventory of flora. We reviewed the results of the previous subalpine studies and, through a preliminary survey, determined that the first appearance point of subalpine vegetation was about 800 m based on the south. Then we conducted a site survey by installing a total of 77 plots, including 12 plots on the northern Baekdamsa-Madeungnyeong trail (BD), 13 plots on the west Hangyeryeong-Kkeutcheong trail (HG), 13 plots on the east side of Sinheungsa-Socheongbong trail (SA), and 39 plots in the southern Osaek-Kkeutcheong, Osaek-Daecheongbong trail (OS), in an interval of 50 m above sea level. The analysis classified 7 communities, including Qercus mongolica-Abies holophylla-Acer pseudosieboldianumcommunity, Q. mongolica-Tilia amurensiscommunity, Q. mongolica-Pinus koraiensiscommunity, Q. mongolica-A. pseudosieboldianumcommunity, Betula ermanii-A. nephrolepiscommunity, P. koraiensis-A. nephrolepiscommunity, and mixed deciduous broad-leaf tree community according to the species composition based on the appearance of the major subalpine plants such as Quercus mongolica, Betula ermanii, and Abies nephrolepis, region, and elevation. 10.68±2.98 species appeared per plot (100 m²), and 110.87±63.89 individuals were identified. The species diversity analysis showed that the subalpine vegetation community of Seoraksan National Park was a mixed forest in which various species appeared as important species. Although there was a difference in the initial elevation for the appearance of major subalpine plants by region, they were distributed intensively in the elevation range of 1,100 to 1,300 m. In the Seoraksan National Park, 322 taxa, 83 families, 193 genera, 196 species, 1 subspecies, 26 varieties, and 4 forms of vascular plants were identified. One taxon of Trientalis europaeavar.arcticawas identified as the protected species. The endemic plants were 19 taxa, and 58 taxa were identified as subalpine plants.

• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.40-50
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• 2024

Pine forests are continuously declining due to competition with broadleaf trees, such as oaks, as a consequence of changes in the natural dynamics of forest ecosystem. This natural decline creates a risk of losing the various benefits pine trees have provided to people in the past. Therefore, it is necessary to prepare future forest management directions by considering the state of pine tree decline in each region. The goal of this study is to understand the characteristics of pine forest changes according to forest dynamics and to predict future regional changes. For this purpose, we evaluated the trend of change in pine forests and extracted various variables(topography, forest stand type, disturbance, and climate) that affect the change, using time-series National Forest Inventory (NFI) data. Also, using selected key variables, a model was developed to predict future changes in pine forests. As a results, it showed that the importance of pine trees in forests across the country has decreased overall over the past 10 years. Also, 75% of the sample points representing pine trees remained unchanged, while the remaining 25% had changed to mixed forests. It was found that these changes mainly occurred in areas with good moisture conditions or disturbance factors inside and outside the forest. In the next 10 years, approximately 14.2% of current pine forests was predicted to convert to mixed forests due to changes in natural forest dynamics. Regionally, the rate of pine forest change was highest in Jeju(42.8%) and Gyeonggi(26.9%) and lowest in Gyeongbuk(8.8%) and Gangwon(13.8%). It was predicted that pine forests would be at a high risk of decline in western areas of the Korean Peninsula, including Gyeonggi, Chungcheong, and Jeonnam. This results can be used to make a management plan for pine forests throughout the country.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.133-146
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• 2013

While the use of internet is routine nowadays, users receive and share information through a variety of media. Through the use of internet, information delivery media is diversifying from traditional media of one-way communication, such as newspaper, TV, and radio, into media of two-way communication. In contrast of traditional media, blogs enable individuals to directly upload and share news, which can be considered to have a differential speed of information diffusion than news media that convey information unilaterally. Therefore this Study focused on the difference between online news and social media blogs. Moreover, there are variations in the speed of information diffusion because that information closely related to one person boosts communications between individuals. We believe that users' standard of evaluation would change based on the types of information. As well, the speed of information diffusion would change based on the level of proximity. Therefore, the purpose of this study is to examine the differences in information diffusion based on the types of media. And then information is segmentalized and an examination is done to see how information diffusion differentiates based on the types of information. This study used the Bass diffusion model, which has been frequently used because this model has higher explanatory power than other models by explaining diffusion of market through innovation effect and imitation effect. Also this model has been applied a lot in other information diffusion related studies. The Bass diffusion model includes an innovation effect and an imitation effect. Innovation effect measures the early-stage impact, while the imitation effect measures the impact of word of mouth at the later stage. According to Mahajan et al. (2000), Innovation effect is emphasized by usefulness and ease-of-use, as well Imitation effect is emphasized by subjective norm and word-of-mouth. Also, according to Lee et al. (2011), Innovation effect is emphasized by mass communication. According to Moore and Benbasat (1996), Innovation effect is emphasized by relative advantage. Because Imitation effect is adopted by within-group influences and Innovation effects is adopted by product's or service's innovation. Therefore, ours study compared online news and social media blogs to examine the differences between media. We also choose different types of information including entertainment related information "Psy Gentelman", Current affair news "Earthquake in Sichuan, China", and product related information "Galaxy S4" in order to examine the variations on information diffusion. We considered that users' information proximity alters based on the types of information. Hence, we chose the three types of information mentioned above, which have different level of proximity from users' standpoint, in order to examine the flow of information diffusion. The first conclusion of this study is that different media has similar effect on information diffusion, even the types of media of information provider are different. Information diffusion has only been distinguished by a disparity between proximity of information. Second, information diffusions differ based on types of information. From the standpoint of users, product and entertainment related information has high imitation effect because of word of mouth. On the other hand, imitation effect dominates innovation effect on Current affair news. From the results of this study, the flow changes of information diffusion is examined and be applied to practical use. This study has some limitations, and those limitations would be able to provide opportunities and suggestions for future research. Presenting the difference of Information diffusion according to media and proximity has difficulties for generalization of theory due to small sample size. Therefore, if further studies adopt to a request for an increase of sample size and media diversity, difference of the information diffusion according to media type and information proximity could be understood more detailed.

• Korean Journal of Heritage: History & Science
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• v.45 no.4
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• pp.140-157
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• 2012

The result of a study on the useful trend of plants related to landscape and how to plant and cultivate through 'ImwonGyeongjaeji Manhakji'of Seoyugu is as follows: First, 'ImwonGyeongjaiji Manhakji', composed of total 5 volumes (General, Fruit trees, vegetables and creeper, plants, others) is a representative literature related to landscape which described the names of plants and varieties, soil condition, how to plant and cultivate, graft, how to prevent the insect attack etc systematically. Second, he recorded the tree planting as Jongjae(種栽) or Jaesik(栽植), and the period to plant the trees as Jaesusihoo(栽樹時候), transplanting as Yijae(移栽), making the fence as Jakwonri(作園籬), the names of varietieis as Myeongpoom(名品), the suitable soil as Toeui(土宜), planting and cultivation as Jongye(種藝), treatment as Euichi(醫治), protection and breeding as Hoyang(護養), garden as Jeongwon(庭園) or Wonpo(園圃), garden manager as Poja(圃者) or Wonjeong(園丁). Third, the appearance frequency of plants was analyzed in the order of flowers, fruits, trees, and creepers and it showed that the gravity of deciduous trees was 3.7 times higher than that of evergreen trees. The preference of flower and trees, fruit trees and deciduous trees and broad-leaved trees includes (1) application of the species of naturally growing trees which are harmonized with the natural environment (2) Aesthetic value which enables to enjoy the beauty of season, (3) the trend of public welfare to take the flowers and fruits, (4) the use of symbolic elements based on the value reference of Neo-Confucianism etc. Fourth, he suggested the optimal planting period as January(上時) and emphasized to transplant by adding lots of fertile soil and cover up the seeds with soil as high as they are buried in accordance with the growing direction and protect them with a support. That is, considering the fact that he described the optimal planting period as January by lunar calendar, this suggests the hints in judging the planting period today. For planting the seeds, he recommended the depth with 1 chi(寸 : approx. 3.3cm), and for planting a cutting, he recommended to plant the finger-thick branch with depth 5 chi(approx. 16.5cm) between January and February. In case of graft of fruit trees, he described that if used the branch stretched to the south, you would get a lot of fruit and if cut the branches in January, the fruits would be appetizing and bigger. Fifth, the hedge(fence tree) is made by seeding the Jujube tree(Zizyphus jujuba var. inermis) in autumn densely and transplanting the jujube tree with 1 ja(尺 : approx. 30cm) interval in a row in next autumn and then binding them with the height of 7 ja(approx. 210cm) in the spring of next year. If planted by mixing a Elm tree(Ulmus davidiana var. japonica) and a Willow(Salix koreensis), the hedge whose branch and leaves are unique and beautiful like a grating can be made. For the hedge(fence tree), he recommended Trifoliolate orange(Poncitus trifoliata), Rose of sharon(Hibiscus syriacus), Willow(Salix koreensis), Spindle tree(Euonymus japonica), Cherry tree(Prunus tomentosa), Acanthopanax tree(Acanthopanax sessiliflorus), Japanese apricot tree(Prunus mume), Chinese wolf berry(Lycium chinense), Cornelian tree(Cornus officinalis), Gardenia(Gardenia jasminoides for. Grandiflora), Mulberry(Morus alba), Wild rosebush(Rosa multiflora) etc.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• 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.