• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.124-138
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• 2018

This research was conducted to examine the availability of spatial data for assessing absorption rates of $CO_2$ in the forest of Ansan-si and evaluate the validity of methods that analyze $CO_2$ absorption. To statistically assess the $CO_2$ absorption rates per year, the 1:5,000 Digital Forest-Map (Lim5000) and Standard Carbon Removal of Major Forest Species (SCRMF) methods were employed. Furthermore, Land Cover Map (LCM) was also used to verify $CO_2$ absorption rate availability per year. Great variations in $CO_2$ absorption rates occurred before and after the year 2010. This was due to improvement in precision and accuracy of the Forest Basic Statistics (FBS) in 2010, which resulted in rapid increase in growing stock. Thus, calibration of data prior to 2010 is necessary, based on recent FBS standards. Previous studies that employed Lim5000 and FBS (2015, 2010) did not take into account the $CO_2$ absorption rates of different tree species, and the combination of SCRMF and Lim5000 resulted in $CO_2$ absorption of 42,369 ton. In contrast to the combination of SCRMF and Lim5000, LCM and SCRMF resulted in $CO_2$ absorption of 40,696 ton. Homoscedasticity tests for Lim5000 and LCM resulted in p-value <0.01, with a difference in $CO_2$ absorption of 1,673 ton. Given that $CO_2$ absorption in forests is an important factor that reduces greenhouse gas emissions, the findings of this study should provide fundamental information for supporting a wide range of decision-making processes for land use and management.

• Journal of Technology Innovation
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• v.22 no.1
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• pp.149-175
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• 2014

This study analyzed the effect of corporate innovation strategies on patent production and ultimately on technological change and new product development of firms in South Korea. The intent was to derive efficient strategies for enhancing technological performance of the firms. For the empirical analysis, three sources of data were combined: four waves of the Human Capital Corporate Panel Survey (HCCP) data collected by the Korea Research Institute for Vocational Education and Training (KRIVET), corporate financial data obtained from the Korea Information Service (KIS), and corporate patent data provided by the Korean Intellectual Property Office (KIPO). The patent production function was estimated by zero-inflated negative binomial (ZINB) regression. The technological performance function was estimated by two-stage regression, taking into account the endogeneity of patent production. An ordered logit model was applied for the second stage regression. Empirical results confirmed the critical role of corporate innovation strategies in patent production and in facilitating technological change and new product development of the firms. In patent production, the firms' R&D investment and human resources were key determinants. Higher R&D intensity led to more patents, yet with decreasing marginal productivity. A larger stock of registered patents also led to a larger flow of new patent production. Firms were more prolific in patent production when they had high-quality personnel, intensely investing in human resource development, and adopting market-leading or fast-follower strategy as compared to stability strategy. In technological performance, the firms' human resources played a key role in accelerating technological change and new product development. R&D intensity expedited new product development of the firm. Firms adopting market-leading or fast-follower strategy were at an advantage than those with stability strategy in technological performance. Firms prolific in patent production were also advanced in terms of technological change and new product development. However, the nexus between patent production and technological performance measures was substantially reduced when controlling for the endogeneity of patent production. These results suggest that firms need to strengthen the linkage between patent production and technological performance, and take strategies that address each firm's capacities and needs.

• Journal of the Korean Society of Groundwater Environment
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• v.5 no.3
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• pp.129-140
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• 1998

In this study two sites were selected to investigate groundwater contamination and spatial relationship between pollution level and its source. One is the Asan area, agricultural district where pollution sources are scattered. The other is the Gurogu area of Seoul city, industrial district where industrial complex and residential areas are located. Groundwater samples collected from these districts were analysis for chemical constituents. The attribute value files of the chemical constituents of groundwater and the spatial layers have been constructed and the pollution properties have been investigated to find out spatial relationships between the groundwater constituents and pollution sources using CIS. Relatively high contents of Si and HCO$_3$ in groundwater from the Asan area reflect the effect of water-rock interaction, whereas high contents of Cl, NO$_3$, SO$_4$and Ca in groundwater from the Gurogu area are due to the pollution of various sources. Pollution over the critical level of Korean Dinking Water Standard has been investigated from 15 sampling sites out of 40 in the Asan area, and 33 sampling sites out of 51 in the Gurogu area. There is pollution of NO$_3$, Cl, Fe, Mn, SO$_4$and Zn in groundwater from the Gurogu area, and that of NO$_3$, SO$_4$and Zn in groundwater from the Asan area. Principal pollution in both areas is NO$_3$contamination. Deep groundwater from the Asan area is not contaminated with NO$_3$except for one site and most of shallow groundwater near the potential point sources such as factory and stock farm is contaminated seriously. Groundwater from the Gurogu area has been already polluted seriously considering the fact of contamination of deep groundwater. This study reports a spatial relationship between the pollution level and pollution source using GIS.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.11
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• pp.1615-1634
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• 2004

A review was undertaken to obtain information on the sustainability of pig free-range production systems including the management, performance and health of pigs in the system. Modern outdoor rearing systems requires simple portable and flexible housing with low cost fencing. Local pig breeds and outdoor-adapted breeds for certain environment are generally more suitable for free-range systems. Free-range farms should be located in a low rainfall area and paddocks should be relatively flat, with light topsoil overlying free-draining subsoil with the absence of sharp stones that can cause foot damage. Huts or shelters are crucial for protecting pigs from direct sun burn and heat stress, especially when shade from trees and other facilities is not available. Pigs commonly graze on strip pastures and are rotated between paddocks. The zones of thermal comfort for the sow and piglet differ markedly; between 12-22$^{\circ}C$ for the sow and 30-37$^{\circ}C$ for piglets. Offering wallows for free-range pigs meets their behavioural requirements, and also overcomes the effects of high ambient temperatures on feed intake. Pigs can increase their evaporative heat loss via an increase in the proportion of wet skin by using a wallow, or through water drips and spray. Mud from wallows can also coat the skin of pigs, preventing sunburn. Under grazing conditions, it is difficult to control the fibre intake of pigs although a high energy, low fibre diet can be used. In some countries outdoor sows are fitted with nose rings to prevent them from uprooting the grass. This reduces nutrient leaching of the land due to less rooting. In general, free-range pigs have a higher mortality compared to intensively housed pigs. Many factors can contribute to the death of the piglet including crushing, disease, heat stress and poor nutrition. With successful management, free-range pigs can have similar production to door pigs, although the growth rate of the litters is affected by season. Piglets grow quicker indoors during the cold season compared to outdoor systems. Pigs reared outdoors show calmer behaviour. Aggressive interactions during feeding are lower compared to indoor pigs while outdoor sows are more active than indoor sows. Outdoor pigs have a higher parasite burden, which increases the nutrient requirement for maintenance and reduces their feed utilization efficiency. Parasite infections in free-range pigs also risks the image of free-range pork as a clean and safe product. Diseases can be controlled to a certain degree by grazing management. Frequent rotation is required although most farmers are keeping their pigs for a longer period before rotating. The concept of using pasture species to minimise nematode infections in grazing pigs looks promising. Plants that can be grown locally and used as part of the normal feeding regime are most likely to be acceptable to farmers, particularly organic farmers. However, one of the key concerns from the public for free-range pig production system is the impact on the environment. In the past, the pigs were held in the same paddock at a high stocking rate, which resulted in damage to the vegetation, nutrient loading in the soil, nitrate leaching and gas emission. To avoid this, outdoor pigs should be integrated in the cropping pasture system, the stock should be mobile and stocking rate related to the amount of feed given to the animals.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Korean Journal of Poultry Science
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• v.39 no.3
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• pp.177-182
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• 2012

This work was carried out to investigate the performance of growing period of two-way crossed of Korean native chickens parental stocks. A total of four hundred eighty female 2-crossbred chicks were used in this study and they were from National Institute of Animal Science. Groups were four crossbreds (4 replications/crossbred, 30 birds/replication) as A) C strain ${\times}$ Y strain, B) C strain ${\times}$ L strain, C) C strain ${\times}$ G strain and D) C strain ${\times}$ W strain. Body weight of A crossbred was the highest at the age of 8 week (P<0.05) and that of D strain was the lowest for growing period (P<0.05). Body weights of A and B crossbreds were higher than those of C and D crossbreds at the 12 and 16 weeks (P<0.05). Weekly body weights of A and B crossbreds were higher than C and D crossbreds (P<0.05), and weekly body weight of B crossbred was higher compared to other crossbreds at 0~20 weeks old. Weekly feed intake of D crossbred was the lowest among all crossbreds at 0~12, 0~16 and 0~20 weeks old (P<0.05). Weekly feed conversion ratio of C crossbred was the highest among crossbreds (P<0.05). These results can give the basic information for growth related data in 2-way crossbreed Korean Native Chickens, which can be used for the parental stocks for the laying-type of Korean native commercial chickens.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.42-55
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• 2006

We propose a single Index approach for subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single index approach, we can reduce both storage space overhead and index maintenance overhead. Moving average transform is known to reduce the effect of noise and has been used in many areas such as econometrics since it is useful in finding overall trends. However, the previous research results have a problem of occurring index overhead both in storage space and in update maintenance since tile methods build several indexes to support arbitrary orders. In this paper, we first propose the concept of poly-order moving average transform, which uses a set of order values rather than one order value, by extending the original definition of moving average transform. That is, the poly-order transform makes a set of transformed windows from each original window since it transforms each window not for just one order value but for a set of order values. We then present theorems to formally prove the correctness of the poly-order transform based subsequence matching methods. Moreover, we propose two different subsequence matching methods supporting moving average transform of arbitrary order by applying the poly-order transform to the previous subsequence matching methods. Experimental results show that, for all the cases, the proposed methods improve performance significantly over the sequential scan. For real stock data, the proposed methods improve average performance by 22.4${\~}$33.8 times over the sequential scan. And, when comparing with the cases of building each index for all moving average orders, the proposed methods reduce the storage space required for indexes significantly by sacrificing only a little performance degradation(when we use 7 orders, the methods reduce the space by up to 1/7.0 while the performance degradation is only $9\%{\~}42\%$ on the average). In addition to the superiority in performance, index space, and index maintenance, the proposed methods have an advantage of being generalized to many sorts of other transforms including moving average transform. Therefore, we believe that our work can be widely and practically used in many sort of transform based subsequence matching methods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.1-13
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• 2019

As a way to find new and innovative technologies, many companies have invested in and acquired skilled startups. Because startups are usually small in size and have a small history of past business experience, there are many risks involved in acquiring them as they have limited technical skills and business feasibility verification methods. Thus, venture capital plays an important role in discovering and investing competitive startups. While Independent Venture Capital generally values financial returns, Corporate Venture Capital, which plays investment roles in the firm, values business synergies with the parent company from a strategic perspective. In an industry sector where development of technology is rapid and whether new technology is held determines a company's competitiveness, existing companies incorporate startups with innovative technologies into their investment portfolios, collaborate together, and take over for comprehensive cooperation. In addition, new investments and acquisitions are carried out through the management of portfolio companies to obtain and utilize industry information. In this paper, major U.S. companies listed in the U.S. verified their investment activities through corporate venture capital and their impact on parent companies and startups through regression, while the parent company's acquisition performance was analyzed through an event study based on a stock price analysis. The criteria for startup were defined as companies with less than 12 years of experience, and the analysis showed that the parent companies with corporate venture capital with a larger number of investments actively take over startups. In addition, increasing corporate venture capital's financial investment activities shows a negative impact on the parent companies' acquisition activities, and the acquisition performance increased when the parent companies took over startups in its portfolio.

• Korean journal of applied entomology
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• v.61 no.3
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• pp.399-408
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• 2022

There has been much debate on the morphometric divergence between the recently identified Apis cerana koreana and Apis cerana honey bees. The aim of this study was to obtain phenotypic information that can be used to compare A. c. koreana data with other A. cerana subspecies data from open resources and determine breeding results on the basis of morphometric traits. To differentiate A. c. koreana, we investigated 22 classic morphological characteristics; royal jelly secretion; and the weight of workers, queens, and drones of A. c. koreana bred in Korea. To define the selection results, we used the geometric morphometric method. The artificially selected A. c. koreana secreted significantly more royal jelly (1.18 times) than the naturally selected A. c. koreana, which positively influenced the health of the colonies. These honey bees were identified more clearly with the geometric morphometric method than with the classic morphometric method, which is traditionally used to determine the subspecies. Large trends were noted for A. c. koreana on the basis of our results and literature from the 1980s regarding A. cerana sizes in Korea (tarsal index, length of forewing, and cubital index were measured). The cluster analysis revealed the proximity of A. c. koreana, A. cerana in China, and A. c. indica on the basis of eight classic characters, which, perhaps, relay the origin of the honey bees. The results of this study defined the morphometric responses of A. c. koreana honey bees to geographic isolation, climate change, and selection, which are important to identify, protect, and preserve honey bee stock in Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.