• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.289-301
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• 2023

Forests within watersheds are essential in maintaining ecosystems and are the central infrastructure for constructing an ecological network system. However, due to indiscriminate development projects carried out over past decades, forest fragmentation and land use changes have accelerated, and their original functions have been lost. Since a forest's structural pattern directly impacts ecological processes and functions in understanding forest ecosystems, identifying and analyzing change patterns is essential. Therefore, this study analyzed structural changes in the forest landscape according to the time-series land cover changes using the FRAGSTATS model for the dam watershed of the Geum River upstream. Land cover changes in the dam watershed of the Geum River upstream through land cover change detection showed an increase of 33.12 square kilometers (0.62%) of forests and 67.26 square kilometers (1.26%) of urbanized dry areas and a decrease of 148.25 square kilometers (2.79%) in agricultural areas from the 1980s to the 2010s. The results of no-sampling forest landscape analysis within the watershed indicated landscape percentage (PLAND), area-weighted proximity index (CONTIG_AM), average central area (CORE_MN), and adjacency index (PLADJ) increased, and the number of patches (NP), landscape shape index (LSI), and cohesion index (COHESION) decreased. Identification of structural change patterns through a moving window analysis showed the forest landscape in Sangju City, Gyeongsangbuk Province, Boeun County in Chungcheongbuk Province, and Jinan Province in Jeollabuk Province was relatively well preserved, but fragmentation was ongoing at the border between Okcheon County in Chungcheongbuk Province, Yeongdong and Geumsan Counties in Chungcheongnam Province, and the forest landscape in areas adjacent to Muju and Jangsu Counties in Jeollabuk Province. The results indicate that it is necessary to establish afforestation projects for fragmented areas when preparing a future regional forest management strategy. This study derived areas where fragmentation of forest landscapes is expected and the results may be used as basic data for assessing the health of watershed forests and establishing management plans.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.689-698
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• 2021

Since the mine reclamation scheme was implemented from 2007 in Korea, various remediation programs have been decontaminated the pollution associated with mining and 254 mines were managed to reclamation from 2011 to 2015. However, as the total amount of contaminated mine drainage has been increased due to the discovery of potential hazards and contaminated zone, more efficient and economical treatment technology is required. Therefore, in this study, the adsorption properties of arsenic was evaluated according to the adsorbents which were derived from water treatment sludge(Alum based adsorbent, ABA-500) and granular ferric hydroxide(GFH), already commercialized. The alum sludge and GFH adsorbents consisted of aluminum, silica materials and amorphous iron hydroxide, respectively. The point of zero charge of ABA-500 and GFH were 5.27 and 6.72, respectively. The result of the analysis of BET revealed that the specific surface area of GFH(257 m²·g^-1) was larger than ABA-500(126~136 m²·g^-1) and all the adsorbents were mesoporous materials inferred from N₂ adsorption-desorption isotherm. The adsorption capacity of adsorbents was compared with the batch experiments that were performed at different reaction times, pH, temperature and initial concentrations of arsenic. As a result of kinetic study, it was confirmed that arsenic was adsorbed rapidly in the order of GFH, ABA-500(granule) and ABA-500(3mm). The adsorption kinetics were fitted to the pseudo-second-order kinetic model for all three adsorbents. The amount of adsorbed arsenic was increased with low pH and high temperature regardless of adsorbents. When the adsorbents reacted at different initial concentrations of arsenic in an hour, ABA-500(granule) and GFH could remove the arsenic below the standard of drinking water if the concentration was below 0.2 mg·g^-1 and 1 mg·g^-1, respectively. The results suggested that the ABA-500(granule), a low-cost adsorbent, had the potential to field application at low contaminated mine drainage.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.87-96
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• 2007

Risk assessment was carried out in order to improve the remediation and management strategy on a contaminated gunnery site, where a flood control reservoir is under construction nearby. Six chemicals, including explosive chemicals and heavy metals, which were suspected to possess risk to humans by leaching events from the site were the target pollutants for the assessment. A site-specific conceptual site model was constructed based on effective, reasonable exposure pathways to avoid any overestimation of the risk. Also, conservative default values were adapted to prevent underestimation of the risk when site-specific values were not available. The risks of the six contaminants were calculated by API's Decision Support System for Exposure and Risk Assessment with several assumptions. In the crater-formed-area(Ac), the non-carcinogenic risks(i.e., HI values) of TNT(Tri-Nitro-Toluene) and Cd were slightly larger than 1, and for RDX(Royal Demolition Explosives), over 50. The total non-carcinogenic risk of the whole gunnery range calculated to a significantly high value of 62.5. Carcinogenicity of Cd was estimated to be about $10^{-3}$, while that of Pb was about $5\;{\times}\;10^{-4}$, which greatly exceeded the generally acceptable carcinogenic risk level of $10^{-4}{\sim}10^{-6}$. The risk assessment results suggest that an immediate remediation practice for both carcinogens and non-carcinogens are required before the reservoir construction. However, for more accurate risk assessment, more specific estimations on condition shifts due to the construction of the reservoir are required, and more over, the effects of the pollutants to the ecosystem is also necessary to be evaluated.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.177-191
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• 2013

6th Industry reduced agricultural income and rural areas, the economic downturn is going to be activated is attracting attention as an alternative. 6th industry means that the integrated or linked, the manufacture and processing of secondary industry based on primary industry, the distribution and service of tertiary industry. Park Geun-hye government to realize the creative economy in agriculture as an alternative to specifically evaluate the 6th industries and suggests various policy alternatives. In addition, to support the development of models and analysis of best practices, including sleep studies are in progress. However, the 6th Industry management body for performing management level, technical level, the leader in comprehensive evaluation of competencies and indicators on the development of an evaluation study is insufficient. In this regard, the present study performed 6th industry management body for the management level, technical level, the leader competency evaluation indicators to develop a comprehensive evaluation by utilizing AHP method was developed indicators. The results achieved in Korea As different countries and the FTA as cheap agricultural imports increased 6th industry revenues associated with the management body is very likely to be worse. The endless competition to survive in the most important of the strategy for each individual project management body to operate on their own, rather than to strengthen internal capacity by strengthening linkages with other industries, products, and services that promote the sale will be. This also is that you need to improve revenue management body. Thus, all 6th industry management body at the location of their efforts to gain the trust of consumers will require, moreover, for each management body to build cooperation between the various measures will be sought. In addition to the smart era rapidly changing needs of customers, depending on the life cycle of products and services are getting faster and the new consumer is getting more and more tend to find new products. Thus, customers and management body 6th industry changes quickly and accurately predict market trends, and also to market new products and services that further efforts would be needed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.279-282
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• 2006

IP망을 이용한 IPTV 방송 서비스가 새로운 수익 모델로 인정받고 현재 국내의 KT, SKT 등이 IPTV 시범서비스를 준비하거나 진행 중에 있다 이 IPTV 서비스는 이전의 단방향 방송과는 달리 사용자와의 인터렉션을 중시하는 양방향 방송을 표방하기 때문에 지금까지의 방송과는 다른 혁신적인 방송서비스가 기대된다. 하지만 IPTV 서비스에 있어서 여러 통신사와 방송사가 참여할 수 있을 것으로 보여지는 것과는 달리 실상은 몇몇 거대 통신기업이 자신들의 망을 이용하는 가입자들을 상대로 한정된 사업을 벌이고 있다. 이는 IPTV 서비스를 위한 인프라가 구축되어 있지 않고 방통융합망의 개념을 만족시키기 위해 서비스 개발자가 알아야 할 프로토콜들이 너무나 많기 때문이다. 따라서 본 논문에서는 이러한 상황을 타개할 수 있는 수단을 Open API로 제안한다. 맞춤형 방송을 위한 시나리오를 TV-Anytime의 벤치마킹과 유저 시나리오를 참고하여 재구성하고 이 시나리오로부터 IPTV 방송 서비스를 위한 방통융합망의 기본적이고 강력한 기능들을 Open API 함수로 정의하였다. 여기에서의 방송 서비스는 NDR, EPG, 개인 맞춤형 광고 서비스를 말하며 각 서비스를 위한 서버는 통합망 위에 존재하고 이 서버들이 개방하는 API들은 다른 응용프로그램에 의해 사용되는 것이기 때문에 가장 기본적인 기능을 정의하게 된다. 또한, 제안한 Open API 함수를 이용하여 개인 맞춤형 방송 응용 서비스를 구현함으로써 서비스 검증을 하였다. Open API는 웹서비스를 통해 공개된 기능들로써 게이트웨이를 통해 다른 망에서 사용할 수 있게 된다. Open API 함수의 정의는 함수 이름, 기능, 입 출력 파라메터로 이루어져 있다. 사용자 맞춤 서비스를 위해 전달되는 사용자 상세 정보와 콘텐츠 상세 정보는 TV-Anytime 포럼에서 정의한 메타데이터 스키마를 이용하여 정의하였다.가능하게 한다. 제안된 방법은 프레임 간 모드 결정을 고속화함으로써 스케일러블 비디오 부호화기의 연산량과 복잡도를 최대 57%감소시킨다. 그러나 연산량 감소에 따른 비트율의 증가나 화질의 열화는 최대 1.74% 비트율 증가 및 0.08dB PSNR 감소로 무시할 정도로 작다., 반드시 이에 대한 검증이 필요함을 알 수 있었다. 현지관측에 비해 막대한 비용과 시간을 절약할 수 있는 위성영상해석방법을 이용한 방법은 해양수질파악이 가능할 것으로 판단되며, GIS를 이용하여 다양하고 복잡한 자료를 데이터베이스화함으로써 가시화하고, 이를 기초로 공간분석을 실시함으로써 환경요소별 공간분포에 대한 파악을 통해 수치모형실험을 이용한 각종 환경영향의 평가 및 예측을 위한 기초자료로 이용이 가능할 것으로 사료된다.염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전략이고, 이 전략의 유용성은 투자자가 설정한 투자기간보다

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.51-78
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• 2016

Online-to-offline (O2O) commerce is the new trend that merges online commerce with traditional industries in various fields. The primary purpose of this paper is to find out which factors influence customers' intention to switch from call-based driver-for-hire services to O2O app-based services. This study used variables and factors based on Theory of Switching Intention, and Extended Unified Theory of Acceptance and Use of Technology in order to design research questions. We surveyed 500 users of call-based driver-for-hire services. According to the result of this study, dissatisfaction with the current call-based driver-for-hire services is estimated to be a significant factor that strengthens customers' intention to switch from the call-based driver-for-hire services to the app-based services. Loyalty to the previous call-based driver-for-hire services was not seen as a crucial motivator that causes customers to switch to the new O2O driver service. Switching cost also did not play a key role in explaining the relationship between dissatisfaction with the current call-based service and the intention to use the new app-based service. Performance expectancy, easiness in use, the level of user's knowledge or available assistance in relation to the use of app-based services, and expectancy for reasonable price was found to have meaningful impacts on customers' intention to switch from the call-based driver-for-hire services to the app-based services. Age, gender and user experience on the new service were found incapable of moderating the relationship between aforementioned factors which influence customers' choice of the app-based driver-for-hire service, and customers' intent to switch to the app-based service.