• Korean Chemical Engineering Research
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• v.51 no.2
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• pp.267-271
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• 2013

Test bed studies with highly efficient amine $CO_2$ solvent (KoSol-4) developed by KEPCO research institute were performed. For the first time in Korea, evaluation of post-combustion $CO_2$ capture technology to capture 2 ton $CO_2$/day from a slipstream of the flue gas from a coal-fired power station was performed. Also the analysis of solvent regeneration energy was conducted to suggest the reliable performance data of the KoSol-4 solvent. For this purpose, we have tested 5 campaigns changing the operating conditions of the solvent flow rate and the stripper pressure. The overall results of these campaigns showed that the $CO_2$ removal rate met the technical guideline ($CO_2$ removal rate: 90%) suggested by IEA-GHG and that the regeneration energy of the KoSol-4 showed about 3.0~3.2 GJ/$tCO_2$ which was, compared to that of the commercial solvent MEA (Monoethanolamine), about 25% reduction of regeneration energy. Based on these results, we could confirm the good performance of the KoSol-4 solvent and the $CO_2$ capture process developed by KEPCO research institute. And also it was expected that the cost of $CO_2$ avoided could be reduced drastically if the KoSol-4 is applied to the commercial scale $CO_2$ capture plant.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.7 no.1
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• pp.58-72
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• 2000

This study was an attempt to encourage the development of a rehabilitation delivery system and programs as a substitute service for hospitalization on the case of car accident patients, such as hospital based home health care nursing services. Various substitute services for hospitalization are required to curtail the length of stay for inpatients who were hospitalized with car accident compensation insurance. It focused on developing an estimation an early discharge day for car accident inpatients based on detailed statements of treatment for 111 inpatients who were hospitalized at the General Hospital in 1997. This study had four specific purposes as follows. First. to find out the utilization of medical services. Second, to estimate the time of early discharge and income increasing effect based on early discharge for those patients. Third, to identify the factors affecting total medical expenditure and the length of stay for those inpatients. Forth, to figure out the need of utilizing home health care nursing service for accident patients. In order to analyze the length of stay and medical expenditure for inpatients who were hospitalized due to car accidents, the authors conducted micro- and macro-analysis of medical and medical expenditure records. Micro-analysis was done by nominal group discussion of 4 expertise with the critical criteria, such as a decrease in the amount of treatment after surgery, treatments, tests, drugs and changes in the test consistency, drug methods, vital signs, start of ROM exercise, doctor's order, patient's outside visiting ability, and stable conditions. In addition to identifying variables affecting medical expenditure, and the length of stay and income effect due to early discharge day, the data was analyzed with a multiple regression analysis and linear regression analysis model by SPSS-PC for windows and Excell program. Results of this study were as follows. First. the mean length of stay was 50.3 days. whereas the mean length of stay due to early discharge was 34.3 days at the hospital. The estimation of time of early discharge depended on the length of stay. The longer the length of stay, the longer the length of time of early discharge : for instance a length of stay under 10 days was estimated as correlating to a mean length of stay of 6.6 days and early discharge of 6.5. The mean length of stay was 217.4 days and the time of early discharge was 110.1 respectively. The mean medical expenditure per day was found to be 169.085 Won and the mean medical expenditure per day showed negative linear trends according to the length of stay at the hospital. The estimation results of the income effect due to being discharged 16 days early was around 2,244,000 won per bed. However. this sum does not represent the real benefits resulting from early discharge, but rather the income increasing amount without considering medical prime cost in the general hospital. Therefore, further analysis is required on the cost containments and benefits as turn over rate per bed as the medical prime costs. The length of stay was most significant and was positive to the total medical expenditure, as expected. Surgery and patient's residential area was also an important variable in explaining medical expenditure. The level of complications was the most significant variable in explaining the length of stay. There was a high level for need a home health care nursing service which further supports early discharge for accident patients. In addition, when the patient was discharged. they needed follow up care for complications suffered during the car accident. $86.8\%$ of discharged patients responded that they needed home health services after early discharge. From these research findings, the following suggestions have been drawn. Strategies on a health care delivery system must be developed in order to focus on the consumer's needs and being planned for 21 century health policy in Korea. Community based intermediate facilities or home health care should be developed for rehabilitation services as a substitute for hospitalization in order to shorten the length of stay would be. A hospital based home health care nursing service. it would be available immediately to utilize by patients who want rehabilitation services as a substitute for hospitalization with the cooperation of car insurance companies.

• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.188-205
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• 2001

Insurer's duty of declaration is based on reciprocity of principle of the highest good, and recently it is widely recognized in the British and American insurance circles. The conception of fiduciary relationship is no longer equity or the legal theory which is only confined to the nations with Anglo-American laws. Therefore, recognizing the fiduciary relationship as the essence of insurance contract, which is more closely related to public interest than any other fields. will serve an efficient measure to seek fair and reasonable relationship with contractor, and provide legal foundation which permits contractor to bring an action for damage against violation of insurer's duty of declaration. In the future, only when the fiduciary relationship is approved as the essence of insurance contract, the business performance and quality of insurance industry is expected to increase. Therefore, to keep well this fiduciary relationship, or increase the fiduciary relationship persistency rates seems to be the bottom line in the insurance industry. In this paper, we developed a fiduciary relationship maintenance ratio based on comparison by case, which is represented with usually maintained contract months to paid months, based on each contract of the basis point. In this paper we have developed a new business model seeking the maximum profit with low cost and high efficiency, management policy of putting its priority on its substantiality, as an improvement measure to break away from the vicious circle of high cost and low efficiency, and management policy of putting its priority on its external growth(expansion of market share).

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.751-764
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• 2023

Gangwon State is centered on the Taebaek Mountains with very different climate characteristics depending on the region, and localized heavy rainfall is a frequent occurrence. Heavy rain disasters have a short duration and high spatial and temporal variability, causing many casualties and property damage. In the last 10 years (2012~2021), the number of heavy rain disasters in Gangwon State was 28, with an average cost of 45.6 billion won. To reduce heavy rain disasters, it is necessary to establish a disaster management plan at the local level. In particular, the current criteria for heavy rain warnings are uniform and do not consider local characteristics. Therefore, this study aims to propose a heavy rainfall warning criteria that considers the threshold rainfall for the advisory areas located in Gangwon State. As a result of analyzing the representative value of threshold rainfall by advisory area, the Mean value was similar to the criteria for issuing a heavy rain warning, and it was selected as the criteria for a heavy rain warning in this study. The rainfall events of Typhoon Mitag in 2019, Typhoons Maysak and Haishen in 2020, and Typhoon Khanun in 2023 were applied as rainfall events to review the criteria for heavy rainfall warnings, as a result of Hit Rate accuracy verification, this study reflects the actual warning well with 72% in Gangneung Plain and 98% in Wonju. The criteria for heavy rain warnings in this study are the same as the crisis warning stages (Attention, Caution, Alert, and Danger), which are considered to be possible for preemptive rain disaster response. The results of this study are expected to complement the uniform decision-making system for responding to heavy rain disasters in the future and can be used as a basis for heavy rain warnings that consider disaster risk by region.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• 한국석유지질학회:학술대회논문집
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• autumn
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• pp.28-46
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• 2001

Natural gas is a mixture of hydrocarbon gases and impurities such as nitrogen, hydrogen sulfide, and carbon dioxide and a clean energy producing no pollution materials for combustion. Currently, the demand of the natural gas is rapidly increasing due to worldwide environmental problems. According to Hubbert's study in the past, the natural gas was predicted as rapidly depleted resources, and then the results led to high gas price and limitation of usage during 1980s. Afterward, the study of natural gas resources based on geology identified the additional natural gas resources that were not considered in Hubbert's study. They are unconventional gas, additional resources in the existed reservoirs, and natural gas in deep subsurface areas. Such additional resouces made the future of natural gas bright and pormised low and stable gas price in the future. Deep natural gas is defined as the gas existing at or below 15,000ft$(4,752{\cal}m)$ in depth from the surface. According to the study from the U.S. Geological Survey(USGS) in 1995, 1,412 TCF of technically recoverable natural gas was remained to be discovered or developed in the onshore of United States. A significant part of that resource base, 114 TCF, exists at deep sedimentary basins, and it shows wide distribution with various geological environments. In 1995, the deep gas contributed to $6.7\% of total supply amount of natural gas in the United States and is expected to be $18.7\% by 201.5. However, the development of the deep gas is a high risky business due to expensive investment and high portion of dry holes, although it is developed. Thus, for developing the deep gas economically, it is necessary to overcome many technical challenges. In this paper, for increasing success rate of the deep gas, 1) geologic and compositional characteristics, and production cost have been analyzed according to depth, 2) technical problems related to deep gas production have been summarized, and 3) finally future study areas for increasing application of the deep gas have been suggested. For reference, this paper was written based on the study results from USGS and Gas Research Institute(GRI), for the United States is doing the most active R&D in the deep gas area, and thus, has many reliable data.

• Journal of the Korean Geosynthetics Society
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• v.12 no.4
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• pp.67-76
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• 2013

Landfill require special care due to the dangers of nearby surface water and underground water pollution caused by leakage of leachate. The leachate does not leak due to the installation of the geomembrane but sharp wastes or landfill equipment can damage the geomembrane and therefore a means of protecting the geomembrane is required. In Korea, in accordance with the waste control act being modified in 1999, protecting the geosynthetics liner on top of the slope of landfill and installing a drainage layer to fluently drain leachate became mandatory, and technologies are being researched to both protect the geomembrane and quickly drain leachate simultaneously. Therefore, this research has its purpose in studying the drainage functions of leachate and protection functions of the geomembrane in order to examine the application possibilities of Geo-Multicell-Composite (GMC) as a Leachate Collection Removal and Protection System (LCRPs) at the slope on top of the geomembrane of landfill by observing methods of inserting filler with high-quality water permeability at the drainage net. GMC's horizontal permeability coefficient is $8.0{\times}10^{-4}m^2/s$ to legal standards satisfeid. Also crash gravel used as filler respected by vertical permeability is 5.0 cm/s, embroidering puncture strength 140.2 kgf. A result of storm drain using artificial rain in GMC model facility, maxinum flow rate of 1,120 L/hr even spray without surface runoff was about 92~97% penetration. Further study, instead of crash gravel used as a filler, such as using recycled aggregate utilization increases and the resulting construction cost is expected to savings.

• Korean Journal of Environmental Biology
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• v.26 no.3
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• pp.129-137
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• 2008

Alien apple snails (Pomacea canaliculata, Pomacea insularus) used in environmentally-friendly agriculture are different from indigenous snails found in Korea. Due to high herbicidal effects and cost-effectiveness, the number of farmers using the snails has been growing every year since 2000. Moreover, in 2008, because of the outbreaks of avian influenza throughout the country from March to May, 2008, central and local governments recommended the use of alien apple snails in agriculture as an alternative to the ducks-oriented environmentally-friendly agriculture. Therefore, it is expected that the use of alien apple snails in agriculture should be expanded in a near future. Since alien apple snails lay eggs with 95.8% of eclosion rate, they are considered to be potential pests unlike indigenous snails. In addition, Japan, Taiwan and most of the southeast Asian countries had already experienced severe ecological and agricultural damage by the alien apple snails. Subsequently, International Union for Conservation of Nature and Natural Resources (IUCN) designated P. canaliculata as one of "the 100 of the world's worst invasive alien species". It seems highly likely that the alien apple snails in Korea pose a potential threat to conservation of ecosystem and biodiversity since the snails were either found or invaded into the natural environments in some regions of Gangwon-Do and southern parts of Korean Peninsula. However, just recently, agricultural authorities and farmers using alien apple snails in agriculture opposed a proposition of designating the alien apple snails as an ecosystem-disturbing animal described by the Wildlife Protection Act. This is because there has been no concrete evidence of the ecological risk imposed by the alien snails up to now in Korea. Subsequently, in this paper, we analysed the ecological and agricultural risks imposed by the alien snails from the studies done in domestic and abroad. In addition, we proposed an urgent need and reasoning for ecological risk management of the alien snails at the national level as well as using the snails in agriculture.