• Journal of Energy Engineering
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• v.28 no.3
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• pp.65-79
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• 2019

The study attempted to estimate the optimal facility capacity by combining renewable energy sources that can be connected with gas CHP in industrial complexes. In particular, we reviewed industrial complexes subject to energy use plan from 2013 to 2016. Although the regional designation was excluded, Sejong industrial complex, which has a fuel usage of 38 thousand TOE annually and a high heat density of $92.6Gcal/km^2{\cdot}h$, was selected for research. And we analyzed the optimal operation model of CHP Hybrid System linking fuel cell and photovoltaic power generation using HOMER Pro, a renewable energy hybrid system economic analysis program. In addition, in order to improve the reliability of the research by analyzing not only the heat demand but also the heat demand patterns for the dominant sectors in the thermal energy, the main supply energy source of CHP, the economic benefits were added to compare the relative benefits. As a result, the total indirect heat demand of Sejong industrial complex under construction was 378,282 Gcal per year, of which paper industry accounted for 77.7%, which is 293,754 Gcal per year. For the entire industrial complex indirect heat demand, a single CHP has an optimal capacity of 30,000 kW. In this case, CHP shares 275,707 Gcal and 72.8% of heat production, while peak load boiler PLB shares 103,240 Gcal and 27.2%. In the CHP, fuel cell, and photovoltaic combinations, the optimum capacity is 30,000 kW, 5,000 kW, and 1,980 kW, respectively. At this time, CHP shared 275,940 Gcal, 72.8%, fuel cell 12,390 Gcal, 3.3%, and PLB 90,620 Gcal, 23.9%. The CHP capacity was not reduced because an uneconomical alternative was found that required excessive operation of the PLB for insufficient heat production resulting from the CHP capacity reduction. On the other hand, in terms of indirect heat demand for the paper industry, which is the dominant industry, the optimal capacity of CHP, fuel cell, and photovoltaic combination is 25,000 kW, 5,000 kW, and 2,000 kW. The heat production was analyzed to be CHP 225,053 Gcal, 76.5%, fuel cell 11,215 Gcal, 3.8%, PLB 58,012 Gcal, 19.7%. However, the economic analysis results of the current electricity market and gas market confirm that the return on investment is impossible. However, we confirmed that the CHP Hybrid System, which combines CHP, fuel cell, and solar power, can improve management conditions of about KRW 9.3 billion annually for a single CHP system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.167-185
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• 2019

As most G-20 countries expect medical spending to grow rapidly over the next few decades, the burden of healthcare costs continues to grow globally due to an increase in the elderly population and chronic illnesses, and the ongoing quality improvement of health care services. However, under the rapidly changing technological environment of healthcare and IT convergence, the problem may become even bigger if not properly recognized and not properly prepared. In the context of the paradigm shift and the increasing problem of the medical field, complex responses in technical, institutional and business aspects are urgently needed. The key is to derive a business model that is appropriate for businesses that integrate IT in the medical field. With the arrival of the era of the 4th industrial revolution, new technologies such as Internet of Things have been applied to eHealthcare, and the need for new business models has emerged.In the e-healthcare of the Internet era, it became a traditional firm-based business model. However, due to the characteristics of dynamics and complexity of things Internet in the Internet of things, A business ecosystem-based approach is needed. In this paper, we present and analyze the major success factors of the ecosystem based on the 3 - layer structure of the e - healthcare business ecosystem as a result of research on e - healthcare business ecosystem based on emerging technology such as Internet of things. The three-layer business ecosystem was defined as (1) Infrastructure Layer, (2) Character Layer, and (3) Stakeholder Layer. As the key success factors for the eHealthCare business ecosystem, the following four factors are suggested: (1) introduction of the iHealthcare concept, (2) expansion of the business ecosystem, (3) business ecosystem change process innovation, and (4) business ecosystem leadership innovation.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.101-114
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• 2019

In this study, we carry out the numerical simulation to trace the yearly shoreline change of Mang-Bang beach, which is suffering from erosion problem. We obtain the basic equation (One Line Model for shoreline) for the numerical simulation by assuming that the amount of shoreline retreat or advance is balanced by the net influx of longshore and cross-shore sediment into the unit discretized shoreline segment. In doing so, the energy flux model for the longshore sediment transport rate is also evoked. For the case of cross sediment transport, the modified Bailard's model (1981) by Cho and Kim (2019) is utilized. At each time step of the numerical simulation, we adjust a closure depth according to pertinent wave conditions based on the Hallermeier's analytical model (1978) having its roots on the Shield's parameter. Numerical results show that from 2017.4.26 to 2017.10.15 during which swells are prevailing, a shoreline advances due to the sustained supply of cross-shore sediment. It is also shown that a shoreline temporarily retreats due to the erosion by the yearly highest waves sequentially occurring from mid-October to the end of October, and is followed by gradual recovery of shoreline as high waves subdue and swells prevail. It is worth mentioning that great yearly circulation of shoreline completes when a shoreline retreats due to the erosion by the higher waves occurring from mid-March to the end of March. The great yearly circulation of shoreline mentioned above can also be found in the measured locations of shoreline on 2017.4.5, 2017.9.7, 2017.11.7, 2018.3.14. However, numerically simulated amount of shoreline retreat or advance is more significant than the physically measured one, and it should be noted that these discrepancies become more substantial for the case of RUN II where a closure depth is sustained to be as in the most morphology models like the Genesis (Hanson and Kraus, 1989).

• Journal of Hospice and Palliative Care
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• v.22 no.3
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• pp.105-116
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• 2019

Purpose: Pediatric palliative care (PPC) is emphasized as standard care for children with life-limiting conditions to improve the quality of life. In Korea, a government-funded pilot program was launched only in July 2018. Given that, this study examined various PPC delivery models in other countries to refine the PPC model in Korea. Methods: Target countries were selected based on the level of PPC provided there: the United Kingdom, the United States, Japan, and Singapore. Relevant literature, websites, and consultations from specialists were analyzed by the integrative review method. Literature search was conducted in PubMed, Google, and Google Scholar, focusing publications since 1990, and on-site visits were conducted to ensure reliability. Analysis was performed on each country's process to develop its PPC scheme, policy, funding model, target population, delivery system, and quality assurance. Results: In the United Kingdom, community-based free-standing facilities work closely with primary care and exchange advice and referrals with specialized PPC consult teams of children's hospitals. In the United States, hospital-based specialized PPC consult teams set up networks with hospice agencies and home healthcare agencies and provide PPC by designating care coordinators. In Japan, palliative care is provided through several services such as palliative care for cancer patients, home care for technology-dependent patients, other support services for children with disabilities and/or chronic conditions. In Singapore, a home-based PPC association plays a pivotal role in providing PPC by taking advantage of geographic accessibility and cooperating with tertiary hospitals. Conclusion: It is warranted to identify unmet needs and establish an appropriate PPD model to provide need-based individualized care and optimize PPC in South Korea.

• Clean Technology
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• v.25 no.2
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• pp.129-139
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• 2019

Bamboo is an evergreen perennial plant, and it is known as one of the most productive and fastest-growing plants in the world. It grows quickly in moderate climates with only moderate water and fertilizer. Traditionally in Asia, bamboo is used for building materials, as a food source, and as versatile raw materials. Bamboo as a biomass feedstock can be transformed to prepare activated carbon using the thermal treatment of pyrolysis. The effect of process variables such as carbonization temperature, activation temperature, activation time, the amount of steam, and the mixing ratio of phosphoric acid and bamboo were systematically investigated to optimize the preparation conditions. Steam activation was proceeded after carbonization with a vapor flow rate of $0.8{\sim}1.8mL-H_2O\;g-char^{-1}\;h^{-1}$ and activation time of 1 ~ 3 h at $700{\sim}900^{\circ}C$. Carbon yield and surface area reached 2.04 ~ 20.59 wt% and $499.17{\sim}1074.04m^2\;g^{-1}$, respectively, with a steam flow rate of $1.4mL-H_2O\;g-char^{-1}\;h^{-1}$ for 2 h. Also, the carbon yield and surface area were 24.67 wt% and $1389.59m^2\;g^{-1}$, respectively, when the bamboo and phosphoric acid were mixed in a 1:1 weight ratio ($700^{\circ}C$, 2 h, $1.4mL-H_2O\;g-char^{-1}\;h^{-1}$). The adsorption of methylene blue into the bamboo activated carbon was studied based on pseudo first order and second order kinetics models. The adsorption kinetics were found to follow the pseudo second order model, which is governed by chemisorption.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.6
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• pp.49-62
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• 2018

The purpose of this study is to develop an assessment index for the selection of promising start-ups, which will enhance the efficiency of program that support start-ups. In order to develop assessment models for selecting start-ups, three major research steps were conducted. First, this study attempted to theoretically redefine the assessment index from the perspective of the Balanced Scorecard (BSC) through a literature review. Second, major assessment index were derived using Delphi technique for experts in start-up areas. Third, weights were derived by applying AHP technique to calculate the importance of each index. The results of this study are summarized as follows. First, this study attempted to apply the assessment model for selecting start-ups from the Balanced Scorecard (BSC) view through the previous study review. Second, the final major questions were derived with sufficient opinions collected and structured survey of leading start-up experts in areas related to research subjects and elicited the most representative questions. Third, the results of applying the weights of the main selected assessment index, commercialization viewpoint is the most priority, followed by market view, technology development viewpoint, and organizational capability viewpoint. In the middle section, th ability to make products in the commercialization viewpoint, market competitiveness in the market, product discrimination capacity in the technology development perspective, and the ability of the entrepreneur in the organizational capacity perspective were important. Overall important items were found to be in the order of the capabilities of entrepreneurs, market competitiveness, product fire capability, and product discrimination. The importance of small items was highest priority for comparative excellence of competing products, and the degree of marketability, capacity of entrepreneurship, ability to raise capital, desire for entrepreneurship, and passion were shown. The results of this study presented a conceptual alternative to the preceding study on the development of existing selection assessment indexes. And it provides meaningful and important implications as an attempt to develop more sophisticated indicators by overcoming the limitations of empirical research on only some of the evaluation metrics.

• Food Engineering Progress
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• v.21 no.1
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• pp.42-48
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• 2017

The purpose of this study was to verify the drying characteristics of steamed sweet potato and to establish optimal drying conditions for far-infrared drying of steamed sweet potato. 4 kg of steamed sweet potato was sliced to thicknesses of 8 and 10 mm, and dried by a far-infrared dryer until a final moisture content of $25{\pm}0.5%$. The far-infrared dryer conditions were an air velocity of 0.6, 0.8 m/s and drying temperature of 60, 70, and $80^{\circ}C$. The results can be summarized as follows. The drying time tended to be reduced as temperature and air velocity for drying increased. The Lewis and Modified Wang and Singh models were found to be suitable for drying of steamed sweet potato by a far-infrared dryer. The color difference was 35.09 on the following conditions: Thickness of 8 mm, temperature of $80^{\circ}C$, and air velocity of 0.8 m/s. The highest sugar content ($59.11^{\circ}Brix$) was observed on the conditions of a thickness of 8 mm, temperature of 80, and air velocity of 0.8 m/s. Energy consumption decreased on the conditions of higher temperature, slower air velocity, and thinner steamed sweet potato.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.