• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.31-37
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• 2018

This study evaluates the dynamic tensile behavior of HPFRCC according to compressive strength levels of 100, 140 and 180 MPa. Firstly, the compressive stress-strain relationship of 100, 140 and 180 MPa class HPFRCC was analyzed. As a result, the compressive strengths were 112, 150 and 202 MPa, respectively, and the elastic modulus increased with increasing compressive strength. The static tensile strengths of HPFRCC of 100, 140 and 180 MPa were 10.7, 11.5 and 16.5 MPa, and tensile strength also increased with increasing compressive strength. On the other hand, static tensile strength and energy absorption capacity at 100 and 140 MPa class HPFRCC showed no significant difference according to the compressive strength level. It was influenced by the specification of specimen and the arrangement of steel fiber. As a result of evaluating the dynamic impact tensile strength of HPFRCC, tensile strength and dynamic impact factor of all HPFRCCs tended to increase with increasing strain rate from 10-1/s to 150/s. In the same strain rate range, the DIF of the tensile strength was measured higher as the compressive strength of HPFRCC was lower. It is considered that HPFRCC of 100 MPa is the best in terms of efficiency. Therefore, it is advantageous to use HPFRCC with high compressive strength when a high level of tensile performance is required, and it is preferable to use HPFRCC close to the target compressive strength for more efficient approach at a high strain rate such as explosion.

• Nuclear Engineering and Technology
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• v.47 no.6
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• pp.678-699
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• 2015

Although the harsh space environment imposes many severe challenges to space pioneers, space exploration is a realistic and profitable goal for long-term humanity survival. One of the viable and promising options to overcome the harsh environment of space is nuclear propulsion. Particularly, the Nuclear Thermal Rocket (NTR) is a leading candidate for nearterm human missions to Mars and beyond due to its relatively high thrust and efficiency. Traditional NTR designs use typically high power reactors with fast or epithermal neutron spectrums to simplify core design and to maximize thrust. In parallel there are a series of new NTR designs with lower thrust and higher efficiency, designed to enhance mission versatility and safety through the use of redundant engines (when used in a clustered engine arrangement) for future commercialization. This paper proposes a new NTR design of the second design philosophy, Korea Advanced NUclear Thermal Engine Rocket (KANUTER), for future space applications. The KANUTER consists of an Extremely High Temperature Gas cooled Reactor (EHTGR) utilizing hydrogen propellant, a propulsion system, and an optional electricity generation system to provide propulsion as well as electricity generation. The innovatively small engine has the characteristics of high efficiency, being compact and lightweight, and bimodal capability. The notable characteristics result from the moderated EHTGR design, uniquely utilizing the integrated fuel element with an ultra heat-resistant carbide fuel, an efficient metal hydride moderator, protectively cooling channels and an individual pressure tube in an all-in-one package. The EHTGR can be bimodally operated in a propulsion mode of $100MW_{th}$ and an electricity generation mode of $100MW_{th}$, equipped with a dynamic energy conversion system. To investigate the design features of the new reactor and to estimate referential engine performance, a preliminary design study in terms of neutronics and thermohydraulics was carried out. The result indicates that the innovative design has great potential for high propellant efficiency and thrust-to-weight of engine ratio, compared with the existing NTR designs. However, the build-up of fission products in fuel has a significant impact on the bimodal operation of the moderated reactor such as xenon-induced dead time. This issue can be overcome by building in excess reactivity and control margin for the reactor design.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.202-211
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• 2015

Companies build the factory automation system to improve management effectiveness and productivity as prime strategies for sustainable growth. But most companies undergo various trials and errors while carrying out the project without elaborate preparation stage for factory automation. In this study, we tried to verify what factors are critical to effectively building distribution automation system, which is a branch of factory automation system. A consulting model for setting up a Material Handling Automation System by utilizing the Stage-Gate Process, which is product development process was studied. 29 material handling automation projects carried out between the year 1990 to 2013 at K-Company were selected. Interviews with the project managers, operators and maintenance personnels, various records and current status of the projects were used as data for structural equations based on the Milan consulting model and existing researches of factory automation, CIM for material handling automation. Creating effective basis of production, material handling system and energy saving system with expert review, when preparing a material handling automation project, help promote the project planning thus contributing to the performance of the resulting system, which appears though rather weakly in our data. Also the effect of material handling automation can be enhanced through sufficient and effective links to the relevant environments such as production logistics management and automated warehouses. More detailed planning characteristics of project promotion or some time-series data of effective Material Handling Automation System could enhace furthur studies. We propose a consulting model for setting up an efficient material handling automation system.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.201-210
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• 2022

Expanded polystyrene (EPS) is the most commonly used fresh food refrigeration insulation in Korea and Europe. Moreover, as the use of disposable packaging materials has increased significantly along with non-face-to-face delivery services since the COVID-19 crisis, social issues related to waste disposal are also being raised. Therefore, in this study, the life cycle of EPS boxes for fresh food is focused on the factors that have a large difference between incineration and landfill including recycling in Europe and Korea in the disposal process after use, and raw materials and energy in the manufacturing process, which account for a large portion of the environmental impact value. We tried to compare the environmental impact of evaluation. Overall, the raw material production stage, box manufacturing stage, and packaging stage have similar processes in Europe and Korea, but unlike Europe, Korea, which lacks landfills and incineration facilities, has focused on expanding the recycling rate. It was necessary to do an environmental impact assessment. Data affecting the environment were derived based on 2019 and 2020 data for Korea and 2017 and 2020 data for Europe. In order to predict the future environmental impact assessment, assumptions about the disposal rate in 2025 and 2030 were introduced and evaluated. As a result of this study, it was found that the raw material production stage of EPS boxes, which have similar processes in both Korea and Europe, has the greatest effect on the global warming effect of Korean EPS boxes. However, Korea, which has a relatively high recycling rate in the disposal process compared to incineration and landfill, showed better environmental performance than Europe in most impact indicators except freshwater eutrophication. In particular, Korea has increased the overall recycling rate compared to Europe by replacing various recyclable materials such as building materials and sundries with XPS (extruded polystyrene) recycled materials. In conclusion, it was found that increasing the recycling rate rather than incinerating and landfilling EPS boxes for fresh food in the domestic EPS industry has relatively less environmental load compared to Europe.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Bio-Environment Control
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• v.18 no.3
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• pp.177-184
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• 2009

For the purpose of reducing the cost for greenhouse operation by acquiring the electric power necessary for it, this study installed a solar photovoltaic system on the roof of the building adjacent to green-houses and experimentally examined the quantity of power generation based on weather conditions. The results of the study are as per the below: The maximum, average and minimum temperature while the experiment was conducted was $0.4{\sim}34.1,\;-6.1{\sim}22.2$, and $-14.1{\sim}16.7^{\circ}C$ respectively, and the solar radiation was $28.8MJ{\cdot}m^{-2}$ (maximum), $14.9MJ{\cdot}m^{-2}$ (average), and $0.6MJ{\cdot}m^{-2}$ (minimum). The quantity of electric power didn't increase in proportion to the quantity of solar radiation and instead, it was almost consistent around 750W. Daily maximum, average and minimum consumption of electric power was 5.2kWh, 2.5kWh and 0kWh respectively. Based on the average electric power consumption of the system used for this experiment, it was sufficient in case the capacity and the working time of a hot blast heater are small, but it was short in case they are big. In case the capacity of the hot blast heater is big, the average electric power quantity will be sufficient for array area $21m^2$, about three times of the present area. In summer when the temperature of the array becomes high, the generation of electric power didn't increase in proportion to the quantity of solar radiation, but this experiment result shows a high correlation between two factors (coefficient of correlation 0.84).