• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.6
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• pp.585-592
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• 2011

The enhancement of the cold-start capability of polymer electrolyte fuel cells is of great importance in terms of the durability and reliability of fuel-cell vehicles. In this study, vanadium oxide films deposited onto the flat surface of metallic bipolar plates were synthesized to investigate the feasibility of their use as an efficient self-heating source to expedite the temperature rise during startup at subzero temperatures. Samples were prepared through the dip-coating technique using the hydrolytic sol-gel route, and the chemical compositions and microstructures of the films were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and field-emission scanning electron microscopy. In addition, the electrical resistance hysteresis loop of the films was measured over a temperature range from -20 to $80^{\circ}C$ using a four-terminal technique. Experimentally, it was found that the thermal energy (Joule heating) resulting from self-heating of the films was sufficient to provide the substantial amount of energy required for thawing at subzero temperatures.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• 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.

• Applied Microscopy
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• v.39 no.4
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• pp.311-317
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• 2009

This study was performed using animals to confirm the effect of tourmaline-ionized water (TIW) the properties of which were changed by tourmaline energy and electric discharge. In the ICR mice fed high-fat diet, body weight increasing rate of the TIW-treated group (Exp) was generally decreased and moreover exhibited significance at 11th week (P<0.05) compared with the control (Con) group fed distilled water, although water intake of the Exp group was lower than that of the Con group. In the ICR mice with $CCl_4$-induced hepatotoxicity, AST and ALT activities of the Exp group were not significant but showed some decreasing trend, and histological damage of liver was less compared with thatof the Con group. On the study of ethanol-induced hangovers in Sprague-Dawley rat, blood alcohol concentration was significantly decreased (P<0.01), activity of GST, antioxidant enzyme related to the alcohol metabolism, was increased in liver tissue (P<0.05), and AST and ALT show a tendency to be decreasedin the Exp group. These results suggest that drinking TIWhas not only some obesity preventing effect but also an alcohol detoxification effect and liver protecting effect in vivo. It is supposed due to a structural change of water cluster and a property which maintains the changed structure through tourmaline energy and electric discharge. Therefore, TIW has a potentiality to be developed as functional water with several beneficial effects as well as for daily drinking, but further study on the mechanism related with efficacy will be necessary.

• Journal of the Korean Wood Science and Technology
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• v.48 no.3
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• pp.271-282
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• 2020

Sewage treatment plants are social infrastructure of cities. The sewage distribution rate in Korea is reaching 94% based on the sewage statistics based in the year of 2017. In Korean sewage treatment plants, use of PAC (Poly Aluminum Chloride) accounts for 58%. It contains a large amount of impurities (heavy metal) according to the quality standards, however, there have been insufficient efforts to reinforce the standards or technically improve the quality, which resulted in secondary pollution problems from injecting excessive coagulant. Also, the increase in the use of chemicals is leading to the increases in the annual amount of sewage sludge generated in 2017 and the need to reuse sludge. As such, this study aims to verify the possibility of reusing sludge by evaluating the stability of heavy metals based on the injection of coagulant mixture during water treatment which uses the torrefield wood powder and natural materials, and evaluating the sedimentation and heating value of sewage sludge. As a result of analyzing heavy metals (Cr, Fe, Zn, Cu, Cd, As, Pb, and Ni) from the coagulant mixture and PAC (10%), Cr, Cd, Pb, Ni, and Hg were not detected. As for Zn, while its concentration notified in the quality standards for drinking water is 3 mg/L, only a small amount of 0.007 mg/L was detected in the coagulant mixture. Maximum amounts of over double amounts of Fe, Cu, and As were found with PAC (10%) compared to the coagulant mixture. Also, an analysis of sludge sedimentation found that the coagulant mixture showed a better performance of up to double the speed of the conventional coagulant, PAC (10%). The dry-basis lower heating value of sewage sludge produced by injecting the coagulant mixture was 3,378 kcal/kg, while that of sewage sludge generated due to PAC (10%) was 3,171 kcal/kg; although both coagulants met the requirements to be used as auxiliary fuel at thermal power plants, the coagulant mixture developed in this study could secure heating values 200 kal/kg higher than the counterpart. Therefore, utilization of the coagulant mixture for water treatment rather than PAC (10%) is expected to be more environmentally stable and effective, as it helps generating sludge with better stability against heavy metals, having a faster sludge sedimentation, and higher heating value.