• Research in Plant Disease
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• v.12 no.2
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• pp.69-74
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• 2006

Basal rot of freesia caused by a Sclerotium sp. occurred at Incheon areas. Incidence of the disease reached up to 45% and averaged 17.0% in the fields. Typical symptoms consisted of sheath dry and leaf blight due to rots on basal leaves. The causal fungus was identified as Sclerotium sp. based on following mycological characteristics. The fungus formed sclerotia on cultural media and plant tissues, but did not produce asexual spores. On cultural medium, aerial mycelia of the fungus changed color from white to clay with cultural age and smelled musty odor. Numerous irregular and elliptical black microsclerotia of the fungus were formed on potato dextrose agar (PDA) after 5 days of incubation at $25^{\circ}C$ and sized $115{\sim}200{\times}95{\sim}150 (av. 145{\sim}126.5){\mu}m$. The fungus grew at $10{\sim}32^{\circ}C$ and $pH 4.0{\sim}8.5$. However, the optimal temperature and pH for mycelial growth of the fungus were $24^{\circ}C$ and 5.5 respectively. The isolate showed present pathogenicity to not only freesia but gladiolus in the pathogenicity test, and the symptoms were similar to those observed in the fields. Basal rot of freesia caused by Sclerotium sp. is firstly reported in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.493-502
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• 2019

Recently, the installation of renewable energy including PV systems in distribution systems has increased energetically to cope with climate change and energy shortages according to the government's policy of renewable energy 3020. On the other hand, the electrical performance and lifespan of PV modules installed outdoors can be decreased considerably due to a range of deterioration phenomena depending on the ambient environmental factors. To overcome these problems, replacement of degraded PV modules with new ones is increased before the lifespan guaranteed by the makers of PV modules. Therefore, this paper proposes a disposal diagnosis algorithm to evaluate the time interval of the optimal replacement for PV modules according to performance degradation rate of PV modules. In addition, this study modeled an economic evaluation, which is composed of the cost and benefit of PV systems. From the simulation results based on the proposed modeling and algorithm to consider the performance degradation rate specifically, the replacement approach was found to have the best B/C ratio in 10th year with a 3[%] degradation rate and the disposal diagnosis algorithm of PV modules are useful and practical tools for economic evaluations of the replacement of PV modules.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.509-514
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• 2015

Indicators of environmental conditions qualitatively and quantitatively describe the state of the environment and natural resources, and the OECD (Organization for Economic Cooperation and Development) has initiated and suggested AEIs (Agri-Environmental Indicators) to assess trends over time of the effects of agriculture on the environment and the effectiveness and efficiency of agricultural and environmental policy measures since 1990's. This study aims to develop the state indicators of water quality for agricultural water, surface and groundwater, to evaluate the environmental impacts of agricultural activities and policies by qualifying the environmental levels of a nation. Status indicators were calculated according to the agricultural water quality standards of OECD and Korea, and their trends were analyzed over time. Particularly, nitrate ($NO_3{^-}$) status indicators of ground water in 2013 were significantly lower than the ones in 2000. Overall, the water quality indicators of surface water in 2013 were higher than the ones in 2012, except for pH and DO. The groundwater quality indicators in 2013 were lower than those of previous years. The optimal management indicators were calculated to assess agricultural surface water and groundwater quality. The findings of this study indicated that the state indicators could play a significant role to establish policies and procedures for managing and conserving water resources. This study also discussed water pollution caused by agricultural and industrial effluents.

• 한국노년학
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• v.38 no.4
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• pp.963-979
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• 2018

The purpose of this study was to identify frailty profiles based on physical, psychological, and social domains of functioning and to examine the associated factors showing the differences among frailty profiles. Respondents were 70 years and older(n=403) and latent class analysis was applied to determine the optimal subgroups based on Tilberg Frailty Indicators which comprised of three domains(the physical, psychological, and social domain). Also, we performed multinominal logistic regression analysis to find out factors making differences among frailty profiles. Latent class analysis(LCA) identified three distinct types: multi-frail type(27.0%), psychologically frail type(26.8%), inadequate support type(46.2%). All three types had common difficulties in dealing with daily life problems and did not receive enough help with theses difficulties. Based on the results of the LCA three-class models, people in multi-frail type accumulated problems in physical and psychological domains and had partially social domain. On the other hands, psychologically frail type showed a relatively high anxiety disorder and depression. Lastly, people in inadequate support type reported the lack of helps, but they were relatively healthy. Comparing these groups with inadequate support type, people with multi-frail had lower educational level, poor nutritional management status and were less likely to participate in labor market. People in psychologically frail type were more likely to be male, to live in big cities rather than middle and small cities, and less likely to smoke. Based on these results, our results showed the multifaceted concept of frailty among Korean elderly people and we suggested several implications for preventing frail process.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.385-413
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• 2019

This study examines the determinants of net export of electricity among 30 European countries sharing electricity grid during the period of 1990~2014 by separating the sample period before and after 2005 in which ETS was introduced in Europe. The empirical method used in this study is generalize least squared one considering both heterogeneous and serial correlation in the balanced panel data. According to the empirical results, after 2005 introducing the ETS, holing energy resources, concentrating only on few electricity generation resources, and nuclear electricity generation had played more important role in net export of electricity, while renewable energy had negative effect on net export of electricity and coal and gas generation have no effect on net export after introduction of ETS in Europe probably because of high environmental cost. The policy implication of the results would be that reconsidering each country's optimal generation mix strategy and its role in case freely trading electricity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.626-631
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• 2019

The policy-making and technological development for the supply expansion of eco-friendly automobiles has been continuing, but the internal combustion engines still accounts for about 95%. Also, in order to meet the stricter emission regulations of internal combustion engines based on fossil fuels, the proportion of after-treatments for vehicles and (ocean going) vessels is gradually increasing. This study is a basic study for the post-Euro-VI exhaust response of CNG buses, and it is to investigate the basic characteristics according to Pd substitution transition metal effect, catalyst volume effect and space velocity. A catalysts was prepared and tested using a model gas reactor. The NGOC catalyst with 3Pd exhibited the highest catalytic activity with 22% at $300^{\circ}C$, 48% at $350^{\circ}C$ and about 75% at $500^{\circ}C$. 3Co NGOC containing 3wt% of transition metal was excellent in oxidation ability, and it was small in size of 2nm, and the degree of catalyst dispersion was improved and de-NO/CO conversion was high. The volume of the NGOC-LNT-SCR catalyst system was optimal in the combination of 1.5+0.5+0.5 with a total score of 165, considering $de-CH_4/NOx$ performance and catalyst cost. For SV $14,000h^{-1}$, the $CH_4$ reduction performance was the highest at about 20%, while the SV $56,000h^{-1}$ was the lowest at about 5%. If the space velocity is small, the flow velocity decreases and the time remaining in the catalyst volume become long, so that the harmful gas was reduced.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.147-176
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• 2019

This paper uses research on the Levelized Cost of Electricity (LCOE) in South Korea to conduct a simulation analysis on the impact of nuclear power dependency and usage rates on the social costs of power generation. We compare the $7^{th}$ basic plan for long-term electricity supply and demand, which was designed to increase nuclear power generation, to the $8^{th}$ basic plan for long-term electricity supply and demand that decreased nuclear power generation and increased renewable energy generation in order to estimate changes in social costs and electricity rates according to the power generation mix. Our environmental generation mix simulation results indicate that social costs may increase by 22% within 10 years while direct generation cost and electricity rates based on generation and other production costs may increase by as much as 22% and 18%, respectively. Thus we confirm that the power generation mix from the $8^{th}$ basic plan for long-term electricity supply and demand compared to the $7^{th}$ plan increases social costs of generation, which include environmental external costs.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.111-128
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• 2019

Subways are eco-friendly public transportation that can transport large numbers of passengers safely and quickly. It is necessary to predict the accurate number of passengers in order to increase public interest in subway. This study groups stations on Lines 1 to 9 of the Seoul Metropolitan Subway using clustering analysis. We propose one final prediction model for all stations and three optimal prediction models for each cluster. We found three groups of stations out of 294 total subway stations. The Group 1 area is industrial and commercial, the Group 2 ares is residential and commercial, and the Group 3 area is residential districts. Various data mining techniques were conducted for each group, as well as driving some influential factors on demand prediction. We use our model to predict the number of passengers for 8 new stations which are part of the 3rd extension plan of Seoul metro line 9 opened in October 2018. The estimated average number of passengers per hour is from 241 to 452 and the estimated maximum number of passengers per hour is from 969 to 1515. We believe our analysis can help improve the efficiency of public transportation policy.