• Research in Plant Disease
• /
• v.21 no.4
• /
• pp.309-314
• /
• 2015

Xylogone ganodermophthora (Xg) is an ascomycetous fungus that causes yellow rot on cultivated Ganoderma lucidum. Previously, we reported in vitro antifungal activities of a Xg culture extract against several watermelon pathogens. In 2014, we conducted greenhouse experiments to evaluate the control efficacy of a water extract of cultured Xg on watermelon powdery mildew (WPM). The test material (stock solution, ca. $4,000{\mu}g/ml$) was prepared by an autoclaved Xg culture in water at a ratio of 800 g of culture per 6 liter of water, and then filtering it through filter paper. Six foliar applications of the solutions (diluted 100- and 1,000-fold) significantly suppressed the formation of conidiophores and conidia. The inhibitory effect of aqueous potassium phosphonate solution on the disease and its phytotoxicity was tested. Phytotoxicity on watermelon plants was observed at concentrations of 1,000 and $2,000{\mu}g/ml$ as irregular brownish spots. The control efficacies against WPM were 91.9% at $2,000{\mu}g/ml$, 64.9% at $1,000{\mu}g/ml$, and 62.2% at $500{\mu}g/ml$.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.697-703
• /
• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of Korean Society of Transportation
• /
• v.22 no.6
• /
• pp.159-173
• /
• 2004

Using a generalized translog multiproduct cost function model, this paper examines economies of scale and scope in the vertically-integrated Korean railway industry. The paper then conceptualizes that the Korea National Railroad (KNR) produces four outputs (passenger-kilometers, ton-kilometers of freight, average length of passenger trips, and average length of freight haul) using three input factors(labor, fuel and maintenance, and rolling stock and capital). Using time series data collected from the KNR's annual records for the years from 1977 to 2002, the simultaneous equation system consisting of a cost function and two input share equatins is estimated with the Zellner's iterative seemingly unrelated regression. The findings show that the cost function corresponding to a non-Cobb-Douglas, non-homothetic, and non-homogeneous production technology adequately represents the KNR's cost structure. On the other hand, the Korean railway industry experiences sizeable overall scale economies, which result from substantial product-specific scale economies associated with passenger-kilometers and freight ton-kilometers and from scope economies associated with their joint production. In addition, the magnitude of economies of scope is influenced largely by the ratio of passenger trips, and has increased over time as the former has increased while the latter has decreased.

• The Korean Journal of Applied Statistics
• /
• v.23 no.1
• /
• pp.63-71
• /
• 2010

Business entrepreneurs reflect their views of domestic and foreign economic activities on their operation for the growth of their business. The decision, forecasting, and planning based on their economic sentiment affect business operation such as production, investment, and hiring and consequently affect condition of national economy. Business survey index(BSI) is compiled to get the information of business entrepreneurs' economic sentiment for the analysis of business condition. BSI has been used as an important variable in the short-term forecasting models for business cycle analysis, especially during the the period of extreme business fluctuations. Recent financial crisis has arised extreme business fluctuations similar to those caused by currency crisis at the end of 1997, and brought back the importance of BSI as a variable for the economic forecasting. In this paper, the meaning of BSI as an economic sentiment index is reviewed and a GUIDE regression tree is constructed to find out the factors which affect on BSI. The result shows that the variables related to the stability of financial market such as kospi index(Korea composite stock price index) and exchange rate as well as manufacturing operation ratio and consumer goods sales are main factors which affect business entrepreneurs' economic sentiment.

• The KIPS Transactions:PartD
• /
• v.9D no.2
• /
• pp.185-194
• /
• 2002

Most of mobile computing systems allow mostly read-only transactions from mobile clients for retrieving various types of Information such as stock data, traffic information and news updates. Since previous concurrence control protocols, however, do not consider such a particular characteristics, the performance degradation occurs when previous schemes are applied to the broadcast environment. In this paper, we propose OCC/UTF(Optimistic Concurrence Control with Update Transaction First) that is most appropriate for broadcast environment. OCC/UTF lets a query transaction, that has already read the data item which was invalidated by update transaction, read again the same data item without the abort of the query transaction due to non-serializability. Therefore, serializable order is maintained and the query transaction is committed safely regardless of commitment of update transactions. In OCC/UTF, Clients need not require server to commit their query transactions. Because of broadcasting the validation reports including values updated recently to clients, it reduces the overhead of requesting recent values from the server and the server need not also re-broadcast the newest values. As a result, OCC/UTF makes full use of the asymmetric bandwidth. It can also improve transaction throughput by increasing the commit ratio of query transactions as much as possible.

• Korean Journal of Soil Science and Fertilizer
• /
• v.29 no.1
• /
• pp.44-52
• /
• 1996

Humic substances of 17 organic fertilizers available on the market were the objects of study. The list of ingredients for formulation of them comprised fish meal. bone meal, oil-cakes, brewer's grains, peat, sawdust, wood bark, zeolite, soil conditioner, live-stock droppings, amino acid fermentation byproduct, chaff, limestone and others. Humic and fulvic acids were isolated from those substances and given chemical and spectroscopic analyses. Nutritional values of the organic fertilizers showed big diversity. Humification of organic matter was incomplete for some of the fertilizers as indicated by a high C/N ratio. Extractable humic acid percentage was higher, in general, than that of fulvic acid. Also the relative content of humin increased with advanced humification. Total acidity was closely related to phenolic hydroxyl groups. Relationships between carboxyl and hydroxyl groups. and carboxyl and alcoholic hydroxyl groups were very significant. Ultraviolet and visible light absorption spectra of humic and fulvic acids were substantially similar. The types of humic acids were B. P, and Rp. Two humic acids of the 17 samples belonged to B type. 3 to P type and all the rest to Rp type.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.2D
• /
• pp.299-309
• /
• 2006

Railway noise and vibration has been recognized as major problems with the speed-up of rolling stock. As a kind of solution to these problems, the decrease of stiffness of track pad have been tried. However, in this case, overturning of rail due to lateral force should be considered because it can have effect on the safety of running train. Therefore, above two things - decrease of stiffness of track pad and overturning of rail due to lateral force - should be considered simultaneously for the appropriate determination of spring coefficient of track pad. With this viewpoint, minimum spring coefficient of track pad is estimated through the comparison between the theoretical relationship about the overturning of rail and 3-dimensional FE analysis result. Two kinds of Lateral force and wheel load are used as input loads. Extracted values from the conventional estimation formula and the Shinkansen design loads are used. It is found that the overturning of rail changes corresponding to the change of the stiffness of track pad and the ratio of lateral force to wheel load. Moreover, it is found that the analysis model can have influence on the results. Through these procedure, minimum spring coefficient of track pad is estimated.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.139-153
• /
• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Bio-Environment Control
• /
• v.27 no.4
• /
• pp.363-370
• /
• 2018

The succulent plants of Echeveria genus are in increasing demand worldwide, but it is difficult to supply good quality young plants throughout the year because propagation efficiencies are depend on cultivar and environmental factors. This study was carried out to investigate the propagation efficiencies of leaf cutting in Echeveria cultivars at different LED light qualities in a closed-type plant factory system. Leaf cuttings cut from stock plants of six difficult-to-propagated cultivars 'Afterglow (AG)', 'Berkeley Light (BL)', 'Mason (MS)', 'Subsessilis Light (SL)', 'Cream Tea (CT)', and 'Ben Badis (BB)' were put into cutting media in the plant factory system maintained at a temperature of $24{\pm}2^{\circ}C$ and relative humidity of $60{\pm}10%$, and watered with over-head irrigation twice a week. Cuttings were irradiated with sole or mixed red (R, 660 nm), blue (B, 450 nm), green (G, 530 nm), and far-red (FR, 730 nm) LEDs as follows: R10, R8+B2, R5+B5, R7+B2+FR1, and R7+B2+G1. PPFD just above the cuttings was $200{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ and photoperiod was 16/8 (light/dark) hours. As a result, propagation efficiencies were dependent on cultivar. Rooting and shooting were relatively easy in 'SL' but shoot formation in 'AG' was very difficult. Light qualities from LEDs also affected plant regeneration. Light conditions with a higher ratio of B, R5+B5, R7+B2+FR1, and R7+B2+G1, promoted shoot formation and growth but inhibited rooting and root growth. R10 and R8+B2 with a higher ratio of R promoted rooting and root growth and inhibited shoot formation and growth of cuttings. In addition, the treatment with FR increased leaf size and biomass of the all plants. Therefore, further studies are needed to investigate the optimum compositions of LED light quality for the improvement of leaf cutting efficiency in difficultto-propagated Echeveria cultivars.

• Korean Journal of Poultry Science
• /
• v.39 no.3
• /
• pp.177-182
• /
• 2012

This work was carried out to investigate the performance of growing period of two-way crossed of Korean native chickens parental stocks. A total of four hundred eighty female 2-crossbred chicks were used in this study and they were from National Institute of Animal Science. Groups were four crossbreds (4 replications/crossbred, 30 birds/replication) as A) C strain ${\times}$ Y strain, B) C strain ${\times}$ L strain, C) C strain ${\times}$ G strain and D) C strain ${\times}$ W strain. Body weight of A crossbred was the highest at the age of 8 week (P<0.05) and that of D strain was the lowest for growing period (P<0.05). Body weights of A and B crossbreds were higher than those of C and D crossbreds at the 12 and 16 weeks (P<0.05). Weekly body weights of A and B crossbreds were higher than C and D crossbreds (P<0.05), and weekly body weight of B crossbred was higher compared to other crossbreds at 0~20 weeks old. Weekly feed intake of D crossbred was the lowest among all crossbreds at 0~12, 0~16 and 0~20 weeks old (P<0.05). Weekly feed conversion ratio of C crossbred was the highest among crossbreds (P<0.05). These results can give the basic information for growth related data in 2-way crossbreed Korean Native Chickens, which can be used for the parental stocks for the laying-type of Korean native commercial chickens.