• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.847-852
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• 2011

Green manure crops are mainly used to reduce the application of chemical fertilizers. Mixture of green manure crops have beneficial effects in agroecosystem. In this study, experiments were conducted to evaluate the effects of monoculture and mixtures of crimson clover (Trifolium incarnatum) on rice growth and yield in paddy. This experiment was conducted at Sinheung series (fine loamy, mixed, nonacid, mesic family of Fluvaquentic Endoaquepts) from Oct. 2007 to Oct. 2009 at the National Institute of Crop Science (NICS), RDA, Suwon, Gyeonggi province, Korea. Seeding rates of crimson clover (CC) were consisted of monoculture (CC2, 3, 4 kg and hairy vetch 5 kg $10a^{-1}$) and mixtures (CC 2 + barley 7, CC 3 + barley 7, CC 4 + barley 7, and CC2 + hairy vetch $5kg\;10a^{-1}$). Seeds were drilled by partial tillage machine on 9th Oct. in 2007. Monoculture and mixture of crimson clover as a green manure crop was incorporated in soil for rice cultivation on 15th May in 2008. Chemical fertilizers had not been applied to monoculture and mixture plots. The biomass and N production of monoculture plots were lower than mixture plots. The biomass and N production of CC 2 + hairy vetch $5kg\;10a^{-1}$ plot were the highest among mixtures treatments. In rice growing season, ammonium nitrogen concentrations in soil were a little high trends at CC 2 + hairy vetch $5kg\;10a^{-1}$ plot. And soil bulk density and porosity were improved at mixture plot after rice harvesting. The rice yield of CC 2 + hairy vetch $5kg\;10a^{-1}$ plot was not significantly different from conventional practice plot. These results indicated that cropping of crimson clover with hairy vetch mixture was better than barley mixture for environmental friendly rice cultivation.

• Journal of Food Hygiene and Safety
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• v.36 no.5
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• pp.400-406
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• 2021

The purpose of this study was to evaluate the microbial contamination level of Korean traditional rice cakes (Garaetteok, Injeolmi, Gyeongdan), as well as manufacturing environment of small-sized businesses in Korea. The contamination levels of total aerobic bacteria, coliforms, and Bacillus cereus in raw materials were 3.76-4.48, 2.21-4.14, and 1.02-1.15 log CFU/g respectively. On the other hand, Escherichia coli was not found. It has been found that the contamination level of total aerobic bacteria, coliforms, and B. cereus in the raw material decreased after the washing process, but it increased again during the soaking and grinding process. However, after the steaming stage, the contamination level increased again during the molding and cooling process, suggesting the need to take cautions in managing cooling water and molded rice cakes in the process. These results suggest that the safe management of cooling water and taking cautions in the drying process after steaming of rice cakes are necessary for controlling cross-contamination. No E. coli was detected during the manufacturing process involving all tested rice cakes. The microbial contamination level of manufacturing environment such as rice grinder and rice cake forming machine was high. Therefore, in terms of food safety strategy, it is necessary to consider introducing systematic cleansing and disinfection procedure to processing equipment and environment for the sake of reducing microbiological risks.

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.29-40
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• 2019

The paper investigates the minimal pair of Korean phonemes quantitatively. To achieve this goal, I calculated the number of consonant minimal pairs in the syllable-initial position as both raw counts and relative counts, and analyzed the part of speech relations of the two words in the minimal pair. "Urimalsaem" was chosen as the object of this study because it was judged that the minimal pair analysis should be done through a dictionary and it is the largest among Korean dictionaries. The results of the study are summarized as follows. First, there were 153 types of minimal pairs out of 337,135 examples. The ranking of phoneme pairs from highest to lowest was 'ㅅ-ㅈ, ㄱ-ㅅ, ㄱ-ㅈ, ㄱ-ㅂ, ㄱ-ㅎ, ${\ldots}$, ㅆ-ㅋ, ㄸ-ㅋ, ㅉ-ㅋ, ㄹ-ㅃ, ㅃ-ㅋ'. The phonemes that played a major role in the formation of the minimal pair were /ㄱ, ㅅ, ㅈ, ㅂ, ㅊ/, in that order, which showed a high proportion of palatals. The correlation between the raw count of minimal pairs and the relative count of minimal pairs was found to be quite high r=0.937. Second, 87.91% of the minimal pairs shared the part of speech (same syntactic category). The most frequently observed type has been 'noun-noun' pair (70.25%), and 'vowel-vowel' pair (14.77%) was the next ranking. It can be indicated that the minimal pair could be grouped into similar categories in terms of semantics. The results of this study can be useful for various research in Korean linguistics, speech-language pathology, language education, language acquisition, speech synthesis, and artificial intelligence-machine learning as basic data related to Korean phonemes.

• Journal of the Korean Society of International Agriculture
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• v.30 no.4
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• pp.292-299
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• 2018

In a survey in which farmers were asked about their levels of satisfaction with agricultural machines, Japanese products scored higher than local products by 1.2, 1.3, and 1.4 times for tractors, combine harvesters, and rice transplanter, respectively. Japanese products corresponded to generally high satisfaction levels in terms of operating performance, operability, frequency of breakdowns, and durability, excluding sales price and after-sales services. Effective countermeasures through quality improvement are therefore necessary for Korean products. Furthermore, a survey of dealers showed that the components and consumables for core agricultural machines had high frequencies of breakdowns and repairs. Four major components of tractors represented 85.3% of all breakdowns and repairs, five components of combine harvesters represented 89.6%, and three components of rice transplanters represented 80.5%. Moreover, a comparison of the technological levels between local and imported machines showed that the local machines' levels were at 60-100% for tractors, 70-100% for combine harvesters, and 70-95% for rice transplanters. Small and mid-sized tractors, 4 interrow combine harvesters, and 6 interrow rice transplanters showed similar levels of technology. The results of the analysis suggest that action is urgently needed at a policy level to establish an agricultural machinery component research center for the development, production, and supply of commonly-used components, with the participation of manufacturers of agricultural machines and components, in order to enhance the competitiveness of local manufacturers and to revitalize the agricultural machine market.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.15-33
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• 2021

In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.423-431
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• 2022

Due to increase of interest in 'carbon neutrality', attempts at agricultural use of CO₂ are increasing. In this study, we used the dry-ice made by CO₂ as by-product in thermoelectric power plant on CO₂ fertilization for production of leafy vegetable in greenhouses. The dry-ice was supplied on three leafy vegetable farms (Allium tuberosum Rottl. ex Spreng, Aster scaber, and Oenanthe stolonifera DC.) located in Hadong, Gyeongsangnamdo. Two greenhouses were used in each leaf vegetable crops, one greenhouse used as the control (non-treatment), other greenhouse used as supplied CO₂. For CO₂ fertilization, a gas sublimated from dry ice was supplied to the greenhouse using a specially designed prototype supply machine. A. tuberosum greenhouse has no difference of CO₂ concentration between the control, and CO₂ fertilization and shown high CO₂ concentration both greenhouses. However, the CO₂ concentrations in A. scaber and O. stolonifera greenhouses were increased in CO₂ fertilization treatment. The growth of A. scaber and O. stolonifera were increased in CO₂ fertilization, and the yield also increased to 36% and 25% than the control, respectively. As a result of economic analysis, the A. scaber has increase of income rate, however A. tuberosum and O. stolonifera has decreased income rate. Thus, the use of the dry-ice made by CO₂ as by-product in thermoelectric power plant has possibility to increase productivity of the leafy vegetable in greenhouse and have agricultural use value.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.22-27
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• 2023

This experiment was conducted to confirm the possibility of preparing Sorghum×sudangrass hybrid artificial hay using far-infrared rays in Korea. The machine used in this experiment is a drying device based on far-infrared rays, and is designed to control temperature, air flow rate, far-infrared radiation amount, and air flow speed. The Sorghum×sudangrass hybrids harvested in late September were wilted in the field for one day, and a drying test was performed on them. Conditions for drying were performed by selecting a total of 7 conditions, and each condition induced a change in radiation amount in a single condition (42%) and two steps (4 treatments) and three steps (2 treatments). The speed of the air flow in the device was fixed at 60 m/s, and the run time was changed to 30, 60, and 90 minutes. The average dry matter (DM) content was 82.84%. The DM content was 59.94 and 76.91%, respectively, in drying conditions 1 and 3, which were not suitable for hay. In terms of drying rate, it was significantly higher than 80% in the 5, 6 and 7 treatment, and power consumption was slightly high with an average of 5.7 kw/h. As for the feed value according to each drying condition, the crude protein (CP) content increased as the drying time increased, and there was no significant difference between treatments in ADF, NDF, IVDMD and TDN content. In terms of RFV, treatment 1, which is a single condition, was significantly lower than the complex condition. Through the above results, it was determined that the drying conditions 4 and 5 were the most advantageous when considering the drying speed, power consumption, and quality.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Journal of Biosystems Engineering
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• v.16 no.4
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• pp.384-398
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• 1991

A computer program was developed to select the optimum size of farm machine and analyze its operation costs according to various farming conditions. It was written in FORTRAN 77 and BASIC languages and can be run on any personal computer having Korean Standard Complete Type and Korean Language Code. The program was developed as a user-friendly type so that users can carry out easily the costs analysis for the whole farm work or respective operation in rice production, and for plowing, rotarying and pest controlling in upland. The program can analyze simultaneously three different machines in plowing & rotarying and two machines in transplanting, pest controlling and harvesting operations. The input data are the sizes of arable lands, possible working days and number of laborers during the opimum working period, and custom rates varying depending on regions and individual farming conditions. We can find out the results such as the selected optimum combination farm machines, the overs and shorts of working days relative to the planned working period, capacities of the machines, break-even points by custom rate, fixed costs for a month, and utilization costs in a hectare.