• Journal of Mushroom
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• v.17 no.4
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• pp.224-229
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• 2019

The two most common mushroom species grown in Korea are pearl oyster mushroom (Pleurotus ostreatus) and king oyster mushroom (P. eryngii). In recent years, the production of king oyster mushroom greatly increased due to the automation of the cultivation facilities, and it became a major export mushroom owing to its excellent shelf life. However, the increase in the production of king oyster mushroom led to a decline in its market price; thus, necessitating the development of new mushroom species that could replace king oyster mushroom, to diversify the mushroom market for the benefit of both, the producers and the consumers. The Mushroom division at the National Institute of Horticultural & Herbal Science (NIHHS) reported the development of a new interspecific hybrid between P. ferulae and P. tuoliensis, referred to as 'Creamy.' Two parental strains KMCC00430 (Bisan2ho, P. ferulae) and KMCC00461 (P. tuoliensis) were selected based on the results of genetic resource analysis, and their monokaryons were collected. About 1,000 Mon-Mon crosses were performed and 73 of them were selected. Following repeated cultivation tests and strain analyses, we selected strain 7773, which had a bright creamy pileus and a thick straight stipe, and named it 'Creamy.' Optimum temperature for mycelial growth of Creamy was 25-30℃, and that for fruiting body growth was 16℃. The pileus, which had a brighter creamy color, was small in size with a diameter of 61.2 mm. Although it was cultivated in suboptimal conditions, such as low temperature and high CO₂ concentration, Creamy was characterized by its straight and smooth stipe. Field production tests and further analyses indicated that the yield of Creamy was 5% higher than that of Baekhwang. It is expected that Creamy, the new interspecific hybrid with a bright creamy pileus and a pleasant flavor, will help create new opportunities for mushroom farmers and diversify the mushroom market.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.1
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• pp.23-29
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• 1989

In order to investigate developmental habit and morphology of maize tillers, time and location of tiller development. number of tillers per plant, tiller angle, height and diameter of tillers and root systems of tillers were examined under field condition for maize with tillers. Materials used were mostly from Korean local lines and a few lines from other countries were also included for comparison. The time of the first tiller development was about 18 to 20 days after emergence when planted on May in Yusong. The second tiller appeared about 4 to 5 days after the first tiller appeared. The tiller number per plant varied with lines and hybrids and ranged from two to ten. The location of tiller development was usually basal nodes of the main stem. Each tiller appeared to have its own root system. The angle between tillers and main stem was variable depending upon the maizes and the tiller angle could be classified into three categories. The height of tillers was also variable and seemed to be under genetic control. The most productive tillers were found among the Korean local derivatives.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.1
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• pp.30-39
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• 1989

Forage productivity of cropping systems of rye - silage corn, silage corn - oats, silage corn - rape was studied in the south-eastern part of Korea where rice black-streaked dwarf virus(RBSDV) infection of corn are severe. Rye(cv. Paldanghomil) was planted on Oct. 20 of 1986 and harvested 10 times from April 5 to May 5 at the 5-day intervals in 1987, corn (cv. Suweon 19 and Jinjuok) was planted 5 times from April 5 to May 15 at the 10-day intervals in 1987, and oats(cv. Megwiri) and rape (cv. Velox) were planted 4 times from Sept: 4 to 25 at the 7-day intervals and harvested 4 times from Nov. 10 to Dec. 10 at the 10-day intervals in 1987. Considering yield, nutrition value, and in vitro dry matter digestibility (IVDMD), forage productivity of the cropping systems was compared. As harvesting time of rye delayed, plant height, dry matter(DM) yield, percent DM, crude fiber, and digestible DM yield increased, but crude protein, crude fat, and IVDMD decreased. However, nitrogen free extract was not different among the harvesting dates. As planting date of corn delayed, RBSDV infection rate increased. but DM yield of silage decreased. However, silage yield of Jinjuok was higher, but RBSDV infection rate was lower compared with Suweon 19 at all planting dates. DM yield of oats and rape decreased as planting date delayed. However, at Sept. 4 and 11 plantings yield of oats on Nov. 10 was much lower than that of rape, but the differences in yield between two crops decreased with delayed harvesting, and yield was similar on Dec. 10. A cropping system harvesting rye around April 20 and followed by planting corn in late April was best among the rye-corn systems considering yield and nutrition value of both crops. However, among the corn-oats or corn-rape cropping systems early April planting of corn and followed by early Sept. planting of oats or rape showed best results with similar yield potential of the best rye-corn cropping system.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.651-662
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• 2022

The electrochemical impedance spectroscopy(EIS) method was used to evaluate the concrete deterioration process related to chloride-induced steel corrosion with various corrosion levels(initiation, rust propagation and acceleration periods). The impressed current technique, with four total current levels of 0C, 13C, 65C and 130C, was used to accelerate steel corrosion in concrete cylinder samples with w/c ratio of 0.4, 0.5, and 0.6, immersed in a 0.5M NaCl solution. A series of EIS measurements was performed to monitor concrete deterioration during the accelerated corrosion test in this study. Some critical parameters of the equivalent circuit were obtained through the EIS analysis. It was observed that the charge transfer resistance(R_c) dropped sharply as the impressed current increased from 0C to 13C, indicating a value of approximately 10kΩcm². However, the sensitivity of R_c significantly decreased when the impressed current was further increased from 13C to 130C after corrosion of steel had been initiated. Meanwhile, the double-layer capacitance value(C_dl) linearly increased from 50×10^-6μF/cm² to 250×10^-6μF/cm² as the impressed current in creased from 0C to 130C. The results in this study showed that monitoring C_dl is an effective measurement parameter for evaluating the progress of internal concrete damages(de-bonding between steel and concrete, micro-cracks, and surface-breaking cracks) induced by steel corrosion. The findings of this study provide a fundamental basis for developing an embedded sensor and signal interpretation method for monitoring concrete deterioration due to steel corrosion at various corrosion levels.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Mushroom
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• v.22 no.3
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• pp.128-132
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• 2024

Oyster mushroom is one of the most widely cultivated and consumed mushrooms in Korea, and mechanization and automation of cultivation systems have enabled mass production. Many cultivars have been developed to replace the old ones such as 'Suhan' and 'Chunchuneutari 2 ho,' which have been cultivated for over 20 years. Among these, 'Soltari' was developed in 2015. Although it has excellent quality, its cultivation is challenging and the productivity is somewhat lower. To address these issues, the Mushroom Division at the National Institute of Horticultural and Herbal Science selected the genetic resource KMCC05165 and attempted hybridization between monokaryons from KMCC05165 and 'Soltari(KMCC04940)'. Through repeated cultivation tests and evaluation of fruiting body characteristics, the superior strain 'Po-2019-smj22' was selected and finally named 'Otari'. The optimal mycelial growth temperature of 'Otari' was between 25 and 30℃ and optimal fruiting body growth temperature was between 13 and 18℃. Mycelial growth on PDA medium was best at 25℃, and at the same temperature, mycelial growth was similar across four media: PDA, MEA, MCM, and YM. In 1,100 mL bottle cultivation, the yield was approximately 174 g, which is about 5% higher than the control cultivar 'Soltari', and the number of valid individuals was also higher at about 25. The diameter and height of the pileus were 29.8 mm and 17.6 mm, respectively, slightly smaller than 'Soltari', and the stipe was thin and long with a thickness of 12.2 mm. Additionally, the pileus' lightness index (L index) was 30.7, indicating a darker brown color compared to 'Soltari.' With excellent mycelial growth, ease of cultivation, and high yield, the new cultivar 'Otari' is expected to be widely adopted by domestic oyster mushroom farms.

• Investigative Magnetic Resonance Imaging
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• v.8 no.2
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• pp.79-85
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• 2004

Purpose : To evaluate the usefulness and reproducibility of $^1H$ MRS in different 1.5 T MR machines with different coils to compare the SNR, scan time and the spectral patterns in different brain regions in normal volunteers. Materials and Methods : Localized $^1H$ MR spectroscopy ($^1H$ MRS) was performed in a total of 10 normal volunteers (age; 20-45 years) with spectral parameters adjusted by the autoprescan routine (PROBE package). In all volunteers, MRS was performed in a three times using conventional MRS (Signa Horizon) with 1 channel coil and upgraded MRS (Echospeed plus with EXCITE) with both 1 channel and 8 channel coil. Using these three different machines and coils, SNRs of the spectra in both phantom and volunteers and (pre)scan time of MRS were compared. Two regions of the human brain (basal ganglia and deep white matter) were examined and relative metabolite ratios (NAA/Cr, Cho/Cr, and mI/Cr ratios) were measured in all volunteers. For all spectra, a STEAM localization sequence with three-pulse CHESS $H_2O$ suppression was used, with the following acquisition parameters: TR=3.0/2.0 sec, TE=30 msec, TM=13.7 msec, SW=2500 Hz, SI=2048 pts, AVG : 64/128, and NEX=2/8 (Signa/Echospeed). Results : The SNR was about over $30\%$ higher in Echospeed machine and time for prescan and scan was almost same in different machines and coils. Reliable spectra were obtained on both MRS systems and there were no significant differences in spectral patterns and relative metabolite ratios in two brain regions (p>0.05). Conclusion : Both conventional and new MRI systems are highly reliable and reproducible for $^1H$ MR spectroscopic examinations in human brains and there are no significant differences in applications for $^1H$ MRS between two different MRI systems.