• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.333-337
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• 2009

A new malting barley variety, "Daho", was developed from the cross between "Milyang85 and Suwon335" at the Department of Rice and Winter Cereal Crop (DRWCC) NICS, in 2007. An elite line, YMB2064-B-8-2-4-1-1, was selected in 2004 and designated as "Milyang134". It showed good agronomic performance in the regional adaptation yield trials (RYT) from 2005 to 2007 and was released with the name of "Daho", having high yielding and BaYMV resistance. The average heading and maturing dates of "Daho" were April 19 and May 27, which were 2 days later and 1 day earlier than those of "Jinyang", leading variety, at the regional adaptation yield trials (RYT), respectively. "Daho" had longer culm length (84 cm), more spikes per $m^2$ (915) and higher 1,000 grain weight (39.2 g) than those of "Jinyang" in paddy field condition. "Daho" was showed resistance to BaYMV at the regions of Naju, Jinju, and Milyang but moderately resistance at Iksan. However, the response of "Daho" to other environmental stresses was similar to "Jinyang". The yields of "Daho" at upland and paddy fields were about 5.20 MT/ha, 4.81 MT/ha, respectively, which is about 38%, 25% higher than those of "Jinyang" in the regional adaptation yield trials (RYT), respectively. It has higher grain assortment, germination capacity, water sensitivity and Kolback index but lower malt extract, diastatic power and filtration speed than those of "Jinyang".

• Journal of Life Science
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• v.31 no.12
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• pp.1079-1087
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• 2021

To analyze gut microbiota of livestock in Korea and compare taxonomic differences, we conducted 16S rRNA metagenomic analysis through next-generation sequencing. Fecal samples from broiler chickens, pigs, and cattle were collected from domestic feedlots randomly. α-diversity results showed that significant differences in estimated species richness estimates (Chao1 and ACE, Abundance-based coverage estimators) and species richness index (OUTs, Operational taxonomic units) were identified among the three groups. However, NPShannon, Shannon, and Simpson indices revealed that abundance and evenness of the species were statistically significant only for poultry (broiler chickens) and mammals (pigs and cattle). Firmicutes was the most predominant phylum in the three groups of fecal samples. Linear discriminant (LDA) effect size (LEfSe) analysis was conducted to reveal the ranking order of abundant taxa in each of the fecal samples. A size-effect over 2.0 on the logarithmic LDA score was used as a discriminative functional biomarker. As shown by the fecal analysis at the genus level, broiler chickens were characterized by the presence of Weissella and Lactobacillus, as well as pigs were characterized by the presence of provetella and cattele were characterized by the presence of Acinetobacter. A permutational multivariate analysis of variance (PERMANOVA) showed that differences of microbial clusters among three groups were significant at the confidence level. (p=0.001). This study provides basic data that could be useful in future research on microorganisms associated with performance growth, as well as in studies on the livestock gut microbiome to increase productivity in the domestic livestock industry.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.167-175
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• 2019

To effectively deal with the fire in tunnel which is mostly the vehicle fire, it's more important to suppress the fire at early stage. In urban tunnel, however, accessibility to the scene of fire by the fire fighter is very limited due to severe traffic congestion which causes the difficulty with firefighting activity in timely manner and such a problem would be further worsened in underground road (double-deck tunnel) which has been increasingly extended and deepened. In preparation for the disaster in Korea, the range of life safety facilities for installation is defined based on category of the extension and fire protection referring to risk hazard index which is determined depending on tunnel length and conditions, and particularly to directly deal with the tunnel fire, fire extinguisher, indoor hydrant and sprinkler are designated as the mandatory facilities depending on category. But such fire extinguishing installations are found inappropriate functionally and technically and thus the measure to improve the system needs to be taken. Particularly in a double-deck tunnel which accommodates the traffic in both directions within a single tunnel of which section is divided by intermediate slab, the facility or the system which functions more rapidly and effectively is more than important. This study, thus, is intended to supplement the problems with existing tunnel life safety system (fire extinguishing) and develop the remote-controlled automatic fire extinguishing system which is optimized for a double-deck tunnel. Consequently, the system considering low floor height and extended length as well as indoor hydrant for a wide range of use have been developed together with the performance verification and the process for commercialization before applying to the tunnel is underway now.

• Applied Chemistry for Engineering
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• v.22 no.2
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• pp.196-202
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• 2011

In this paper, a series of alkyl succinic acid esters for base oil were synthesized by condensation reaction of succinic anhydride and fatty alcohol. The structures of the synthesized esters were confirmed by $^1H-NMR$, FT-IR spectrum and GC analysis. Basic properties of esters such as kinematic viscosity (KV), refractive index (RI), total acid number (TAN) and pour points were measured and lubricating properties such as SRV wear scar diameter (SRV WSD), fraction coefficient (COF) and 4-ball wear (4-ball WSD) were also evaluated. As the results of basic properties, KV, RI and pour point of synthetic esters increased as the carbon chain of the esters increased. Measurement value of total acid number (TAN) was indicated between 0.2~4 mgKOH/g, and that metal working fluids and pressure working oils are acceptable to use as base oil. Also, lubricating properties of the esters showed as follows: 0.391~0.689 mm of SRV WSD, 0.110~0.138 of SRV COF and 0.49~0.55 mm of 4-ball WSD depended on the structure of the esters. In a comparison on the lubrication capacity of the SRV test based on polyester TMPTO, SRV WSD result showed that a better performance caused by the alkyl group. On the other hand, SRV COF test was not influenced of the alkyl group which the capacity of the lubricant was sightly diminished than the comparison material, regardless of the alkyl group.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.267-272
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• 2021

This experiment was conducted to the purpose of evaluating the effect of feeding on Korean Native Cattle to expand the production and utilization of whole crop rice (WCR). TMR (Total mixed ration) feed was prepared by using WCR produced in Pyeongtaek, Gyeonggi-do, and the growth capacity and meat quality characteristics of 16 Korean Native Cattle raised up to 29 months of age were investigated. The produced WCR silage for feed had a moisture content of 64.02% and a crude protein content of 7.54%, and was blended with about 45% during the growing season, and lowered to 35, 15 and 9%, respectively, in the fattening period (early, middle and late stages). The body weight of the WCR-TMR feeding group was significantly higher than that of the control in the middle and late fattening stage, and at the end (29 months of age), the control group was 631 kg/head, but the WCR-TMR feeding group was 647 kg/head, which was higher. The average daily gain was significantly higher in the WCR-TMR feeding group in the growing and early fattening period, and there was no difference in the mid- and late fattening period. In the whole period, 0.71 vs 0.75 kg/head/day, WCR-TMR feeding group was high. In terms of meat quantity, the back fat thickness of the control group (11.7 mm) was significantly thicker than that of the WCR-TMR fed group (9.3 mm) (P<0.05). There was no difference in Rib eye area, Carcass weight and Meat yield index (P>0.05). In terms of meat quality, the Marbling score was higher in the WCR-TMR feeding group (P<0.05), and there were no significant differences in Meat color, Fat color, Texture and Maturity. Considering the above results, TMR feeding mainly on whole crop rice silage for feed improved the productivity of livestock, but there was no significant difference in meat quality. Therefore, it is judged that it is necessary to produce and use the whole crop rice for feed in countries with poor forage conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.48-61
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• 2022

South Korea is quite vegetation rich country which has 63% forests and 16% cropland area. Massive NOx emissions from megacities, therefore, are easily combined with BVOCs emitted from the forest and cropland area, then produce high ozone concentration. BVOCs emissions have been estimated using well-known emission models, such as BEIS (Biogenic Emission Inventory System) or MEGAN (Model of Emission of Gases and Aerosol from Nature) which were developed using non-Korean emission factors. In this study, we ran MEGAN v2.1 model to estimate BVO Cs emissions in Korea. The MO DIS Land Cover and LAI (Leaf Area Index) products over Korea were used to run the MEGAN model for June 2012. Isoprene and Monoterpenes emissions from the model were inter-compared against the enclosure chamber measurements from Taehwa research forest in Korea, during June 11 and 12, 2012. For estimating emission from the enclosed chamber measurement data. The initial results show that isoprene emissions from the MEGAN model were up to 6.4 times higher than those from the enclosure chamber measurement. Monoterpenes from enclosure chamber measurement were up to 5.6 times higher than MEGAN emission. The differences between two datasets, however, were much smaller during the time of high emissions. More inter-comparison results and the possibilities of improving the MEGAN modeling performance using local measurement data over Korea will be presented and discussed.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.384-392
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• 2022

The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R²: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.