• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.75-84
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• 2019

In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.

• Korean Journal of Medicinal Crop Science
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• v.27 no.4
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• pp.247-258
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• 2019

Background: Astragali radix (A) and Lithospermi radix (L) have long been used as traditional medicines due to their known anti-inflammatory effects. This study aimed at evaluating, their optimal mixing ratio and their functional compounds by investigating the inhibitory effects of mixed extracts of A and L and their active compounds on matrix metalloproteinases (MMPs). Methods and Results: A and L extracts were obtained by extraction at $80^{\circ}C$ using 50% and 70% fermented alcohol, respectively, and then mixed at a ratio of 5 : 5, 6 : 4, 7 : 3 and 8 : 2 (w/w). The activities of MMP-1, MMP-3, and MMP-13 were evaluated in interleukin-1beta ($IL-1{\beta}$)-induced SW1353 cells. The extract mixtures showed synergistic inhibitory effects on MMP-3 and MMP-13, higher than the effects of the individual A and L extracts. The 7 : 3 mixture (ALM16) showed the most effective MMPs inhibitory activity, while among the active ingredients, calycosin-7-O-${\beta}$-D-glucoside and lithospermic acid exhibited excellent MMPs inhibitory activity. Additionally, an HPLC method was established for simultaneous quantification of the effective components of the extract mixtures, and validated by measuring the linearity, precision and accuracy of the limit of detection (LOD) and limit of quantification (LOQ). Conclusions: ALM16 showed the most effective MMPs inhibitory activity. Calycosin-O-${\beta}$-D-glucoside, calycosin and lithospermic acid were identified as useful candidates, as they were the major functional compounds in the MMP inhibitory activity. Summarily, ALM16 might be a highly effective in osteoarthritis management, owing to its because it exhibits a protective effect on cartilage via excellent inhibition of MMPs.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.209-217
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• 2019

In this paper, the purpose of this paper is to lay the foundation for the search system by using and building an online search engine suitable for the search domain and enabling search, conversion, integration and sharing of information. It is to use the ontology to infer hierarchical relationships, deduce objects based on that layer, and extract attributes to search areas that are relevant to the data that the user wants. In order to search for information in this way, the information search system was implemented by entering key words related to 'qualifications'. The implemented system arranged the meaning and relationship of each attribute online so that the general public can search information quickly, easily, and accurately. In addition, the implementation results were compared with two different search engines. Comparable search engines are Naver and Daum, the two major search engines. The search engine of this study, which was built using an ontology suitable for the search domain to perform searches using the semantic web, was evaluated to have excellent results. However, it is thought that a more formalized online location is necessary to increase the accuracy and reliability of search engines and to include more comprehensive categories of search terms.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.65-74
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• 2020

Continuous and accurate instrument of river water usage is needed for sustainable river water management. Although the instrument methods applicable to each point of use of river water are different, more precise direct instrument methods are required at the point of major open channel. Users of river water should select appropriate direct instrument methods to measure usage, but there is a lack of standards and verification research. In this study, the H-Q rating curve method, ultrasonic method, and microwave method were applied directly to the test basin in the upper basin of Mangyeong river, and the accuracy of measurement data was evaluated by comparing absolute error between discharge data calculated by instrument method. When comparing the calculated discharge of point units, the ultrasonic method showed the best results of the actual measurement. Through continuous instrument, the sum of the daily and monthly units was compared, and the ultrasonic and microwave methods were shown to be highly accurate. Based on the results of this study, it is hoped that the appropriate direct measurement method can be selected according to the importance of the river water use facility, considering that the ultrasonic method and the microwave method are relatively costly compared to the water level-flow relationship method.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.303-310
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• 2021

Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.207-213
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• 2019

The fuel-cut coast-down driving mode is activated when the acceleration pedal is released with transmission gear engaged, and it's a default function for electronic-controlled engine of vehicles. The fuel economy becomes better because fuel injection stops during fuel-cut driving mode. A fuel-cut detection method is suggested in the study and it's based on the speed, acceleration and road gradient data from GPS sensor. It detects fuel-cut driving mode by comparing calculated acceleration and realtime acceleration value. The one is estimated with driving resistance in the condition of fuel-cut driving and the other is from GPS sensor. The detection accuracy is about 80% when the method is verified with road driving data. The result is estimated with 9,600 data set of vehicle speed, acceleration, fuel consumption and road gradient from test driving on the road of 12km during 16 minutes, and the road slope is rather high. It's easy to detect fuel-cut without injector signal obtained by connecting wire. The detection error is from the fact that the variation range of speed, acceleration and road gradient data, used for road resistance force, is larger than the value of fuel consumption data.

• Animal Bioscience
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• v.34 no.1
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• pp.26-35
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• 2021

Objective: This study was conducted to assess the level of pregnancy-associated glycoprotein (PAG) in whole and skim milk samples, and its suitability for early pregnancy diagnosis in goats. Methods: A two-step sandwich enzyme-linked immunosorbent assay (ELISA) system for estimation of milk PAG was developed and validated, which employed caprine-PAG specific polyclonal antisera. Whole and skim milk samples (n = 210 each) from fifteen multiparous goats were collected on alternate days from d 10 to d 30, and thereafter weekly till d 51 post-mating. PAG levels in milk samples were estimated by ELISA and the pregnancies were confirmed at d40 post-mating by transrectal ultrasonography (TRUS). Results: The level of PAG in whole and skim milk samples of both pregnant and nonpregnant goats remained below the threshold values until d 24 after mating. Thereafter, PAG concentration in whole and skim milk increased steadily in pregnant goats, whereas it continued below the threshold in non-pregnant does. The PAG profiles in whole and skim milk of pregnant goats were almost similar and exhibited strong positive relationship (r = 0.891; p<0.001). Day 26 post-mating was identified as the first time-point for significantly (p<0.05) higher milk PAG concentration in pregnant goats than to non-pregnant goats. When compared to TRUS examination for pregnancy diagnosis, the accuracy and specificity of PAG ELISA using whole and skim milk samples were 94.5% and 95.4%; and 95.3% and 100%, respectively. The high values of area-under-curve (0.904 [whole milk] and 0.922 [skim milk]), demonstrate outstanding discrimination ability of the milk assays. Among the sampling dates chosen, d 37 post-mating was identified as the best suitable time point for collection of milk samples to detect pregnancy in goats. Conclusion: The PAG concentration in whole and skim milk of goats collected between days 26 and 51 post-breeding can be used for the accurate prediction of pregnancy and may be useful for assisting management decisions in goat flocks.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.25-32
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• 2020

The seed, the most basic component, is an important factor in increasing production and efficiency in agriculture. Seeds with superior genes can be expected to improve agricultural productivity, crop survival, and reproduction. Currently, however, screening of superior seeds depends mostly on manual work, which requires a lot of time and manpower. In this paper, we propose a system that can extract the characteristics of seed phenotypes by using computer image processing technology, so that even a small number of people and a short period of time are needed to extract the characteristics of seeds. The proposed system detects individual seeds from images containing large quantities of seeds, and extracts and stores various characteristics such as representative colors, area, perimeter and roundness for each individual seed. Due to the regularity of input images, the accuracy of individual seed extraction in the proposed system is 99.12% for soybean seeds and 99.76% for rice seeds. The extracted data will be used as basic data for various data analyses that reflect the opinions of experts in the future, and will be used as basic data to determine the expressive nature of each seed.