• Korean Journal of Food Science and Technology
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• v.52 no.6
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• pp.580-586
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• 2020

Fishery resources have been widely consumed as protein- and vitamin-rich food sources in the Korean diet. However, information regarding their vitamin levels is extremely limited. In this study, trienzyme-Lactobacillus casei method was validated and used to determine the folate contents in fishery foods. The trienzyme-L. casei method for folate analysis showed excellent accuracy (85.2 to 95.3% recovery) and precision (repeatability 1.4% RSD and reproducibility 2.4% RSD). Folate contents of 20 fish foods (4 fish, 3 crustaceans, 3 sea algae, 3 cephalopods, 4 shellfish, and 3 others) ranged from 1.75 to 97.98 ㎍/100 g. Furthermore, we found that the folate content in seaweed fusiforme was the highest, followed by gulfweed (69.73 ㎍/100 g). Folate analysis using the trienzyme-L. casei method was determined excellent based on the z-score of -0.3 in the Food Analysis Performance Assessment Scheme test. Analytical and method validation data generated in this study could be used to update the national food composition table on vitamin B9 in Korean fishery resources.

• Journal of the Korean GEO-environmental Society
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• v.12 no.7
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• pp.57-65
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• 2011

Numerical simulations by three-dimensional Particle Flow Code($PFC^{3D}$, Itasca) considering distinct element method (DEM) were carried out for prediction of triaxial compression test with sand material. The effect of scale conditions for numerical model and distinct material on final prediction results was analyzed by numerical models under various scale conditions, and following observations were made from the numerical experiments. It is very useful to model the initial material condition without any porosity conversion from 2-D to 3-D DEM. Numerical experiments have shown that in all cases considered, 3D distinct element modeling could provide good agreement on stress-strain behavior, volume change and strength properties with laboratory testing results. It was important thing to assess reasonable scale ratio of numerical model and distinct elements for saving calculation time and securing calculation efficiency under condition with accuracy and appropriateness as numerical laboratory. As results of DEM simulations under various scale conditions, most of results show that shear strength properties as cohesion and internal friction angle are similar in condition of $D_{mod}/D_{gmax}$ < 10. It shows that 3-D distinct element method could be used as efficient tool to assess strength properties by numerical laboratory technique.

• Journal of Korea Society of Waste Management
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• v.35 no.7
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• pp.616-626
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• 2018

The quality standards of solid refuse fuel (SRF) define the values for 12 physico-chemical properties, including moisture, lower heating value, and metal compounds, according to Article 20 of the Enforcement Rules of the Act on Resource Saving and Recycling Promotion. These parameters are evaluated via various SRF Quality Test Methods, but problems related to the heavy metal content have been observed in the microwave acid digestion method. Therefore, these methods and their applicability need improvement. In this study, the appropriate testing conditions were derived by varying the parameters of microwave acid digestion, such as microwave power and pre-treatment time. The pre-treatment of SRF as a function of the microwave power revealed an incomplete decomposition of the sample at 600 W, and the heavy metal content analysis was difficult to perform under 9 mL of nitric acid and 3 mL of hydrochloric acid. The experiments with the reference materials under nitric acid at 600 W lasted 30 minutes, and 1,000 W for 20 or 30 minutes were considered optimal conditions. The results confirmed that a mixture of SRF and an acid would take about 20 minutes to reach $180^{\circ}C$, requiring at least 30 minutes of pre-treatment. The accuracy was within 30% of the standard deviation, with a precision of 70 ~ 130% of the heavy metal recovery rate. By applying these conditions to SRF, the results for each condition were not significantly different and the heavy metal standards for As, Pb, Cd, and Cr were satisfied.

• Korean Journal of Veterinary Service
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• v.43 no.4
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• pp.201-209
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• 2020

In Korea, bovine tuberculosis (bTB) is a representative zoonotic disease that causes considerable economic loss. In determining the positive bTB, the ELISA method for examining the amount of interferon-gamma (IFN-γ) is included in Korea's diagnostic standard method. Recently, commercially available BIONOTE TB-Feron ELISA Plus (TB-Feron Plus) that detects IFN-γ has been introduced. However, since the scientific basis for the performance is limited, we evaluated performance by comparing it with the results of another IFN-γ ELISA assay kit (BOVIGAM®) certified by Office International des Epizooties. In our research, 42 positive blood samples preliminarily tested with a tuberculin skin test and/or BOVIGAM® and 54 negative blood samples collected from three bTB free farms were subjected to IFN-γ assay using the TB-Feron Plus and the BOVIGAM®, respectively. The result shows that the sensitivity, specificity and accuracy were 81.0% (34/42), 100% (54/54), 91.7% (88/96) in TB-Feron Plus kit and 78.6% (33/42), 100% (54/54), 90.6% (87/96) in BOVIGAM® kit, respectively. Moreover, the overall accordance percentage of the two kits was 99.0% (95/96) and there was almost perfect agreement between two assays (Kappa=0.977, P<0.0001). Furthermore, additional studies confirmed that elevated lymphocyte numbers in blood did not interfere with the results of the TB-Feron Plus kit. And, delayed time from sampling to culture decreased the optical density (OD) value. Therefore, we concluded that the TB-Feron Plus kit was not inferior to BOVIGAM® in performance. High lymphocyte numbers in blood did not impact on TB-Feron Plus results, while delayed time before culture interfered with OD value.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.61-66
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• 2020

Recently, a variety of electric anesthetics devices have been developed and used in clinical practice to reduce the fatigue of the operator during local anesthesia for dental procedures and to compensate for the disadvantages of manual anesthesia device. In this electric anesthesia injection device, the accurate and constant delivery of pressure for drug infusion is a very important performance factor. In order to evaluate the accuracy of the transfer pressure, a small pressure gauge using a load cell is often used, but since the elastic body inside the load cell may not be able to accommodate a sufficient displacement, an error may occur when evaluating pressure performance. For these reasons, in this study, we proposed and evaluated a silicon-chrome steel (Si-Cr steel) spring jig that can accommodate relatively large displacements that can be used when evaluating the performance of a pressure-controlled pressure application device using a load cell type pressure gauge. As a result of the pressure transmissibility test and repeated measurement results using a commercial dental anesthesia injection device, a more stable result was obtained when using a spring jig, and it was confirmed that the frequency of abnormally high measurement was reduced.

• Journal of Apiculture
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• v.34 no.1
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• pp.47-55
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• 2019

Honey bees are a vital part of the food chain as the most important pollinators for a broad palette of crops and wild plants. The climate change and colony collapse disorder (CCD) phenomenon make it challenging to develop ICT solutions to predict changes in beehive and alert about potential threats. In this paper, we report the test results of the bee-counting system which stands out against the previous analogues due to its comprehensive components including an improved dual infrared sensor to detect honey bees entering and leaving the hive, environmental sensors that measure ambient and interior, a wireless network with the bluetooth low energy (BLE) to transmit the sensing data in real time to the gateway, and a cloud which accumulate and analyze data. To assess the system accuracy, 3 persons manually counted the outgoing and incoming honey bees using the video record of 360-minute length. The difference between automatic and manual measurements for outgoing and incoming scores were 3.98% and 4.43% respectively. These differences are relatively lower than previous analogues, which inspires a vision that the tested system is a good candidate to use in precise apicultural industry, scientific research and education.

• Korean Journal of Poultry Science
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• v.48 no.1
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• pp.41-50
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• 2021

Although feather-sexing using sex-linked genes related to feather development is a widely used chick sexing method in the poultry industry, the feather-sexing method has yet to be used for Korean native chickens (KNCs). The purpose of this study was to construct a KNC feather-sexing line using early-feathering (EF) and late-feathering (LF) genes for industrial application. Using 557 reddish-brown KNCs as the basal flock, frequencies of the EF (k) and LF (K) genes were estimated to be 0.814 and 0.186, respectively. This indicating that it would be feasible to construct a feather-sexing line using this chicken group, and we accordingly constructed EF paternal and LF maternal lines. On the basis of test-cross for the selection of LF homozygous (KK) males in the maternal line, we confirmed that three of 40 chickens were homozygous males. The survival rate, body weight, days at first egg-laying, hen-day egg production, and egg weight were analyzed to compare the production performance of EF and LF chickens. The results revealed that EF chickens were characterized by a superior survival rate, whereas LF chickens were superior in terms of egg production rate. However, no differences between LF and EF chickens were detected with respect to other production performance parameters. In addition, assessment of the fitness of sexed chicks produced in the established KNC feather-sexing lines revealed that the accuracy of sexing was 98.6%. Collectively, these findings indicate the feasibility of constructing effective KNC feather-sexing lines with potential industrial application.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.