• Bulletin of the Korean Chemical Society
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• v.31 no.5
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• pp.1159-1164
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• 2010

Several triterpenoid saponins from root of Pulsatilla koreana Nakai (Ranunculaceae) were studied and their biological activities were reported. It is difficult to analyze triterpenoid saponins using HPLC-UV due to the lack of chromophores. So, evaporative light scattering detection (ELSD) is used as a valuable alternative to UV detection. More recently, a charged aerosol detection (CAD) has been developed to improve the sensitivity and reproducibility of ELSD. In this study, we developed and validated a novel method of high performance liquid chromatography coupled with a charged aerosol detector for the simultaneous determination of four triterpenoid saponins: pulsatilloside E, pulsatilla saponin H, anemoside B4 and cussosaponin C. Analytes were separated by the Supelco Ascentis$^{(R)}$ Express C18 column (4.6 mm ${\times}$ 150 mm, 2.7 ${\mu}m$) with gradient elution of methanol and water at a flow rate of 0.8 mL/min at $30^{\circ}C$. We examined various factors that could affect the sensitivity of the detectors, including various concentrations of additives, the pH of the mobile phase, and the CAD range. Linear calibration curves were obtained within the concentration ranges of 2 - 200 ${\mu}g$/mL for pulsatilloside E, anemoside $B_4$ and cussosaponin C, and 5 - 500 ${\mu}g$/mL for pulsatilla saponin H with correlation coefficient ($R^2$) greater than 0.995. The limit of detection (LOD) and quantification (LOQ) were 0.04 - 0.2 and 2 - 5 ${\mu}g$/mL, respectively. The validity of the developed HPLC-CAD method was confirmed by satisfactory values of linearity, intra- and inter-day accuracy and precision. This method could be successfully applied to quality evaluation, quality control and monitoring of Pulsatilla koreana.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.547-552
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• 2006

There have been various cohesion measurements studied considering reference relation among attributes and methods in a class. Generally, these cohesion measurement are camed out in one class. If the range of reference relation considered are extended from one class to two classes, we could find out the reference relation between two classes. Tn this paper, we proposed a neural network to determine the method location. Neural network is effective to predict output value from input data not to be included in training and generalize after training input and output pattern repeatedly. Learning vector is generated with 30-dimensional input vector and one target binary values of method location in a constraint that there are two classes which have less than or equal to 5 attributes and methods The result of the proposed neural network is about 95% in cross-validation and 88% in testing.

• Korean Journal of Veterinary Service
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• v.45 no.3
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• pp.243-248
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• 2022

The HPLC conditions for analysis of ivermectin in solutions dosage forms of commercial anthelmintics are different for each product. The purpose of this study was to establish a standardized chromatographic method for the quantification of ivermectin in solution. The separation was achieved on Waters Xbridge C18 column (4.6×150 nm, 5 ㎛) using different kinds of mobile phase composed of water/methanol/acetonitrile (15/34/51, v/v and 19.5/27.5/53, v/v), with UV detection at wavelengths 245 nm and 254 nm. A total of five commercial ivermectin in solution samples were analyzed. In this study, the optimal chromatographic conditions for analysis of ivermectin in solution were mobile phase of water/methanol/acetonitrile (15/34/51, v/v) at a flow rate of 1.0 mL/min and a detection wavelength of 245 nm using a Waters Xbridge C18 column (4.6×250 nm, 5 ㎛) at a column temperature of 25℃. The linearity was observed in the concentration range of 50~150 ㎍/mL, with a correlation coefficient, r²= 0.99999. The limit of detection and the limit of quantification were 0.88 and 2.68 ㎍/mL, respectively. The accuracy (% recovery) was found to be 98.9 to 100.3%. Intra-day and Intermediate precisions with relative standard deviations were less than 1.0%. The content of ivermectin for five market samples ranged 91.2~102.7%. The proposed method was also found to be robust, therefore, the method can be used for the routine analysis of ivermectin in solutions dosage forms.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.300-306
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• 2022

Currently, Korea is building traffic infrastructure using Intelligent Transport Systems (ITS), but the pedestrian traffic accident rate is very high. The purpose of this paper is to prevent the risk of traffic accidents by jaywalking pedestrians. The development of this study aims to detect pedestrians who trespass using the public data set provided by the Artificial Intelligence Hub (AIHub). The data set uses training data: 673,150 pieces and validation data: 131,385 pieces, and the types include snow, rain, fog, etc., and there is a total of 7 types including passenger cars, small buses, large buses, trucks, large trailers, motorcycles, and pedestrians. has a class format of Learning is carried out using YOLOv5 as an implementation model, and as an object detection and edge detection method of an input image, a canny edge model is applied to classify and visualize human objects within the detected road boundary range. In this study, it was designed and implemented to detect pedestrians using the deep learning-based YOLOv5 model. As the final result, the mAP 0.5 showed a real-time detection rate of 61% and 114.9 fps at 338 epochs using the YOLOv5 model.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.8
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• pp.952-956
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• 2017

An high performance liquid chromatography (HPLC) analysis method was developed for standard determination of coixol as a functional cosmetic material in Coix lachryma-jobi var. ma-yuen roots extract. HPLC was performed on a $C_{18}$ Unison US column ($4.6{\times}250mm$, $5{\mu}m$ column) using a gradient elution of 0.1% (v/v) trifluoroacetic acid and acetonitrile at a flow rate of 1.0 mL/min at $30^{\circ}C$. The analyte was detected at 290 nm. The HPLC method was validated in accordance with the International Conference on Harmonization guideline of analytical procedures with respect to specificity, precision, accuracy, and linearity. The limit of detection and quantitation were 0.07 and 0.25 mg/mL, respectively. Calibration curves showed good linearity ($R^2$>0.9995), and the precision of analysis was satisfied (less than 0.29%). Recoveries of quantified compounds ranged from 98.36 to 100.30%. This result indicates that the established HPLC method is very useful for the determination of a marker compound in C. lachryma-jobi var. ma-yuen roots extracts.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.71-78
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• 2017

There are several algorithms to classify emotion, such as Support-vector-machine (SVM), Bayesian decision rule, etc. However, many researchers have insisted that these methods have minor problems. Therefore, in this paper, we propose a novel method for emotion recognition based on Electroencephalogram (EEG) signal using the Gradient method which was proposed by Han. We also utilize a database for emotion analysis using physiological signals (DEAP) to obtain objective data. And we acquire four channel brainwaves, including Fz (${\alpha}$), Fp2 (${\beta}$), F3 (${\alpha}$), F4 (${\alpha}$) which are selected in previous study. We use 4 features which are power spectral density (PSD) of the above channels. According to performance evaluation (4-fold cross validation), we could get 85% accuracy in valence axis and 87.5% in arousal. It is 5-7% higher than existing method's.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Archives of Pharmacal Research
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• v.29 no.9
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• pp.808-813
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• 2006

Two methods for the chiral purity determination of bevantolol were developed, namely capillary electrophoresis (CE) using carboxymethyl-${\beta}$-cyclodextrin (CM-${\beta}$-CD) as a chiral selector and high-perfomance liquid chromatography (HPLC) using a chiral stationary phase. In the HPLC method, the separation of bevantolol enantiomers was performed on a Chiralpak AD-H column by isocratic elution with n-hexane-ethanol-diethylamine (10:90:0.1, v/v/v) as mobile phase. In the CE method, bevantolol enantiomers were separated on an uncoated fused silica capillary with 50 mM amonium phosphate dibasic adjusted to a pH 6.5 with phosphoric acid containing 15 mM CM-${\beta}$-CD as running buffer. Validation data such as linearity, recovery, detection limit, and precision of the two methods are presented. The detection limits of S-(-)-bevantolol were 0.1% and 0.05% for CE and HPLC method, respectively and R-(+)-bevantolol were 0.15% and 0.05% for CE and HPLC method, respectively. There was generally good agreement between the HPLC and CE results.

• Journal of applied mathematics & informatics
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• v.42 no.5
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• pp.1063-1076
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• 2024

This research introduces an innovative approach to revolutionize inventory management strategies amid unpredictable demand and uncertainties. Introducing a Fuzzy Economic Order Quantity (EOQ) model, enriched with the centroid defuzzification method and supervised machine learning, the study offers a comprehensive solution for optimized decision-making. The model transcends traditional inventory paradigms by seamlessly integrating fuzzy logic and advanced machine learning, emphasizing adaptability in fast-paced business landscapes. The research unfolds against the backdrop of agile inventory management advocacy, with key contributions including the centroid defuzzification method for crisp interpretation and the integration of linear regression for cost prediction. The study employs a real-life bakery scenario to demonstrate the efficacy of both crisp and fuzzy models, underscoring the latter's superiority in handling uncertainties. Comparative analysis reveals nuanced impacts of uncertainty on inventory decisions, while linear regression establishes statistical relationships for cost predictions. The findings underscore the pivotal role of fuzzy logic in optimizing inventory management, paving the way for future enhancements, advanced machine learning integration, and real-world validation. This research not only contributes to adaptive inventory management evolution but also sets the stage for further exploration and refinement in dynamic business landscapes.