• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.17-23
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• 2019

In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.987-994
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• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.26-36
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• 2021

In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.72-81
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• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Fisheries and Aquatic Sciences
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• v.26 no.9
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• pp.548-557
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• 2023

The purposes of this research were to identify fish species using DNA barcodes or partial sequences of cytochrome b (Cytb) and to assess the diversity of fish in the Mae Tam reservoir, Phayao province, Thailand. Fish samples were collected 3 times, during the winter, summer, and rainy seasons, from 2 sampling sites using gillnets with 3 mesh sizes (30, 50, and 70 mm). A total of 34 representative samples were classified into 12 species, 7 families and 6 orders by morphological- and DNA barcoding-based identifications. However, one cichlid species, Cichlasoma trimaculatum, could only be identified using DNA barcoding. Family Cyprinidae had the greatest diversity, 50.00%. The diversity, richness and evenness indices ranged from 0.43-0.65, 0.64-1.46, and 0.27-0.40, respectively, indicating that fish diversity at both sampling sites was relatively low. A comparison of the catch per unit effort (CPUE) with 3 different mesh sizes found that the 50 mm mesh size was the best (474.80 ± 171.56 g/100 m²/night), followed by the 70 mm (417.41 ± 176.24 g/100 m²/night) and 30 mm mesh sizes (327.88 ± 115.60 g/100 m²/night). These results indicate that DNA barcoding is a powerful tool for species identification. Our data can be used for planning the sustainable management of fisheries resources in the Mae Tam reservoir.

• (The) Research of the performance art and culture
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• no.23
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• pp.185-212
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• 2011

Buddhist dance (seungmu) is a crux and highlight of Korean traditional dance; its aesthetics and technique are extraordinary, and Korean dance's unique style is well expressed. The Buddhist dance, which has been descended, is divided into Han Yeong Suk style, which is designated as Important Intangible Asset Number 27, and Lee Mae Bang style. While the two dances are same one, area is difference and they have unique style because of genealogical difference. However, studies on Buddhist dance so far have focused on single style's dance, or comparison of regional aspects (Han Yeong Suk dance is from Gyeonggi and Lee Mae Bang dance is from Honam area). But, Lee Byeong Ok suggested traditional artist dance is differed by male dance genealogy and female dance (gibang) genealogy dance, and while folk dance has storng tie with region, but artist dance has weak regional tie. Therefore, the purpose of this thesis is to study genealogical character of Buddhist dance's dancing style, clarifying Han Yeong Suk dance is male dance genealogy and Lee Mae Bang dance is gibang dance genealogy. In other words, among three theses that compared Lee Mae Bang and Han Yeong Suk dances, one analyzing movement, one comparing dance of invocation and one comparing traditional ballad, are re-analyzed from genealogical perspective and characteristics are comparatively analzyed. The overall summary of the genealogical attitude of the Han Yeong Suk and Lee Mae Ban dances is; First, Han's dance has masculinity, upwardness, progressiveness, activeness, outgoing character, boldness and grace, which are character of male dance lineage, while Lee's dance shows feminity, downwardness, backwardness, aesthecity, inwardness, delicacy and coquette. Second, the most expressed parts of the attitude of two dances are genealogical character, and then are original and regional characters. Third, two dances have strong genealogical attitude, but also has anti-genealogical attitude since the gender of descendent was changed, in other words Lee Mae Bang was man, and Han Yeong Suk was woman. Fourth, even though the two Buddhist dances have different genealogy and region, they share similarities as traditional dance descended in the same time period, so there are many common features. In other words, the two dances are Korean nation's dance and from same time period, but they should not be mixed, either. Even though they have small differences, they must keep each genealogy and descend to the next generation.

• Food Science and Preservation
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• v.9 no.3
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• pp.332-337
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• 2002

This study was undertaken in order to compare reflux extraction(RE) and microwave-assisted extraction(MAE) in extraction efficiency and establish optimum microwave extraction conditions in obtaining Ligularia fischeri extracts. A considerable reduction in extraction time was accomplished by MAE. When 70% methanol 50% methanol 70% ethanol, or 50% ethanol was used, MAE extract contained equal levels of soluble solid and total polyphenol as obtained by RE. The optimum microwave-assisted extraction conditions for Ligularia fischeri were achieved by 120∼150 watts of microwave energy and 4∼8 minutes of extraction time. No significant changes were found in antioxidant activity with DPPH scavenging method over the variation of microwave energy or extraction time. The use of diluted methanol or ethanol improved soluble solid content(30%), total polyphenol content(2.7%) and antioxidant activity(68%).

• Journal of Pharmacopuncture
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• v.11 no.2
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• pp.131-140
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• 2008

Objective Spinal SAH is an unusual disease that occasionally occurs spinal cord injury. This report intended to estimate the effect that taken by using oriental treatment on the patient with the sequelae of spinal SAH. Methods We have observed this case of patient treated by Dong's acupuncture therapy, pharmacopuncture therapy, Mae-sun therapy and herbal medication, etc. Results The patient showed improvements in pain, power and sensory function. Conclusion Oriental treatments such as Dong's acupuncture therapy, pharmacopuncture therapy, Mae-sun therapy and herbal medication can be effective for the sequelae of spinal SAH.