• 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.

• Smart Media Journal
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• v.11 no.8
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• pp.65-72
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• 2022

Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this paper, text data is vectorized using FastText after separating consonants, and voice data is vectorized by extracting features using Log Mel Spectrogram and MFCC respectively. We propose a multi-emotion recognition model that recognizes five emotions using vectorized data using an LSTM model. Multi-emotion recognition is calculated using RMSE. As a result of the experiment, the RMSE of the proposed model was 0.2174, which was the lowest error compared to the model using text and voice data, respectively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• 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.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.2
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• pp.223-230
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• 2015

Due to the conversion from the existing address system to road name address system, the versatility of the road name address from the public and private sectors is increasing gradually. Therefor, the reliability of the road name address basic map, of the default data is also an urgent need, this means the accuracy of the data. But, road name address basic map is production time due to the use of different map accuracy is to fall, so is also the situation insufficient of utilization. This study Construction of identify each attempt by the road name address basic map and analysis the location accuracy of road name address basic map. As a result, the RMSE of cadastral survey results showed that appears lower than the RMSE of digital map survey results. The judgment can be utilized as the basis for the proposed improvements for the road name address basic map.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.114-114
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• 2021

본 연구에서는 Sentinel-1 SAR 센서 기반 이미지자료와 딥러닝기법을 이용하여 고해상도 토양수분을 산정하였다. 입력자료는 지표특성(모래함량, 점토함량, 경사도), 인공위성 기반의 강우와 LANDSAT 기반의 이미지자료(NDVI, LST, 공간분포 토양수분)를 사용하였다. 강우자료의 경우 GPM(Global Precipitation Measurement) 일강우 자료를 사용하였으며, 관측일 기준으로 5일전까지의 강우자료와 5일평균강우를 구분하여 사용하였다. LANDSAT 기반의 토양수분 이미지자료와 지점관측 토양수분을 이용하여 검·보정 이후 딥러닝 모형의 입력자료로 사용하였다. 입력자료는 30m × 30m 해상도로 Resample 하여 딥러닝 모형의 학습을 진행하였으며, 학습에 사용된 모형을 이용하여 Sentinel-1 기반의 고해상도(10m × 10m) 토양수분이미지를 산정하였다. 검증지점은 거창군 거창읍, 계룡시 두마면, 장수군 장수읍 및 무주군 무주읍 토양수분 관측지점을 선정하였다. 거창군 거창읍의 산정결과, LANDSAT 기반의 토양수분 이미지와 DNN 기반의 토양수분 이미지가 매우 유사하게 나타났으며, 모의값(DNN 기반 토양수분)이 실측값(LANDSAT 기반의 토양수분)을 잘 반영한 것(R: 0.875 ; RMSE: 0.013)으로 나타났다. 또한 학습모형을 토지피복이 유사한 지역에 적용하여 토양수분을 산정한 결과 검증지점 계룡시(R: 0.897 ; RMSE: 0.014), 장수군(R: 0.770 ; RMSE: 0.024) 및 무주군(R: 0.909 ; RMSE: 0.012)의 모의값이 실측값과 매우 유사한 것으로 나타났다. 이를 바탕으로 Seninel-1 SAR센서 이미지자료와 딥러닝기법을 연계한 고해상도 토양수분자료가 농업, 수문, 환경 등 다양한 분야에서 활용될 수 있을 것으로 판단된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1151-1156
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• 2023

In this paper, we propose a deep learning model that utilizes charge/discharge data from initial lithium-ion batteries to predict the remaining useful life of lithium-ion batteries. We build the DMP using the PNP model. To demonstrate the performance of DMP, we organize DML using the LSTM model and compare the remaining useful life prediction performance of lithium-ion batteries between DMP and DML. We utilize the RMSE and RMSPE error measurement methods to evaluate the performance of DMP and DML models using test data. The results reveal that the RMSE difference between DMP and DML is 144.62 [Cycle], and the RMSPE difference is 3.37 [%]. These results indicate that the DMP model has a lower error rate than DML. Based on the results of our analysis, we have showcased the superior performance of DMP over DML. This demonstrates that in the field of lithium-ion batteries, the PNP model outperforms the LSTM model.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.376-385
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• 2023

Fishing boat capsizing accidents account for more than half of all capsize accidents. These can occur for a variety of reasons, including inexperienced operation, bad weather, and poor maintenance. Due to the size and influence of the industry, technological complexity, and regional diversity, fishing ships are relatively under-researched compared to commercial ships. This study aimed to predict the rolling motion time series of fishing boats using an image-based deep learning model. Image-based deep learning can achieve high performance by learning various patterns in a time series. Three image-based deep learning models were used for this purpose: Xception, ResNet50, and CRNN. Xception and ResNet50 are composed of 177 and 184 layers, respectively, while CRNN is composed of 22 relatively thin layers. The experimental results showed that the Xception deep learning model recorded the lowest Symmetric mean absolute percentage error(sMAPE) of 0.04291 and Root Mean Squared Error(RMSE) of 0.0198. ResNet50 and CRNN recorded an RMSE of 0.0217 and 0.022, respectively. This confirms that the models with relatively deeper layers had higher accuracy.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.213-225
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• 2018

In order to access the accuracy of the gridded daily Advanced Scatterometer (hereafter DASCAT) ocean surface wind data in the surrounding of Korea, the DASCAT was compared with the wind data from buoys. In addition, the reanalysis data for wind at 10 m provided by European Centre for Medium-Range Weather Forecasts (ECMWF, hereafter ECMWF), National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR, hereafter NCEP), Modern Era Retrospective-analysis for Research and Applications-2 (MERRA-2, hereafter MERRA) were compared and analyzed. As a result, the RMSE of DASCAT for the actual wind speed is about 3 m/s. The zonal components of wind of buoys and the DASCAT have strong correlation more than 0.8 and the meridional components of wind them have lower correlation than that of zonal wind and are the lowest in the Yellow Sea (r=0.7). When the actual wind speed is below 10 m/s, the EMCWF has the highest accuracy, followed by DASCAT, MERRA, and NCEP. However, under the wind speed more than 10 m/s, DASCAT shows the highest accuracy. In the nature of error according to the wind direction, when the zonal wind is strong, all dataset has the error of more than $70^{\circ}$ on the average. On the other hand, the RMSE of wind direction was recorded $50^{\circ}$ under the strong meridional winds. ECMWF shows the highest accuracy in these results. The RMSE of the wind speed according to the wind direction varied depending on the actual wind direction. Especially, MERRA has the highest RMSE under the westerly and southerly wind condition, while the NCEP has the highest RMSE under the easterly and northerly wind condition.