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Detection of Arrhythmia using 1D Convolution Neural Network with LSTM Model
Seungwoo Han,Wongyu Lee,Heesang Eom,Juhyeong Kim,Cheolsoo Park 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.4
Considering the high death rate from cardiovascular diseases, it is important to detect an irregular heart rhythm in order to prevent potential tragedy. The purpose of this paper is to present automatic detection of arrhythmia based on electrocardiography. We suggest a one-dimensional convolutional neural network (1D CNN) with long short-term memory (LSTM). The suggested architecture is compared with two other deep learning methods: the 1D CNN and the multi-layer perceptron (MLP) model. To evaluate performance, we measured the overall accuracy, macroaveraged precision, and macro-averaged recall of our proposed method as being 92.03%, 90.98%, and 86.15%, respectively. The results demonstrate that the 1D CNN-with-LSTM model outperforms the two other models.
HAN, Seungwoo,KO, Yong-Ho,HONG, Taehoon,KOO, Choongwan,LEE, Sangyoub Vilnius Gediminas Technical University 2017 Journal of Civil Engineering and Management Vol.23 No.2
<P>As construction projects have become more complicated in design and construction, it is necessary to establish the construction operational plans in advance. However, there were some limitations in analyzing construction productivity due to the difficulty of collecting accurate data. To address this challenge, this study aimed to develop the framework for the validation of simulation-based productivity analysis, which consisted of three measures: (i) validation of the measured productivity data as target variable; (ii) validation of the measured duration data as input variable; and (iii) validation of the simulation model compared to the actual construction process. To verify the feasibility of the proposed framework, this study focused on the curtain wall construction project of “S” office building as a case study. The T-test was applied to investigate the statistical difference between the measure and simulated productivity. It was determined that the significance level α in the T-test for the unloading process was 0.136 with 95% confidence interval; the lifting process, 0.106; and the installing process, 0.311. As a result, there was no significant difference between the measured and simulated productivity. The proposed framework could enable executives and managers in charge of project planning and scheduling to accurately predict the simulation-based productivity.</P>
Elite Polarization in South Korea: Evidence from a Natural Language Processing Model
seungwoo han 동아시아연구원 2022 Journal of East Asian Studies Vol.22 No.1
This study analyzes political polarization among the South Korean elite by examining 17 years’ worth of subcommittee meeting minutes from the South Korean National Assembly's standing committees. Its analysis applies various natural language processing techniques and the bidirectional encoder representations from the transformers model to measure and analyze polarization in the language used during these meetings. Its findings indicate that the degree of political polarization increased and decreased at various times over the study period but has risen sharply since the second half of 2016 and remained high throughout 2020. This result suggests that partisan political gaps between members of the South Korean National Assembly increase substantially.
Housing Market Trend Forecasts through Statistical Comparisons based on Big Data Analytic Methods
Han, Seungwoo,Ko, Yongho,Kim, Jimin,Hong, Taehoon American Society of Civil Engineers 2018 Journal of management in engineering Vol.34 No.2
<P>Assessments and forecasts of housing markets can provide insight into the fundamental sustainability of housing and construction. The home sales index (HSI) is considered one of the most important factors for forecasting economic trends of housing markets in the real estate and construction industry, and researchers have tried to develop relevant forecasting models for the HSI. The autoregressive integrated moving average (ARIMA) has generally been used for forecasting future trends based on time series but without investigating any of the influences of social factors. However, there are many demands for effective HSI forecasting by identifying the various social factors influencing the HSI. The HSI can be effectively forecasted in advance by observing several social factors. Such forecasting methods can be developed using big data analytic methods that focus on the relationship between those factors and HSI using web search data. This study suggests a methodology for forecasting model development with the provision of fundamental attributes and the pros and cons of each model to which the multiple regression analysis (MRA) and the artificial neural network (ANN) were applied. The forecasting performance of these models was compared with that by ARIMA. This study also quantifies the HSI forecasting accuracy between MRA and ANN based on social factors obtained from web search data. The forecast HSI values using ARIMA are more accurate than those of MRA and ANN. The lowest mean absolute error and normalized mean-square error for each model were calculated as 1.680 and 1.089 by MRA, 1.557 and 1.843 by ANN, and 0.173 and 0.294 by ARIMA, respectively. This methodology could allow many researchers to create and develop forecasting models using web search data for HSI forecasting and other related economic indexes. (c) 2017 American Society of Civil Engineers.</P>
SeungWoo Han,HeeSook Kim 인문사회과학기술융합학회 2018 예술인문사회융합멀티미디어논문지 Vol.8 No.3
promotion program using MBTI on self-esteem, depression, and anger of the high school students. Methods: Data was collected from 27 students who were interested with MBTI program at an high school students in D city from October 20, 2014 to December 03, 2014. The collected data were analyzed by frequency, percentage, and paired t-test, with the SPSS/WIN 18.0 program. Results: After participating in the peer relationship promotion program using MBTI, the mean score of differences with depression, self-esteem, and and anger in high school students was 6.0, 2.8, 2.6 respectively. significant differences were found between pre and post-test in self-esteem(t=-3.19, p=.004) and depression(t=3.50, p=.002). However, there was no significant difference in anger. Conclusion: The results suggest that the peer relationship promotion program using MBTI can reduce high school students’s depression by helping them to enhance their self-esteem. Furthermore, MBTI is an effective nursing intervention to improve students’ peer relationship.