• Nuclear Engineering and Technology
• /
• v.53 no.7
• /
• pp.2334-2340
• /
• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Journal of Convergence for Information Technology
• /
• v.11 no.8
• /
• pp.53-61
• /
• 2021

The purpose of this study was to investigate the menstrual pain, clinical practice stress, and clinical competence and identify influencing factors of clinical competence of 3rd nursing students who start clinical practice for the first tim. The survey was conducted on 155 nursing students from June 1 to October 30, 2020. Data were analyzed using t-test, ANOVA, Scheffe test, Pearson correlation coefficients and multiple regression analysis. The degree of influence menstrual pain was 5.01points, clinical practice stress was 2.82points, clinical competence was 3.42points. Menstrual pain was positive correlated with clinical practice stress(r=.319, p=.000), and menstrual pain was negative correlated with clinical competence(r=-.279, p=.000). Clinical practice stress was negative correlated with clinical competence(r=-.333, p=.005). Menstrual pain was main factor that affects clinical competence. The model explained 25.0% of the variables. Therefore, intervention education is needed to reduce menstrual pain in order to improve the clinical competence of nursing students.

• Journal of Convergence for Information Technology
• /
• v.12 no.5
• /
• pp.202-211
• /
• 2022

The purpose of this study is to investigate the relationship between job stress, job autonomy, employee support evaluation, and the turnover intentions of workers in the beauty and cosmetics industry. This study assessed 570 workers with 3 months or more of experience in the beauty and cosmetics industry. Data processing was achieved through analyses of frequency, descriptive statistics, exploratory factors, reliability, correlation, and confirmatory factors, as well as the verification of the structural equation model. The results of the study are as follows: first, employee support evaluation in the beauty and cosmetics industry workers was negatively correlated to job stress. Second, employee support evaluation showed a negative relationship with turnover intentions. Third, job stress was found to have a positive relationship with turnover intention.This study suggests that, in order to reduce the turnover intentions of beauty and cosmetics industry workers, it is necessary for employers to make efforts to manage employees' job autonomy, support evaluation, and stress levels.

• Journal of Convergence for Information Technology
• /
• v.12 no.5
• /
• pp.256-270
• /
• 2022

This paper examined whether quality management(QM) positively impacts Kazakh companies' business performance (financial and non-financial performance). As a result of testing ten hypotheses based on the research model for 287 companies in Almaty, only eight hypotheses were supported. Top management leadership was identified as a critical factor that positively affected financial performance, and customer-centered thinking is strongly related to non-financial performance. Employee participation and quality information analysis factors also positively affected business performance, but their influence was lower than top management leadership and customer-centered thinking factors. Finally, the supplier management factor did not significantly affect business performance, and the two related hypotheses were not supported. These results are presumed to be due to the characteristics of the target companies, such as oil and raw material manufacturing and construction, rather than high-quality finished products.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.151-157
• /
• 2022

The purpose of this study was to investigate the frequency characteristics of forced vibration considering the vehicle progress. And the vibration characteristics in frequency domain that occur, when vehicle passes the bump, were analyzed. The responses such as displacement, velocity and acceleration were obtained through numerical analysis, and FFT processing was performed to analyze the frequency response function(FRF) characteristics. In particular, the location of vehicle eigenmodes and external excitation modes was clearly shown and analyzed. In the forced vibration model by external force, the behavior of the eigenmode in power spectrum and real and imaginary parts were also analyzed. The mode characteristics were also analyzed in each FRF. It was approximated by assuming total excitation force by considering the exciting frequency using impulse and sine wave forces, which can give the amplitude and frequencies. The response characteristics of forced oscillations having different mass, damping and stiffness have been systematically discussed.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.270-279
• /
• 2022

The purpose of this study was to find the influential factors of nursing students' behavioral intention of Recommended Immunizations for Healthcare Personnel(RIHP). The survey was performed on 260 nursing students. Data were collected using a structured questionnaires and analyzed using t-test, ANOVA, Pearson correlation coefficient, and hierarchical regression with SPSS 23.0 program. Results of this study revealed that the influential factors on the behavioral intention of RIHP were the cues to action, self-efficacy, perceived benefits and senior grade. And the explanation power of the regression model appeared as being 36.4%(F=13.35, p<.001). Based on the study findings, further development and application of specific programs to improve nursing students' intention of RIHP in consideration of grade, to emphasize benefits of immunization, are needed to prevent infection in clinical practice.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.116-125
• /
• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.66-74
• /
• 2022

This study identified the impact of information literacy, risk perception, crisis communication on preventive behaviors related to COVID-19 among nursing students. Data were collected from 187 nursing students from 25 June 2020 to 3 July 2020, and analyzed using the SPSS/WIN 26.0 program. As a result of regression analysis, the factors influencing prevention behaviors were crisis communication(β=0.30, p<.001), information literacy(β=0.29, p<.001), and risk perception(β=0.19, p=.004). The explanatory power of the model was 27%. This study suggests that the focus should be on improving the activating crisis communication process among individual, family and society, increasing information literacy and risk perception on crisis when developing program to improve COVID-19 preventive behaviors of nursing students experiencing clinical practice.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.252-258
• /
• 2022

In this study, we propose a method for analyzing students' understanding of probability and statistics in small lectures at universities. A computer-based test for probability and statistics was performed on 95 science and engineering college students. After dividing the students' responses into 7 clusters using the Robust PCA and the Gaussian mixture model, the achievement of each subject was analyzed for each cluster. High-ranking clusters generally showed high achievement on most topics except for statistical estimation, and low-achieving clusters showed strengths and weaknesses on different topics. Compared to the widely used PCA-based dimension reduction followed by clustering analysis, the proposed method showed each group's characteristics more clearly. The characteristics of each cluster can be used to develop an individualized learning strategy.

• Nuclear Engineering and Technology
• /
• v.56 no.4
• /
• pp.1153-1164
• /
• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.