• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.

• The Journal of Korean Association of Computer Education
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• v.14 no.6
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• pp.31-39
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• 2011

Information and communication environment includes a lot of dangerous factors to elementary school children who do not establish their own value sense as well as its related knowledge yet. This paper proposes a case based multiplex teaching and learning model which leads students to participate and be interested in, then to improve the educational level of information and communication ethics in elementary school. This model progresses in centric of the conflict situations in information and communication ethics that can be commonly occurred in actual life. To adapt case based approach, three types of teaching and learning models, value conflict, value clarification, and role playing, are applied in a combined form. Along with applying the models, their educational effects have been compared and analysed in time sequence, in the educational level improvement point of view. The verification has been conducted by using surveys and questionnaires, in the four areas; cognitive, affective, behavioral, and then overall. The verification results show that the proposed method is effective to improve the educational level of information and communication ethics in elementary schooll.

• Journal of Korean Academy of Nursing
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• v.50 no.1
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• pp.26-38
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• 2020

Purpose: The purpose of this study was to explain a structural model of posttraumatic growth among psychiatric nurses based on existing models and a literature review and verify its effectiveness. Methods: Data were collected from psychiatric nurses in one special city, four metropolitan cities, and three regional cities from February to March 2016. Exogenous variables included hardiness and distress perception, while endogenous variables included self-disclosure, social support, deliberate rumination, and posttraumatic growth. Data from 489 psychiatric nurses were analyzed using IBM SPSS Statistics 19.0 and AMOS 20.0. Results: The modified model was a good fit for the data. Tests on significance of the pathways of the modified model showed that nine of the 14 paths were supported, and the explanatory power of posttraumatic growth by included variables in the model was 69.2%. For posttraumatic growth among psychiatric nurses, deliberate rumination had a direct effect as the variable that had the largest influence. Indirect effects were found in the order of hardiness, social support, and distress perception. Self-disclosure showed both direct and indirect effects. Conclusion: A strategy to improve deliberate rumination is necessary when seeking to improve posttraumatic growth among psychiatric nurses. Enhancing psychiatric nurses' hardiness before trauma would enable them to actively express negative emotions after trauma, allowing them to receive more social support. This would improve deliberate rumination and consequently help promote psychological growth among psychiatric nurses who have experienced trauma.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.3
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• pp.457-474
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• 2002

Purpose : The purpose of this study was to construct the Organizational Effectiveness Model that explains and predicts the effectiveness of a nursing unit from the organizational-behavioral perspective. Furthermore, this study arms to develop a comprehensive organizational effectiveness model. Method : The subjects of this study consist of two groups: 455 nurses and 538 patients. Staff nurses who were employed and twenty patients from each ward in four university hospitals located in Seoul, Pusan and Kyungki were involved. Data were collected from October 4th to October 14th in 2000 by self-report questionnaire. Data were analyzed by the SAS for the general characteristics of the subjects, descriptive statistics, test for the reliability and correlations. Fitness of the hypothetical model were tested using Lisral 8.12(a) program. Result : With the findings from this study, duration of employment and the locus of control among the characteristics of the nurses, job enrichment among the characteristics of nursing job were direct or indirect predictors of the organizational effectiveness of the nursing units. Group dynamics in the nursing units and the characteristics of organizational behavior were mediating variables for the organizational effectiveness of the nursing units, and affect directly and indirectly on the individual outcome and group outcome to the great extends. Conclusion : Therefore, nursing managers ought to develop career ladder program and based job enrichment program in order to improve the organizational effectiveness of the nursing units. Additionally, programs to improve organizational effectiveness via improve group dynamics and characteristics of the organizational behaviors should be developed.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.578-586
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• 2021

We propose a semi-supervised learning method based on Variational AutoEncoder (VAE) and Unsupervised Data Augmentation (UDA) to improve the performance of an end-to-end speech recognizer. In the proposed method, first, the VAE-based augmentation model and the baseline end-to-end speech recognizer are trained using the original speech data. Then, the baseline end-to-end speech recognizer is trained again using data augmented from the learned augmentation model. Finally, the learned augmentation model and end-to-end speech recognizer are re-learned using the UDA-based semi-supervised learning method. As a result of the computer simulation, the augmentation model is shown to improve the Word Error Rate (WER) of the baseline end-to-end speech recognizer, and further improve its performance by combining it with the UDA-based learning method.

• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.295-300
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• 2015

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1274-1285
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• 2015

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator.

• Journal of Korean Biological Nursing Science
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• v.22 no.2
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• pp.90-101
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• 2020

Purpose: The purpose of this study was to build and verify a structural model that could predict the severity of irritable bowel syndrome in university students. Methods: Participants were 205 students enrolled in college with irritable bowel syndrome using the irritable bowel syndrome module of the ROME IV Adult Questionnaire. The data were collected using online questionnaires in AprilMay 2019. The data were analyzed using the SPSS WIN 25.0 and AMOS 20.0 programs. Results: 1) The symptom severity that participants experienced were mild (14.6%), moderate (45.4%), and severe (40%). 2) Fit indices of the model were x²= 79.66 (df = 52, p= .009), CFI= .94, TLI= .96, RMSEA= .05, RMR= 1.59, GFI= .94, and TLI= .96.3). The severity of irritable bowel syndrome was influenced directly by anxiety and sleep, and indirectly by family history, perfectionism, social support, coping, and stress. The severity of irritable bowel syndrome was indirectly affected by the following: family history through anxiety; perfectionism through stress, anxiety, and sleep; social support through coping, stress, anxiety, and sleep; coping through stress and anxiety; and stress through anxiety and sleep. Conclusion: Based on the results of this study, a nursing intervention is needed to reduce the anxiety and stress and improve the quality of sleep to improve the health of the college students and manage the symptoms of patients with irritable bowel syndrome.