• Journal of The Korean Society of Integrative Medicine
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• v.2 no.4
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• pp.91-100
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• 2014

Purpose : The purpose of this experiment is to find out the effectiveness which exert influence on cognitive skills by using the Driver Rehabilitation program for senior citizens who are over 65 years old and live in Busan. Method : From July first, 2014 to August 28th, 2014, we researched the 60 elderly people who are over 65 and go to community relief center which is in Busan. - 30 are experimental group and the other is control group. In the experimental group, we used Nintendo wii's driving simulation program and RC Car driving program in the model road. For estimation, we used MVPT-3(Motor-Free Visual Perception Test-3), Trail Making Test - 1, Trail making Test - 2 and LOTCA(Loewenstein Occupational Therapy Cognitive Assessment). Result : Nintendo wii's driving simulation program and RC Car driving program in model road results efficient visual perception ability. This programs results effectively in visual perception ability and space perception ability. This programs results effectively in motor apraxia ability. This programs results effectively in control ability for visual perception. This programs results effectively in thinking operation. Conclusion : Nintendo Wii's driving simulation program and RC Car driving program in model road positively influence improving for visual perceptual ability and cognitive function of elderly people. Also it is considered as being more efficient for improving visual perceptual ability and cognitive function to implement basic rehabilitation training with driving rehabilitation program than basic training itself.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.73-81
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• 2020

This study is a single-group study to check the effectiveness of nursing students on their communication credibility, communication confidence and clinical decision-making ability by implementing SBAR-applied simulation training to improve the difficulty of delivering clear information to medical personnel during transition. By combining simulation practices and SBAR training based on emergency situations of mothers and newborns, programs were developed and applied to communicate clearly and briefly to the medical staff about emergencies and to enhance communication skills. The subjects were 91 fourth-year nursing college students from one university in B metropolitan city. The data were collected from Feb. 18, 2019 to Feb. 28, 2019 and were analyzed using the SPSS/WIN 18.0 program as a paired t-test. The results revealed that the communication clarity measured after the simulation exercise (t=-3.99, p<.001), Communication Confidence (t=-8.60, p<.001), Clinical Decision Capacity (t=-4.66, p<.001) Statistically, it has increased significantly. Therefore, the purpose of this research is significant in that it seeks to promote the expertise of nursing college students by developing and applying simulation practical education programs to enhance the communication skills and clinical decision-making skills of nursing college students as prospective medical personnel.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.259-268
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• 2023

The purpose of this study was to verify the effectiveness of mental simulation practice training using standardized patients for nursing students. This study is a single-group pre- and post-design study, and for data collection, a structured questionnaire was provided to 95 nursing students from a university located in J. The collected data was analyzed using the SPSS/WIN 27.0 program. Results of the study The mental simulation practice training program using standardized patients improved the subject's learning motivation (t=-2.011, p=.046), learning self-efficacy (t=-2.225, p=.027), and learning satisfaction (t=-). 3.428, p=.001) and transfer motivation (t=-2.628, p=.009). In addition, as a result of analyzing the self-assessment contents by text mining, words related to mental simulation practice education using standardized patients included situation, experience, acting, communication, scenario, and mental nursing clinical practice, and words related to satisfaction were actual, There was help, response, understanding, variety, etc. As a result of this study, an environment similar to the actual situation was implemented, and the mental simulation training program applying various cases was found to be effective in practical education of nursing students, so it is necessary to actively utilize it to improve the ability to adapt to the field in the future.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.396-410
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• 2018

To assist operators to properly assess the current situation of the plant, accurate fault diagnosis methodology should be available and used. A reliable fault diagnosis method is beneficial for the safety of nuclear power plants. The major idea proposed in this work is integrating the merits of different fault diagnosis methodologies to offset their obvious disadvantages and enhance the accuracy and credibility of on-line fault diagnosis. This methodology uses the principle component analysis-based model and multi-flow model to diagnose fault type. To ensure the accuracy of results from the multi-flow model, a mechanical simulation model is implemented to do the quantitative calculation. More significantly, mechanism simulation is implemented to provide training data with fault signatures. Furthermore, one of the distance formulas in similarity measurement-Mahalanobis distance-is applied for on-line failure degree evaluation. The performance of this methodology was evaluated by applying it to the reactor coolant system of a pressurized water reactor. The results of simulation analysis show the effectiveness and accuracy of this methodology, leading to better confidence of it being integrated as a part of the computerized operator support system to assist operators in decision-making.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.248-254
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• 2022

Educational innovations are first created, improved or applied educational, didactic, educative, and managerial systems and their components that significantly improve the results of educational activities. The development of pedagogical technology in the global educational space is conventionally divided into three stages. The role of innovative technologies in Higher School practice is substantiated. Factors of effectiveness of the educational process are highlighted. Technology is defined as a phenomenon and its importance is emphasized, it is indicated that it is a component of human history, a form of expression of intelligence focused on solving important problems of being, a synthesis of the mind and human abilities. The most frequently used technologies in practice are classified. Among the priority educational innovations in higher education institutions, the following are highlighted. Introduction of modular training and a rating system for knowledge control (credit-modular system) into the educational process; distance learning system; computerization of libraries using electronic catalog programs and the creation of a fund of electronic educational and methodological materials; electronic system for managing the activities of an educational institution and the educational process. In the educational process, various innovative pedagogical methods are successfully used, the basis of which is interactivity and maximum proximity to the real professional activity of the future specialist. There are simulation technologies (game and discussion forms of organization); technology "case method" (maximum proximity to reality); video training methodology (maximum proximity to reality); computer modeling; interactive technologies; technologies of collective and group training; situational modeling technologies; technologies for working out discussion issues; project technology; Information Technologies; technologies of differentiated training; text-centric training technology and others.

• Radiation Oncology Journal
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• v.37 no.3
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• pp.201-206
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• 2019

Purpose: To observe the effectiveness of the practical instruction sheet and the educational video for left-sided breast treatment in a patient receiving deep inspiration breath hold (DIBH) technique. Two parameters, simulation time and patient satisfaction, were assessed through the questionnaire. Methods: Two different approaches, which were the instruction sheet and educational video, were combinedly used to assist patients during DIBH procedures. The guideline was assigned at least 1 week before the simulation date. On the simulation day, patients would fill the questionnaire regarding their satisfaction with the DIBH instruction. The questionnaire was categorized into five levels: extremely satisfied to dissatisfied, sequentially. The patients were divided into four groups: not DIBH technique, DIBH without instruction materials, the DIBH with instruction sheet or educational video, and DIBH with both of instruction sheet and educational video. Results: Total number of 112 cases of left-sided breast cancer were analyzed. The simulation time during DIBH procedure significantly reduced when patients followed the instruction. There was no significant difference in simulation time on the DIBH procedures between patient compliance via instruction sheet or educational video or even following both of them. The excellent level was found at 4.6 ± 0.1 and 4.5 ± 0.1, for patients coaching via instruction sheet as well as on the educational video, respectively. Conclusion: Patient coaching before simulation could potentially reduce the lengthy time in the simulation process for DIBH technique. Practicing the DIBH technique before treatment is strongly advised.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.