• Quality Improvement in Health Care
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• v.24 no.2
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• pp.26-40
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• 2018

Purpose: The objective of this study was to identify the relationship between knowledge of patient safety, nursing professionalism and patient safety management activities of nursing students with clinical practical experience. Methods: Self-administered questionnaires survey on knowledge of patient safety, nursing professionalism, and patient safety management activities were conducted for the $3^{rd}-year$ and $3^{th}-year$ nursing students. 139 questionnaires were distributed, of which, 131 were used for data analysis. Results: The scores of nursing students' knowledge of patient safety, nursing professionalism and patient safety management activities were $6.76{\pm}1.26$, $65.11{\pm}7.97$ and $67.99{\pm}7.26$, respectively. Knowledge of patient safety differed significantly according to the grade. Nursing professionalism had a difference with major satisfaction, clinical practical satisfaction, and experience of patient safety accident. Patient safety management activities were positively correlated (p<.01) with knowledge of patient safety and nursing professionalism. Patient safety management activities increased significantly with increase in the scores of knowledge of patient safety and nursing professionals. The factors that were related to patient safety management activities of nursing students were knowledge of patient safety and nursing professionalism. Knowledge of patient safety and nursing professionalism were selected as significant variables for explaining the patient safety management activities of nursing students, of which the coefficient of determination was 9.8%. Conclusion: To promote patient safety management activities of nursing students, training programs for patient safety management activities are required. Also, there is the need to increase the knowledge of patient safety and nursing professionalism of nursing students using various educational method.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.165-173
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• 2019

Recently, a variety of programs for elementary school students that utilize play in their curricula are supported. In this study, we are trying to draw up ways to activate play education based on the elements necessary for the play education to be effectively provided on the field and the current operational status. In order to achieve the research goal, nine participants of play experts and parents were selected for the focus group interview (FGI). The FGI consist of five questions: (1) opinions on the establishment and joint operation of the organization to support play and parents' education; (2) opinions based on experience in participating in existing training programs; (3) activation plan of play & education program; (4) competencies required by members of the organization; (5) evaluation of program for quality improvement. Through the FGI survey, we drew ideas for the operation of play & education programs to promote positive growth and support systemic programs of both preschoolers and elementary students. In order for play & education to be active in the field of education, a center where play & education and parents' education can be conducted at the same time should be established and operated so that the education can be integrated with play. Based on these findings, we proposed follow-up research in the direction of achieving specific goals and enhancing the quality of play education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.888-895
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• 2020

In the construction industry, off-site construction (OSC) is drawing attention as a production method due to changes in working hours and the supply and demand of manpower. In 1991, there was a policy of spreading and expanding the use of precast concrete (PC) apartment homes, but they have not been actively used so far since they were discontinued due to quality problems. In this study, policy tasks were analyzed to motivate the application of OSC-based PCs in the apartment housing sector, and policy directions were derived by conducting focus group interviews (FGI). Nine policies are suggested regarding the following topics: PC apartment supply quantity provision, priority application of public housing, priority supply of public housing, preferential floor area ratio, funding, tax support, improvement of business area structure, improvement of delivery method, factory certification system, and training of experts. The results of the FGIs are as follows. First, in order to revitalize PC apartment homes, leading efforts from the public sector are required. Second, rather than reorganizing the business sector or introducing a new delivery method, a policy direction that induces the strengthening of cooperation is desirable. Third, PC activation should be promoted on an institutional basis for securing appropriate construction costs and quality.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.187-194
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• 2021

OSINT(Open Source Intelligence), rapidly increasing on the surface web in various forms, can also be used for criminal investigations by using profiling. This technique has become quite common in foreign investigative agencies such as the United States. On the other hand, in Korea, it is not used a lot, and there is a large deviation in the quantity and quality of information acquired according to the experience and knowledge level of investigator. Unlike Bazzell's most well-known model, we designed a Korean-style OSINT-based profiling technique that considers the Korean web environment and provides timeline information, focusing on the improved workflow. The database schema to improve the efficiency of profiling is also presented. Using this, we can obtain search results that guarantee a certain level of quantity and quality. And it can also be used as a standard training course. To increase the effectiveness and efficiency of criminal investigations using this technique, it is necessary to strengthen the legal basis and to introduce automation technologies.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.23-29
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• 2021

Recent deep learning-based natural language processing studies are conducting research to improve performance by training large amounts of data from various sources together. However, there is a possibility that the methodology of learning by combining data from various sources into one may prevent performance improvement. In the case of machine translation, data deviation occurs due to differences in translation(liberal, literal), style(colloquial, written, formal, etc.), domains, etc. Combining these corpora into one for learning can adversely affect performance. In this paper, we propose a new Corpus Weight Balance(CWB) method that considers the balance between parallel corpora in machine translation. As a result of the experiment, the model trained with balanced corpus showed better performance than the existing model. In addition, we propose an additional corpus construction process that enables coexistence with the human translation market, which can build high-quality parallel corpus even with a monolingual corpus.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.342-350
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• 2022

In this paper, actual drone noise samples are collected for speech processing in disaster environments to build noise-corrupted speech database, and speech enhancement performance is evaluated by applying spectrum subtraction and mask-based speech enhancement techniques. To improve the performance of VoiceFilter (VF), an existing deep neural network-based speech enhancement model, we apply the Self-Attention operation and use the estimated noise information as input to the Attention model. Compared to existing VF model techniques, the experimental results show 3.77%, 1.66% and 0.32% improvements for Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligence (STOI), respectively. When trained with a 75% mix of speech data with drone sounds collected from the Internet, the relative performance drop rates for SDR, PESQ, and STOI are 3.18%, 2.79% and 0.96%, respectively, compared to using only actual drone noise. This confirms that data similar to real data can be collected and effectively used for model training for speech enhancement in environments where real data is difficult to obtain.

• Journal of Korean Elementary Science Education
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• v.41 no.3
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• pp.480-500
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• 2022

In this study, the perception and demands on the use of realistic content were analyzed through in-depth interviews with elementary school teachers experienced in using realistic content in science classes. Specifically, the following questions were investigated: (1) What kind of realistic content and how do elementary school teachers use it in science classes? (2) What are the perceptions and difficulties of elementary school teachers regarding the use of realistic content in science classes? (3) What are the needs of elementary school teachers related to the professional development program for the use of realistic content in science classes? The study revealed the following results. First, elementary school teachers mainly used digital textbooks and realistic content provided by the "Science Level Up" site, and the content types could be classified into "exploration type," "visit type," and "production type," according to the purpose of use. Second, elementary school teachers mentioned the educational advantages of using realistic content to help students understand scientific content, induce interest and curiosity, and become immersed in a sense of reality. Several difficulties related to the use of realistic content were mentioned. Among them, the lack of high-quality educational content suitable for science classes and a lack of examples of specific class cases that use realistic content stood out. Thirdly, regarding the development of teacher expertise to use realistic content, elementary school teachers emphasized the need for information on quality realistic content; teacher training centered on specific class cases; instructional models that can be applied by realistic content type; and information on the purchase, use, management, and operation of necessary devices. Reflecting on these research results, implications for more effective use of realistic content in elementary science classes were discussed.