• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• The Journal of Internal Korean Medicine
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• v.40 no.3
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• pp.331-342
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• 2019

Objectives: Samul-tang is commonly used to alleviate the side effects of chemotherapy. This study aimed to establish an efficient method of extracting ACTS002 based on Samul-tang using the yield, high-performance liquid chromatography (HPLC), and antioxidant assay. Methods: ACTS002 was extracted from each extraction solvent, and the contents of 5-hydroxymethyl-2-furaldehyde (5-HMF), paeoniflorin, and ferulic acid were quantitatively analyzed and compared using HPLC. Moreover, the antioxidant activities of ACTS002 were measured using total flavonoids, total phenolic compounds, 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), and Ferric reducing/antioxidant power (FRAP). Results: All of the components were set as the index contents because they were easy to process. The antioxidant activity of total flavonoids was the highest in 70% ethyl alcohol extracts, and total phenolic compounds were the highest in 50% ethyl alcohol extracts. In DPPH, 50% ethyl alcohol extracts showed the highest activity, and in ABTS 70% ethyl alcohol extracts were the highest. In FRAP, 70% ethyl alcohol extracts showed the highest activity. Conclusions: ACTS002 can control quality by setting 5-HMF, paeoniflorin, and ferulic acid as the index contents. The antioxidant activity measurement was relatively high in the 50% and 70% ethyl alcohol extracts. Our results can predict the possibility of a pharmacological activity and the standardization of ACTS002.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.11-19
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• 2021

In this paper, we propose a non-orthogonal multiple access (NOMA) method based on channel alignment to simultaneously transmit multiple unicast and multicast streams in frequency-efficient manner. In this method, all receivers in a multicast cluster use the receive beamforming vectors that align their channels, and the base station uses the aligned channel information to design the transmit beamforming vectors that eliminate interference between multicast clusters. Using the effective receive channel information combined with the transmit beamforming vectors, unicast receivers design their own receive beamforming vectors that eliminate interference between unicast receivers. Since the proposed method effectively eliminates the interference, it achieves a higher sum rate than the existing orthogonal multiple access (OMA) method in high SNR regions. In addition, we present a hybrid method that exploits the benefits of the proposed NOMA method and the existing OMA method. Depending on the channel state, the hybrid method adaptively employs the existing OMA method, which improves the received signal power, in low SNR regions and the proposed NOMA method, which effectively eliminates the interference, in high SNR regions, thereby achieving a good sum rate over the entire SNR region.

• Design & Manufacturing
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• v.15 no.1
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• pp.26-31
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• 2021

As the aging ages, the disease also increases, and the development of AI technology and X-ray equipment used to treat patients' diseases is also progressing a lot. X-ray tube converts only 1% of electron energy into X-ray and 99% into thermal energy. Therefore, when the cooling time of the anode and the X-ray tube are frequently used in large hospitals, the amount of X-ray emission increases due to temperature rise, the image quality deteriorates due to the difference in X-ray dose, and the lifespan of the overheated X-ray tube may be shortened. Therefore, in this study, temperature rise and cooling time of 60kW, 75kW, and 90kW of X-ray tube anode input power were studied. In the X-ray Tube One shot 0.1s, the section where the temperature rises fastest is 0.03s from 0s, and it is judged that the temperature has risen by more than 50%. The section in which the temperature drop changes most rapidly at 20 seconds of cooling time for the X-ray tube is 0.1 seconds to 0.2 seconds, and it is judged that a high temperature drop of about 65% or more has occurred. After 20 seconds of cooling time from 0 seconds to 0.1 seconds of the X-ray tube, the temperature is expected to rise by more than 3.7% from the beginning. In particular, since 90kW can be damaged by thermal shock at high temperatures, it is necessary to increase the surface area of the anode or to require an efficient cooling system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.295-305
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• 2021

In this study, we constructed using Random Forest(RF) by selecting the meteorological factors related to the occurrence of frost. As a result, when constructing a classification model for frost occurrence, even if the amount of data set is large, the imbalance in the data set for development of model has been analyzed to have a bad effect on the predictive power of the model. It was found that building a single integrated model by grouping meteorological factors related to frost occurrence by region is more efficient than building each model reflecting high-importance meteorological factors. Based on our results, it is expected that a high-accuracy frost occurrence prediction model will be able to be constructed as further studies meteorological factors for frost prediction.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• Applied Chemistry for Engineering
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• v.35 no.2
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• pp.128-133
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• 2024

Although antisolvent-assisted crystallization is one of the promising processes to produce high-quality perovskite films, general antisolvents such as chlorobenzene (CB) have toxic and volatile properties. In addition, CB is not suitable to control the crystallization of perovskite in the atmospheric air. In this work, isopropyl acetate (IA) is used as an eco-friendly antisolvent to demonstrate air-processed perovskite solar cells, and ethyl-4-cyanocinnamate (E4CN) with a cyano group, carbonyl group, and aromatic ring is introduced in IA to improve the performance and stability of devices. Defects at the surface and grain boundaries of the perovskite layer, such as un-coordinated Pb²⁺ and iodine, can be decreased resulting from the interaction of E4CN and perovskite, and thus reduced recombination and enhanced carrier transport can be expected. As a result, the perovskite device with E4CN achieves a high maximum power conversion efficiency (PCE) of 18.89% and outstanding stability, maintaining 60% of the initial efficiency for 300 h in the air without any encapsulation.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.880-885
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• 2024

For Safety Assisted Engineering works, real-time simulators have emerged as a mandatory tool among all the key actors involved in the nuclear industry (utilities, designers and safety authorities). EDF, Electricité de France, as the leading worldwide nuclear power plant operator, has a crucial need for efficient and updated simulation tools for training, operating and safety analysis support. This paper will present the work performed at EDF/DT to develop a new generation of engineering simulator to fulfil these tasks. The project is called SiRENE, which is the acronym of Re-hosted Engineering Simulator in French. The project has been economically challenging. Therefore, to benefit from existing tools and experience, the SiRENE project combines: - A part of the process issued from the operating fleet training full-scope simulator. - An improvement of the simulator prediction reliability with the integration of High-Fidelity models, used in Safety Analysis. These High-Fidelity models address Nuclear Steam Supply System code, with CATHARE thermal-hydraulics system code and neutronics, with COCCINELLE code. - And taking advantage of the last generation and improvements of instructor station. The intensive and challenging uses of the new SiRENE engineering simulator are also discussed. The SiRENE simulator has to address different topics such as verification and validation of operating procedures, identification of safety paths, tests of I&C developments or modifications, tests on hydraulics system components (pump, valve etc.), support studies for Probabilistic Safety Analysis (PSA). etc. It also emerges that SiRENE simulator is a valuable tool for self-training of the newcomers in EDF nuclear engineering centers. As a modifiable tool and thanks to a skillful team managing the SiRENE project, specific and adapted modifications can be taken into account very quickly, in order to provide the best answers for our users' specific issues. Finally, the SiRENE simulator, and the associated configurations, has been distributed among the different engineering centers at EDF (DT in Lyon, DIPDE in Marseille and CNEPE in Tours). This distribution highlights a strong synergy and complementarity of the different engineering institutes at EDF, working together for a safer and a more profitable operating fleet.

• Women's Health Nursing
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• v.29 no.4
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• pp.274-290
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• 2023

Purpose: This study investigated the effects of prenatal education characteristics, pandemic-related pregnancy stress, and health behaviors during pregnancy on prenatal depression in pregnant women during the coronavirus disease 2019 (COVID-19) pandemic. Methods: The participants were 180 pregnant Korean women, recruited from internet communities for pregnancy preparation, childbirth, and childcare, from July 5 to 15, 2022. The collected data were analyzed using the t-test, analysis of variance, the Mann-Whitney U-test, the Kruskal-Wallis test, and multiple regression analysis. Results: The scores for pandemic-related pregnancy stress (24.50±6.37) and health behaviors during pregnancy (67.07±9.20) were high. Nearly half of the participants (n=89, 49.4%) presented with prenatal depression, with scores of 10 or greater. Prenatal depression had a positive correlation with gestational age (r=.18, p=.019) and pandemic-related pregnancy stress (r=.27, p<.001), and a negative correlation with health behaviors during pregnancy (r=-.42, p<.001). The factors associated with prenatal depression were pandemic-related pregnancy stress (t=4.70, p<.001), marital satisfaction (dissatisfied) (t=3.66, p<.001), pregnancy healthcare practice behaviors (t=-3.31, p=.001), family type (weekend couple) (t=2.84, p=.005), and gestational age (t=2.32, p=.022). The explanatory power of these variables was 38.2%. Conclusion: Since participants had a high level of prenatal depression during the pandemic, and infectious diseases such as COVID-19 may recur, strategies should be developed to improve pregnant women's mental health with consideration of the unique variables that are relevant in a pandemic. It is also necessary to develop efficient online prenatal education programs that can be implemented even in special circumstances such as social distancing, and to evaluate their effectiveness.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.260-276
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• 2024

The purpose of this study is to explore whether pre-service teachers majoring in earth science improve their perception of computational thinking through STEAM classes focused on engineering-based wave power plants. The STEAM class involved designing the most efficient wave power plant model. The survey on computational thinking practices, developed from previous research, was administered to 15 Earth science pre-service teachers to gauge their understanding of computational thinking. Each group developed an efficient wave power plant model based on the scientific principal of turbine operation using waves. The activities included problem recognition (problem solving), coding (coding and programming), creating a wave power plant model using a 3D printer (design and create model), and evaluating the output to correct errors (debugging). The pre-service teachers showed a high level of recognition of computational thinking practices, particularly in "logical thinking," with the top five practices out of 14 averaging five points each. However, participants lacked a clear understanding of certain computational thinking practices such as abstraction, problem decomposition, and using bid data, with their comprehension of these decreasing after the STEAM lesson. Although there was a significant reduction in the misconception that computational thinking is "playing online games" (from 4.06 to 0.86), some participants still equated it with "thinking like a computer" and "using a computer to do calculations". The study found slight improvements in "problem solving" (3.73 to 4.33), "pattern recognition" (3.53 to 3.66), and "best tool selection" (4.26 to 4.66). To enhance computational thinking skills, a practice-oriented curriculum should be offered. Additional STEAM classes on diverse topics could lead to a significant improvement in computational thinking practices. Therefore, establishing an educational curriculum for multisituational learning is essential.