• Journal of Digital Convergence
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• v.18 no.7
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• pp.229-236
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• 2020

This study aimed to analyze the effects of nursing students' select task value and academic self-efficacy on learner's learning engagement. The subjects of this study consisted of 186 nursing students who completed the major course with Smart Learning of a university in G city. Data were collected from September 1 to November 30. 2018. This study was designed as a research study and multiple regression analysis was conducted to analyze the effects of task value and academic self-efficacy on learning engagement. The results showed that the degree of influence on learning engagement was in order of academic self-efficacy(β=.515) and task value(β=.244). It was found that both task value (r=.52, p<.001)and academic self-efficacy(r=.64, p<.001) had a significant positive effect on learning engagement. Based on the results of this study, we proposed teaching and learning strategies and suggestions for strengthening learner's learning engagement in smart learning which has recently been applied to increase the effectiveness of education.

• The Korean Journal of Food And Nutrition
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• v.36 no.1
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• pp.6-16
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• 2023

To investigate the anti-inflammatory activity of submerged culture using Cordyceps militaris mycelium, culture-including mycelia was extracted and lyophilized into postbiotics (hot-water extract; CM-HW). HW was fractionated into crude polysaccharide (CM-CP) by ethanol precipitation, and CM-CP was further dialyzed into CM-DCP by dialysis with running water using 12~14 kDa dialysis tube. When the cytotoxicity of subfractions against cells was assessed, no subfraction had a cytotoxic impact that was substantially different from the control groups. In an inflammatory model using LPS-stimulated RAW 264.7 cells, CM-DCP significantly decreased IL-6 and MCP-1 production levels compared to the LPS-control group. CM-DCP also inhibited IL-6 and IL-8 secretion in HaCaT keratinocytes stimulated with TNF-α and IFN-γ. In the meanwhile, the neutral sugar content and mannose ratio of anti-inflammatory CM-DCP were higher than the other fractions, and CM-DCP contained β-1,3/1,6-glucan of 216.1 mg/g. High pressure size exclusion chromatography revealed that CM-DCP contained molecules with a molecular weight range of 5.6 to 144.0 kDa. In conclusion, postbiotics of C. militaris mycelium significantly promoted anti-inflammatory activity, suggesting that neutral polysaccharides including Glc and Man contribute to the anti-inflammation in RAW 264.7 or HaCaT cells.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.515-522
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• 2024

Energy consumption is steadily increasing, and buildings in particular account for more than 20% of the total energy consumption around the world. As an effort to cost-effectively manage the energy consumption of buildings, many research groups have recently focused on Smart Building Energy Management Systems (BEMS), which are deepening the research depth by applying artificial intelligence(AI). In this paper, we propose a reinforcement learning-based energy control method for smart energy buildings integrated with V2G station, which aims to reduce the total energy cost of the building. The results of performance evaluation based on the energy consumption data measured in the real-world building shows that the proposed method can gradually reduce the total energy costs of the building as the learning process progresses.

• The Korean Journal of Food And Nutrition
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• v.35 no.1
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• pp.7-15
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• 2022

Maca roots (Lepidium meyenii) are an important medicinal herb that have long been used by the Andes-indigenous peoples and South Americans. In Korea, recently, it has attracted attention as a health food material because of nutritional values and physiological activities. The purpose of this study was to investigate the industrial applicability of maca (red and golden varieties; R&G) as immunostimulating materials. In the macrophage stimulating assay using RAW 264.7 cells at 125~500 ㎍/mL of non-cytotoxicity doses, G-HW showed the most potent production of TNF-α, IL-6 and nitric oxide compared to red maca or the other extracts. The general component analysis results showed that all extracts comprised more than 90% neutral sugars with small amounts of uronic acid and protein. Meanwhile, component sugar analysis showed the difference in the content of uronic acids of cold- and hot-water extract. Additionally, the further fractionation of G-HW into crude polysaccharide (G-CP) greatly enhanced the macrophage stimulating activity, and G-CP contained macromolecules over 144 kDa, comprised mainly of glucose and galacturonic acid (51.0 and 34.9%). In conclusion, crude polysaccharide from maca showed industrial applicability as immunostimulating material, and especially golden maca showed higher macrophage stimulating activity than red maca.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.197-199
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• 2021

High speed railway system is progressed to SRN(Smart Railway Network) having entirely automation function beyond each componet automations. It is necessity to use mobile communication technology of LTE-R(Long Term Evolution - Railway) and 5G-R(5th Generation - Railway) and information technology of convergence based on AI, Big Data, Deep Learning to construct this smart railway networks. In this paper, a communication structure is suggested for SRN. This suggested communication structure for SRN is composed to include safety operation of high speed train, railway system management and customer services, and also have complexing function of these each functions. Results of this study can be used for SRN construction and opeation, and development of railway communication standards.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1173-1181
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• 2020

Recently, as local heavy rains occur frequently in a short period of time, economic and social impacts are increasing beyond the simple primary damage. In advanced meteorologically advanced countries, realistic and reliable impact forecasts are conducted by analyzing socio-economic impacts, not information transmission as simple weather forecasts. In this paper, the degree of flooding was derived using the Spatial Runoff Assessment Tool (S-RAT) and FLO-2D models to calculate the threshold rainfall that can affect human walking, and the threshold rainfall of the concept of Grid to Grid (G2G) was calculated. In addition, although it was used a lot in the medical field in the past, a quantitative accuracy analysis was performed through the ROC analysis technique, which is widely used in natural phenomena such as drought or flood and machine learning. As a result of the analysis, the results of the time period similar to that of the actual and simulated immersion were obtained, and as a result of the ROC (Receiver Operating Characteristic) curve, the adequacy of the fair stage was secured with more than 0.7.

• Applied Chemistry for Engineering
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• v.31 no.5
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• pp.520-525
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• 2020

In this study, we developed a machine learning-based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM₁₂₈ was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R² of 0.924.

• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.61-68
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• 2007

The Cyber Home Learning System(CHLS) is a representative, nationwide e-learning system specially for G1-12 in Korea. It was launched at 16 MPOE in 2005 and has been evolved through every year-its evaluation and sharing best practices. In terms of evaluation, learners' satisfaction is one of essential and indispensable factors to improve CHLS. In this research, evaluation criteria on learners's satisfaction were developed, and also, the developed evaluation criteria were verified through the process of item goodness analysis and item characteristic analysis. These evaluation criteria are expected to contribute to analysing learners' satisfaction more objectively and quantitatively.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.