• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.452-459
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• 2023

The core power control is an important issue for the study of dynamic characteristics in China initiative accelerator driven subcritical system (CiADS), which has direct impact on the control strategy and safety analysis process. The CiADS is an experimental facility that is only controlled by the proton beam intensity without considering the control rods in the current engineering design stage. In order to get the optimized operation scheme with the stable and reliable features, the variation of beam intensity using the continuous and periodic control approaches has been adopted, and the change of collimator and the adjusting of duty ratio have been proposed in the power control process. Considering the neutronics and the thermal-hydraulics characteristics in CiADS, the physical model for the core power control has been established by means of the point reactor kinetics method and the lumped parameter method. Moreover, the multi-inputs single-output (MISO) logical structure for the power control process has been constructed using proportional integral derivative (PID) controller, and the meta-heuristic algorithm has been employed to obtain the global optimized parameters for the stable running mode without producing large perturbations. Finally, the verification and validation of the control method have been tested based on the reference scenarios in considering the disturbances of spallation neutron source and inlet temperature respectively, where all the numerical results reveal that the optimization method has satisfactory performance in the CiADS core power control scenarios.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.537-541
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• 2022

As technology advances, various applications linked to the music industry are becoming popular through new media. There have been many changes in the music market. Beyond the existing music market, where music copyright and performance rights were the center of the music business, we are unifying and operating communication channels that connect artists and fans, such as investment products derived from music copyrights. The technology that connects the fandom with additional business digital content has transformed into global platforms such as HYBE Entertainment's and YG Entertainment's Weverse, as well as SM Entertainment's Bubble. In addition, various national support projects to build a 5G MEC (MobileEdge Computing) environment to quickly respond to the rapidly changing 5G industry ecosystem are supporting for the immersive content demonstration, immersive content testing, and technical analysis, we are laying the groundwork to efficiently respond to the ever-expanding metaverse content market. Technology is changing dramatically. Therefore, we would like to study to changes in the music market brought about by technology and suggest strategies for a new era in the music business.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• ETRI Journal
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• v.46 no.2
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• Journal of the Korea Computer Graphics Society
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• v.30 no.4
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• pp.11-18
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• 2024

In partial nephrectomy for kidney cancer treatment, accurate segmentation of the kidney tumor is crucial for surgical planning, as it provides essential information on the precise size and location of the tumor. However, it is challenging due to the tumor's similar intensity to surrounding organs and the variability in its location and size across patients. In this study, we propose a hybrid network that integrates a convolutional neural network and a transformer to capture both local and global features, aiming to improve the segmentation performance of kidney tumors. We validated our method through comparative experiments with UNETR++, outperforming it with a Dice Similarity Coefficient (DSC) of 78.54% and a precision of 85.0 7%. Moreover, in the analysis by tumor size, our method demonstrated improvements by reducing over-segmentation and outlier cases observed in UNETR++.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.524-531
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• 2024

In a global environment, airlines are striving to secure competitiveness and generate profits through customer satisfaction. The first thing to do in order to provide services to customers is the safety of the aircraft, and the basis for realizing safety is the continuous maintenance of maintenance quality. The degree of development of aviation technology is progressing unimaginably fast, but the overall environment of aviation mechanics working at the front line to maintain maintenance quality remains largely unchanged. This study aims to identify the level of education and training provided to aircraft maintenance engineers and safety culture, and to examine the effect of self-esteem on maintenance quality. Previous studies have been conducted on maintenance engineers' education and training, organizational performance, and job stress, but there has been a lack of studies on maintenance quality. Therefore, this study aims to identify the relationship between variables affecting maintenance quality and to suggest implications for improving maintenance quality through empirical analysis.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.221-235
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• 2024

Errors included in Global Navigation Satellite System (GNSS) measurements degrade the performance of user position estimation but can be mitigated by spatial correlation properties. Augmentation systems providing correction data can be broadly categorized into State Space Representation (SSR) and Observation Space Representation (OSR) methods. The satellite-based cm-level augmentation service based on the SSR broadcasts correction data via satellite signals, unlike the traditional Real-Time Kinematic (RTK) and Network RTK methods, which use OSR. To provide a large amount of correction data via the limited bandwidth of the satellite communication, efficient message structure design considering service area, correction generation, and broadcast intervals is necessary. For systematic message design, it is necessary to analyze the influence of error components included in GNSS measurements. In this study, errors in satellite orbits, satellite clocks for GPS, Galileo, BeiDou, and QZSS satellite constellations ionospheric and tropospheric delays over one year were analyzed, and their spatial decorrelations and linear modeling characteristics were examined.

• The Korean Journal of Quaternary Research
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• v.33 no.1_2
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• pp.25-35
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• 2021

In this study, we identified characteristics of heatwaves on the Korean Peninsula and related atmospheric circulation patterns using data on the daily maximum temperature (TMX) and reanalysis data for the past 42 years (1979-2020) and analyzed their connection to the Arctic oscillation (AO). The heatwave on the Korean Peninsula showed to be stronger and more frequent in the 2000s. The recent strong and frequent heatwaves on the Korean Peninsula are mainly affected by abnormal high-pressure over the Korean Peninsula on the middle/upper-level atmosphere and the strengthening of the North Pacific high pressure. Interestingly, composite difference of sea level pressure showed very similar results to the positive AO pattern. The correlation coefficients between the summertime AO and the TMX and HWD of the Korean Peninsula were 0.407 and 0.437, respectively, which showed a statistical significance in 1%, and showed a clear relationship with the abnormal high-pressure over the Korean Peninsula and the strengthening of the North Pacific high pressure. In addition, in the positive AO phase, the TMX and HWD of the Korean peninsula were approximately 30.1 ℃ and 14.6 days, which were about 1.2 ℃ and 8.8 days higher than in the negative AO phase, respectively. As a result of the 15-year moving average correlation analysis, the relationship between the heatwave and AO on the Korean Peninsula has increased significantly since 2003, and the linear relationship between them has become more apparent. Moreover, after the 2000s, when the relationship developed, AO had more strongly induced the atmospheric circulation pattern to be more favorable to the occurrence of heatwaves in the Korean Peninsula. This study implies that understanding the AO, which is the large-scale variability in the Northern Hemisphere, and the Arctic-mid latitude teleconnection, can improve the performance of global climate models and help predict the seasonality of the summer heatwave on the Korean Peninsula.