• Journal of radiological science and technology
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• v.46 no.2
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• pp.131-139
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• 2023

High-dose I-131 used for the treatment of thyroid cancer causes localized exposure among radiology technologists handling it. There is a delay between the calibration date and when the dose of I-131 is administered to a patient. Therefore, it is necessary to directly measure the radioactivity of the administered dose using a dose calibrator. In this study, we attempted to apply machine learning modeling to measured external dose rates from shielded I-131 in order to predict their radioactivity. External dose rates were measured at 1 m, 0.3 m, and 0.1 m distances from a shielded container with the I-131, with a total of 868 sets of measurements taken. For the modeling process, we utilized the hold-out method to partition the data with a 7:3 ratio (609 for the training set:259 for the test set). For the machine learning algorithms, we chose linear regression, decision tree, random forest and XGBoost. To evaluate the models, we calculated root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) to evaluate accuracy and R² to evaluate explanatory power. Evaluation results are as follows. Linear regression (RMSE 268.15, MSE 71901.87, MAE 231.68, R² 0.92), decision tree (RMSE 108.89, MSE 11856.92, MAE 19.24, R² 0.99), random forest (RMSE 8.89, MSE 79.10, MAE 6.55, R² 0.99), XGBoost (RMSE 10.21, MSE 104.22, MAE 7.68, R² 0.99). The random forest model achieved the highest predictive ability. Improving the model's performance in the future is expected to contribute to lowering exposure among radiology technologists.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.1
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• pp.57-64
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• 2023

Recently, the increased use of anti-ship guided missiles, a weapon system that detects and attacks targets in naval engagement, has come to pose a major threat to the survivability of ships. In order to improve the survivability of ships in response to such anti-ship guided missiles, many studies of means to counteract them have been conducted in militarily advanced countries. The integrated survivability of a ship can be largely divided into susceptibility, vulnerability, and recoverability, and is expressed as the conditional probability, if the ship is hit, of damage and recovery. However, as research on susceptibility is a major military secret of each country, access to it is very limited and there are few publicly available data. Therefore, in this study, a possibility of estimating the susceptibility of ships using an anti-air defense system corresponding to anti-ship guided missiles was reviewed. To this, scenarios during engagement, weapon systems mounted to counter threats, and maximum detection/battle range according to the operational situation of the defense weapon system were defined. In addition, the effectiveness of the anti-air defense system and susceptibility was calculated based on the performance of the weapon system, the crew's ability to operate the weapon system, and the detection probability of the detection/defense system. To evaluate the susceptibility estimation feasibility, the sensitivity of the detailed variables was reviewed, and the usefulness of the established process was confirmed through sensitivity analysis.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• Composites Research
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• v.19 no.4
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• pp.23-30
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• 2006

In the development of a cryogenic propellant tank, the proper selection of adhesives to bond composite and metal liner is important for the safety of operation. In this study, 3 types of adhesives were tested for the ability to bond CFRP composites developed for cryogenic use and aluminum alloy (Al 6061-T6) for lining the tank using double-lap joint specimens. The double-lap joint specimens were tested inside an environmental chamber at room temperature and cryogenic temperature ($-150^{\circ}C$) respectively to compare the bond strength of each adhesive and fracture characteristics. The material properties with temperature of component materials of double-lap joints were measured. In addition, ABAQUS was used for the purpose of analyzing the experimental results.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.157-162
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• 2022

In a society full of knowledge and information, digital literacy and artificial intelligence (AI) education that can utilize AI technology is needed to solve numerous everyday problems based on computational thinking. In this study, data-centered AI education was conducted while teaching computer programming to non-computer programming students at universities, and the correlation between major factors related to academic performance was analyzed in addition to student satisfaction surveys. The results indicated that there was a strong correlation between grades and problem-solving ability-based tasks, and learning satisfaction. Multiple regression analysis also showed a significant effect on grades (F=225.859, p<0.001), and student satisfaction was high. The non-computer programming students were also able to understand the importance of data and the concept of AI models, focusing on specific examples of project types, and confirmed that they could use AI smoothly in their fields of interest. If further cases of AI education are explored and students' AI education is activated, it will be possible to suggest its direction that can collaborate with experts through interest in AI technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.107-111
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• 2023

Journaling techniques are widely used to maintain a consistent file system state in the event of a system crash. As existing journaling techniques are designed for block storage such as HDDs, they are not efficient for byte-addressable persistent memory media. This paper proposes a metadata journaling technique for in-memory file systems that has the ability of avoiding inconsistent file system states in crash situations. The proposed journaling technique reduces a large amount of writing by making use of the byte-addressable feature of memory media and bypasses heavy software I/O stack. Experimental results with the IOzone benchmark show that the proposed journaling technique improves the performance of Ext4 by 49.2% on average.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.213-218
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• 2022

In this paper, a technology for a high-efficiency wireless power transmission transmitting and receiving coil that can wirelessly charge a drone is introduced. The drone station implements the ability to charge the battery wirelessly without the need to remove the battery to charge the drone's battery. In order to charge the drone's battery in the shortest time, wireless charging efficiency must be high. In order to increase the wireless charging efficiency of the drone station, a method for manufacturing high-efficiency transmitting and receiving coils and a performance measurement method are presented. Transmitting and receiving coils were manufactured considering the size and weight of the drone so as not to interfere with the flight of the drone. Efficiency of 88% or more was realized at a distance of 40mm or more between the transmitting and receiving coils.

• Korea Trade Review
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• v.41 no.5
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• pp.285-304
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• 2016

e-Commerce in Small and Medium-sized Enterprises(SMEs) provides a platform for fair competition between SMEs and large enterprises, and brings economic benefits to SMEs. Thus in the recent years, the e-Commerce in SMEs developed rapidly. However, the overall proportion of e-Commerce in SMEs in China remains low, and many SMEs' decision-makers tend to feel that their business is relatively weak in terms of information construction of scale and financial management, thus they keep the 'wait and see' attitude about the e-Commerce development decisions. Therefore, SMEs are facing three puzzles about e-Commerce application. First, if the companies need to implement e-Commerce, what are the e-Commerce adoption decision factors. Second, what are the successful implementation factors of e-Commerce. And how about the relationship between them. The third is how to measure the implementation effect of e-Commerce. What is the performance evaluation factors of e-Commerce. In this paper, the theoretical and empirical exploration and research are conducted towards these SMEs. Considering the actual situation of SMEs, this paper builds a theoretical model, then puts forward relevant hypothesis. This paper analyzes present influencing factors based on enormous research papers, and finally discovers the critical successful factors in doing business with e-Commerce in SMEs by conducting Structural Equation Modeling. Five critical factors are verified by cases of enterprise by demonstration study. Lastly, we can draw a conclusion that the innovation ability of leaders, the IT support of leaders and e-Commerce strategies are the success factors of e-Commerce for Chinese SMEs.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.673-686
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• 2023

Determining the properties of pile from cone penetration test (CPT) is costly, and need several in-situ tests. At the present study, two novel hybrid learning models, namely PSO-RF and HHO-RF, which are an amalgamation of random forest (RF) with particle swarm optimization (PSO) and Harris hawks optimization (HHO) were developed and applied to predict the pile set-up parameter "A" from CPT for the design aim of the projects. To forecast the "A," CPT data along were collected from different sites in Louisiana, where the selected variables as input were plasticity index (PI), undrained shear strength (S_u), and over consolidation ratio (OCR). Results show that both PSO-RF and HHO-RF models have acceptable performance in predicting the set-up parameter "A," with R² larger than 0.9094, representing the admissible correlation between observed and predicted values. HHO-RF has better proficiency than the PSO-RF model, with R² and RMSE equal to 0.9328 and 0.0292 for the training phase and 0.9729 and 0.024 for testing data, respectively. Moreover, PI and OBJ indices are considered, in which the HHO-RF model has lower results which leads to outperforming this hybrid algorithm with respect to PSO-RF for predicting the pile set-up parameter "A," consequently being specified as the proposed model. Therefore, the results demonstrate the ability of the HHO algorithm in determining the optimal value of RF hyperparameters than PSO.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.346-347
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• 2021

For pattern recognition for machine learning, the larger the amount of learning data, the better its performance. However, it is not always possible to secure a large amount of learning data with the types and information of patterns that must be detected in daily life. Therefore, it is necessary to significantly inflate a small data set for general machine learning. In this study, we study techniques to augment data so that machine learning can be performed. A representative method of performing machine learning using a small data set is the transfer learning technique. Transfer learning is a method of obtaining a result by performing basic learning with a general-purpose data set and then substituting the target data set into the final stage. In this study, a learning model trained with a general-purpose data set such as ImageNet is used as a feature extraction set using augmented data to detect a desired pattern.