• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.2
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• pp.287-294
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• 2006

Purpose: The purpose of this study was to identify the change of critical thinking disposition and problem solving process in students who experienced problem-based learning. Method: This research design was one group pre-post test design. Twenty-five nursing students who participated in ‘'Nursing Process' course with two PBL packages for a semester in 2004 were the subjects of this study. The data were analyzed by repeated measures of ANOVA, and content analysis. Result: The problem defining in problem solving process was improved significantly, but there was no significant difference in the critical thinking disposition. Conclusion: The results of this study suggest that PBL has a positive effect on nursing students' problem solving process, But for a more significant effect on a continuous base for critical thinking of nursing students, faculties should use web based and simulation-based education for self directed learning along with clinical situation-based scenarios.

• Journal of the Korea Society for Simulation
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• v.30 no.3
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• pp.1-9
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• 2021

Commander and staffs on the battlefield are aware of the situation and, based on the results, they perform military activities through their military decisions. Recently, with the development of information technology, the demand for artificial intelligence to support military decisions has increased. It is essential to identify, collect, and pre-process the data set for reinforcement learning to utilize artificial intelligence. However, data on enemies lacking in terms of accuracy, timeliness, and abundance is not suitable for use as AI learning data, so a training model is needed to collect AI learning data. In this paper, a methodology for learning artificial intelligence was presented using the constructive wargame model exercise data. First, the role and scope of artificial intelligence to support the commander and staff in the military decision-making process were specified, and to train artificial intelligence according to the role, learning data was identified in the Chang-Jo 21 model exercise data and the learning results were simulated. The simulation data set was created as imaginary sample data, and the doctrine of ROK Army, which is restricted to disclosure, was utilized with US Army's doctrine that can be collected on the Internet.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.325-330
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• 2023

This paper proposes to apply machine learning to the process of predicting the slab thickness based on the structural analysis results or experience and subjectivity of engineers in the design of bridge data construction to enable digital-based decision-making. This study aims to build a reliable design environment by utilizing machine learning techniques to provide guide values to engineers in addition to structural analysis for slab thickness selection. Based on girder bridges, which account for the largest proportion of bridge data, a prediction model process for predicting slab thickness among superstructures was defined. Various machine learning models (Linear Regress, Decision Tree, Random Forest, and Muliti-layer Perceptron) were competed for each process to produce the prediction value for each process, and the optimal model was derived. Through this study, the applicability of machine learning techniques was confirmed in areas where slab thickness was predicted only through existing structural analysis, and an accuracy of 95.4% was also obtained. models can be utilized in a more reliable construction environment if the accuracy of the prediction model is improved by expanding the process

• Journal of the Korean Society of Earth Science Education
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• v.3 no.3
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• pp.239-247
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• 2010

The purpose of this study is to examine the effects of an free inquiry method based on PBL(Problem-Based Learning: PBL) to improve students science process skills and self-directed learning characteristics. To verify this research, twenty-two third-grade students were selected from Chung-ryeol Elementary School located in Busan. They have received pre-test and post-test about their abilities in their science process skills and abilities for self-directed learning characteristics. Also, their self-reflection data was analyzed. The teaching and learning PBL process is to provide the information named 'I am the expert on Earth and Moon' which is recreated by analyzing the science curriculum and characteristics of students from Lesson 3 'Earth and Moon', and to make plans for solving the information with K/NK method. Then, to solve the information is gathered and investigated using the PBL workbook. Lastly, students present their finding using the free inquiry method in a group. The post-test showed following results : first, the free inquiry method based on PBL stimulates inquisitiveness in students about science learning and the research group shows improved science process skill. It shows us that using the free inquiry method based on PBL can be used effects to elevate science process skill. Second, the free inquiry method based on PBL has a positive effect on self-directed learning. The research tells us that using the free inquiry method based on PBL can improve a student self-directed learning characteristics.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.47-52
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• 2022

The purpose of this study is to confirm the effect of nursing process education applying action learning on the critical thinking tendency, self-leadership, and self-directed learning ability of nursing college students. A total of 96 subjects were studied, and data collection was from September to December 2021. For data analysis, the frequency, percentage, and corresponding sample t-test were used using spss/win 23. Results show that self-directed learning ability(t=-3.76, p<.001) was significantly improved. In addition, critical thinking disposition and self-leadership(r=.730, p<.001), critical thinking disposition and self-directed learning ability (r=.701, p<.001), self-leadership and self-directed learning ability(r=.734 p<.001) had a statistically significant positive correlation between them. As a result of this study, it can be seen that the nursing course education applied to action learning has a positive effect on the self-directed learning ability of nursing college students. In the future, research is needed to confirm the effects of various teaching and learning methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.7-15
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• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.421-422
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• 2015

Deep learning, a deepen neural network technology that demonstrates the enhanced performance of neural network analysis, has been getting the spotlight in recent years. The present study proposed a process to test the error rates of certain variables and predict big data by using R, a analysis visualization tool based on deep learning, applying the RBM(Restricted Boltzmann Machine) algorithm to deep learning. The weighted value of each dependent variable was also applied after the classification of dependent variables. The investigator tested input data with the RBM algorithm and designed a process to detect error rates with the application of R.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.3
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• pp.251-260
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• 2021

Purpose: This study uses a descriptive research design to identify the influence of critical thinking disposition, deep approaches to learning, and interaction between learners on the degree of nursing process confidence for nursing students. Methods: The subjects of the study were second-year students in the Department of Nursing at a university in G city. The data included general characteristics, critical thinking disposition, deep approaches to learning, learner-to-learner interaction, and nursing process confidence were analyzed utilizing an independent t-test, one-way ANOVA, and Scheffe's test to identify differences in the variables according to general characteristics. To identify the correlation between the factors related to the nursing process and nursing process confidence, Pearson's correlation was analyzed, and hierarchical regression was used to determine the factors affecting the confidence of the subject's nursing process. Results: Gender, critical thinking disposition, and in-depth learning approach were statistically significant as factors affecting the nursing process confidence of nursing students, and these factors were shown to explain 62% of nursing course performance (F=23.80, p<.001), among which in-depth learning access has the greatest influence (β=.41, p<.001). Conclusion: Critical thinking disposition and deep approaches to learning arbitration program development are necessary to improve nursing students' nursing process confidence.