• Computers and Concrete
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• v.30 no.1
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• pp.43-74
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• 2022

Machine learning technique is recently opening new opportunities to identify the complex shear transfer mechanisms of reinforced concrete (RC) beam members. This study employed 1224 shear test specimens to train decision tree-based machine learning (ML) programs, by which strong correlations between shear capacity of RC beams and key input parameters were affirmed. In addition, shear contributions of concrete and shear reinforcement (the so-called V_c and V_s) were identified by establishing three independent ML models trained under different strategies with various combinations of datasets. Detailed parametric studies were then conducted by utilizing the well-trained ML models. It appeared that the presence of shear reinforcement can make the predicted shear contribution from concrete in RC beams larger than the pure shear contribution of concrete due to the intervention effect between shear reinforcement and concrete. On the other hand, the size effect also brought a significant impact on the shear contribution of concrete (V_c), whereas, the addition of shear reinforcements can effectively mitigate the size effect. It was also found that concrete tends to be the primary source of shear resistance when shear span-depth ratio a/d<1.0 while shear reinforcements become the primary source of shear resistance when a/d>2.0.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• ETRI Journal
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• v.46 no.4
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• pp.671-682
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• 2024

Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short-term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1-score. The use of ResNet50V2 for feature extraction can improve model performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.145-159
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• 2013

The purpose of this study is to analyze the structural relationship as to how learning flow and self-directed learning are linked to learning motives and academic self-efficacy in the learning setting of high school students. To accomplish such purpose, based on theoretical backgrounds and preceding research findings evaluation models were put to verification for a valid research model for this study. The initial hypothetical model was that self-directed learning ability would have a direct influence on learning motive, academic efficacy and learning flow, while having an indirect influence on learning flow with learning motive and self-efficacy acting as a mediating variable. But the hypothetical model showed low significance level between self-directed learning and learning motive, and learning motive and learning flow. Therefore, links were adjusted to create the final model within the scope that the adequacy of the model might not be compromised. To verify the model, 900 high school students in Seoul were surveyed and the collected data were statistically analyzed using AMOS v21.0 and SPSS v21.0 But 815 surveys were excluded because they were not sufficiently answered. From the analysis, it was found that self-directed learning and academic efficacy have a direct influence on learning flow while self-directed learning and academic efficacy have an indirect leaning motive and learning flow. This finding means that, in the relationship of self-directed learning and learning flow, learning motive and learning efficacy are positive factors that help high school students experience learning flow. Thus, in order to enhance the experience of self-directed learning ability of high school students, various educational endeavors are needed to draw the experience of learning flow during the regular course of study. In addition, customized educational methods and environments are required to increase academic efficacy of the students.

• The Journal of Korean Association of Computer Education
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• v.10 no.2
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• pp.27-38
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• 2007

The objective of this research is to propose a e-learning standardization roadmap based on the future scenarios. First of all, a e-learning standardization committee was organized to collect ideas on the visions of the future e-learning, in which experts from the technological, educational, and standardization field were invited. They made a great contribution to the success of this research by furnishing us with valuable advices and feedbacks. The first step of the research was to survey the current e-learning standardization proposals suggested by some of standard organizations in and out of the country. We developed three 2015 scenarios for e-learning in elementary and secondary education, in university education, and in life-long education respectively by using a top-down roadmap development strategy. In the second step, we drew a new e-learning standardization roadmap v2 out of the future scenarios by gap analysis between the current and the future e-learning standardization elements. These future e-learning scenarios and e-learning standardization roadmap are very helpful to teachers or educational policy makers for understanding future e-learning and e-learning standardization.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.313-325
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• 2022

This study aims to identify the conditional direct and indirect effects by Gender in the relationship between Learning Agility, Unlearning and Learning Flow for university students. Based on the data collected from 265 university students the conditional effectiveness of the research model was analyzed using Process v3.4 for SPSS. As a result, first, the Learning Agility of university students had a statistically significant effect Unlearning and Learning Flow. Second, Learning Agility had a statistically significant direct effect on Learning Flow by gender but, the moderated effect of Learning Flow had a significant negative effect. Third, the moderated mediating effect of Unlearning by gender was found to be significant in the relationship between Learning Agility and Learning Flow of university students. Based on these results, implications of education at university were presented.

• Wind and Structures
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• v.34 no.6
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• pp.511-523
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• 2022

Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.405-419
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• 2021

This study aims to explore the moderated moderation effect of learning flow and professor-student interaction in the relationship between learning agility and job preparation behavior among engineering college students. Based on the data collected from 206 second-year students at P-college, the conditional effects of the research model (direct effect and moderated moderation effect) was analyzed using Process v3.5.3 for SPSS. As a result, first, the learning agility of engineering college students had a statistically significant and positive effect on job preparation behavior. Second, learning flow had a significantly negative and moderating effect in the relationship between learning agility and job preparation behavior. Third, the moderating effect of learning flow was moderated by the frequency of interaction between professor-student in the relationship between learning agility and job preparation behavior. And its effects were statistically significant and positive. Based on these results, academic and practical discussions and implications were presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.36-43
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• 2021

In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV.