• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.709-722
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• 2014

In the $21^{st}$ century, IT reform has led to the development of cyber-infrastructure owing to the outstanding enhancement of computer and network performance. The ripple effect has continued to increase. Accordingly, this study suggests a new computational research environment using mobile devices. In order to simplify the access of supercomputer, Science AppStore, task management and virtualization technologies are developed on mobile devices. User can be able to research by utilizing computational science SW such as compressible flow solver and nano device simulation tool that in installed on supercomputer in mobile environments. Also, this research environment makes it possible to monitor the simulation result and covers 14 university, 33 subjects, and 1,202 individuals.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.66-73
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• 2010

The curriculum of programming education including algorithm has been recognized as a very important subject to many students majoring in natural sciences and engineering including electronic engineering and computer related departments. However, many students have had difficulties with it due to its characteristics; as a consequence, they have been in trouble taking upper-level subjects. Flow chart is a diagram that expresses logical stages necessary to solve certain problems and has been widely used to have an understanding of the flow of algorithm. The practice-oriented education of algorithm and programming would be very important to assist the understanding of operation processes. Furthermore, it has been desperately required to the necessity of auxiliary programs that could enhance an understanding of the concept of algorithm and program execution process. This study was aimed to design and embody the learning system of programming languages using basic algorithms so as for students to easily learn basic algorithm among the entire programming curriculum.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.137-148
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• 2023

This study investigated the influence of major satisfaction on learning flow and self-efficacy of students majoring in culinary arts at local junior colleges. In the 2022-2 semester, 260 freshmen and sophomore college students majoring in culinary from five junior colleges in the Gwangju and Jeonnam regions were analyzed. For data processing, SPSS Ver. 25.0 was used. The data is used to measure reliability by Cronbach's α, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. The results of this study are as follows : First, there was a difference in satisfaction between freshmen and sophomores in major satisfaction with cooking related departments at local junior colleges. Second, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on learning immersion. Third, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on self-efficacy. In conclusion, it was found that major satisfaction affects learning immersion and self-efficacy for both students enrolled in cooking-related departments at local junior colleges. In the future, we suggest follow-up research on educational measures to increase learning immersion and self-efficacy for students who are not majoring in cooking in the high school curriculum and students who are insufficient in major classes due to part-time jobs during the semester.

• Journal of The Korean Association of Information Education
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• v.20 no.2
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• pp.109-120
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• 2016

The purpose of this paper is to develop and apply instructional model to improve awareness of information accessibility for gifted students in computer science. The model applied to class is designed based on Problem-Based Learning(PBL). The class was for 42 students(22 elementary school students, 20 middle school students), and the questions, cognition of the necessity for accessibility, behavior intent for accessibility were given before and after class. Additionally, interest, satisfaction and flow were given after the class. The results of this study are as follows. Firstly, it shows a difference on the changes of cognition on the necessity and behavior intent. As a gender difference, specifically, it shows a difference on the cognition of female students's necessity and behavior intent of male students. As a class level difference, specifically, it statistically shows a meaningful difference on cognitive of the necessity of elementary school students and behavior intent of middle school students. Secondly, after class, it shows a high level of interest, satisfaction. But it shows a general level of flow, so various strategies should be developed for covering the flow level.

• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.521-543
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• 2023

Reynolds-averaged Navier-Stokes (RANS) models are extensively employed in industrial settings for the purpose of simulating intricate fluid flows. However, these models are subject to certain limitations. Notably, disparities persist in the Reynolds stresses when comparing the RANS model with high-fidelity data obtained from Direct Numerical Simulation (DNS) or experimental measurements. In this work we propose an approach to mitigate these discrepancies while retaining the favorable attributes of the Menter Shear Stress Transport (SST) model, such as its significantly lower computational expense compared to DNS simulations. This strategy entails incorporating an explicit algebraic model and employing a neural network to correct the turbulent characteristic time. The imposition of realizability constraints is investigated through the introduction of penalization terms. The assimilated Reynolds stress model demonstrates good predictive performance in both in-sample and out-of-sample flow configurations. This suggests that the model can effectively capture the turbulent characteristics of the flow and produce physically realistic predictions.

• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.43-53
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• 2017

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the emoticons that users often use, you can identify facial expressions with acamera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, in order to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar e xpressions, reached 66%.It doesn't need to search for emoticons. If you use the camera to recognize the expression, itwill appear emoticons immediately. So this service is the emoticons used when people send messages to others, and it can feel a lot of convenience. In countless emoticons, there is no need to find emoticons, which is an increasing trend in deep learning. So we need to use more suitable algorithm for expression recognition, and then improve accuracy.

• Journal of Dental Rehabilitation and Applied Science
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• v.23 no.2
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• pp.179-186
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• 2007

The decrease of masticatory function caused by tooth loss leads to a decrease of cerebral blood flow volume resulting in impairment of cognitive function and learning memory disorder. However, the reduced mastication-mediated morphological alteration in the central nervous system (CNS) responsible for senile deficit of cognition, learning and memory has not been well documented. In this study, the effect of the loss of the molar teeth (molarless condition) on the hippocampal expression of glial fibrillary acidic protein (GFAP) protein was studied by immunohistochemical techniques. The results were as follows : 1. The molarless mice showed a lower density of pyramidal cells in the cornu ammonis 1 (CA1) and dentate gyrus (DG) region of the hippocampus than control mice. 2. Immunohistochemical analysis showed that the molarless condition enhanced the time-dependent increase in the cell density and hypertrophy of GFAP immunoreactivity in the CA1 region of the hippocampus. The molarless condition enhanced an time-dependent decrease in the number of neurons in the hippocampal formation and the time-dependent increase in the number and hypertrophy of GFAP-labeled cells in the same region. The data suggest a possible link between reduced mastication and histological changes in hippocampal formation that may be one risk factor for senile impairment of cognitive function and spatial learning memory.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.113-116
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• 2021

As artificial intelligence technologies, including deep learning, develop, these technologies are being introduced to code similarity analysis. In the traditional analysis method of calculating the graph edit distance (GED) after converting the source code into a control flow graph (CFG), there are studies that calculate the GED through a trained graph neural network (GNN) with the converted CFG, Methods for analyzing code similarity through CNN by imaging CFG are also being studied. In this paper, to determine which approach will be effective and efficient in researching code similarity analysis methods using artificial intelligence in the future, code similarity is measured through funcGNN, which measures code similarity using GNN, and Siamese Network, which is an image similarity analysis model. The accuracy was compared and analyzed. As a result of the analysis, the error rate (0.0458) of the Siamese network was bigger than that of the funcGNN (0.0362).