• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.773-782
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• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.977-985
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• 2020

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.505-512
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• 2007

This paper presents the quantitative evaluation of the fearfulness of the human driver in the case of the short range (time) on the highway. The driving situation is realized by using the driving simulator based on CAVE, which provides three-dimensional stereoscopic immersive visual information. The examinees' responses and personal information are categorized reasonably by applying the competitive learning algorithm. The characteristics of each group are analyzed. The following two situations are also compared: (1) the active approaching situation where the examinee drives the vehicle near the preceding vehicle, and (2) the passive approaching situation where the preceding vehicle nears the examinee's vehicle by gradually decelerating. The range time that the examinee feels fear in the active approaching case tends to be shorter than that in the passive approaching case.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.473-474
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• 2017

본 연구는 TBL에서 학생을 보다 효율적으로 평가하기 위해 학생평가방법에 따른 평가점수 간 관계를 비교하고 그 관대함 정도를 분석하였다. 치위생학과 학생의 구강보건교육학 및 구강보건교육학 실습 교과목에서 학생평가방법에 따른 평가점수 간 관계를 살펴본 결과 지필시험과 팀별평가만 유의미한 중간 정도의 정적 상관관계가 나타났고, 그 외의 관계는 모두 유의하지 않았다. 학생평가방법에 따른 평가점수 간 관대함 정도는 팀원평가, 지필시험, 팀별평가 순으로 관대한 것으로 나타났다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.510-518
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• 2020

With the promotion of digitalization in the construction industry, BIM has become an indispensable technology. On the other hand, it has not been actively utilized in practice because of the difficulty of BIM modeling. The reason is that 3D modeling is less productive not only because of the difficulty of learning BIM software but also the modeling work is done manually. Therefore, this study proposes a method and system that can improve the productivity of BIM-based modeling. For this reason, in the study, a slit caisson, which is a typical structure of a port, was selected as a development target, and various parameters were derived through interviews with experts so that it could be used in practice. This study presents a UI construction plan that considers user convenience for efficient management and operation of diverse and complex parameters. Based on this, this study used visual programming and Excel VBA to develop a BIM-based design automation system for slit caissons. The developed system can use many parameters to quickly develop slit caisson models suitable for various design conditions that can contribute to BIM-based modeling and productivity improvement.

• Journal of Engineering Education Research
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• v.25 no.5
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• pp.85-93
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• 2022

Capstone Design, a project-based learning technique, is the most important curriculum that clarifying major knowledge and cultivating the ability to apply through the process of solving problems in the industrial field centered on the student project team. Accordingly, various and extensive studies are being conducted for the successful implementation of capstone design courses. Unlike previous studies, this study aimed to quantitatively analyze the opinions that recorded the experiences and feelings of students who performed capstone design, and used text mining methodologies such as frequency analysis, correlation analysis, topic modeling, and sentiment analysis. As a result of examining the overall opinions of the latter period through frequency analysis and correlation analysis, there was a difference between the languages used by the students in the opinions according to gender and project results. Through topic modeling analysis, 'topic selection' and 'the relationship between team members' showed an increase in occupancy or high occupancy, and topics such as 'presentation', 'leadership', and 'feeling what they felt' showed a tendency to decreasing occupancy. Lastly, sentiment analysis has found that female students showed more neutral emotions than male students, and the passed group showed more negative emotions than the non-passed group and less neutral emotions. Based on these findings, students' practical recognition of the curriculum was considered and implications for the improvement of capstone design were presented.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.79-86
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• 2022

This paper focuses on realizing design improvement and high quality through visualization of reverse engineering-based software. As new technologies and complex software emerge in various areas of the fourth industry in the future, software verification with both stability and reliability is becoming an issue. We propose a reverse engineering-based UML design extraction and visualization for high-quality software ranging from simple computational software to machine learning-based data-oriented software. Through this study, it is expected to improve software quality through design improvement by checking the accuracy of the target design and identifying the code complexity.

• Fourth Industrial Review
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• v.4 no.1
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• pp.19-27
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• 2024

Purpose: This study is based on the results of the National Information Society Agency's the Report on the Digital Divide in 2022. This study sought to develop digital literacy education programs for senior citizens, a digitally disadvantaged group, and to utilize smart devices to enhance their digital capabilities. Research design, data and methodology: Based on Gagné's nine events of instruction, a total of 7-session educational programs using smart devices were developed, and teaching-learning goals were set at a level that older learners can realistically perform. In preparation for the era of digital transformation, AI utilization methods are introduced and utilized in some sessions of the educational program. Results: Among a total of 7 sessions of the educational program, 5 sessions using KakaoTalk and Naver App, and 2 sessions using other apps were developed. There are a total of three sessions using AI. Conclusions: This study presented a digital literacy education program that combined AI, addressing the insufficiency of AI-based education programs targeting senior citizens. It is expected that this educational program will be able to improve the digital literacy skills and provide a basis for fulfilling their responsibilities as digital citizens by suggesting a direction for AI utilization education for senior citizens.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1965-1974
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• 2021

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.