• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1193-1198
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• 2022

As unmanned processing systems emerged socially due to the rapid development of modern society, a face recognition attendance management system using Raspberry Pi 4 was studied and conceived to automatically analyze and process images and produce meaningful results using OpenCV. Based on Raspberry Pi 4, the software is designed with Python 3 and consists of technologies such as OpenCV, Haarcascade, Kakao API, and Google Drive, which are open sources, and can communicate with users in real time through Kakao API for face registration and face recognition.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.6
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• pp.556-561
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• 2022

Computational material science as an application of Density Functional Theory (DFT) to the discipline of material science has emerged and applied to the research and development of energy materials and electronic materials such as semiconductor. However, there are a few difficulties, such as generating input files for various types of materials in both the same calculating condition and appropriate parameters, which is essential in comparing results of DFT calculation in the right way. In this tutorial status report, we will introduce how to create crystal structures and to prepare input files automatically for the Vienna Ab initio Simulation Package (VASP) and Gaussian, the most popular DFT calculation programs. We anticipate this tutorial makes DFT calculation easier for the ones who are not experts on DFT programs.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.694-697
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• 2017

최근 인터넷 기사 중에는 부정확한 제목이나 자극적인 단어를 사용하는 경우가 많아 구독자에게 불편함을 준다. 본 논문에서는 이러한 기사들의 헤드라인을 삭제하고, 기사의 내용을 3문장으로 요약해 주어, 구독자가 원하는 기사를 효율적으로 파악할 수 있게 하는 시스템을 제안한다. 제안하는 본 시스템은 파이썬 언어의 KoNLPy 패키지를 사용하여 기사의 단어들을 형태소 단위로 분석하며, 추출된 키워드를 토대로 워드 클라우드를 생성한다. 사용자가 클라우드의 특정 단어를 선택하면, 해당 신문기사들의 본문을 분석하여 각 신문 기사만의 핵심적인 문장을 3문장으로 출력해 준다.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.386-398
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• 2020

We developed an integrative annotation program that can perform data labeling process for deep learning models in object recognition. The program utilizes the basic GUI library of Python and configures crawler functions that allow data collection in real time. Retinanet was used to implement an automatic annotation function. In addition, different data labeling formats for Pascal-VOC, YOLO and Retinanet were generated. Through the experiment of the proposed method, a domestic vehicle image dataset was built, and it is applied to Retinanet and YOLO as the training and test set. The proposed system classified the vehicle model with the accuracy of about 94%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.127-134
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• 2018

In this paper, we have developed the Auto Lip-Sync Maya plug-in for extracting Korean phonemes from voice data and text information based on Korean and produce high quality 3D lip-sync animation using divided phonemes. In the developed system, phoneme separation was classified into 8 vowels and 13 consonants used in Korean, referring to 49 phonemes provided by Microsoft Speech API engine SAPI. In addition, the pronunciation of vowels and consonants has variety Mouth Shapes, but the same Viseme can be applied to some identical ones. Based on this, we have developed Auto Lip-sync Maya Plug-in based on Python to enable lip-sync animation to be implemented automatically at once.

• Communications of Mathematical Education
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• v.37 no.3
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• pp.467-482
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• 2023

In this paper, we present our new teaching and learning materials on gradient descent method, which is widely used in artificial intelligence, available for college mathematics. These materials provide a good explanation of gradient descent method at the level of college calculus, and the presented SageMath code can help students to solve minimization problems easily. And we introduce how to solve least squares problem using gradient descent method. This study can be helpful to instructors who teach various college-level mathematics subjects such as calculus, engineering mathematics, numerical analysis, and applied mathematics.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.151-157
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• 2024

Most of the existing game-based education programmes for lower grades are simple block-coding studies, and there is a lack of examples of programming-intensive classes. In this study, we implemented a Minecraft-based Python coding fundamentals class for 3 classes at a local elementary school during a 2-week school holiday. The learning programme was reorganised from the standard learning programme on the official website, such as building quests through LAN-PARTY and self-scripting in-game, to improve class interest and motivation. In addition, we analysed the satisfaction and preferences of the class topics through a survey, and obtained meaningful results for future educational program development. This study is significant as a basic research for the design and development of game-based educational programmes for all age groups.

• Journal of The Korean Association of Information Education
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• v.22 no.2
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• pp.205-212
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• 2018

As a way to improve the software education ability of the pre - service teachers, we conducted programming learning using two types of programming tools (Python and Scratch) at the regular course time. In programming learning, various types of errors, which are factors that continuously hinder interest, achievement and creativity, were collected and analyzed by type. By using the analyzed data, it is possible to improve the ability of pre-service teachers to cope with the errors that can occur in the software education to be taught in the elementary school, and to improve the learning effect. In this study, logic error (37.63%) was the most frequent type that caused the most errors in programming in both conventional language that input text and language that assembles block. In addition, the detailed errors that show a lot of differences in the two languages are the errors of Python (14.3%) and scratch (3.5%) due to insufficient use of grammar and other errors.

• Journal of the Korea Institute of Building Construction
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• v.22 no.2
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• pp.171-182
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• 2022

The regular assessment of a building is important to understand structural safety and latent risk in the early stages of building life cycle. However, methods of traditional assessment are subjective, atypical, labor-intensive, and time-consuming and as such the reliability of these results has been questioned. This study proposed a method to bring accurate results using a 3D laser scanner and integrate them in Building Information Modeling (BIM) to visualize defective condition. The specific process for this study was as follows: (1) semi-automated data acquisition using 3D laser scanner and python script, (2) scan-to-BIM process, (3) integrating and visualizing defective conditions data using dynamo. The method proposed in this study improved efficiency and productivity in a building assessment through omitting the additional process of measurement and documentation. The visualized 3D model allows building facility managers to make more effective decisions. Ultimately, this is expected to improve the efficiency of building maintenance works.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.132-137
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• 2019

The amount of data generated from medical images is increasingly exceeding the limits of professional visual analysis, and the need for automated medical image analysis is increasing. For this reason, this study evaluated the classification and accuracy according to the presence or absence of tumor using Inception V3 deep learning model, using MRI medical images showing normal and tumor findings. As a result, the accuracy of the deep learning model was 90% for the training data set and 86% for the validation data set. The loss rate was 0.56 for the training data set and 1.28 for the validation data set. In future studies, it is necessary to secure the data of publicly available medical images to improve the performance of the deep learning model and to ensure the reliability of the evaluation, and to implement modeling by improving the accuracy of labeling through labeling classification.