• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.75-86
• /
• 2017

In this study, I propose a method to automate the method to diagnose the quality of big data. The reason for automating the quality diagnosis of Big Data is that as the Fourth Industrial Revolution becomes a issue, there is a growing demand for more volumes of data to be generated and utilized. Data is growing rapidly. However, if it takes a lot of time to diagnose the quality of the data, it can take a long time to utilize the data or the quality of the data may be lowered. If you make decisions or predictions from these low-quality data, then the results will also give you the wrong direction. To solve this problem, I have developed a model that can automate diagnosis for improving the quality of Big Data using machine learning which can quickly diagnose and improve the data. Machine learning is used to automate domain classification tasks to prevent errors that may occur during domain classification and reduce work time. Based on the results of the research, I can contribute to the improvement of data quality to utilize big data by continuing research on the importance of data conversion, learning methods for unlearned data, and development of classification models for each domain.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.935-938
• /
• 2021

데이터 베이스에 저장된 사용자의 위치, 성격정보를 자동으로 받아서 머신러닝으로 회귀분석하여 방문 장소에 대한 선호도를 예측한다. 사람의 성격 요소로는 BFF 와 다른 기본 요소들을 사용하였다. 이를 위하여 자동화된 시스템을 구성하였고 위치 방문 선호도를 예측하기 위한 머신러닝 기법으로는 앙상블기법을 사용하였다. 예측 결과는 장소 카테고리별로 방문 선호도가 나타나고 이를 사용자 별로 나누어 저장할 예정이다. 데이터의 양이 많아지면서 나타나는 문제들을 해결하여 향후 연구에 도움이 될 것이다.

• Smart Media Journal
• /
• v.13 no.3
• /
• pp.45-52
• /
• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

• Korea Information Processing Society Review
• /
• v.28 no.3
• /
• pp.29-36
• /
• 2021

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.295-296
• /
• 2020

본 논문에서는 RPA(Robotic Process Automation) Tool 개발 과정 중 머신 러닝, 딥러닝에 필요한 이미지 크롤링 및 전처리 기능을 이용한 가공된 데이터 셋 처리 과정을 기술한다. 개발된 RPA 툴에서 머신러닝 및 딥러닝에 사용될 데이터 확보 기능을 제공하며, 세부적으로 이미지 전처리(Convert Gray, Histogram Equalization, Binary, Resize)등 반복적으로 사용되는 기능들을 제공한다. 개발된 툴을 통해 RPA의 자동화 기능과, 전처리 기능의 융합을 통해 업무의 효율성을 제공한다.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.11-18
• /
• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of The Korean Association For Science Education
• /
• v.38 no.2
• /
• pp.219-234
• /
• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.530-532
• /
• 2019

제안하는 서비스는 OCR(Optical Character Recognition, 광학문자인식)과 딥러닝 패턴분석 알고리즘을 활용하여 문서를 효율적으로 관리하는 서비스로 필기를 많이 하는 사용자를 위한 기능을 제공한다. 최근 다양한 분야에서의 머신러닝 기반의 OCR의 활용이 증가했지만 기존의 애플리케이션은 패턴 분석 알고리즘과 통계 기반의 OCR을 혼합하여 사용하기 때문에 필기체에 대한 인식률이 높지 않다. 이에 본 논문에서는 OCR과 패턴분석 알고리즘을 활용하여 필기체에 대한 높은 인식률을 제공하는 서비스를 제안한다.

• Magazine of korean Tunnelling and Underground Space Association
• /
• v.25 no.2
• /
• pp.16-26
• /
• 2023

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.758-760
• /
• 2022

본 연구는 머신러닝의 한 종류인 YOLOv5를 이용하여 기존 육안 선별작업을 자동화 하는 기계를 설계하는 것이다. 본 연구에서는 영상촬영과 선별작업을 진행하는 컨베이어 기구와 선별 프로그램을 제작하고, 모든 표면을 검사해 사과의 품질을 3단계로 구별하는 작업을 진행하였다. 결과적으로 투입된 사과의 품질을 성공적으로 분류 하였다.