• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.248-251
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• 2020

전 반도체 제조 및 검사 공정 과정을 자동화하는 스마트 팩토리의 실현에 있어 제품 검수를 위한 검사 장비는 필수적이다. 하지만 딥 러닝 모델 학습을 위한 데이터 처리 과정에서 엔지니어가 전체 웨이퍼 영상에 대하여 결함 항목 라벨을 매칭하는 것은 현실적으로 불가능하기 때문에 소량의 라벨 (labeled) 데이터와 나머지 라벨이 없는 (unlabeled) 데이터를 적절히 활용해야 한다. 또한, 웨이퍼 영상에서 결함이 발생하는 빈도가 결함 종류별로 크게 차이가 나기 때문에 빈도가 적은 (minor) 결함은 잡음처럼 취급되어 올바른 분류가 되지 않는다. 본 논문에서는 소량의 라벨 데이터와 대량의 라벨이 없는 데이터를 동시에 활용하면서 결함 사이의 발생 빈도 불균등 문제를 해결하는 점진적 데이터 평준화 (progressive pseudo-labeling balancer)를 제안한다. 점진적 데이터 평준화를 이용해 분류 네트워크를 학습시키는 경우, 기존의 테스트 정확도인 71.19%에서 6.07%-p 상승한 77.26%로 약 40%의 라벨 데이터가 추가된 것과 같은 성능을 보였다.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.302-313
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• 2023

This paper aims to develop a framework that can fully automate the quality management of training data used in large-scale Artificial Intelligence (AI) models built by the Ministry of Science and ICT (MSIT) in the 'AI Hub Data Dam' project, which has invested more than 1 trillion won since 2017. Autonomous driving technology using AI has achieved excellent performance through many studies, but it requires a large amount of high-quality data to train the model. Moreover, it is still difficult for humans to directly inspect the processed data and prove it is valid, and a model trained with erroneous data can cause fatal problems in real life. This paper presents a dataset reconstruction framework that removes abnormal data from the constructed dataset and introduces strategies to improve the performance of AI models by reconstructing them into a reliable dataset to increase the efficiency of model training. The framework's validity was verified through an experiment on the autonomous driving dataset published through the AI Hub of the National Information Society Agency (NIA). As a result, it was confirmed that it could be rebuilt as a reliable dataset from which abnormal data has been removed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.41-49
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• 2010

Being popularized the use of portable entertainment/information devices, the demand on flash memory has been also increased radically. In general, flash memory reveals various error patterns by the devices it is mounted, and thus the memory makers are trying to minimize error ratio in the final process through not only the electric test but also the data integrity test under the same condition as real application devices. This process is called an application-level memory test. Though currently various flash memory testing devices have been used in the production lines, most of the works related to memory test depend on the sensual abilities of human testers. In case of testing the flash memory for MP3 devices, the human testers are checking if the memory has some errors by hearing the audio played on the memory testing device. The memory testing process like this has become a bottleneck in the flash memory production line. In this paper, we propose an audio comparison technique to support the efficient flash memory test for MP3 devices. The technique proposed in this paper compares the variance change rate between the source binary file and the decoded analog signal and checks automatically if the memory errors are occurred or not.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.31-36
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• 2020

Virtual simulation education, which is one of the methods of executing engineering education, is spreading. In general online education, only theoretical learning-centered lessons and practical training of simple small projects are conducted remotely, and it is necessary to disseminate various educational contents. Due to the spread of smart factories these days, most producers use automatic control to produce, inspect and package their products. The operation of automation equipment is controlled by using electricity, and electricity-related learning is operated in various departments. Due to the characteristics of electricity, it is difficult to learn online due to safety issues and high cost of practical equipment. In this paper, we provide a simulation-based electrical control panel distance learning model to improve the sense of accomplishment of education related to electrical training. Through the experiment of the proposed model, it was confirmed that the learning was more satisfied with the virtual simulation education than the online education using the existing equipment. It is expected that it can be used as a basic course for automation equipment education in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.21-28
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• 2023

In manufacturing companies that focus on small-scale production of multiple product varieties, defective products are manually selected by workers rather than relying on automated inspection. Consequently, there is a higher risk of incorrect sorting due to variations in selection criteria based on the workers' experience and expertise, without consistent standards. Moreover, for non-standardized flexible objects with varying sizes and shapes, there can be even greater deviations in the selection criteria. To address these issues, this paper designs a quality inspection system using artificial intelligence-based unsupervised learning methods and conducts research by experimenting with accuracy using a dataset obtained from real manufacturing environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2568-2574
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• 2011

31 December 2012 will be the end of terrestrial analog broadcasting. Digital broadcasting will begin. The picture quality on analog broadcasting, was not a problem. but, In digital broadcasting, is a problem. Service for streaming on the digital broadcasting MPEG video compression. And the content is added block noise. These block noise measured by people's eyes. but, people's eyes is subjective. In this paper, the on-reference methods to detect block noise. And detected by measuring the distribution of block noise, to quantify the levels of block-noise. With this study, block noise by visual inspection using an automated tool, by being objective measure of information and communication will contribute to the development of the video.