• Journal of KIISE
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• 제44권12호
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• pp.1275-1281
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• 2017

The supervised learning approach is suitable for classification of insulting sentences, but pre-decided training sentences are necessary. Since a Character-level Convolution Neural Network is robust for each character, so is appropriate for classifying abusive sentences, however, has a drawback that demanding a lot of training sentences. In this paper, we propose transfer learning method that reusing the trained filters in the real classification process after the filters get the characteristics of offensive words by generated abusive/normal pair of sentences. We got higher performances of the classifier by decreasing the effects of data shortage and class imbalance. We executed experiments and evaluations for three datasets and got higher F1-score of character-level CNN classifier when applying transfer learning in all datasets.

• Journal of the Korea Convergence Society
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• 제12권10호
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• pp.27-35
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• 2021

Automatic post editing is a research field that aims to automatically correct errors in machine translation results. This research is mainly being focus on high resource language pairs, such as English-German. Recent APE studies are mainly adopting transfer learning based research, where pre-training language models, or translation models generated through self-supervised learning methodologies are utilized. While translation based APE model shows superior performance in recent researches, as such researches are conducted on the high resource languages, the same perspective cannot be directly applied to the low resource languages. In this work, we apply two transfer learning strategies to Korean-English APE studies and show that transfer learning with translation model can significantly improves APE performance.

• Journal of the Korean Recycled Construction Resources Institute
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• 제9권3호
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• pp.348-355
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• 2021

Asphalt, widely used for road pavement, has different required physical properties depending on the environment to which the road is exposed. Therefore, it is essential to maximize the life of asphalt roads by evaluating the physical properties of asphalt according to additives and selecting an appropriate formulation considering road traffic and climatic environment. Dynamic shear rheometer(DSR) test is mainly used to measure resistance to rutting among various physical properties of asphalt. However, the DSR test has limitations in that the results are different depending on the experimental setting and can only be measured within a specific temperature range. Therefore, in this study, to overcome the limitations of the DSR test, the rheological characteristics were predicted by learning the images collected from atomic force microscopy. Images and rheology properties were trained through EfficientNet, one of the deep learning architectures, and transfer learning was used to overcome the limitation of the deep learning model, which require many data. The trained model predicted the rheological properties of the asphalt binder with high accuracy even though different types of additives were used. In particular, it was possible to train faster than when transfer learning was not used.

• The Journal of the Convergence on Culture Technology
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• 제9권4호
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• pp.23-30
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• 2023

This study was attempted to be used as basic data for efficient clinical practice education operation by identifying differences in program outcomes before and after integrated clinical practice of nursing students. Data collection was conducted before and after 6 weeks of integrated clinical practice for 38 nursing college students from July 19 to September 10, 2021 with a single group pre- and post-design. The collected data were analyzed by frequency, Wilcoxon sign rank, and Pearson correlation using SPSS/Win 23.0. After the integrated clinical practice, 1st program outcomes(Care integration)(Z=-4.63, p<.001), 2nd(Core practice)(Z=-3.99, p<.001), 6th(Critical thinking)(Z=-3.60, p<.001) and 11th(Research practice)(Z=-2.76, p=.005) increased significantly compared to before practice. Since integrated clinical practice was found to be an effective practice to improve program outcomes, it is suggested to actively utilize it as a strategy for improving program outcomes.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2016년도 추계학술발표대회
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• pp.617-620
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• 2016

본 논문에서는 이미지 캡션 생성과 모델 전이에 효과적인 심층 신경망 모델을 제시한다. 본 모델은 멀티 모달 순환 신경망 모델의 하나로서, 이미지로부터 시각 정보를 추출하는 컨볼루션 신경망 층, 각 단어를 저차원의 특징으로 변환하는 임베딩 층, 캡션 문장 구조를 학습하는 순환 신경망 층, 시각 정보와 언어 정보를 결합하는 멀티 모달 층 등 총 5 개의 계층들로 구성된다. 특히 본 모델에서는 시퀀스 패턴 학습과 모델 전이에 우수한 LSTM 유닛을 이용하여 순환 신경망 층을 구성하고, 컨볼루션 신경망 층의 출력을 임베딩 층뿐만 아니라 멀티 모달 층에도 연결함으로써, 캡션 문장 생성을 위한 매 단계마다 이미지의 시각 정보를 이용할 수 있는 연결 구조를 가진다. Flickr8k, Flickr30k, MSCOCO 등의 공개 데이터 집합들을 이용한 다양한 비교 실험을 통해, 캡션의 정확도와 모델 전이의 효과 면에서 본 논문에서 제시한 멀티 모달 순환 신경망 모델의 우수성을 입증하였다.

• Annual Conference on Human and Language Technology
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• 한국정보과학회언어공학연구회 2023년도 제35회 한글 및 한국어 정보처리 학술대회
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• pp.301-305
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• 2023

최근 등장한 Large Language Models (LLM)은 자연어 처리 분야에서 눈에 띄는 성과를 보여주었지만, 주로 영어 중심의 연구로 진행되어 그 한계를 가지고 있다. 본 연구는 사전 학습된 LLM의 언어별 지식 전이 가능성을 한국어를 중심으로 탐구하였다. 이를 위해 한국어와 영어로 구성된 코드 스위칭 코퍼스를 구축하였으며, 기본 모델인 LLAMA-2와 코드 스위칭 코퍼스를 추가 학습한 모델 간의 성능 비교를 수행하였다. 결과적으로, 제안하는 방법론으로 학습한 모델은 두 언어 간의 희미론적 정보가 효과적으로 전이됐으며, 두 언어 간의 지식 정보 연계가 가능했다. 이 연구는 다양한 언어와 문화를 반영하는 다국어 LLM 연구와, 소수 언어를 포함한 AI 기술의 확산 및 민주화에 기여할 수 있을 것으로 기대된다.

• The Journal of the Korea Contents Association
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• 제17권9호
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• pp.661-671
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• 2017

The purpose of this study is to identify the correlations among learning self-efficacy, confidence in performance, perception of importance and transfer intention for core basic nursing skill in nursing students. The subjects of this study were 2nd grade students at a nursing university. The collected data were analyzed using SPSS 21.0 program. As a result, the transfer intention had a correlation with the learning self-efficacy (r=.49, p<.001), confidence in performance (r=.30, p=.006), perception of the importance (r=.31, p=.005). The results of this study suggest that further research is necessary to verify the causal relationship between the transfer intention and the related variables in order to develop an effective education program for promoting the transfer intention.

• Journal of the Korea Convergence Society
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• 제12권10호
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• pp.333-342
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• 2021

The purpose of this study is to compare the differences in transfer motivation and learning self-efficacy before and after applying virtual simulation for nursing in psychiatric nursing practice, and to provide them as basic data for effective psychiatric practical education. This study was conducted from October to December 2020. The subjects were 41 people who were enrolled in the third year of a located in U city, and who had received psychiatric nursing practice education using virtual simulation for nursing. Data were analyzed by paired t-test and pearson's correlation coefficient. After practice compared to before psychiatric nursing practice with virtual simulation nursing applied, transfer motivation was significantly increased and learner self-efficacy increased, but it was not statistically significant. Therefore, It was confirmed that psychiatric nursing practice education using virtual simulation for nursing is partly an effective practice strategy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.346-347
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• 2021

For pattern recognition for machine learning, the larger the amount of learning data, the better its performance. However, it is not always possible to secure a large amount of learning data with the types and information of patterns that must be detected in daily life. Therefore, it is necessary to significantly inflate a small data set for general machine learning. In this study, we study techniques to augment data so that machine learning can be performed. A representative method of performing machine learning using a small data set is the transfer learning technique. Transfer learning is a method of obtaining a result by performing basic learning with a general-purpose data set and then substituting the target data set into the final stage. In this study, a learning model trained with a general-purpose data set such as ImageNet is used as a feature extraction set using augmented data to detect a desired pattern.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2004년도 하계학술대회
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• pp.389-396
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• 2004

입문기 아동의 수학 교육에서의 첫 걸음은 수 개념 형성으로부터 시작되는데 취학 전에 발달 단계나 학습 순차를 무시한 형식적인 수지도가 수 개념을 형성하는데 별다른 도움을 주지 못하는 것으로 나타나 있다. 수학 교과가 가지고 있는 논리적 위계성을 감안할 때 이전 학년에서 발생된 학습 결손이나 개념 이해부족은 다음 학년의 학습을 지속해 나가기가 어렵다. 이와 같은 학습의 과정이 반복되면 학습 부진을 증대시켜 학습에 흥미를 잃게 한다. 이에 본 연구는 수학과 기초 학습력을 신장시키며 취학 전에 발달단계를 무시한 잘못된 수 경험을 개선하고자 멀티미디어 웹 코스웨어인 '50까지의 수'를 설계 및 구현하였다.