• Journal of Information Science Theory and Practice
• /
• 제1권1호
• /
• pp.10-26
• /
• 2013

Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2023년도 추계학술발표대회
• /
• pp.934-935
• /
• 2023

반려묘 수가 늘어나는 현대 사회에서 동물 의료 낙후 지역의 보호자는 고양이의 정확한 건강 상태를 파악하기 어렵다. 본 논문에서는 고양이가 가장 흔하게 걸리는 질병인 '결막염'을 비대면으로 진단하고자, 전이학습(Transfer Learning) 기반의 딥러닝 모델을 이용한 웹 애플리케이션을 개발 및 배포하였다. 이를 통해 고양이 결막염 발병 여부 조기 진단 및 치료비 절감, 반려묘 보호자의 편의성 증대 및 동물 의료 서비스의 지역 편차를 줄이는데 기여하고자 한다.

• International Journal of Computer Science & Network Security
• /
• 제21권9호
• /
• pp.223-229
• /
• 2021

Classification of different blood cell types is an essential task for human's medical treatment. The white blood cells have different types of cells. Counting total White Blood Cells (WBC) and differential of the WBC types are required by the physicians to diagnose the disease correctly. This paper used transfer learning methods to the pre-trained deep learning models to classify different WBCs. The best pre-trained model was Inception ResNetV2 with Adam optimizer that produced classification accuracy of 98.4% for the dataset comprising four types of WBCs.

• 대한원격탐사학회지
• /
• 제39권5_4호
• /
• pp.1111-1123
• /
• 2023

원격탐사 영상을 이용한 지표 속성의 변화를 모니터링 하기 위해서 딥러닝(deep learning) 모델을 이용한 의미론적 영상 분할 방법이 최근에 널리 사용되고 있다. 대표적인 의미론적 영상 분할 딥러닝 모델인 UNet 모델을 비롯하여 다양한 종류의 UNet 기반의 딥러닝 모델들의 성능 향상을 위해서는 학습 데이터셋의 크기가 충분해야 한다. 학습 데이터셋의 크기가 커지면 이를 처리하는 하드웨어 요구 사항도 커지고 학습에 소요되는 시간도 크게 증가되는 문제점이 발생한다. 이런 문제를 해결할 수 있는 방법인 전이학습은 대규모의 학습 데이터 셋이 없어도 모델 성능을 향상시킬 수 있는 효과적인 방법이다. 본 논문에서는 UNet 기반의 딥러닝 모델들을 대표적인 사전 학습 모델(pretrained model)인 VGG19 모델 및 ResNet50 모델과 결합한 세 종류의 전이학습 모델인 UNet-ResNet50 모델, UNet-VGG19 모델, CBAM-DRUNet-VGG19 모델을 제시하고 이를 건물 추출에 적용하여 전이학습 적용에 따른 정확도 향상을 분석하였다. 딥러닝 모델의 성능이 학습률의 영향을 많이 받는 점을 고려하여 학습률 설정에 따른 각 모델별 성능 변화도 함께 분석하였다. 건물 추출 결과의 성능 평가를 위해서 Kompsat-3A 데이터셋, WHU 데이터셋, INRIA 데이터셋을 사용하였으며 세 종류의 데이터셋에 대한 정확도 향상의 평균은 UNet 모델 대비 UNet-ResNet50 모델이 5.1%, UNet-VGG19 모델과 CBAM-DRUNet-VGG19 모델은 동일하게 7.2%의 결과를 얻었다.

• 전기전자학회논문지
• /
• 제26권4호
• /
• pp.639-646
• /
• 2022

딥러닝의 발전으로 딥러닝 모델들이 이미지 인식, 음성 인식 등 여러 분야에서 활발하게 사용 중이다. 하지만 이 딥러닝을 효과적으로 사용하기 위해서는 대형 데이터 세트가 필요하지만 이를 구축하기에는 많은 시간과 노력 그리고 비용이 필요하다. 본 논문에서는 웹 크롤링이라는 이미지 수집 방법을 통해서 이미지를 수집하고 데이터 전처리 과정을 거쳐 이미지 분류 모델에 사용할 수 있게 데이터 세트를 구축한다. 더 나아가 전이학습을 이미지 분류 모델에 접목해 카테고리값을 넣어 자동으로 이미지를 분류할 수 있는 경량화된 모델과 적은 훈련 시간 및 높은 정확도를 얻을 수 있는 이미지 분류 모델을 제안한다.

• International Journal of Internet, Broadcasting and Communication
• /
• 제15권1호
• /
• pp.157-163
• /
• 2023

Recently, as businesses and data types become more complex and diverse, efficient data analysis using machine learning is required. However, since communication in the cloud environment is greatly affected by network latency, data analysis is not smooth if information delay occurs. In this paper, SPT (Safe Proper Time) was applied to the cluster-based machine learning data analysis agent proposed in previous studies to solve this delay problem. SPT is a method of remotely and directly accessing memory to a cluster that processes data between layers, effectively improving data transfer speed and ensuring timeliness and reliability of data transfer.

• ETRI Journal
• /
• 제46권1호
• /
• pp.59-70
• /
• 2024

We introduce a high-performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta-pseudo-label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human- and pseudo-labeled data. Second, the influence of noisy pseudo-labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human-labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제31권2호
• /
• pp.93-107
• /
• 1992

The purpose of this study is to make out teaching-learning method for developing mathematical abilities of the 1st grade children in elementary school by investigating cognitive effects which mathematical pre-experiences given intentionally by teachers have on children's learning mathematics. The research questions for this purpose are as follows: In learning effects through mathematical pre-experiences given intentionally by teachers. 1) is there any differences between children with pre-experiences and children without them in Mathematics Achievement Test\ulcorner 2) is there any differences between children with pre-experiences and children without them in Transfer Test for learning effects\ulcorner For this study, a class with 41 children in H elementary school located in a Myon near Chong-ju was selected as an experimental group and a class with 43 children in G elementary school in the same Myon was selected as a control group. Nonequivalent Control Group Design of Quasi-Experimental Design was applied to this study. To give pre-experiences to the children in experimental group, their classroom was equipped with materials for pre-experiences, so children could always observe the materials and play with them. The materials were a round-clock on the wall, two pairs of scales, fifty dice, some small pebbles, two pairs of weight scales, two rulers on the wall, and various cards for playing games. Pre-experiences were given to the children repeatedly through games and observations during free time in the morning (00:20-09:00) and intervals between periods. There was a pretest for homogeneity of mathematics achievement between the two groups and were Mathematics Achievement Test (30 items) and Transfer Test (25 items) for learning effects as post-tests. The data were collected from the pretest on April 8 (control group), on April 11 (experimental group) and from the Mathematics Achievement Test and Transfer Test on July 15 (experimental group) and on July 16 (control group). T-test was used to analyze if there were any differences in the results of the test. The results of the analysis were as follows: (1) As the result of pretest, there was not a significance difference between the experimental group (M=17.10. SD=7.465) and the control group (M=16.31, SD=6.974) at p<.05 (p=0.632). (2) For the question 1. in the Mathematics Achievement Test, there was a significant difference between the experimental group (M=26.08, SD=4.827) and the control group (M=22.28. SD=5.913) at p<.01 (p=.003). (3) For the question 2. in the Transfer Test for learning effects. there was a significant difference between the experimental group (M=16.41, SD=5.800) and the control group (M=11.84, SD=4.815) at p<001, (p=.000). From the results of the analyses obtained in this study. the following conclusions can be drawn: First, mathematical pre-experiences given by teachers are effective in increasing mathematical achievement and transfer in learning mathematics. Second, games. observations, and experiments given intentionally by teachers can make children's mathematical experiences rich and various, and are effective in adjusting individual differences for the mathematical experiences obtained before they entered elementary schools. Third, it is necessary for teachers to give mathematical pre-experiences with close attention in order to stimulate children's mathematical interests and intellectual curiosity.

• 산업융합연구
• /
• 제20권2호
• /
• pp.61-70
• /
• 2022

본 연구는 COVID-19 pandemic으로 간호대학생의 정신간호학 임상실습이 제한되면서 블랜디드 러닝을 기반으로 한 정신간호학실습 프로그램을 개발하고 의사소통 능력, 협력적 자기효능감 및 학습전이동기에 미치는 효과를 확인하기 위한 연구이다. 연구는 2021년 10월 18일부터 2021년 12월 11일까지 간호대학생 64명이 참여하였으며 온라인 구글설문지를 이용하여 실습 전과 실습 후에 설문지를 완성하였다. 수집된 자료는 SPSS 25.0 프로그램을 이용하여 기술적 통계, paired t-test로 분석하였다. 연구결과, 대상자는 블렌디드 러닝을 기반으로 한 정신간호학실습프로그램 전에 비해 후에 의사소통 능력, 협력적 자기효능감과 학습전이동기가 유의미하게 상승되었다. 본 연구결과를 통하여 블렌디드 러닝을 기반으로 한 정신간호학실습프로그램의 효과를 확인할 수 있었다. 추후 포스트 코로나 상황에서도 임상현장실습을 대체하여 적용할 수 있는 정신간호학실습교육의 실행성과 후속연구의 근거자료로 활용될 수 있을 것이다.

• 지식경영연구
• /
• 제11권4호
• /
• pp.41-66
• /
• 2010

This research examines the effect of the relatedness and the gap between Resources and mechanisms on effectiveness of inter-organizational knowledge transfer. According to the literature, there has been a competing theory between two claims; one is that inter-organizational knowledge transfer will be more effective due to the reduction of the transaction cost as the relatedness increases. And the other is that the mutual complementarity of different organizational characteristics will increase synergy. In total, the relatedness and the gap of the Resource and mechanism makes the inverted U-shaped relationship with the inter-organizational knowledge transfer. As the result of empirical analysis about 109 Korean-based Joint Ventures entered country, it shows that the relatedness of parent company's production Resources, learning mechanisms, and coordination mechanisms made the inverted U-shaped relations with the inter-organizational knowledge transfer and the gap of production Resources and adjustment mechanism formed the same relationship. However, the U-shaped relationship has been established in the relatedness of market Resources, but the gap of market Resources and the learning mechanism was not statistically significant. Through this study, I can draw a best conclusion that the inter-organizational knowledge transfer will be more effective when the relatedness and the gap of management resources and mechanisms is in optimal level. However, when it comes to market Resources, it can be inferred that the result could be the opposite because the partner country's market environment would be different.