• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• 경영과정보연구
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• 제37권3호
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• pp.1-25
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• 2018

본 연구는 이러닝 학습 경험이 있는 국내외 기업 종업원 390명을 대상으로 지각된 유용성, 사용용이성, 자기효능감 및 업무환경이 학습전이에 미치는 영향을 실증 분석하였다. 또한 각 요인의 학습전이에 대한 직접효과와 더불어 자기효능감과 업무환경의 매개효과를 분석하였다. 분석결과 이러닝 학습자의 지각된 유용성과 사용용이성은 자기효능감에 유의미한 정(＋)의 영향을 보였으며, 상사 및 동료 지원과 조직 분위기에도 유의미한 정(＋)의 영향을 갖는 것으로 분석되었다. 자기효능감은 학습전이에 유의미한 정(＋)의 영향을 보였으며, 상사 및 동료 지원과 조직 분위기도 학습전이에 유의미한 정(＋)의 영향을 갖는 것으로 나타났다. 지각된 유용성 또한 학습전이에 유의미한 영향을 갖는 것으로 분석되었다. 하지만 지각된 사용용이성은 학습전이에 유의미한 영향을 미치지 않았다. 매개효과 분석결과 자기효능감과 업무환경은 각각 지각된 유용성, 지각된 사용용이성과 학습전이에 대해 모두 매개효과를 갖는 것으로 분석되었다. 본 연구에서 제시한 시사점은 첫째, 기업교육에서 보편화된 이러닝에 대해 기술수용 단계를 벗어나 수용 후 실질적인 학습전이 효과에 대한 영향요인을 반영한 새로운 연구 모형을 제시하였다. 기술수용모델에서 외부 특성요인에 대한 매개변수로 사용되는 지각된 유용성과 지각된 사용 용이성을 독립변수로, 외부 특성요인으로 연구되었던 자기효능감과 조직 환경을 매개변수로 사용한 연구모형을 도출하였다. 둘째, 기술수용과 학습전이에 관한 연구는 단일국가를 대상으로 한 연구들이 대부분이다. 26개 국가의 표본을 대상으로 다양한 샘플을 사용하여 연구 모형을 검증함으로써 신뢰성을 높였다. 셋째, 기존의 연구에서 지각된 유용성과, 사용용이성을 수용의향 및 학습전이의 주요 결정요인으로 고려하였다. 본 연구는 수용된 정보기술에 대해 학습자 및 환경 요인의 매개효과를 탐색하여 지각된 유용성, 사용용이성의 학습전이에 대한 경로를 강화하고 보완하였다. 본 연구에서 활용된 다양한 국가의 표본 분석을 기반으로 향후 국제비교연구도 가능할 것으로 기대된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.178-179
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• 2018

최근 자율주행 및 음성인식 등 인공지능 분야에서 기계학습을 이용한 방법이 활발히 연구되고 있다. 디지털 영상에서 특정 사물이나 영역을 인식하기 위해 고전적인 경계검출 및 패턴인식 등의 고전적인 영상처리 방법으로는 많은 한계를 가지고 있으나 deep-learning 등 기계학습 방법을 이용하면 사람의 인지수준에 근접한 결과를 얻을 수 있다. 하지만 기본적으로 deep-learning 등 기계학습은 방대한 양의 학습데이터가 확보되어야 한다. 따라서 환경 분석을 위한 항공사진처럼 데이터의 양이 매우 적은 경우 영역 구분을 위해 기계학습을 적용하기 어렵다. 본 연구에서는 입력영상의 dataset 크기가 적고 입력 영상의 형태가 training dataset의 category에 포함되지 않는 경우 사용할 수 있는 transfer-learning 기법을 적용하며 이를 이용하여 영상 내에서 특정 영역 검출을 수행한다.

• Investigative Magnetic Resonance Imaging
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• 제25권1호
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• pp.10-22
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• 2021

Purpose: To overcome the difficulty in building a large data set with a high-quality in medical imaging, a concept of 'blended-transfer learning' (BTL) using a combination of both source data and target data is proposed for the target task. Materials and Methods: Source and target tasks were defined as training of the source and target networks to reconstruct cardiac CINE images from undersampled data, respectively. In transfer learning (TL), the entire neural network (NN) or some parts of the NN after conducting a source task using an open data set was adopted in the target network as the initial network to improve the learning speed and the performance of the target task. Using BTL, an NN effectively learned the target data while preserving knowledge from the source data to the maximum extent possible. The ratio of the source data to the target data was reduced stepwise from 1 in the initial stage to 0 in the final stage. Results: NN that performed BTL showed an improved performance compared to those that performed TL or standalone learning (SL). Generalization of NN was also better achieved. The learning curve was evaluated using normalized mean square error (NMSE) of reconstructed images for both target data and source data. BTL reduced the learning time by 1.25 to 100 times and provided better image quality. Its NMSE was 3% to 8% lower than with SL. Conclusion: The NN that performed the proposed BTL showed the best performance in terms of learning speed and learning curve. It also showed the highest reconstructed-image quality with the lowest NMSE for the test data set. Thus, BTL is an effective way of learning for NNs in the medical-imaging domain where both quality and quantity of data are always limited.

• 디지털융복합연구
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• 제20권5호
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• pp.897-908
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• 2022

본 연구의 목적은 개인의 학습 자기효능과 기업의 혁신적 지식전달과 같은 학습전이풍토가 디자이너의 특성을 고려한 디자인 역량강화교육과 개인성과와의 관계에서 매개역할을 할 것이라는 가설을 증명하는 것에 있다. 이는 단순히 교육의 만족도를 조사하는 기존의 연구와는 달리 디자이너의 특성에 근거한 학습 자기효능감과 디자이너가 조직의 문화에 영향을 주는 학습전이 풍토에 대한 정량적 분석을 하여 디자인 교육훈련의 의미를 확장하는데 의의가 있다. 구체적으로 본 연구에서는 7개의 가설을 설정하였고, 그 결과 디자이너 대상 디자인 역량강화 교육과 학습 자기효능감, 기업의 학습전이풍토는 개인성과에 유의한 영향을 미치는 것으로 나타났다.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1387-1395
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• 2018

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

• Design & Manufacturing
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• 제15권3호
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• pp.1-6
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• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Journal of Computing Science and Engineering
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• 제5권3호
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• pp.257-268
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• 2011

Machine learning and data mining have found many applications in biological domains, where we look to build predictive models based on labeled training data. However, in practice, high quality labeled data is scarce, and to label new data incurs high costs. Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the target domain. In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. In particular, we survey several key bioinformatics application areas, including sequence classification, gene expression data analysis, biological network reconstruction and biomedical applications.

• 반도체디스플레이기술학회지
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• 제20권1호
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• pp.64-69
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• 2021

In this paper, a method for improving the defect classification performance in low contrast, ununiformity and featureless steel plate surfaces has been studied based on deep convolution neural network and transfer-learning neural network. The steel plate surface images have low contrast, ununiformity, and featureless, so that the contrast between defect and defect-free regions are not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. A classifier based on a deep convolution neural network is constructed to extract features automatically for effective classification of images with these characteristics. As results of the experiment, AlexNet-based transfer-learning classifier showed excellent classification performance of 99.43% with less than 160 seconds of training time. The proposed classification system showed excellent classification performance for low contrast, ununiformity, and featureless surface images.