• 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.

• 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.

• 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.

• Journal of Positioning, Navigation, and Timing
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• 제11권3호
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3333-3350
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• 2019

The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions.

• 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.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.294-301
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• 2022

COVID-19 is a crisis with numerous casualties. The World Health Organization (WHO) has declared the use of masks as an essential safety measure during the COVID-19 pandemic. Therefore, whether or not to wear a mask is an important issue when entering and exiting public places and institutions. However, this makes face recognition a very difficult task because certain parts of the face are hidden. As a result, face identification and identity verification in the access system became difficult. In this paper, we propose a system that can detect masked face using transfer learning of Yolov5s and recognize the user using transfer learning of Facenet. Transfer learning preforms by changing the learning rate, epoch, and batch size, their results are evaluated, and the best model is selected as representative model. It has been confirmed that the proposed model is good at detecting masked face and masked face recognition.

• 대한임베디드공학회논문지
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• 제14권6호
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• pp.287-294
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• 2019

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

• 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.