• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• 인터넷정보학회논문지
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• 제21권5호
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.

• 한국멀티미디어학회논문지
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• 제24권7호
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• pp.849-859
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• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• 한국유통학회지:유통연구
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• 제13권4호
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• pp.1-19
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• 2008

본 논문에서는 학습조직이론을 바탕으로 소매 노하우의 국제이전을 사례연구를 통하여 검토하였다. 연구의 대상으로서는 세계적인 편의점 체인인 7-Eleven을 선정하여 그 노하우가 어떻게 일본에 성공적으로 이전되었는지를 정성적으로 분석하였다. 분석결과 편의점 노하우의 국제이전에 있어서는 본질학습과 적응학습이 대단히 중요한 역할을 하였다. 본질학습의 내용으로는 편의점의 기본컨셉트와 점포운영 기본3원칙, 최저이익보증제도, 이익배분방식이 있었으며 적응학습의 내용으로는 출점방식과 점포규모, 점포입지, 상품구성 등과 같은 소매믹스가 있었다. 또한 적응학습에는 가설검증방식이라는 학습방법론이 사용되었으며 이 방식을 계속적으로 적용한 결과 경쟁기업이 모방하기 힘든 혁신을 이룩하였다. 다만 본질학습에서 학습한 내용이 적응학습에 원칙과 방향을 제시해 주었다. 이처럼 본질학습과 적응학습이 서로 맞물려 잘 이루어져야 소매 노하우의 국제이전은 성공할 수 있는 것이다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권4호
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• pp.105-112
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• 2022

최근 환경의 변화에 적응적이고 특정 목적에 부합하는 오토스케일링 정책을 만들기 위해 강화학습 기반 오토스케일링을 사용하는 연구가 많이 이루어지고 있다. 하지만 실제 환경에서 강화학습 기반 수평적 파드 오토스케일러(HPA, Horizontal Pod Autoscaler)의 정책을 학습하기 위해서는 많은 비용과 시간이 요구되며, 서비스를 배포할 때마다 실제 환경에서 강화학습 기반 HPA 정책을 처음부터 다시 학습하는 것은 실용적이지 않다. 본 논문에서는 쿠버네티스에서 강화학습 기반 HPA를 구현하고, 강화학습 기반 HPA 정책에 대한 학습을 가속화하기 위해 대기행렬 모델 기반 시뮬레이션을 활용한 전이 학습 기법을 제안한다. 시뮬레이션을 활용한 사전 학습을 수행함으로써 실제 환경에서 시간과 자원을 소모하며 학습을 수행하지 않아도 시뮬레이션 경험을 통해 정책 학습이 이루어질 수 있도록 하였고, 전이 학습 기법을 사용함으로써 전이 학습 기법을 사용하지 않았을 때보다 약 42.6%의 비용을 절감할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제25권3호
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• pp.27-32
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• 2020

본 논문에서는 금속스크랩이 쌓이는 스크랩박스의 적치 상태를 측정하는 알고리즘을 제안한다. 적치 상태 측정 문제를 다중 클래스 분류 문제로 정의하여, 딥러닝 기법을 이용해 스크랩박스 촬영 영상만으로 적치 상태를 구분하도록 하였다. Transfer Learning 방식으로 학습을 진행하였으며, 딥러닝 모델은 NASNet-A를 이용하였다. 더불어 분류 모델의 정확도를 높이기 위해 학습된 NASNet-A에 랜덤포레스트 분류기를 결합하였으며, 후처리를 통해 안전성을 높였다. 현장에서 수집된 4,195개의 데이터로 테스트한 결과 NASNet-A만 적용했을때 정확도 55%를 보였으며, 제안 방식인 Random Forest를 결합한 NASNet은 88%로 향상된 정확도를 달성하였다.

• Educational Technology International
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• 제9권1호
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• pp.79-96
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• 2008

The issue of connection between entities has a lengthy history in educational research, especially since it provides the necessary bridge between base and target in analogical transfer. Recently, the connection has been viewed through the application of technology to bridge between sequences in order to be cognitively useful. This study reports the effect of sequence type (AT vs. TA) and connection type (fading vs. popping) on the achievement and analogical transfer in a multimedia application. In the current research, 10th -grade and 11th -grade biology students in Korea were randomly assigned to five groups to test the effects of presentation sequence and entity connection type on analogical transfer. Consistent with previous studies, sequence type has a significant effect: analogical transfer performance was better when base representations were presented first followed by target representations rather than the reverse order. This is probably because presenting a familiar base first helps in understanding a less familiar target. However, no fully significant differences were found with the entity connection types (fading vs. popping) in analogical transfer. According to the Markman and Gentner's (2005) spatial model, analogy in a space is influenced only by the differences between concepts, not by distance in space. Thus connection types fail on the basis of this spatial model in analogical transfer test. The findings and their implications for sequence and connection research and practice are discussed. Leveraging on the analogical learning process, specific implications for scaffolding learning processes and the development of adaptive expertise are drawn.