• Journal of Information Processing Systems
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• 제16권5호
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• 대한임베디드공학회논문지
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• 제13권6호
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• pp.313-319
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• 2018

We propose a post-processing algorithm to improve the quality of the RGB image generated by deep learning based colorization from the gray-scale image of an infrared camera. Wavelet fusion is used to generate a new luminance component of the RGB image luminance component from the deep learning model and the luminance component of the infrared camera. PSNR is increased for all experimental images by applying the proposed algorithm to RGB images generated by two deep learning models of SegNet and DCGAN. For the SegNet model, the average PSNR is improved by 1.3906dB at level 1 of the Haar wavelet method. For the DCGAN model, PSNR is improved 0.0759dB on the average at level 5 of the Daubechies wavelet method. It is also confirmed that the edge components are emphasized by the post-processing and the visibility is improved.

• 한국재료학회지
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• 제32권8호
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• Smart Structures and Systems
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• 제31권5호
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• 전기전자학회논문지
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• 제25권4호
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• pp.644-649
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• 2021

Self-Imitation Learning은 간단한 비활성 정책 actor-critic 알고리즘으로써 에이전트가 과거의 좋은 경험을 활용하여 최적의 정책을 찾을 수 있도록 해준다. 그리고 actor-critic 구조를 갖는 강화학습 알고리즘에 결합되어 다양한 환경들에서 알고리즘의 상당한 개선을 보여주었다. 하지만 Self-Imitation Learning이 강화학습에 큰 도움을 준다고 하더라도 그 적용 분야는 actor-critic architecture를 가지는 강화학습 알고리즘으로 제한되어 있다. 본 논문에서 Self-Imitation Learning의 알고리즘을 가치 기반 강화학습 알고리즘인 DQN에 적용하는 방법을 제안하고, Self-Imitation Learning이 적용된 DQN 알고리즘의 학습을 다양한 환경에서 진행한다. 아울러 그 결과를 기존의 결과와 비교함으로써 Self-Imitation Leaning이 DQN에도 적용될 수 있으며 DQN의 성능을 개선할 수 있음을 보인다.

• 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.

• Journal of information and communication convergence engineering
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• 제16권2호
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• Asia pacific journal of information systems
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• 제32권2호
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• 전자통신동향분석
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• 제33권4호
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• pp.43-53
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• 2018

As face recognition (FR) has been well studied over the past decades, FR technology has been applied to many real-world applications such as surveillance and biometric systems. However, in the real-world scenarios, FR performances have been known to be significantly degraded owing to variations in face images, such as the pose, illumination, and low-resolution. Recently, visual intelligence technology has been rapidly growing owing to advances in deep learning, which has also improved the FR performance. Furthermore, the FR performance based on deep learning has been reported to surpass the performance level of human perception. In this article, we discuss deep-learning based high-performance FR technologies in terms of representative deep-learning based FR architectures and recent FR algorithms robust to face image variations (i.e., pose-robust FR, illumination-robust FR, and video FR). In addition, we investigate big face image datasets widely adopted for performance evaluations of the most recent deep-learning based FR algorithms.