• 디지털융복합연구
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• 제19권2호
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• pp.215-220
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• 2021

최근 국내 미세먼지 발생의 증가에 따라 발생하는 인체에 유해한 영향을 줄이기 위하여, 미세먼지 수치를 예측하고 사전 조치를 취할 수 있도록 돕는 기술이 필요해지고 있다. 본 논문에서는 국내 미세먼지 위험 수준을 예측하기 위한 1D Convolutional to Recurrent Neural Network (1-D CRNN) 모델을 제안한다. 제안 된 모델은 딥러닝 신경망의 CNN과 RNN을 결합한 구조이며, 다른 종류의 데이터로 구성된 시계열 데이터 세트에서 데이터 예측을 수행 할 수 있다. 데이터 예측을 위해 국내·외 미세먼지, 풍향, 풍속 데이터를 사용한다. 제안된 모델은 약 76%(부분 최대 84%)의 정확도를 달성했으며, 일반 RNN 모델(53%)보다 정확한 예측 결과를 얻었을 수 있었다. 제안된 모델은 향후 여러 개의 시계열 데이터 세트를 고려해야 하는 데이터 예측 모델 학습 및 실험을 목표로 한다.

• 한국정보통신학회논문지
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• 제24권6호
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• pp.759-767
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• 2020

본 논문에서는 인지 무선 통신을 위한 새로운 순환 신경망 기반 스펙트럼 센싱 기법을 제안한다. 제안하는 기법은 주사용자에 대한 정보가 전혀 없는 상황에서 에너지 검출을 통해 신호 존재 유무를 판단한다. 제안 기법은 센싱하고자 하는 전체 대역을 고려하여 수신신호를 고속으로 샘플링 후 이 신호의 FFT (fast Fourier transform)를 통해 주파수 스펙트럼으로 변환한다. 이 스펙트럼 신호는 채널 대역폭 단위로 자른 후 순환 신경망에 입력하여 해당 채널이 사용중인지 비어있는지 판정한다. 제안하는 기법의 성능은 컴퓨터 모의실험을 통해 확인하는데 그 결과에 따르면 기존 문턱값 기반 기법보다 2 [dB] 이상 우수하며 합성곱 신경망 기법과 유사한 성능을 보인다. 또한, 실제 실내환경에서 실험도 수행하는데 이 결과에 따르면 제안하는 기법이 기존 문턱값 기반 방식 및 합성곱 신경망 방식보다 4 [dB] 이상 우수한 성능을 보인다.

• 로봇학회논문지
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• 제13권4호
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• pp.230-236
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• 2018

3D depth perception has played an important role in robotics, and many sensory methods have also proposed for it. As a photodetector for 3D sensing, single photon avalanche diode (SPAD) is suggested due to sensitivity and accuracy. We have researched for applying a SPAD chip in our fusion system of time-of-fight (ToF) sensor and stereo camera. Our goal is to upsample of SPAD resolution using RGB stereo camera. Currently, we have 64 x 32 resolution SPAD ToF Sensor, even though there are higher resolution depth sensors such as Kinect V2 and Cube-Eye. This may be a weak point of our system, however we exploit this gap using a transition of idea. A convolution neural network (CNN) is designed to upsample our low resolution depth map using the data of the higher resolution depth as label data. Then, the upsampled depth data using CNN and stereo camera depth data are fused using semi-global matching (SGM) algorithm. We proposed simplified fusion method created for the embedded system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1494-1507
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• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• 한국가시화정보학회지
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• 제20권3호
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• pp.109-116
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• 2022

The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.104-113
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• 2022

본 논문에서는 OFDM 레이다를 위한 딥러닝 기반 표적의 거리 및 속도 추정 기법을 제안한다. 제안하는 기법은 표적으로부터 반사된 수신 신호를 받아 변조신호 제거 후 2차원 FFT를 통해 2차원 주기도를 얻는다. 주기도는 기존 및 제안 방법에서 표적의 거리 및 속도를 추정하는 입력신호이다. 주기도에서 정점은 표적의 위치를 나타내는데 표적의 거리 및 속도 추정을 위해 널리 사용되는 기존 기법은 CFAR (Constant False Alarm Rate) 알고리즘이다. 반면 제안하는 기법은 다중 출력 CNN (Convolutional Neural Network)을 이용하여 거리 및 속도를 추정한다. 기존 기법과 달리 제안 기법은 주기도 이외에 잡음 전력과 같이 추가적인 정보가 필요하지 않아 사용하기 편리하다. 컴퓨터 시뮬레이션 결과에 따르면 제안 추정 기법은 기존 기법보다 거리 및 속도 추정 MSE (Mean Square Error)오차 성능을 5배 이상 개선하며 송신 OFDM 심볼 개수가 증가할수록 정확도가 향상되는 특성을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• 한국군사과학기술학회지
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• 제22권2호
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• pp.170-178
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• 2019

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.