• Title/Summary/Keyword: CNN (Convolutional Neural Networks)

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Epileptic Seizure Detection for Multi-channel EEG with Recurrent Convolutional Neural Networks (순환 합성곱 신경망를 이용한 다채널 뇌파 분석의 간질 발작 탐지)

  • Yoo, Ji-Hyun
    • Journal of IKEEE
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    • v.22 no.4
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    • pp.1175-1179
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    • 2018
  • In this paper, we propose recurrent CNN(Convolutional Neural Networks) for detecting seizures among patients using EEG signals. In the proposed method, data were mapped by image to preserve the spectral characteristics of the EEG signal and the position of the electrode. After the spectral preprocessing, we input it into CNN and extracted the spatial and temporal features without wavelet transform. Results from the Children's Hospital of Boston Massachusetts Institute of Technology (CHB-MIT) dataset showed a sensitivity of 90% and a false positive rate (FPR) of 0.85 per hour.

Target/non-target classification using active sonar spectrogram image and CNN (능동소나 스펙트로그램 이미지와 CNN을 사용한 표적/비표적 식별)

  • Kim, Dong-Wook;Seok, Jong-Won;Bae, Keun-Sung
    • Journal of IKEEE
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    • v.22 no.4
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    • pp.1044-1049
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    • 2018
  • CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

Implementation of Face Mask Detection (얼굴 마스크 탐지의 구현)

  • Park, Seong Hwan;Jung, Yuchul
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.17-19
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    • 2021
  • 본 논문에서는 코로나19 사태에 대비하여 실시간으로 마스크를 제대로 쓴 사람과 제대로 쓰지 않은 사람을 구분하는 시스템을 제안한다. 이 시스템을 사용하기 위하여 모델 학습 시에 합성곱 신경망(CNN : Convolutional Neural Networks)를 사용한다. 학습된 모델을 토대로 영상에 적용 시 하르 특징 분류기(Haar Cascade Classifier)로 얼굴을 탐지하여 마스크 여부를 판단한다.

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Active pulse classification algorithm using convolutional neural networks (콘볼루션 신경회로망을 이용한 능동펄스 식별 알고리즘)

  • Kim, Geunhwan;Choi, Seung-Ryul;Yoon, Kyung-Sik;Lee, Kyun-Kyung;Lee, Donghwa
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.1
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    • pp.106-113
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    • 2019
  • In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

Face Recognition in Visual and Infra-Red Complex Images (가시광-근적외선 혼합 영상에서의 얼굴인식에 관한 연구)

  • Kim, Kwang-Ju;Won, Chulho
    • Journal of Korea Multimedia Society
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    • v.22 no.8
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    • pp.844-851
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    • 2019
  • In this paper, we propose a loss function in CNN that introduces inter-class amplitudes to increase inter-class loss and reduce intra-class loss to increase of face recognition performance. This loss function increases the distance between the classes and decreases the distance in the class, thereby improving the performance of the face recognition finally. It is confirmed that the accuracy of face recognition for visible light image of proposed loss function is 99.62%, which is better than other loss functions. We also applied it to face recognition of visible and near-infrared complex images to obtain satisfactory results of 99.76%.

Comparison of CNN and YOLO for Object Detection (객체 검출을 위한 CNN과 YOLO 성능 비교 실험)

  • Lee, Yong-Hwan;Kim, Youngseop
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.1
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    • pp.85-92
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    • 2020
  • Object detection plays a critical role in the field of computer vision, and various researches have rapidly increased along with applying convolutional neural network and its modified structures since 2012. There are representative object detection algorithms, which are convolutional neural networks and YOLO. This paper presents two representative algorithm series, based on CNN and YOLO which solves the problem of CNN bounding box. We compare the performance of algorithm series in terms of accuracy, speed and cost. Compared with the latest advanced solution, YOLO v3 achieves a good trade-off between speed and accuracy.

PCB Defect Inspection using Deep Learning (딥러닝을 이용한 PCB 불량 검출)

  • Baek, Yeong-Tae;Sim, Jae-Gyu;Pak, Chan-Young;Lee, Se-Hoon
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.325-326
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    • 2018
  • 본 논문에서는 PCB 공정상의 육안검사를 통한 불량 분류 방식에서 CNN을 이용한 PCB 불량 분류 방식을 제안한다. 이 방식은 육안검사의 문제점인 작업자의 숙련도에 따른 검사 효율을 자동화 검사 시스템에 의해 해결하며, 불량 위치와 종류를 결과 이미지에 표시한다. 또한 이미지 분류 결과를 모니터링할 수 있도록 시리얼 통신을 통하여 Darknet 프레임워크와 LCD를 연동하였다. 적은 량의 데이터 셋으로도 좋은 결과를 냈으며, 다양한 데이터 셋을 이용해 훈련할 시 전반적인 PCB 불량의 분류가 가능할 것으로 예상된다.

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Implementation of Turtle Neck Syndrome Diagnosis using CNN (CNN을 이용한 거북목 증후군 진단기의 구현)

  • Son, Dong-Hyeop;Jung, Yuchul
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.7-10
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    • 2021
  • 최근 스마트폰과 컴퓨터 등의 비중이 커지면서 거북목 증후군의 관심사가 커졌다. 거북목 증후군은 잘못된 자세로 인해 어깨의 근육과 인대가 늘어나 통증이 생기는 증상을 의미한다. 이러한 잘못된 자세에는 대표적으로 일자목과 역c자목이 있으며 일자목은 7개의 목뼈로 이루어진 경추라인이 c자 라인에서 일직선으로 뼈의 형태가 바뀌어 디스크가 일어나 통증을 유발하는 증상이고 역c자목은 정상의 목뼈 구조를 잃어버린 형태로 곧 디스크를 보이며 고개를 드는 것이 힘드며 구부정한 자세를 취하게 되는 증상이다. 본 연구에서는 컨볼루션 신경망 (CNN) 학습 모델을 구현하여 주어진 자세가 올바른 자세인지 일자목인지 c자목인지를 진단할 수 있는 분류기를 구현하였다. 또한, 최근 코로나 사태로 인해 마스크 장착이 일상화되고 있는데, 추가 데이터를 보강하여, 마스크 착용상태에서도 적용가능한 모델로 확장하였다.

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Real-Time Hand Gesture Recognition Based on Deep Learning (딥러닝 기반 실시간 손 제스처 인식)

  • Kim, Gyu-Min;Baek, Joong-Hwan
    • Journal of Korea Multimedia Society
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    • v.22 no.4
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    • pp.424-431
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    • 2019
  • In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

Active Sonar Target/Non-target Classification using Convolutional Neural Networks (CNN을 이용한 능동 소나 표적/비표적 분류)

  • Kim, Dongwook;Seok, Jongwon;Bae, Keunsung
    • Journal of Korea Multimedia Society
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    • v.21 no.9
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    • pp.1062-1067
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    • 2018
  • Conventional active sonar technology has relied heavily on the hearing of sonar operator, but recently, many techniques for automatic detection and classification have been studied. In this paper, we extract the image data from the spectrogram of the active sonar signal and classify the extracted data using CNN(convolutional neural networks), which has recently presented excellent performance improvement in the field of pattern recognition. First, we divided entire data set into eight classes depending on the ratio containing the target. Then, experiments were conducted to classify the eight classes data using proposed CNN structure, and the results were analyzed.