• Title/Summary/Keyword: Convolutional Network (CNN)

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Convolutional neural network based amphibian sound classification using covariance and modulogram (공분산과 모듈로그램을 이용한 콘볼루션 신경망 기반 양서류 울음소리 구별)

  • Ko, Kyungdeuk;Park, Sangwook;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.37 no.1
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    • pp.60-65
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    • 2018
  • In this paper, a covariance matrix and modulogram are proposed for realizing amphibian sound classification using CNN (Convolutional Neural Network). First of all, a database is established by collecting amphibians sounds including endangered species in natural environment. In order to apply the database to CNN, it is necessary to standardize acoustic signals with different lengths. To standardize the acoustic signals, covariance matrix that gives distribution information and modulogram that contains the information about change over time are extracted and used as input to CNN. The experiment is conducted by varying the number of a convolutional layer and a fully-connected layer. For performance assessment, several conventional methods are considered representing various feature extraction and classification approaches. From the results, it is confirmed that convolutional layer has a greater impact on performance than the fully-connected layer. Also, the performance based on CNN shows attaining the highest recognition rate with 99.07 % among the considered methods.

Comparison of Convolutional Neural Network Models for Image Super Resolution

  • Jian, Chen;Yu, Songhyun;Jeong, Jechang
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2018.06a
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    • pp.63-66
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    • 2018
  • Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

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Facial Expression Classification Using Deep Convolutional Neural Network (깊은 Convolutional Neural Network를 이용한 얼굴표정 분류 기법)

  • Choi, In-kyu;Song, Hyok;Lee, Sangyong;Yoo, Jisang
    • Journal of Broadcast Engineering
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    • v.22 no.2
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    • pp.162-172
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    • 2017
  • In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. To overcome the disadvantages of existing facial expression databases, various databases are used. In the proposed technique, we construct six facial expression data sets such as 'expressionless', 'happiness', 'sadness', 'angry', 'surprise', and 'disgust'. Pre-processing and data augmentation techniques are also applied to improve efficient learning and classification performance. In the existing CNN structure, the optimal CNN structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of fully-connected layer nodes. Experimental results show that the proposed scheme achieves the highest classification performance of 96.88% while it takes the least time to pass through the CNN structure compared to other models.

Implementation of Handwriting Number Recognition using Convolutional Neural Network (콘볼류션 신경망을 이용한 손글씨 숫자 인식 구현)

  • Park, Tae-Ju;Song, Teuk-Seob
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.561-562
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    • 2021
  • CNN (Convolutional Neural Network) is widely used to recognize various images. In this presentation, a single digit handwritten by humans was recognized by applying the CNN technique of deep learning. The deep learning network consists of a convolutional layer, a pooling layer, and a platen layer, and finally, we set an optimization method, learning rate and loss functions.

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Analysis of Evolutionary Optimization Methods for CNN Structures (CNN 구조의 진화 최적화 방식 분석)

  • Seo, Kisung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.6
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    • pp.767-772
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    • 2018
  • Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

Extraction of Protein-Protein Interactions based on Convolutional Neural Network (CNN) (Convolutional Neural Network (CNN) 기반의 단백질 간 상호 작용 추출)

  • Choi, Sung-Pil
    • KIISE Transactions on Computing Practices
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    • v.23 no.3
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    • pp.194-198
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    • 2017
  • In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

Design and Implementation of Automotive Intrusion Detection System Using Ultra-Lightweight Convolutional Neural Network (초경량 Convolutional Neural Network를 이용한 차량용 Intrusion Detection System의 설계 및 구현)

  • Myeongjin Lee;Hyungchul Im;Minseok Choi;Minjae Cha;Seongsoo Lee
    • Journal of IKEEE
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    • v.27 no.4
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    • pp.524-530
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    • 2023
  • This paper proposes an efficient algorithm to detect CAN (Controller Area Network) bus attack based on a lightweight CNN (Convolutional Neural Network), and an IDS(Intrusion Detection System) was designed, implemented, and verified with FPGA. Compared to conventional CNN-based IDS, the proposed IDS detects CAN bus attack on a frame-by-frame basis, enabling accurate and rapid response. Furthermore, the proposed IDS can significantly reduce hardware since it exploits only one convolutional layer, compared to conventional CNN-based IDS. Simulation and implementation results show that the proposed IDS effectively detects various attacks on the CAN bus.

CCTV Object Detection with Background Subtraction and Convolutional Neural Network (배경 차분과 CNN 기반의 CCTV 객체 검출)

  • Kim, Young-Min;Lee, Jiyoung;Yoon, Illo;Han, Taekjin;Kim, Chulyeon
    • KIISE Transactions on Computing Practices
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    • v.24 no.3
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    • pp.151-156
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    • 2018
  • In this paper, a method to classify objects in outdoor CCTV images using Convolutional Neural Network(CNN) and background subtraction is proposed. Object candidates are extracted using background subtraction and they are classified with CNN to detect objects in the image. At the end, computation complexity is highly reduced in comparison to other object detection algorithms. A database is constructed by filming alleys and playgrounds, places where crime occurs mainly. In experiments, different image sizes and experimental settings are tested to construct a best classifier detecting person. And the final classification accuracy became 80% for same camera data and 67.5% for a different camera.

Speech Emotion Recognition Using 2D-CNN with Mel-Frequency Cepstrum Coefficients

  • Eom, Youngsik;Bang, Junseong
    • Journal of information and communication convergence engineering
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    • v.19 no.3
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    • pp.148-154
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    • 2021
  • With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.

Performance Improvement of Object Recognition System in Broadcast Media Using Hierarchical CNN (계층적 CNN을 이용한 방송 매체 내의 객체 인식 시스템 성능향상 방안)

  • Kwon, Myung-Kyu;Yang, Hyo-Sik
    • Journal of Digital Convergence
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    • v.15 no.3
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    • pp.201-209
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    • 2017
  • This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.