• Journal of information and communication convergence engineering
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• 제16권3호
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.31-41
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• 2018

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1387-1395
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• 2018

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

• 한국멀티미디어학회논문지
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• 제25권4호
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• pp.568-574
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• 2022

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.516-518
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• 2020

통계청에서는 지역별고용조사, 인구총조사 등 다양한 조사를 실시하고 있다. 이러한 조사에서는 응답자의 사업체명, 사업체가 주로 하는 일, 응딥자가 한 일, 부서 및 직책 정보 등을 조사해서 조사되어진 자료를 토대로 한국 표준 산업분류 형태로 코드를 부여해 주고 있다. 각 조사에서는 자연어 형태로 입력을 받아서 자료처리 기간에 코딩작업을 하는 조사가 있고 조사원이 입력을 하면서 자동코딩시스템을 이용해서 산업분류 코드를 입력하는 방식도 있다. 본 연구에서는 전자의 방법을 자동화하는 것에 초점을 두었다. 딥러닝 알고리즘을 이용해서 기존에 코드부여가 완료된 자료를 가지고 실험을 해본 결과 조사된 모든 항목을 사용했을 때에는 CNN이 81.36%로 가장 좋은 성능을 보였고, 항목을 2가지로 (사업체가 주로 하는 일/응딥자가 한 일) 줄였을 경우 전체적으로 더 좋은 성능을 보였다. 그 중에 CNN-LSTM이 85.91%로 가장 좋은 성능을 보였다.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.159-172
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• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• Journal of Platform Technology
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• 제12권1호
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• pp.3-11
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• 2024

Deaf people who use sign language as their first language sometimes have difficulty communicating because they do not know spoken Korean. Deaf people are also members of society, so we must support to create a society where everyone can live together. In this paper, we present a method to increase the recognition rate of Korean sign language using a CNN model. When the original image was used as input to the CNN model, the accuracy was 0.96, and when the image corresponding to the skin area in the YCbCr color space was used as input, the accuracy was 0.72. It was confirmed that inserting the original image itself would lead to better results. In other studies, the accuracy of the combined Conv1d and LSTM model was 0.92, and the accuracy of the AlexNet model was 0.92. The CNN model proposed in this paper is 0.96 and is proven to be helpful in recognizing Korean sign language.

• 방송공학회논문지
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• 제23권3호
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• pp.369-382
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• 2018

본 논문은 딥 컨볼루션 뉴럴 네트워크(CNN)를 이용하여 행 별로 서로 다른 노출로 촬영된 단일 영상을 HDR 영상으로 변환하는 기법을 제안한다. 제안하는 알고리즘은 먼저 입력 영상에서 저조도 또는 포화로 인해 발생하는 정보 손실 영역을 CNN을 이용하여 복원하여 휘도맵을 생성한다. 또한, CNN 학습 과정에서 인간의 시각 인지 특성을 고려할 수 있는 손실 함수를 제안한다. 마지막으로 복원된 휘도맵에 디모자이킹 필터를 적용하여 최종 HDR 영상을 획득한다. 컴퓨터 모의실험을 통해 제안하는 알고리즘이 기존의 기법에 비해서 높은 품질의 HDR 영상을 취득하는 것을 확인한다.

• 한국컴퓨터정보학회논문지
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• 제26권10호
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• pp.19-26
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• 2021

딥러닝 기반 악성코드 탐지 및 분류모델의 성능은 특성집합을 어떻게 구성하느냐에 따라 크게 좌우된다. 본 논문에서는 CNN 기반의 안드로이드 악성코드 탐지 시 탐지성능을 극대화할 수 있는 최적의 특성집합(feature set)을 선정하는 방법을 제안한다. 특성집합에 포함될 특성은 기계학습 및 딥러닝에서 특성추출을 위해 널리 사용되는 Chi-Square test 알고리즘을 사용하여 선정하였다. CICANDMAL2017 데이터세트를 대상으로 선정된 36개의 특성을 이용하여 CNN 모델을 학습시킨 후 악성코드 탐지성능을 측정한 결과 이진분류에서는 99.99%, 다중분류에서는 98.55%의 Accuracy를 달성하였다.

• Smart Structures and Systems
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• 제23권5호
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.