• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.397-399
• /
• 2022

Digit recognition is one of the applications of deep learning, which appears in many fields. CNN network enables us to recognize handwritten digits. Also, It can process various types of data. As we stack more layers in CNN network, we expect more performance improvements. In this paper, we added a convolution layer. as a result, we achieved an accuracy improvement from 76.96% to 98.87%, which is a nearly 21.81% increase.

• Journal of Broadcast Engineering
• /
• v.23 no.1
• /
• pp.3-10
• /
• 2018

Depth map has been utilized in a varity of fields. Recently research on generating depth map by artificial neural network (ANN) has gained much interest. This paper validates the feasibility of implementing the ready-made depth map generation by convolutional neural network (CNN). First, for a given image, a depth map is generated by the weighted average of a saliency map as well as a motion history image. Then CNN network is trained by test images and depth maps. The objective and subjective experiments are performed on the CNN and showed that the CNN can replace the ready-made depth generation method.

• Journal of Bio-Environment Control
• /
• v.29 no.4
• /
• pp.381-387
• /
• 2020

Various studies have been attempted to estimate and measure the fresh weight of crops. However, no studies have used raw images of sweet peppers to estimate fresh weight. Recently, image processing research using convolution neural network (CNN) that can use raw data is increasing. In this study, the crop fresh weight was estimated by using the images of sweet peppers as inputs of CNN. The experiment was performed in a greenhouse growing sweet pepper (Capsicum annuum L.). The fresh weight, the output of the CNN, was regressed based on the data collected through destructive investigation. The highest coefficient of determination (R²) of the trained CNN was 0.95. The estimated fresh weight showed a very similar trend to the actual measured value.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.865-867
• /
• 2017

본 논문에서는 데이터스트림 환경에서 개념 변화를 탐지하기 위해 합성곱 신경망(CNN)을 사용하는 방법을 제시한다. 데이터스트림 환경에서 입력될 수 있는 데이터를 패턴화하여 신경망 모델에 학습시키고, 패턴화한 데이터를 학습시킨 신경망 모델을 이용하여 스트림 환경에서 개념 변화를 검출 가능함을 보인다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.11a
• /
• pp.76-79
• /
• 2019

합성곱 신경망의 성능이 증가하면서 다양한 영상 처리 문제를 해결하기 위해 합성곱 신경망을 적용한 시도들이 증가하고 있다. 고해상도 복원 문제도 그 중 하나였으며, 보다 높은 성능을 얻기 위해 주로 신경망의 깊이를 깊게 하는 시도들이 있었다. 본 논문에서는 고해상도 복원 작업을 위한 합성곱 신경망의 성능 향상을 위해 깊이를 증가시키는 접근법이 아닌 수용영역을 확장시키는 접근법을 시도하였다. 논문에서 제시한 모델은 신경망 내부에 두 개의 브랜치를 두어, 하나의 브랜치는 Dilated Convolution 을 이용해 수용영역을 확장하는데 사용되며, 다른 하나는 이 브랜치를 통해 나온 feature 를 가공하는데 사용된다. 기본 모델은 EDSR 을 사용하였으며, 최종적으로 4.79M 의 파라미터로 평균 32.46dB 의 PSNR 을 보여주었다. 하지만 모델의 구조가 복잡하여 깊이를 늘이는 접근법을 적용하기 어렵다는 한계점이 있다.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.5
• /
• pp.568-573
• /
• 2019

In the diagnosis of pulmonary diseases, auscultation technique is simpler than the other methods, and lung sounds can be used for predicting the types of pulmonary diseases as well as identifying patients with pulmonary diseases. Therefore, in this paper, we identify patients with pulmonary diseases and classify lung sounds according to their sound characteristics using various convolutional neural networks, and compare the classification performance of each neural network method. First, lung sounds over affected areas of the chest with pulmonary diseases are collected by using a single-channel lung sound recording device, and spectral features are extracted from the collected sounds in time domain and applied to each neural network. As classification methods, we use general, parallel, and residual convolutional neural network, and compare lung sound classification performance of each neural network through experiments.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.3
• /
• pp.521-526
• /
• 2020

This paper presents an improved HOLP neural network that adds 25 average values to a typical HOLP neural network using 25 feature vector values as input values by applying high-order local autocorrelation function, which is excellent for extracting immutable feature values of iris images. Compared with deep learning structures with different types, we compared the recognition rate of iris recognition using Back-Propagation neural network, which shows excellent performance in voice and image field, and synthetic product neural network that integrates feature extractor and classifier.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.75-80
• /
• 2021

Artificial intelligence is becoming an important part of our lives providing incredible benefits. In this respect, facial expression recognition has been one of the hot topics among computer vision researchers in recent decades. Classifying small dataset of low resolution images requires the development of a new small scale deep CNN model. To do this, we propose a method suitable for small datasets. Compared to the traditional deep CNN models, this model uses only a fraction of the memory in terms of total learnable weights, but it shows very similar results for the FER2013 and FERPlus datasets.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.6
• /
• pp.615-621
• /
• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Journal of Digital Convergence
• /
• v.17 no.1
• /
• pp.123-130
• /
• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.