• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.349-354
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• 2020

The preliminary investigation to expand the green space requires a lot of cost and time. In this paper, we solve the problem by measuring the ratio of green space in a specific region through a convolutional neural network based the green space classification using Google Earth images. First, the proposed method collects various region images in Google Earth and learns them by using the convolutional neural network. The proposed method divides the image recursively to measure the green space ratio of the specific region, and it determines whether the divided image is green space using a trained convolutional neural network model, and then the green space ratio is calculated using the regions determined as the green space. Experimental results show that the proposed method shows high performance in measuring green space ratios in various regions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.518-524
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• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.414-423
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• 2020

In this paper, we propose a Dilated Convolution Gate Linear Unit (DCGLU) to mitigate the lack of sparsity and small receptive field problems caused by the segmentation map extraction process in sound event detection with weak labels. In the advent of deep learning framework, segmentation map extraction approaches have shown improved performance in noisy environments. However, these methods are forced to maintain the size of the feature map to extract the segmentation map as the model would be constructed without a pooling operation. As a result, the performance of these methods is deteriorated with a lack of sparsity and a small receptive field. To mitigate these problems, we utilize GLU to control the flow of information and Dilated Convolutional Neural Networks (DCNNs) to increase the receptive field without additional learning parameters. For the performance evaluation, we employ a URBAN-SED and self-organized bird sound dataset. The relevant experiments show that our proposed DCGLU model outperforms over other baselines. In particular, our method is shown to exhibit robustness against nature sound noises with three Signal to Noise Ratio (SNR) levels (20 dB, 10 dB and 0 dB).

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.749-752
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• 2018

깊은 신경망 모델을 이용한 차량 번호판 검출과 번호판 문자 인식 시스템을 제안한다. 차량 번호판 인식 시스템은 세 가지 종류의 깊은 신경망 모델로 구성된다. 기존의 영상처리 기반의 차량 번호판 검출과 문자 인식을 전부 신경망으로 대체함으로써 영상의 밝기, 회전, 왜곡 등의 변형에 강인한 성능을 얻을 수 있다. 차량 번호판 검출률은 99.3%, 문자 영역 검출률은 99%, 문자 인식률을 98.5%를 얻었다.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.484-494
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• 2018

The uniform motion blur removing algorithm of Cho et al. has the problem that the edge region of the image cannot be restored clearly. We propose the effective algorithm to overcome this problem by using shock filter that reconstructs a blurred step signal into a sharp edge, and convolutional neural network (CNN) that learns by extracting features from the image. Then uniform motion blur kernel is estimated from the latent sharp image to remove blur in the image. The proposed algorithm improved the disadvantages of the conventional algorithm by reconstructing the latent sharp image using shock filter and CNN. Through the experimental results, it was confirmed that the proposed algorithm shows excellent reconstruction performance in objective and subjective image quality than the conventional algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.84-86
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• 2018

요즘 자율주행과 같은 최신 기술의 발전과 더불어 촬영된 영상 장면에 대한 깊이있는 이해가 필요하게 되었다. 특히, 기계학습 기술이 발전하면서 카메라로 찍은 영상에 대한 의미론적 분할 기술에 대한 연구도 활발히 진행되고 있다. FuseNet은 인코더-디코더 구조를 이용하여 장면 내에 있는 객체에 대한 의미론적 분할 기술을 적용할 수 있는 신경망 모델이다. FuseNet은 오직 RGB 입력을 받는 기존의 FCN보다 깊이정보까지 활용하여 RGB 정보를 기반으로 추출한 특징지도와의 요소합 연산을 통해 멀티 모달 구조를 구현했다. 의미론적 분할 연구에서는 객체의 전역 컨텍스트가 고려되는 것이 중요한데, 이를 위해 여러 계층을 깊게 쌓으면 연산량이 많아지는 단점이 있다. 이를 극복하기 위해서 기존의 합성곱 방식을 벗어나 새롭게 제안된 팽창 합성곱 연산(Dilated Convolution)을 이용하면 객체의 수용 영역이 효과적으로 넓어지고 연산량이 적어질 수 있다. 본 논문에서는 컨볼루션 연산의 새로운 방법론적 접근 중 하나인 팽창된 합성곱 연산을 이용해 의미론적 분할 연구에서 새로운 멀티 모달 네트워크의 성능 향상 방법을 적용하여 계층을 더 깊게 쌓지 않더라도 파라미터의 증가 없이 해상도를 유지하면서 네트워크의 전체 성능을 향상할 수 있는 최적화된 방법을 제안한다.