• 한국BIM학회 논문집
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• 제9권1호
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• pp.42-51
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• 2019

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.173-180
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• 2018

This paper proposes a method to improve the performance of face recognition via super-resolution method using sparse representation and deep learning from low-resolution facial images. Recently, there have been many researches on ultra-high-resolution images using deep learning techniques, but studies are still under way in real-time face recognition. In this paper, we combine the sparse representation and deep learning to generate super-resolution images to improve the performance of face recognition. We have also improved the processing speed by designing in parallel structure when applying sparse representation. Finally, experimental results show that the proposed method is superior to conventional methods on various images.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.147-154
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• 2019

In this paper, we conducted a study that utilizes deep learning to calculate appropriate physical exercise information when basic human factors such as sex, age, height, and weight of users come in. To apply deep learning, a method was applied to calculate the amount of fat needed to calculate the amount of one repetition maximum by utilizing the structure of the basic Deep Neural Network. By applying Accuracy improvement methods such as Relu, Weight initialization, and Dropout to existing deep learning structures, we have improved Accuracy to derive a lean body weight that is closer to actual results. In addition, the results were derived by applying a formula for calculating the one repetition maximum load on upper and lower body movements for use in actual physical exercise. If studies continue, such as the way they are applied in this paper, they will be able to suggest effective physical exercise options for different conditions as well as conditions for users.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• 대한토목학회논문집
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• 제40권2호
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• pp.239-245
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• 2020

최근 BIM (Building Information Modeling)이 건설 산업계에서 폭넓게 활용되고 있다. 하지만 과거에 시공이 된 구조물에 경우 대부분 BIM이 구축되어 있지 않다. BIM이 구축되지 않은 구조물의 경우, 카메라로부터 얻은 2D 이미지에 SfM (Structure from Motion) 기법을 활용하면 3D 모델의 점군 데이터(Point cloud)를 생성하고 BIM을 구축할 수 있다. 하지만 이렇게 생성된 점군 데이터는 의미론적 정보가 포함되어 있지 않기 때문에, 수작업으로 구조물의 어떤 요소인지 분류해 주어야 한다. 따라서 본 연구에서는 구조물 구성요소를 분류하는 과정을 자동화하기 위하여 딥러닝을 적용하였다. 딥러닝 네트워크 구축에는 CNN (Convolutional Neural Network) 구조의 Inception-ResNet-v2를 사용하였고, 전이학습을 통하여 교량 구조물의 구성요소를 학습하였다. 개발된 시스템을 검증하기 위하여 수집한 데이터를 이용하여 구성요소를 분류한 결과, 교량의 구성요소를 96.13 %의 정확도로 분류할 수 있었다.

• 한국컴퓨터그래픽스학회논문지
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• 제27권5호
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• pp.73-79
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• 2021

최근 컴퓨터 애니메이션 분야에서는 기존의 유한상태기계나 그래프 기반의 방식들에서 벗어나 딥러닝을 이용한 동작 생성 방식이 많이 연구되고있다. 동작 학습에 요구되는 네트워크의 표현력은 학습해야하는 동작의 단순한 길이보다는 그 안에 포함된 동작의 다양성에 더 큰 영향을 받는다. 본 연구는 이처럼 학습해야하는 동작의 종류가 다양한 경우에 효율적인 네트워크 구조를 찾는것을 목표로 한다. 기본적인 fully-connected 구조, 여러개의 fully-connected 레이어를 병렬적으로 사용하는 mixture of experts구조, seq2seq처리에 널리 사용되는 순환신경망(RNN), 그리고 최근 시퀀스 형태의 데이터 처리를 위해 자연어 처리 분야에서 사용되고있는 transformer구조의 네트워크들을 각각 학습하고 비교한다.

• 한국농공학회논문집
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• 제61권3호
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• 한국멀티미디어학회논문지
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• 제23권9호
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• 한국구조물진단유지관리공학회 논문집
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• 제23권7호
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• pp.80-86
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• 2019

컴퓨터 성능 향상으로 다양한 분야에서 딥러닝을 활용한 연구가 활발히 진행되고 있으며 최근에는 구조물 안전성 평가 연구에도 그 적용이 이루어지고 있다. 특히 터빈의 내부 블레이드는 분리가 쉽지 않고 어두운 주변 환경으로 인해 블레이드의 표면 결함 검출은 전문 인력의 경험에 의존하고 있으며, 점검시간도 상당히 소요되고 있는 실정이다. 따라서, 본 연구에서는 딥러닝 기술을 적용하여 터빈 구조의 부재 중 하나인 내부 블레이드에 발생하는 결함을 검출할 수 있는 효율적인 방법을 제시하였다. Faster R-CNN 인공신경망 기법을 활용하여 결함의 이미지 데이터를 학습하였고 부족한 이미지는 필터링과 Image Data Generator를 이용하여 데이터를 확장하였다. 그 결과 블레이드의 결함을 학습한 딥러닝 모델은 평균적으로 약 96.1%의 정확도와 재현율은 95.3%, 정밀도는 96%의 성능을 보였다. 재현율을 통해 제시된 딥러닝 모델이 결함을 탐지하지 못하는 경우는 4.7% 로 나타났다. 재현율의 성능은 여러 환경의 많은 결함 이미지 데이터를 수집하고 확장하여 딥러닝 학습에 적용함으로써 더욱 향상되리라 판단된다. 이러한 실제 블레이드의 결함 이미지 데이터 확보와 학습을 통해 향후 터빈엔진 정비에 적용 가능한 결함 검출 시스템으로 발전할 수 있을 것이다.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.