• 한국컴퓨터그래픽스학회논문지
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• 제24권5호
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• pp.41-48
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• 2018

가상 환경에서 몰입감을 높이고 자유로운 상호작용을 제공하기 위한 가장 자연스러운 방법은 사용자의 손을 이용한 제스처 인터페이스를 제공하는 것이다. 그러나 손 제스처 인식에 관한 기존의 연구들은 특화된 센서나 장비를 요구하거나 낮은 인식률을 보이는 단점이 있다. 본 논문은 손 제스처 입력을 위한 RGB 카메라 이외 별도 센서나 장비 없이 손 제스처 인식이 가능한 3차원 DenseNet 합성곱 신경망 모델을 제안하고 이를 기반으로 한 가상현실 게임을 소개한다. 4개의 정적 손 제스처와 6개의 동적 손 제스처 인터페이스에 대해 실험한 결과 평균 50ms의 속도로 94.2%의 인식률을 보여 가상현실 게임의 실시간 사용자 인터페이스로 사용 가능함을 알 수 있었다. 본 연구의 결과는 게임 뿐 아니라 교육, 의료, 쇼핑 등 다양한 분야에서 손 제스처 인터페이스로 활용될 수 있다.

• Journal of the Korean Wood Science and Technology
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• 제49권5호
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• pp.491-503
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• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• ETRI Journal
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• 제45권4호
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• pp.666-677
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• 2023

In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side. To reduce the computational complexity of CNNs for edge computing, a lightweight algorithm, such as a MobileNet, is proposed. Although MobileNet is lighter than other CNN models, it commonly achieves lower classification accuracy. Hence, to find a balance between complexity and accuracy, additional hyperparameters for adjusting the size of the model have recently been proposed. However, significantly increasing the number of parameters makes models dense and unsuitable for devices with limited computational resources. In this study, we propose a novel MobileNet architecture, in which the number of parameters is adaptively increased according to the importance of feature maps. We show that our proposed network achieves better classification accuracy with fewer parameters than the conventional MobileNet.

• 한국정보통신학회논문지
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• 제19권6호
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• pp.1307-1312
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• 2015

스마트폰의 대중화에 따라 무선 데이터 트래픽이 기하급수적으로 증가하고 있으며, 이러한 데이터 트래픽을 원활히 수용하기 위하여 차세대 이동통신 네트워크에 대한 연구가 활발히 진행 중이다. 특히, 매크로 셀과 소형 셀을 활용하여 공간 재활용성을 높임으로써 네트워크 용량을 획기적으로 개선할 수 있는 이종 이동 통신망이 많은 관심을 끌고 있다. 이종 이동 통신망에서는 매크로 기지국과 소형 기지국 간의 송신전력의 차이로 인하여 부하 불균형과 간섭등의 문제가 발생하며, 이를 해결하기 위하여 cell range expansion (CRE) 기술을 활용한다. 본 논문에서는, 초밀집 이종 이동 통신망 에서 CRE bias (CREB)를 적응적으로 적용하는 새로운 셀 선택 방식을 제안하고 시스템 레벨 시뮬레이션을 통하여 셀 평균 전송률을 분석하고, 기존의 셀 선택 방식과 비교 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2882-2903
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• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.63-66
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• 2015

스마트폰의 대중화에 따라 무선 데이터 트래픽이 기하급수적으로 증가하고 있으며, 이러한 데이터 트래픽을 원활히 수용하기 위하여 차세대 이동통신 네트워크에 대한 연구가 활발히 진행 중이다. 특히, 매크로 셀과 소형 셀을 활용하여 공간 재활용성을 높임으로써 네트워크 용량을 획기적으로 개선할 수 있는 이종 이동 통신망이 많은 관심을 끌고 있다. 이종 이동 통신망에서는 매크로 기지국과 소형 기지국 간의 송신전력의 차이로 인하여 부하 불균형과 간섭 등의 문제가 발생하며, 이를 해결하기 위하여 cell range expansion (CRE) 기술을 활용한다. 본 논문에서는, 초밀집 이종 이동 통신망에서 CRE bias를 적응적으로 적용하는 새로운 셀 선택 방식을 제안하고 시스템 레벨 시뮬레이션을 통하여 셀 평균 전송률을 분석하고, 기존의 셀 선택 방식과 비교 한다.

• Imaging Science in Dentistry
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• 제52권3호
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.464-466
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• 2018

본 논문은 복수의 안테나를 보유한 기지국이 잡음제한 된(noise-limited) 셀룰러 환경에서 복수의 단일 안테나 사용자와 동시에 통신 하는 시스템을 고려한다. 각 사용자는 제한된 피드백을 통해 송신부에 자신의 채널 상태 정보를 공유하고 송신부는 공유된 채널 정보를 바탕으로 다중사용자 기반의 공간다중화를 진행한다. 채널 정보가 제한되어있기 때문에 시스템의 하향링크 데이터 율은 채널 정보의 피드백 양에 비례한다. 하지만 피드백 양이 증가하면 피드백에 사용되는 상향링크의 자원소모가 동시에 증가하기 때문에, 실제의 통신시스템은 하향링크 통신량과 상향링크 제어정보량 사이의 균형을 적절히 고려할 필요가 있다. 본 논문에서는 이러한 측면을 고려하여 정의된 순(net) 주파수 효율을 사용하며 이를 최대화 하는 최적의 피드백 양을 분석한다. 특히, 단말의 수신 파워가 수신 노이즈에 비해 낮을 때의 시스템의 최적 피드백 양을 주로 연구한다.