• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.178-188
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• 2020

This paper proposes a lane recognition CNN network using split-attention network as a backbone to extract feature. Split-attention is a method of assigning weight to each channel of a feature map in the CNN feature extraction process; it can reliably extract the features of an image during the rapidly changing driving environment of a vehicle. The proposed deep neural networks in this paper were trained and evaluated using the Tusimple data set. The change in performance according to the number of layers of the backbone network was compared and analyzed. A result comparable to the latest research was obtained with an accuracy of up to 96.26, and FN showed the best result. Therefore, even in the driving environment of an actual vehicle, stable lane recognition is possible without misrecognition using the model proposed in this study.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.18-25
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• 2020

In the fifth generation (5G) and beyond 5G (B5G) mobile communication, non-orthogonal multiple access (NOMA) has attracted great attention due to higher spectral efficiency and massive connectivity. We investigate the impacts of the channel estimation errors on the bit-error rate (BER) of NOMA, especially with the single-user decoding (SUD) receiver, which does not perform successive interference cancellation (SIC), in contrast to the conventional SIC NOMA scheme. First, an analytical expression of the BER for SUD NOMA with channel estimation errors is derived. Then, it is demonstrated that the BER performance degrades severely up to the power allocation less than about 20%. Additionally, we show that for the fixed power allocation of 10% in such power allocation range, the signal-to-noise (SNR) loss owing to channel estimation errors is about 5 dB. As a consequence, the channel estimation error should be considered for the design of the SUD NOMA scheme.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.11
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• pp.1751-1758
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• 2001

Among severe plastic deformation processes, ECAP has drawn much attention due to its advantages including ultra-fine grain size material production. In this paper, ECAP process with pure -Zirconium is investigated due to its applicability to nuclear reactors. The finite element method is employed to investigate the deformation behavior of materials during ECAP process. In particular, effects of process parameters such as die deformation and channel angles on the material behaviors have been investigated. Experimental studies have also been performed to verify the numerical results.

• Asia pacific journal of information systems
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• v.31 no.3
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• pp.277-295
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• 2021

This paper examines the features that make a YouTube channel attractive to users. Considering that drawing users' attention is challenging on this platform, where voluminous amounts of videos are available, it is crucial to identify the factors that make users intend to subscribe to a YouTube channel. In this study, we used an online survey to collect data from 1125 respondents and an SEM model using Smart PLS 3.2.8 to analyze it. The results show that integrity and familiarity with a YouTube channel are positively correlated with trust in its creator, which leads to subscribing to the YouTube channel; value and accuracy also positively affect intention to subscribe to a YouTube channel via trust in content. This study enriches the field of research about trust in the creator and trust in content.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.33-43
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• 2022

In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.245-253
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• 2010

In this paper, we derive the performance analysis of the coded cooperation over a Rician fading channel. A new scheme called coded cooperation was suggested by using user cooperation and channel codes simultaneously. In previous works, it was verified that the coded cooperation schemes have better performance than other relay schemes in a Rayleigh fading channel. However, the high speed short range indoor wireless communication system has recently attracted research attention and its channel with very high carrier frequency(60GHz) can be typically modeled as a Rician fading channel. We derive analytical outage probabilities and bit error probabilities of the coded cooperation over the Rician fading channel and prove it to have full diversity order.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.1-9
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• 2019

The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.295-296
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• 2022

작물을 경작 해충과 질병은 오랫동안 주요 관심사였다. 농업에서 병해충을 탐지하기 위해 전통적인 방법을 사용하는 것은 더 이상 높은 효율성을 제공하지 않는다. 오늘날 과학과 인공 지능의 폭발적인 발달로 인해 농업분야의 연구원들은 병해충을 탐지하기 위해 딥 러닝을 적용하고 있다. 최근에 다양한 분야의 문제들을 해결하기 위해 수많은 모델들이 발표되었지만, 많은 병해충 진단 딥러닝을 사용한 방법들은 하드웨어 리소스를 낭비하고 실제 농장에서 사용하기 어렵다. 따라서 본 논문에서는 작물의 병해충을 분류하기 위해 Select Kernel Attention(SK Attention)을 Channel Attention Module 로 변경하여 Decoupling-and-Attention network (DANet)을 하드웨어 리소스 사용을 최소화한다.