• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.2
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• pp.1-9
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• 1980

A voltage synthesizing channel selection circuit was designed to improve on the conventional vol tape synthesizer which has been memorized each charnel's tuning vol cage itself. In the course of this study, tuning voltage was calculated by channel number entered from 10 keys. Then this circuit has tie function of direct access channel selection and rear display of channel number for the whole range of UHF and VHF, Attention was also given to realize the fine tuning by searching each commended channel, and the sequential selection by using 2keys, and the flash of channel indicator in case of inactive station.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2782-2804
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• 2024

The integration of artificial intelligence technology with medicine has rapidly evolved, with increasing demands for quality of life. However, falls remain a significant risk leading to severe injuries and fatalities, especially among the elderly. Therefore, the development and application of computer vision-based fall detection technologies have become increasingly important. In this paper, firstly, the keypoint detection algorithm ViTPose++ is used to obtain the coordinates of human body keypoints from the camera images. Human skeletal feature maps are generated from this keypoint coordinate information. Meanwhile, human dense feature maps are produced based on the DensePose algorithm. Then, these two types of feature maps are confused as dual-channel inputs for the model. The convolutional gated recurrent unit is introduced to extract the frame-to-frame relevance in the process of falling. To further integrate features across three dimensions (spatio-temporal-channel), a dual-channel fall detection algorithm based on video streams is proposed by combining the Convolutional Block Attention Module (CBAM) with the ConvGRU. Finally, experiments on the public UR Fall Detection Dataset demonstrate that the improved ConvGRU-CBAM achieves an F1 score of 92.86% and an AUC of 95.34%.

• 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.