• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.185-193
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• 2023

As many different over-the-top (OTT) services become ubiquitous, demands for high-quality content are increasing. However, high dynamic range (HDR) contents, which can provide more realistic scenes, are still insufficient. In this regard, we propose a new HDR video reconstruction technique using multi-exposure low dynamic range (LDR) videos. First, we align a reference and its neighboring frames to compensate for motions between them. In the alignment stage, we perform content-based alignment to improve accuracy, and we also present a high-resolution (HR) module to enhance details. Then, we merge the aligned features to generate a final HDR frame. Experimental results demonstrate that our method outperforms existing methods.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.83-90
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• 2010

Sensor is a key element in various fields of applications such as sensor networks. However, they could not be easily developed because of several factors such as temperature dependence of output characteristics and/or nonlinearity. Calibration of sensor is also needed to solve these problems. Conventional calibration process required a lot of time and expenses. Therefore, it is important to develop sensor systems which can shorten development time and minimize expense. In this paper, we develop CO and $CO_2$ Sensor modules and propose a multiple sensor calibration system to resolve problems of conventional calibration process. A proposed system is composed of sensor module, system board and monitor program. Regression analysis method based on the least mean squares is used for calibration. We introduced the structure of calibration systems and experimental results. Calibration results can be used to confirm the effectiveness of the proposed system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Convergence Security Journal
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• v.22 no.1
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• pp.29-38
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• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.611-617
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• 2019

Purpose: This study applied simulation practice to nursing university students to see if they are related to teamwork capability, perceived interactions. Methods: This study single-group experimental study for 36 students who take a fourth-grade simulation course for nursing college students located in G city and the effects after the practical application. The analysis of the research was done using the SPSS 19.0 program to perform frequency analysis and technical statistical analysis of general characteristics and to identify paired t-test of Teamwork competence and perceived interactions between measurement variables. Results: After the simulation practical application, it was shown that Teamwork competence were improved. Conclusion: The results of this study show that the required teaming capabilities of the clinical site can be verified through simulation practical application, and the detailed design of the module that can develop the Teamwork competence in the future.

• Smart Structures and Systems
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• v.32 no.3
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.166-173
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• 2024

This study explores lip-sync detection, focusing on the synchronization between lip movements and voices in videos. Typically, lip-sync detection techniques involve cropping the facial area of a given video, utilizing the lower half of the cropped box as input for the visual encoder to extract visual features. To enhance the emphasis on the articulatory region of lips for more accurate lip-sync detection, we propose utilizing a pre-trained visual attention-based encoder. The Visual Transformer Pooling (VTP) module is employed as the visual encoder, originally designed for the lip-reading task, predicting the script based solely on visual information without audio. Our experimental results demonstrate that, despite having fewer learning parameters, our proposed method outperforms the latest model, VocaList, on the LRS2 dataset, achieving a lip-sync detection accuracy of 94.5% based on five context frames. Moreover, our approach exhibits an approximately 8% superiority over VocaList in lip-sync detection accuracy, even on an untrained dataset, Acappella.

• Journal of Hydrogen and New Energy
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• v.35 no.3
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• pp.324-335
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• 2024

The container package type sealed water electrolysis production system installs mechanical balance of plant and electrical balance of plant as an integrated unit to enable independent operation within the package module. The auxiliary equipment required to operate the water electrolysis system must be integrated to reduce the installation area and shorten the installation time. At this time, as leak risk factors are placed in a dense space, when a hydrogen gas leak accident occurs, it can have a mutual influence on other adjacent facilities, so it contains various risk factors. In this study, when a gas leak occurs in a container packaged water electrolysis system, possible sources of leakage in the system according to the KS C IEC 60079-10-1:2015 and KGS GC101 standards were identified, and the leak rate and leak characteristics were calculated. did. The hazardous area and its range were calculated according to ventilation and dilution characteristics. In order to optimize ventilation characteristics, design of experiment was used to analyze the influence to evaluate the adequacy of ventilation, and overseas ventilation standards were analyzed and compared. In addition, the optimal ventilation structure and characteristics of the container packaged water electrolysis system were presented according to the results of the experimental design method.

• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.