• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• Journal of Biomedical Engineering Research
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• v.40 no.3
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• pp.105-115
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• 2019

Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.319-324
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• 2019

A depth-encoding detector module with silicon photomultipliers(SiPMs) using two layers of scintillation crystal array was designed, and the position measurement capability was verified using DETECT2000. The depth of interaction of the crystal pixels with the gamma rays was tracked through the image acquired with the combination of surface treatment of the crystal pixels and reflectors. The bottom layer was treated as a reflector except for the optically coupled surfaces, and the crystals of top layer were optically coupled each other except for the outer surfaces so that the light sharing was made easier than the bottom layer. Flood images were obtained through the combination of specular reflectors and random reflectors, grounded and polished surfaces of crystal pixels, and the positions at which layer images were generated were measured and analyzed. The images were reconstructed using the Anger algorithm, whose the SiPM signals were reduced as the 16-channels to 4-channels. In the combination of the grounded surface and all reflectors, the depth positions were discriminated into two layers, whereas it was impossible to separate the two layers in the all polished surface combinations. Therefore, using the combination of grounded surface crystal pixels and reflectors could improve the spatial resolution at the outside of the field of view by measuring the depth position in preclinical positron emission tomography.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.661-666
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• 2019

Diverging collimators is used to obtain reduced images of an object, or to detect a wide filed-of-view (FOV) using a small gamma camera. In the gamma camera using the diverging collimators, the block scintillator, and the pixel scintillator array, gamma rays are obliquely incident on the scintillator surface when the source is located the periphery of the FOV. Therefore, the spatial resolution is reduced because it is obliquely detected in depth direction. In this study, we designed a novel system to improve the spatial resolution in the periphery of the FOV. Using a tapered crystal array to configure the scintillation pixels to coincide with the angle of the collimator's hole allows imaging to one scintillation pixel location, even if events occur to different depths. That is, even if is detected at various points in the diagonal direction, the gamma rays interact with one crystal pixel, so resolution does not degrade. The resolution of the block scintillator and the tapered crystal array was compared and evaluated through Geant4 Application for Tomographic Emission (GATE) simulation. The spatial resolution of the obtained image was 4.05 mm in the block scintillator and 2.97 mm in the tapered crystal array. There was a 26.67% spatial resolution improvement in the tapered crystal array compared to the block scintillation.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.463-471
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• 2019

This paper proposes a method for reducing the complexity of LIC (Local Illuminance Compensation) for bi-directional inter prediction. The LIC performs local illumination compensation using neighboring reconstruction samples of the current block and the reference block to improve the accuracy of the inter prediction. Since the weight and offset required for local illumination compensation are calculated at both sides of the encoder and decoder using the reconstructed samples, there is an advantage that the coding efficiency is improved without signaling any information. Since the weight and the offset are obtained in the encoding prediction step and the decoding step, encoder and decoder complexity are increased. This paper proposes two methods for low complexity LIC. The first method is a method of applying illumination compensation with offset only in bi-directional prediction, and the second is a method of applying LIC after weighted average step of reference block obtained by bidirectional prediction. To evaluate the performance of the proposed method, BD-rate is compared with BMS-2.0.1 using B, C, and D classes of MPEG standard experimental image under RA (Random Access) condition. Experimental results show that the proposed method reduces the average of 0.29%, 0.23%, 0.04% for Y, U, and V in terms of BD-rate performance compared to BMS-2.0.1 and encoding/decoding time is almost same. Although the BD-rate was lost, the calculation complexity of the LIC was greatly reduced as the multiplication operation was removed and the addition operation was halved in the LIC parameter derivation process.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.161-167
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• 2019

In this paper, the performance verification of an around-view sonar and an excavation depth measuring sonar applicable to track-based ROVs (Remotely Operated underwater Vehicles) for heavy duty work is studied. For the performance verification, an experiment is carried out in a water tank and at sea by attaching the around-view sonar and the excavation depth measuring sonar for a heavy work ROV. In the case of the around-view sonar, image sonars are mounted on ROV in four directions (front, back, left and right) and in the case of the excavation depth measuring sonar, the same kind of MBES (Multi Beam Echo Sounder) is mounted on the front of the ROV. The result of an operation test of the ROV equipped with these sonars shows that the sonar systems are rarely affected by high turbidity due to sedimentation during the operation. In the case of the around-view sonar, it is possible to see rock formation, gravel and sandbank 30 m ahead of the ROV. It is confirmed that the excavation depth can be measured after the ROV has performed the excavation. This experiment demonstrates that the ROV can improve the efficiency of the work by utilizing the around-view sonar and the excavation depth measuring sonar.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.500-507
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• 2021

The virtual reality industry has an opportunity to take another leap forward with the surge in demand for non-face-to-face content and interest in the metaverse after Covid-19. Therefore, in order to popularize virtual reality content along with this trend, high-quality content production and storytelling research suitable for the characteristics of virtual reality should be continuously conducted. In order for content to which virtual reality characteristics are applied to be effectively produced through user feedback, a quantitative index that can evaluate the content is needed. In this study, the process of viewing virtual reality contents was analyzed and head movement was set as a quantitative indicator. Afterwards, the experimenter watched five animations and analyzed the correlation between recorded head movement information and immersion. As a result of the analysis, high immersion was shown when the head movement speed was relatively slow, and it was found that the head movement speed can be used significantly as an index indicating the degree of content immersion. The result derived in this way can be used as a quantitative indicator that can verify the validity of the storytelling method applied after the prototype is produced when the creator creates virtual reality content. This method can improve the quality of content by quickly identifying the problems of the proposed storytelling method and suggesting a better method. This study aims to contribute to the production of high-quality virtual reality content and the popularization of virtual reality content as a basic research to analyze immersion based on the quantitative indicator of head movement speed.

• The Korean Journal of Franchise Management
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• v.10 no.2
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• pp.49-58
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• 2019

Purpose - As the growth potential of the cultural contents industry steadily grows, the cinemas industry is growing fast especially due to popularity and commerciality. The recent in the cinemas industry is evolving on multiplex cinemas that combine cultural facilities such as theaters, restaurants, and shopping centers. Due to the rapid growth of multiplex cinemas, many researchers have studied the characteristics of visiting customers in Korea. Among them, selection attribute of multiplex cinemas, including the service quality and physical environment, is important because it may examines the cause of customer 's behavior. Thus, this research focuses on the effects of the selection attributes of the multiplex cinemas on customer satisfaction and their loyalty. This research suggests the guidelines for how cinemas should manage their customers and build their customers satisfaction and loyalty that improve business performance. Research design, data, and methodology - This study tests the structural relationship between selection attributes of multiplex cinemas, customer satisfaction, and loyalty. selection attributes of multiplex cinemas divide into five sub-dimensions such as movies facility quality, services, cleanliness, accessibility, and snack bars quality. In order to examine the purposes of this research, research model and hypotheses were developed. All constructs were measured with multiple items developed and tested in the previous studies. The data were collected from 100 students in their 10-20s and were analyzed using SPSS 22.0, SmartPLS 3.0 and fsQCA program. Result - The findings of this research are as follows. First, all selection attributes except cleanliness have significant positive impacts on customer satisfaction and loyalty. Second, customer satisfaction has significant positive impact on loyalty. Third, as a result of fsQCA, high satisfaction and high accessibility were the necessary conditions on loyalty. Fourth, the necessary conditions for male and female groups were different. Conclusions - The implications of this study are as follows. Overall, multiplex cinemas should manage selection attributes basically regardless of the type of theater. Especially, cleanliness was not significant, but the customer probably consider it an essential and basic factors. Also, they are able to manage the selection attributes differently depending on the type of gender. For the male customers, it is effective that centralized strategy and for female customers, it is effective that emphasized the multiplex cinemas image.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.115-122
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• 2022

Recently, the importance of CPR training for the layperson has been emphasized to improve the survival rate of out-of-hospital cardiac arrest patients. An accurate and realistic training strategy is required for the CPR training effect for laypersons. In this study, we develop an extended reality (XR) based CPR training system and evaluate its usability. The XR based CPR training system consisted of three applications. First, a 3D heart anatomy image registered to the manikin is transmitted to the smart glasses to guide the chest compression point. The second application provides visual and auditory information about the CPR process through smart glasses. At the same time, the smartwatch sends a vibration notification to guide the compression rate. The 'Add-on-kit' is a device that detects the depth and speed of chest compression via sensors installed on the manikin and sends immediate feedback to the smartphone. One hundred laypersons who participated in this study agreed that the XR based CPR training system has realism and effectiveness. XR based registration technology will contribute to improving the efficiency of CPR training by enhancing realism, immersion, and self-directed learning.