• 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 electronic communication sciences
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• v.18 no.4
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• pp.723-730
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• 2023

Due to the recent development of battery technology, various types of means of transportation such as electric kickboards, Segways, and electric bicycles have emerged, which can be defined as Personal Mobility. In this paper, as the incidence of safety accidents increases due to the increase in the number of users of Personal Mobility, safety helmet devices that strengthen safety capabilities and peripheral recognition functions were studied. In order for the helmet to send a safety signal, Arduino was used as a base to set the value of the sensor according to changes in distance and angle using the ultrasonic sensor to minimize errors and ensure smooth recognition. In addition, a gyro sensor was used to turn on the direction indicator according to each slope. Using a CDS sensor, the LED is designed to turn on when it goes below 150 lux at night. Finally, it is possible to check whether a helmet is worn within 5cm, and when driving at an average speed, the direction indicator light is turned on at 10 degrees, and the LED is turned on at less than 150 lux.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.881-895
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• 2023

Modern image inpainting techniques based on deep learning have achieved remarkable performance, and more and more people are working on repairing more complex and larger missing areas, although this is still challenging, especially for facial image inpainting. For a face image with a huge missing area, there are very few valid pixels available; however, people have an ability to imagine the complete picture in their mind according to their subjective will. It is important to simulate this capability while maintaining the identity features of the face as much as possible. To achieve this goal, we propose a three-stage network model, which we refer to as the identity and structure feature refinement network (ISFRNet). ISFRNet is based on 1) a pre-trained pSp-styleGAN model that generates an extremely realistic face image with rich structural features; 2) a shallow structured network with a small receptive field; and 3) a modified U-net with two encoders and a decoder, which has a large receptive field. We choose structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), L1 Loss and learned perceptual image patch similarity (LPIPS) to evaluate our model. When the missing region is 20%-40%, the above four metric scores of our model are 28.12, 0.942, 0.015 and 0.090, respectively. When the lost area is between 40% and 60%, the metric scores are 23.31, 0.840, 0.053 and 0.177, respectively. Our inpainting network not only guarantees excellent face identity feature recovery but also exhibits state-of-the-art performance compared to other multi-stage refinement models.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4431-4440
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• 2022

An advanced nuclear reactor based on molten salts including a molten salt reactor and pyroprocessing needs a sensitive monitoring system suitable for operation in harsh environments with limited access. Multi-element detection is challenging with the conventional technologies that are compatible with the in-situ operation; hence laser-induced breakdown spectroscopy (LIBS) has been investigated as a potential alternative. However, limited precision is a chronic problem with LIBS. We increased the precision of LIBS under high temperature by protecting optics using a gas protective layer and correcting for shotto-shot variance and lens-to-sample distance using a laser-induced acoustic signal. This study investigates cerium as a surrogate for uranium and corrosion products for simulating corrosive environments in LiCl-KCl. While the un-corrected limit of detection (LOD) range is 425-513 ppm, the acoustic-corrected LOD range is 360-397 ppm. The typical cerium concentrations in pyroprocessing are about two orders of magnitude higher than the LOD found in this study. A LIBS monitoring system that adopts these methods could have a significant impact on the ability to monitor and provide early detection of the transient behavior of salt composition in advanced molten salt-based nuclear reactors.

• Journal of the Korean Geotechnical Society
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• v.24 no.4
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• pp.115-123
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• 2008

The dispersive phase velocity of a wave propagating through multilayered systems such as a soil site is an important parameter and carries valuable information in non-destructive site characterization tests. The dispersive phase velocity of a wave can be determined using the phase spectrum, which is easily evaluated through the cross power spectrum. However, the phase spectrum determined using the cross power spectrum is easily distorted by background noise which always exists in the field. This causes distortion of measured signal and difficulties in the determination of the dispersive phase velocities. In this paper, a new method to evaluate the phase spectrum using the harmonic wavelet transform is proposed and the phase spectrum by the proposed method is applied to the determination of dispersion curve. The proposed method can successfully remove background noise effects. To evaluate the validity of the proposed method, numerical simulations of multi-layered systems were performed. Phase spectrums and dispersion curves determined by the proposed method were found to be in good agreement with the actual phase spectrums and dispersion curves biased by heavy background noise. The comparison manifests the proposed method to be a very useful tool to overcome noise effects.

• The Journal of Korean Medicine
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• v.43 no.4
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• pp.20-32
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• 2022

Objectives: Kyungok-go (KOG) is a traditional multi-herbal medicine commonly used for enforcing weakened immunity for long time. Recently, there are several reports that KOG has anti-inflammatory and immuno-stimulatory activities in many experimental models. However, the protective effects of KOG on neuronal inflammation are still undiscovered. Thus, we investigated the neuro-protective activity of KOG on lipopolysaccharide (LPS)-stimulated mouse microglia cells. To find out KOG's anti-neuroinflammatory effects on microglial cells, we examined the production of nitrite using griess assay, and mRNA expressions of inducible nitric oxide synthase (iNOS), cyclooxygenase (COX)-2 and interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α using real time RT-PCR. In addition, to examine the regulating mechanisms of KOG, we investigated the protein expression of mitogen-activated protein kinases (MAPKs) and Iκ-Bα by western blot. KOG inhibited the elevation of nitrite, iNOS and COX-2 on LPS-stimulated BV2 cells. Also, KOG significantly inhibited the pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α on LPS-stimulated BV2 microglial cells. Moreover, KOG inhibited the activation of c-Jun N-terminal kinase (JNK), P38 and degradation of Iκ-Bα but not the activation of extracellular signal regulated kinase (ERK) on LPS-stimulated BV2 microglial cells. These results showed KOG has the anti-inflammatory effects through the inhibition on nitrite, iNOS, COX-2, IL-1β, IL-6, and TNF-α via the deactivation of JNK, p38 and nuclear factor (NF)-κB on LPS-stimulated BV2 microglial cells. Thereby, KOG could offer the new and promising treatment for neurodegenerative disease related to neuroinflammation.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.68.1-68.1
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• 2019

Turbulence plays a crucial role in controlling star formation as it produces density fluctuation as well as non-thermal pressure against gravity. Therefore, turbulence controls the mode and tempo of star formation. However, despite a plenty of previous studies, the properties of turbulence remain poorly understood. As part of the Taeduk Radio Astronomy Observatory (TRAO) Key Science Program (KSP), "mapping Turbulent properties In star-forming MolEcular clouds down to the Sonic scale (TIMES; PI: Jeong-Eun Lee)", we mapped the Orion A and the Ophiuchus clouds, in three sets of lines (13CO 1-0/C18O 1-0, HCN 1-0/HCO+ 1-0, and CS 2-1/N2H+ 1-0) with a high-velocity resolution (~0.1 km/s) using the TRAO 14-m telescope. The mean Trms for the observed maps are less than 0.25 K, and all these maps show uniform Trms values throughout the observed area. These homogeneous and high signal-to-noise ratio data provide the best chance to probe the nature of turbulence in two different star-forming clouds, the Orion A and Ophiuchus clouds. We present comparisons between the line intensities of different molecular tracers as well as the results of a Principal Component Analysis (PCA).

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1073-1080
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• 2023

The development and speed improvement of the Internet network, and the development of many platforms based on it, have brought about a rapid expansion of production and consumption of various contents. However, the existing IP-based Internet system cannot efficiently cope with such an urgent increase in data. Accordingly, an alternative called the CCN(Contents Centric Network) has emerged, enabling more efficient data transmission and reception centered on content rather than host. In this paper, we will deal with the mobility of intermediate nodes in CCN real-time streaming service, which is one of the major research fields of CCN, and minimize network overload through more efficient path switching through RSSI detection. In other words, by improving the method of selecting and switching a spare path when an intermediate node located between the requester(consumer) and the provider moves, a mechanism for managing data transmission is not interrupted and unnecessary load due to route switching does not occur in the network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.217-228
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• 2024

LiDAR(Light Detection And Ranging) sensors, which play a pivotal role among cameras, RADAR(RAdio Detection And Ranging), and ultrasonic sensors for the safe operation of autonomous vehicles, can recognize and detect objects in 360 degrees. However, since LiDAR sensors use lasers to measure distance, they are vulnerable to attackers and face various security threats. In this paper, we examine several security threats against LiDAR sensors: relay, spoofing, and replay attacks, analyze the possibility and impact of physical jamming attacks, and analyze the risk these attacks pose to the reliability of autonomous driving systems. Through experiments, we show that jamming attacks can cause errors in the ranging ability of LiDAR sensors. With vehicle-to-vehicle (V2V) communication, multi-sensor fusion under development and LiDAR anomaly data detection, this work aims to provide a basic direction for countermeasures against these threats enhancing the security of autonomous vehicles, and verify the practical applicability and effectiveness of the proposed countermeasures in future research.