• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.69-78
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• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.17-23
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• 1997

A 3-D vision system is developed to evaluate blanks' line up and monitor gap and thickness difference between blanks in tailored blank welding system. A structured lighting method is used for 3-D vision recognition. Images of sheared portion in blanks are irregular according to roughness of blank surface, shape of sheared geometry and blurring. It is difficult to get accurate and reliable informations in the case of using binary image processing or contour detection techniques in real time for such images. We propoe a new energy integration method robust to blurring and changes of illumination. The method is computationally simple, and uses feature restoration concept, different to another digital image restoration methods which aim image itself restoration and may be used in conventional applications using structured line lighting technique. Experimental results show this system measuring repeatability is .+-. pixel for gap and thickness difference in static and dynamic tests. The data are expected to be useful for preview gap control.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.29-41
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• 2023

The 3D geospatial modeling of geotechnical information can aid in understanding the geotechnical characteristic values of the continuous subsurface at construction sites. In this study, a geostatistical optimization model for the three-dimensional (3D) mapping of subsurface stratification and the SPT-N value based on a trial-and-error rule was developed and applied to a dam emergency spillway site in South Korea. Geospatial database development for a geotechnical investigation, reconstitution of the target grid volume, and detection of outliers in the borehole dataset were implemented prior to the 3D modeling. For the site-specific subsurface stratification of the engineering geo-layer, we developed an integration method for the borehole and geophysical survey datasets based on the geostatistical optimization procedure of ordinary kriging and sequential Gaussian simulation (SGS) by comparing their cross-validation-based prediction residuals. We also developed an optimization technique based on SGS for estimating the 3D geometry of the SPT-N value. This method involves quantitatively testing the reliability of SGS and selecting the realizations with a high estimation accuracy. Boring tests were performed for validation, and the proposed method yielded more accurate prediction results and reproduced the spatial distribution of geotechnical information more effectively than the conventional geostatistical approach.

• Smart Structures and Systems
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• v.31 no.6
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• pp.573-583
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• 2023

Although unmanned aerial vehicles have been used to overcome the limited accessibility of human-based visual inspection, unresolved issues still remain. Onsite inspectors face difficulty finding previously detected damage locations and tracking their status onsite. For example, an inspector still marks the damage location on a target structure with chalk or drawings while comparing the current status of existing damages to their previous status, as documented onsite. In this study, an augmented-reality-based structural inspection system with onsite damage information marking was developed to enhance the convenience of inspectors. The developed system detects structural damage, creates a holographic marker with damage information on the actual physical damage, and displays the marker onsite via an augmented reality headset. Because inspectors can view a marker with damage information in real time on the display, they can easily identify where the previous damage has occurred and whether the size of the damage is increasing. The performance of the developed system was validated through a field test, demonstrating that the system can enhance convenience by accelerating the inspector's essential tasks such as detecting damages, measuring their size, manually recording their information, and locating previous damages.

• Restorative Dentistry and Endodontics
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• v.46 no.1
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• pp.12.1-12.9
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• 2021

Objectives: The present study aims to compare the obturation quality of 2 confluence confirmation techniques in artificial maxillary first premolars showing Vertucci type II root canal configuration. Materials and Methods: Thirty artificial maxillary premolars having Vertucci type II root canal configuration were made. They were divided into 3 groups according to the confluence confirmation technique as follows. Gutta-percha indentation (GPI) group (confluence confirmation using a gutta-percha cone and a K file); electronic apex locator (EAL) group (confluence confirmation using K files and EAL); and no confluence detection (NCD) group. In the GPI group and the EAL group, shaping and obturation were performed with the modified working length (WL). In the NCD group, shaping was performed without WL adjustment and obturation was carried out with an adjusted master cone. Micro-computed tomography was used before preparation and after obturation to calculate the percentage of gutta-percha occupied volume (%GPv) and the volume increase in the apical 4 mm. Data were analyzed using 1-way analysis of variance and post hoc Tukey's test. Results: Statistically significant difference was not found in terms of the %GPv from the apex to apical 4 mm. However, the NCD group showed a statistically significant volume increase compared with the EAL group (p < 0.05). Conclusions: In terms of gutta-percha occupied volume, no significant difference was observed among the 3 groups. Confluence confirmation using an EAL in teeth with Vertucci type II configuration showed less volume increase during canal shaping compared with no confluence confirmation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.217-218
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• 2021

The development of artificial intelligence in the field of construction and construction is revitalizing. The performance and development techniques of artificial intelligence are changing rapidly, but if you look at the cases of domestic construction sites, they are using technologies from 5 to 7 years ago. It is right to follow a stable method in consideration of commercialization, but the previous AI development method requires more manpower and time to develop than the current technology. In addition, in order to actively utilize artificial intelligence technology, customized artificial intelligence is required to be applied to ever-changing changes in construction sites. it is the reality As a result, even if good AI technology is secured at the construction site, it is reluctant to introduce it because there is no advantage in terms of time and cost compared to the existing method to apply it only to some processes. Currently, an AI technique with a faster development process and accurate recognition has been developed to cope with a fluid situation, so it will be important to understand and introduce the rapidly changing AI development method.

• Current Optics and Photonics
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• v.7 no.4
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• pp.345-353
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• 2023

We propose a turbulence-tolerant Manchester on-off keying (M-OOK) transmission for free-space optical (FSO) communication. At the transmitter end, a M-OOK signal featuring a spectrum with low-frequency components absent is modulated and transmitted into a turbulent channel. At the receiver end, a low-pass filter (LPF) -based adaptive-threshold decision (ATD) with LPF-extracted channel-state information (CSI) and a high-pass filter (HPF)-based fixed-threshold decision (FTD) are employed to compensate for the effects of turbulence, owing to the low-frequency spectral characteristics of the turbulent channel. The performance of LPF-based ATD and HPF-based FTD are evaluated for various cutoff frequencies for the LPF and HPF. Besides, the proposed M-OOK transmission is compared to conventional non-return-to-zero OOK (NRZ-OOK) for different data rates. The proposed technique is verified in simulation. The simulation results show that the proposed M-OOK detection with optimized cutoff frequencies of LPF and HPF has better bit-error-rate (BER) performance compared to NRZ-OOK, and it is close to the theoretical ATD with the knowledge of precise CSI under various degrees of turbulence effects.

• Korean Journal of Pharmacognosy
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• v.54 no.1
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• pp.38-43
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• 2023

A simple and accurate assay was developed for the quantitative analysis of 1-deoxynojirimycin (1-DNJ) derived from the silkworm (Bombyx mori). Normal-phase high-performance liquid chromatography coupled with an evaporative light scattering detector (HPLC-ELSD) and a hydrophilic interaction liquid chromatography column was used. Various parameters were applied to optimize the analysis method. The limits of detection and quantification of 1-DNJ were 2.97 × 10^-3 and 9.00 × 10^-3 mg/mL, respectively. The calibration curve showed good linearity results. The concentration range and the r² value were 0.0625-1.0 mg/mL and 0.9997, respectively. The accuracy test demonstrated a significantly high recovery rate (89.95-103.22%). The relative standard deviation was ≤ 1.00%. Thus, a method for the accurate identification and quantitative analysis of 1-DNJ in silkworms was developed. Moreover, in this procedure, the process of derivatization of 1-DNJ, which was required in previous experiments, could be eliminated. This technique may be actively utilized for the development of pharmaceuticals and health functional foods using 1-DNJ.

• Annual Conference of KIPS
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• 2022.11a
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• pp.541-543
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• 2022

In recent years, retinal disorders have become a serious health concern. Retinal disorders develop slowly and without obvious signs. To avoid vision deterioration, early detection and treatment are critical. Optical coherence tomography (OCT) is a non-invasive and non-contact medical imaging technique used to acquire informative and high-resolution image of retinal area and underlying layers. Disease signs are difficult to detect because OCT images have many areas which are not related to any disease. In this paper, we present a deep learning-based method to perform multi-label classification on a long-tailed OCT dataset. Our method first extracts the region of interest and then performs the classification task. We achieve 98% accuracy, 92% sensitivity, and 99% specificity on our private OCT dataset. Using the heatmap generated from trained convolutional neural network, our method is more robust and explainable than previous approaches because it focuses on areas that contain disease signs.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.