• Imaging Science in Dentistry
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• v.53 no.2
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• pp.137-144
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• 2023

Purpose: This study aimed to evaluate the anatomic circle around the impacted lower third molar to show, document, and correlate essential findings that should be included in the routine radiographic assessment protocol as clinically meaningful factors in overall case evaluation and treatment planning. Materials and Methods: Cone-beam computed tomographic images of impacted lower third molars were selected according to specific inclusion criteria. Impacted teeth were classified according to their position before assessment. The adjacent second molars were assessed for distal caries, distal bone loss, and root resorption. The fourth finding was the presence of a retromolar canal distal to the impaction. Communication with the dentist responsible for each case was done to determine whether these findings were detected or undetected by them before communication. Results: Statistically significant correlations were found between impaction position, distal bone loss, and detected distal caries associated with the adjacent second molar. The greatest percentage of undetected findings was found in the evaluation of distal bone status, followed by missed detection of the retromolar canal. Conclusion: The radiographic assessment protocol for impacted third molars should consider a step-by-step evaluation for second molars, and clinicians should be aware of the high prevalence of second molar affection in horizontal and mesioangular impactions. They also should search for the retromolar canal due to its associated clinical considerations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4C
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• pp.239-245
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• 2008

In this study, a fundamental-level study was performed to establish knowledge-base for the development of optimal surface-wave method for urban areas with adjacent structures. First, theoretical modelling was performed to investigate the influence of adjacent structures on dispersion characteristics of surface waves. Later, the geotechnical sites with a concrete model of adjacent structure and a real subway box structure were tested by surface-wave method to investigate the influence of adjacent structures. The major influencing factors of adjacent structures on surface-wave propagation were direct distance between measurement array and adjacent structure, stiffness contrast between layers and type of seismic source.

• Fisheries and Aquatic Sciences
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• v.26 no.6
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• pp.406-422
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• 2023

Sarcopenia is an age-related, progressive skeletal muscle disorder involving the loss of muscle mass and strength. Previous studies have shown that γ-aminobutyric acid (GABA) from fermented oysters aids in regulatory T cells (Tregs) cell expansion and function by enhancing autophagy, and concomitantly mediate muscle regeneration by modulating muscle inflammation and satellite cell function. The fermentation process of oysters not only increases the GABA content but also enhances the content of branched amino acids and free amino acids that aid the level of protein absorption and muscle strength, mass, and repair. In this study, the effect of GABA-enriched fermented sarco oyster extract (FSO) on reduced muscle mass and functions via Treg modulation and enhanced autophagy in aged mice was investigated. Results showed that FSO enhanced the expression of autophagy markers (autophagy-related gene 5 [ATG5] and GABA receptor-associated protein [GABARAP]), forkhead box protein 3 (FoxP3) expression, and levels of anti-inflammatory cytokines (interleukin [IL]-10 and transforming growth factor [TGF]-β) secreted by Tregs while reducing pro-inflammatory cytokine levels (IL-17A and interferon [IFN]-γ). Furthermore, FSO increased the expression of IL-33 and its receptor IL-1 receptor-like 1 (ST2); well-known signaling pathways that increase amphiregulin (Areg) secretion and expression of myogenesis markers (myogenic factor 5, myoblast determination protein 1, and myogenin). Muscle mass and function were also enhanced via FSO. Overall, the current study suggests that FSO increased autophagy, which enhanced Treg accumulation and function, decreased muscle inflammation, and increased satellite cell function for muscle regeneration and therefore could decrease the loss of muscle mass and function with aging.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.38.2-38.2
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• 2020

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1 kpc. This enormous dynamic range allows us to simultaneously capture the physics of the cosmic web on very large scales and account for the formation and evolution of dwarf galaxies on much smaller scales. Inside the simulation box. we zoom-in on a high-resolution cuboid region with a volume of 1049 × 114 × 114 Mpc3. The subgrid physics chosen to model galaxy formation includes radiative heating/cooling, reionization, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OpenMP version of the RAMSES code, specifically targeted for modern many-core many thread parallel architectures. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder to analyse the large outputs of HR5. The simulation successfully reproduces many observations, such as the cosmic star formation history, connectivity of galaxy distribution and stellar mass functions. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.271-271
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• 2019

Estimating ship motion is difficult because it take place in complex environments.. Estimating ship motion is an important factor in ensuring the safety of ship, so accurate estimates are needed. Existing motion-related studies compare the apparent motion of the model acquired and the reference model by experimenting with the ship motion on a particular alignment, making it difficult to intuitively estimate the hull motion. This study introduces the concept of estimating the characteristics of ship motion as a transfer function through pole-zero interpretation and frequency response analysis by applying the method of transfer function of Linear-Time Invariant system. Ship motion analysis model using Linear-Time Invariant system is consist with 1) wave as input signal 2) ship motion as output signal 3) hull defined as black box. This model can be defined by numericalizing the ship motion as a transfer function and is expected to facilitate the characterization of the ship motion through pole-zero analysis and frequency response analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1841-1857
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• 2023

CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object's height and width. However, because the objects have different sizes, directly regressing their height and width will make the model difficult to converge and lose the intrinsic relationship between object's width and height, thereby reducing the stability of the model and the consistency of prediction accuracy. For this problem, we proposed an algorithm based on the regression of the diagonal half-length and the center angle, which significantly compresses the solution space of the regression components and enhances the intrinsic relationship between the decoded components. First, encode the object's width and height into the diagonal half-length and the center angle, where the center angle is the angle between the diagonal and the vertical centreline. Secondly, the predicted diagonal half-length and center angle are decoded into two length components. Finally, the position of the object bounding box can be accurately obtained by combining the corresponding center point coordinates. Experiments show that, when using CenterNet as the improved baseline and resnet50 as the Backbone, the improved model achieved 81.6% and 79.7% mAP on the VOC 2007 and 2012 test sets, respectively. When using Hourglass-104 as the Backbone, the improved model achieved 43.3% mAP on the COCO 2017 test sets. Compared with CenterNet, the improved model has a faster convergence rate and significantly improved the stability and prediction accuracy.

• Convergence Security Journal
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• v.22 no.3
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• pp.101-110
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• 2022

As the cases of using artificial intelligence in various fields increase, attempts to solve various issues through artificial intelligence in the intrusion detection field are also increasing. However, the black box basis, which cannot explain or trace the reasons for the predicted results through machine learning, presents difficulties for security professionals who must use it. To solve this problem, research on explainable AI(XAI), which helps interpret and understand decisions in machine learning, is increasing in various fields. Therefore, in this paper, we propose an explanatory AI to enhance the reliability of machine learning-based intrusion detection prediction results. First, the intrusion detection model is implemented through XGBoost, and the description of the model is implemented using SHAP. And it provides reliability for security experts to make decisions by comparing and analyzing the existing feature importance and the results using SHAP. For this experiment, PKDD2007 dataset was used, and the association between existing feature importance and SHAP Value was analyzed, and it was verified that SHAP-based explainable AI was valid to give security experts the reliability of the prediction results of intrusion detection models.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Progress in Medical Physics
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• v.34 no.2
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• pp.15-22
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• 2023

This study compared the dose calculated using the electron Monte Carlo (eMC) dose calculation algorithm employing the old version (eMC V13.7) of the Varian Eclipse treatment-planning system (TPS) and its newer version (eMC V16.1). The eMC V16.1 was configured using the same beam data as the eMC V13.7. Beam data measured using the VitalBeam linear accelerator were implemented. A box-shaped water phantom (30×30×30 cm³) was generated in the TPS. Consequently, the TPS with eMC V13.7 and eMC V16.1 calculated the dose to the water phantom delivered by electron beams of various energies with a field size of 10×10 cm². The calculations were repeated while changing the dose-smoothing levels and normalization method. Subsequently, the percentage depth dose and lateral profile of the dose distributions acquired by eMC V13.7 and eMC V16.1 were analyzed. In addition, the dose-volume histogram (DVH) differences between the two versions for the heterogeneous phantom with bone and lung inserted were compared. The doses calculated using eMC V16.1 were similar to those calculated using eMC V13.7 for the homogenous phantoms. However, a DVH difference was observed in the heterogeneous phantom, particularly in the bone material. The dose distribution calculated using eMC V16.1 was comparable to that of eMC V13.7 in the case of homogenous phantoms. The version changes resulted in a different DVH for the heterogeneous phantoms. However, further investigations to assess the DVH differences in patients and experimental validations for eMC V16.1, particularly for heterogeneous geometry, are required.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.