• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Wind and Structures
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• v.5 no.2_3_4
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• pp.267-276
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• 2002

Research being undertaken at the University of Auckland has enabled Vortec Energy to improve the performance of the Vortec 7 Diffuser Augmented Wind Turbine. Computational Fluid Dynamic (CFD) modelling of the Vortec 7 was used to ascertain the effectiveness of geometric modifications to the Vortec 7. The CFD work was then developed to look at new geometries, and refinement of these led to greater power augmentation for a given diffuser exit area ratio. Both full scale analysis of the Vortec 7 and a wind tunnel investigation of the development design have been used for comparison with the CFD model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.946-947
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• 2016

This paper is for analyzing not only DGNSS navigation signal but also the navigation parameter of MSAS. The sufficient navigation satellites to determine 3-D position based on DGNSS are simultaneously available at MSAS monitering station and the test region of Korean peninsula. It was verified that the carrier to noise signal is stable to maintain the reliable positioning.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.323-332
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• 2023

Today, services using Positioning, Navigation, and Timing (PNT) technology are provided in various fields, such as smartphone Location-Based Service (LBS) and autonomous driving. Generally, outdoor positioning techniques depend on the Global Navigation Satellite System (GNSS), and the need for positioning techniques that guarantee positioning accuracy, availability, and continuity is emerging with advances in service. In particular, continuity is not guaranteed in urban canyons where it is challenging to secure visible satellites with standalone GNSS, and even if more than four satellites are visible, the positioning accuracy and stability are reduced due to multipath channels. Research using Low Earth Orbit (LEO) satellites is already underway to overcome these limitations. In this study, we conducted a trend analysis of LEO-PNT research, an LEO satellite-based navigation and augmentation system. Through comparison with GNSS, the differentiation of LEO-PNT was confirmed, and the system design and receiver processing were analyzed according to LEO-PNT classification. Lastly, the current status of LEO-PNT development by country and institution was confirmed.

• Implantology
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• v.22 no.4
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• pp.242-254
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• 2018

The aim of the current study was to evaluate the results of horizontal guided bone regeneration (GBR) with xenograf t (deproteinized bovine bone mineral, DBBM), allograf t (irradiated allogenic cancellous bone and marrow), titanium membrane, resorbable collagen membrane, and advanced platelet-rich fibrin (A-PRF) in the anterior maxilla. The titanium membrane was used in this study has a three-dimensional (3D) shape that can cover ridge defects. Case 1. A 32-year-old female patient presented with discomfort due to mobility and pus discharge on tooth #11. Three months after extracting tooth #11, diagnostic software (R2 GATE diagnostic software, Megagen, Daegu, Korea) was used to establish the treatment plan for implant placement. At the first stage of implant surgery, GBR for horizontal augmentation was performed with DBBM ($Bio-Oss^{(R)}$, Geistlich, Wolhusen, Switzerland), irradiated allogenic cancellous bone and marrow (ICB $cancellous^{(R)}$, Rocky Mountain Tissue Bank, Denver, USA), 3D-titanium membrane ($i-Gen^{(R)}$, Megagen, Daegu, Korea), resorbable collagen membrane (Collagen $membrane^{(R)}$, Genoss, Suwon, Korea), and A-PRF because there was approximately 4 mm labial dehiscence after implant placement. Five months after placing the implant, the second stage of implant surgery was performed, and healing abutment was connected after removal of the 3D-titanium membrane. Five months after the second stage of implant surgery was done, the final prosthesis was then delivered. Case 2. A 35-year-old female patient presented with discomfort due to pain and mobility of implant #21. Removal of implant #21 fixture was planned simultaneously with placement of the new implant fixture. At the first stage of implant surgery, GBR for horizontal augmentation was performed with DBBM ($Bio-Oss^{(R)}$), irradiated allogenic cancellous bone and marrow (ICB $cancellous^{(R)}$), 3D-titanium membrane ($i-Gen^{(R)}$), resorbable collagen membrane (Ossix $plus^{(R)}$, Datum, Telrad, Israel), and A-PRF because there was approximately 7 mm labial dehiscence after implant placement. At the second stage of implant surgery six months after implant placement, healing abutment was connected after removing the 3D-titanium membrane. Nine months after the second stage of implant surgery was done, the final prosthesis was then delivered. In these two clinical cases, wound healing of the operation sites was uneventful. All implants were clinically stable without inflammation or additional bone loss, and there was no discomfort to the patient. With the non-resorbable titanium membrane, the ability of bone formation in the space was stably maintained in three dimensions, and A-PRF might influence soft tissue healing. This limited study suggests that aesthetic results can be achieved with GBR using 3D-titanium membrane and A-PRF in the anterior maxilla. However, long-term follow-up evaluation should be performed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.4
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• pp.475-484
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• 2001

The purpose of this study is to investigate the heat transfer augmentation using the perforated plate placed in front of a target plate in an axisymmetric impinging air jet system. The new liquid crystal technique using neural networks with median filtering is used to determine the Nusselt number distributions on the target surface. The experiments were made for the jet Reynolds number (Re) 23,000. The effects of the pitch-to-diameter (p/d1) from 1.5 to 2.5 in the perforated plate, the hole diameter on perforated plate (d1) from 4㎜ to 12㎜, the perforated plate to target surface distance (z/d1) from 1 to 3, and the nozzle-to-target surface distance (L/d) from 2 to 10 on the heat transfer characteristics were experimentally investigated. It was found that when the perforated plate was located between the nozzle exit and the target plate, the average heat transfer rate at the stagnation region corresponding to r/d$\leq$1.0 was increased up to the maximum 2.3 times compared to the case without the perforated plate.

• The Journal of Korean Medicine
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• v.30 no.3
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• pp.98-105
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• 2009

Objective: Increased aortic and carotid arterial augmentation index (AI) is associated with the risk of cardiovascular disease. The most widely used approach for determining central arterial AI is by calculating the aortic pressure waveform from radial arterial waveforms using a transfer function. But how the change of waveform by applied pressure and the pattern of the change rely on subject's characteristics has not been recognized. In this study, we use a new method for measuring radial waveform and observe the change of waveform and the deviation of radial AI in the same position by applied pressure. Method: Forty-six non-patient volunteers (31 men and 15 women, age range 21-58 years) were enrolled for this study. Informed consent in a form approved by the institutional review board was obtained in all subjects. Blood pressure was measured on the left upper arm using an oscillometric method, radial pressure waves were recorded with the use of an improved automated tonometry device. DMP-3000(DAEYOMEDI Co., Ltd. Ansan, Korea) has robotics mechanism to scan and trace automatically. For each subject, we performed the procedure 5 times for each applied pressure level. We could thus obtain 5 different radial pulse waveforms for the same person's same position at different applied pressures. All these processes were repeated twice for test reproducibility. Result: Aortic AI, peripheral AI and radial AI were higher in women than in men (P<0.01), radial AI strongly correlated with aortic AI, and radial AI was consistently approximately 39% higher than aortic AI. Relationship between representative radial AI of DMP-3000 and peripheral AI of SphygmoCor had strongly correlation. And there were three patterns in change of pulse waveform. Conclusion: In this study, it is revealed the new device was sufficient to measure how radial AI and radial waveform from the same person at the same time change under applied pressure and it had inverse-proportion to applied pressure.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.