• Journal of Life Science
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• v.29 no.5
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• pp.621-630
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• 2019

Fertilization is the beginning of a new life that occurs in the female uterine. The female reproductive tract is composed ovary, oviduct, uterine, vagina and cervix, their physiological features are regulated by estrous cycle. Of these, uterine is a main point to establish embryo development and implantation, and intercommunication between embryo and uterine environment is necessary for suitable pregnancy. Endometrium is part of the uterine, its morphology is repetitively changed by hormones, and characteristic of uterine fluid from endometrium is also changed. Recently, massive proteins of endometrium and uterine fluid can be detected according to develop proteomics and bioinformatics and have been accelerated the understanding of the reproductive biology fields. Moreover, the massive protein information is actively studying with deeply studied theory such as sex hormone signal pathway and angiogenesis in mammals. In this paper, we review understanding of endometrium remodeling, uterine gland and fluid during estrous cycle, additionally studies on endometrium and uterine fluid based on proteomics techniques. Lastly, we introduced methods of the protein-protein correlation using bioinformatics tool that interaction with hormone receptors, representative angiogenetic factors and detected proteins using proteomics in endometrium and uterine fluid. This review will be useful to understanding the study on search of new cell mechanism in endometrium and uterine fluid.

• Journal of Electrodiagnosis and Neuromuscular Diseases
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• v.20 no.2
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• pp.106-111
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• 2018

Transcranial magnetic stimulation (TMS) has been a gold standard for investigating central motor pathways in humans. Diffusion tensor imaging with fiber tractography (DTI FT) is known for its usefulness in detecting white matter lesion in vivo. We investigated the clinical usefulness of elucidating the integrity and continuity of corticospinal tract (CST) by combined use of TMS and DTI FT in this study. We report two cases who have presented with left hemiparesis and evaluated by both TMS and DTI FT; 10-year-old boy with Mitochondrial Encephalomyopathy with Lactic Acidosis and Stroke-like episode syndrome and 20-year-old woman with traumatic brain injury. Combined use of TMS and DTI FT successfully led to localize the brain lesion that might cause motor impairment in patients with abnormal signal intensities in MRI. The results of this study suggest that TMS and DTI FT might provide the detailed information between function and anatomy of the CST, complementarily.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.139-147
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• 2021

This paper introduces and implements a Sound Event Detection (SED) system based on weakly-supervised learning where only part of the data is labeled, and analyzes the effect of parameters. The SED system estimates the classes and onset/offset times of events in the acoustic signal. In order to train the model, all information on the event class and onset/offset times must be provided. Unfortunately, the onset/offset times are hard to be labeled exactly. Therefore, in the weakly-supervised task, the SED model is trained by "strongly labeled data" including the event class and activations, "weakly labeled data" including the event class, and "unlabeled data" without any label. Recently, the SED systems using the mean-teacher model are widely used for the task with several parameters. These parameters should be chosen carefully because they may affect the performance. In this paper, performance analysis was performed on parameters, such as the feature, moving average parameter, weight of the consistency cost function, ramp-up length, and maximum learning rate, using the data of DCASE 2020 Task 4. Effects and the optimal values of the parameters were discussed.

• Journal of Navigation and Port Research
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• v.45 no.1
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• pp.26-32
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• 2021

In ocean field, the spread of the Fourth Industrial Revolution based on information and communication technology requires high precision and stable PNT&D (Position, Navigation, Timing and Data). As the IMO (International Maritime Organization) and IALA (The International Association of Marine Aids to Navigation and Lighthouse Authorities) are requiring backup systems due to mitigate vulnerabilities and the increase of dependency on GNSS (Global Navigation Satellite System), Korea is conducting a research & development of R-Mode. An DGPS (Differentiate Global Positioning System) reference station that uses MF, an existing maritime infrastructure, and AIS (Automatic Identification System) base stations that use 34 integrity station and VHF will be utilized in this study to avoid redundant investment. Because there are radio shadow areas that display low signal levels in the west sea, the establishment of new R-Mode reference and integrity station will be intended to resolve problems regrading the radio shadow area. Because the frequency has a characteristic in that radio wave transmits well along the ground (water surface) in low frequency band, simulation and measurement were conducted therefore this paper to propose candidate sites for R-Mode reference and integrity station resulted through p wave's propagation characteristics analysis. Using this paper, R-Mode reference and integrity station can be established at appropriate locations to resolve radio shadow areas in other regions.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.189-194
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• 2021

The purpose of this study was to investigate the effect of reducing noise and streaking artefacts by applying the Boost3D algorithm in the case of noise and streaking artifacts generated during computed tomography of the shoulder joint. A phantom study using a thoracic phantom including shoulder joint and clinical evaluation were conducted through shoulder joint images of 35 patients who underwent computed tomography of the shoulder joint from September 2020 to October 2020. The evaluation was divided into groups before and after the application of the Boost3D algorithm, and the noise values, signal to noise ratio, and mean to standard deviation ratio values were analyzed. Both noise values and mean to standard deviation ratio values analyzed in phantom image evaluation and clinical image evaluation were statistically significantly lower in the group after Boost3D was applied (p<0.05). Through this study, it was found that noise and streak artifacts were reduced through the application of Boost3D, and the mean to standard deviation ratio was high, which can be judged as an excellent image. If the Boost3D algorithm is used for computed tomography of the shoulder joint, it is thought that excellent images can be obtained with reduced noise and streaking artifacts that may occur in the shoulder joint area.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.307-313
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• 2022

As the development of renewable energy expands internationally to cope with global warming and climate change, the share of wind power generation has been gradually increasing. Although wind farms can produce electric power for 24 h a day compared to solar power plants, Their interfere with the operation of nearby radars or communication equipment must be analyzed because large-scale wind power turbines are installed. This study analyzed whether a land radio station can receive sufficient signals when a ship sailing outside the offshore wind farm transmits distress signals on the VHF band. Based on the geographic information system digital map around the target area, wind turbine CAD model, and wind farm layout, the area of interest and wind farm were modeled to enable numerical analysis. Among the high frequency analysis techniques suitable for radio wave analysis in a wide area, a dedicated program applying physical optics (PO) and shooting and bouncing ray (SBR) techniques were used. Consequently, the land radio station could receive the electromagnetic field above the threshold of the VHF receiver when a ship outside the offshore wind farm transmitted a distress communication signal. When the line of sight between the ships and the land station are completely blocked, the strength of the received field decreases, but it is still above the threshold. Hence, although a wind farm is a huge complex, a land station can receive the electromagnetic field from the ship's VHF transmitter because the wave length of the VHF band is sufficiently long to have effects such as diffraction or reflection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.119-129
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• 2023

Sleep apnea causes various secondary disease such as hypertension, stroke, myocardial infarction, depression and cognitive impairment. Early detection and continuous management of sleep apnea are urgently needed since it causes cardio-cerebrovascular diseases. In this study, wearable device for monitoring respiration during sleep using PVDF film was developed to detect vibration through trachea caused by breathing, which determines normal breathing and sleep apnea. Variables such as respiration rate and apnea were extracted based on the detected breathing sound data, and a noise reduction algorithm was established to minimize the effect even when there is a noise signal. In addition, it was confirmed that irregular breathing patterns can be analyzed by establishing a moving threshold algorithm. The results show that the accuracy of the respiratory rate from the developed device was 98.7% comparing with the polysomnogrphy result. Accuracy of detection for sleep apnea event was 92.6% and that of the sleep apnea duration was 94.0%. The results of this study will be of great help to the management of sleep disorders and confirmation of treatment by commercialization of wearable devices that can monitor sleep information easily and accurately at home during daily life and confirm the progress of treatment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.