• Tuberculosis and Respiratory Diseases
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• v.82 no.4
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• pp.319-327
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• 2019

Background: Radial probe endobronchial ultrasound (R-EBUS), is effective for tissue diagnosis of lung lesions. We evaluated the diagnostic performance of R-EBUS both a guide-sheath and fluoroscopy and identified factors associated with accurate diagnosis. The feasibility of molecular and genetic testing, using specimens obtained by R-EBUS, was also investigated. Methods: The study retrospectively reviewed 211 patients undergoing R-EBUS without a guide-sheath and fluoroscopy, June 2016-May 2017. After excluding 27 patients of which the target lesion was not reached, 184 were finally included. Multivariate logistic regression was used, to identify factors associated with accurate diagnosis. Results: Among 184 patients, R-EBUS-guided biopsy diagnosed malignancy in 109 patients (59%). The remaining 75 patients (41%) with non-malignant results underwent additional work-ups, and 34 were diagnosed with malignancy. Based on final diagnosis, diagnostic accuracy was 80% (136/170), and sensitivity and specificity for malignancy were 76% (109/143) and 100% (27/27), respectively. In multivariate analysis, peripheral location (adjusted odds ratio [aOR], 3.925; 95% confidence interval [CI], 1.203-12.811; p=0.023), and central position of the probe (aOR, 2.435; 95% CI, 1.424-7.013; p=0.035), were associated with accurate diagnosis of malignancy. Molecular and genetic analyses were successful, in all but one case, with inadequate specimens. Conclusion: R-EBUS-guided biopsy without equipment, is effective for tissue diagnosis. Peripheral location and central position of the radial probe, were crucial for accurate diagnosis. Performance of molecular and genetic testing, using samples obtained by R-EBUS, was satisfactory.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.418-426
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• 2019

Recently, many studies on vital sign detection using a radar sensor related to Internet of Things(IoT) smart home systems have been conducted. Because vital signs such as respiration and cardiac rates generally cause micro-motions in the chest or back, the phase of the received echo signal from a target fluctuates according to the micro-motion. Therefore, vital signs are usually detected via spectral analysis of the phase. However, the probability of false alarms in cardiac rate detection increases as a result of various problems in the measurement environment, such as very weak phase fluctuations caused by the cardiac rate. Therefore, this study analyzes the difficulties of vital sign detection and proposes an efficient vital sign detection algorithm consisting of four main stages: 1) phase decomposition, 2) phase differentiation and filtering, 3) vital sign detection, and 4) reduction of the probability of false alarm. Experimental results using impulse-radio ultra-wideband radar show that the proposed algorithm is very efficient in terms of computation and accuracy.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.122-130
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• 2021

A wind-speed estimation at the arbitrary elevations is key component for the design of the offshore wind energy structures and the computation of the wind-wave generation. However, the wind-speed estimation of the target elevation has been carried out by using the typical functions and their typical parameters, e.g., power and logarithmic functions because the available wind speed data is limited to the specific elevation, such as 2~3m, 10 m, and so on. In this study, the parameters of the vertical profile functions are estimated with optimal and analyzed the parameter ranges using the HeMOSU-1 platform wind data monitored at the eight different locations. The results show that the mean value of the exponent of the power function is 0.1, which is significantly lower than the typically recommended value, 0.14. The values of the exponent, the friction velocity, and the roughness parameters are in the ranges 0.0~0.3, 0~10 (m/s), and 0.0~1.0 (m), respectively. The parameter ranges differ from the typical ranges because the atmospheric stability condition is assumed as the neutral condition. To improve the estimation accuracy, the atmospheric condition should be considered, and a more general (non-linear) vertical profile functions should be introduced to fit the diverse profile patterns and parameters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.705-713
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• 2021

Because the cage assembly serves as the launch platform, an accurate aim is essential to ensure shooting accuracy for the target. On the other hand, the abnormal rotation of the cage due to the directional errors of the K-MLRS has continuously caused quality problems. The quality problem of weapon systems may have a negative impact on the military's power loss. In this study, improvement plans were derived by examining the defects and analyzing the directional errors of the K-MLRS launcher. In addition, all possible causes of directional errors were derived from the flow diagram for cage directionality. Based on the results, the defense design through the software program was intended to prevent the loss of direction. Through this study, the signal error of the resolver was improved by preventing unspecific signals in the data. Furthermore, the directional judgment method was improved to minimize the impact of data distortion. Lastly, directional storage and verification methods were improved so that data for the cage rotation direction would not be affected by errors. For the design improvement method, the reliability was verified through the system applicability. This study is expected to be a reference for failure analysis and design for similar weapon systems in the future.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.59-69
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• 2021

Recently, large-scale offshore and coastal structures have been constructed owing to the increasing interest in eco-friendly energy development. To achieve this, precise simulations of waves are necessary to ensure the safe operations of marine structures. Several experiments are required in the field to understand the offshore wave; however, in terms of scale, it is difficult to control variables, and the cost is significant. In this study, numerical waves under various wave conditions are produced using a piston-type wavemaker, and the produced wave profiles are verified by comparing with the results from a numerical wave tank (NWT) modeled using the smoothed particle hydrodynamics (SPH) method and theoretical equations. To minimize the effect by the reflected wave, a mass-weighted damping zone is set at the right end of the NWT, and therefore, stable and uniform waves are simulated. The waves are generated using the linear and Stokes wave theories, and it is observed that the numerical wave profiles calculated by the Stokes wave theory yield high accuracy. When the relative depth is smaller than two, the results show good agreement irrespective of the wave steepness. However, when the relative depth and wave steepness are larger than 2 and 0.04, respectively, the errors are negligible if the measurement position is close to the excitation plate. However, the error is 10% or larger if the measurement position is away from the excitation location. Applicable target wave ranges are confirmed through various case studies.

• Publications of The Korean Astronomical Society
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• v.35 no.3
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• pp.29-41
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• 2020

Korea has numerous astronomical resources, such as observational records, star maps, and a wealth of literature, covering the period from the Three Kingdoms (54 BC - 932 AD) to the Joseon Dynasty (1392 - 1910 AD). The research activities related to these resources have been limited to those by individual researchers. It is now necessary to conduct research by efficiently and systematically collecting and managing Korean astronomical records using an accessible Web environment. The purpose of this study is to complete a system that enables researchers systematically to collect and verify a large number of historical records related to astronomical phenomena in a Web environment. In 2017, a preliminary survey was conducted, and the requirements pertaining to an implementation target system were devised. In addition, a joint development plan was carried out by the developer, lasting three months in 2018. Although the system is relatively simple, it is the first system to be attempted in the historical astronomy field. In order to proceed with the systematic development, the software development methodology is applied to the entire process from deriving the requirements of researchers to completing the system. The completed system is verified through integrated function and performance tests. The functional test is repeated while modifying and testing the system based on various test scenarios. The performance test uses a performance measurement test tool that takes measurements by setting up a virtual operation environment. The developed system is now in normal operation after a one-year trial period. Researchers who become authorized to use the system can use it to verify the accuracy of data and to suggest improvements. The collected feedback will be reflected in future systems, and Korean astronomical records will be available for use internationally through a multilingual service.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.1
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• pp.9-17
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• 2022

Rural spaces are increasingly valuable as areas for introducing renewable energy infrastructure to achieve carbon neutrality. Rural areas are the living grounds of rural residents, and the balance of conservation and development for rural areas is important for the introduction of reasonable facilities. In order to maintain a balance between development and preservation and to introduce reasonable renewable energy facilities, it is necessary to develop a current status survey and an effective survey method to utilize a space capable of introducing renewable energy facilities such as idle land and vacant houses. Therefore, this study was conducted to verify the readability using an unmanned aerial vehicle, and the main results are as follows. The detection of photovoltaic power generation facilities using unmanned aerial vehicles was effective in analyzing the location and area of photovoltaic panels located on the roofs of buildings, and it was possible to calculate the expected power generation by region through the area calculation of photovoltaic panels. The vacant house detection can be used to select an investigation target for an vacant house condition survey as it can identify damage to buildings that are expected to be empty houses, management status, and electricity supply facilities through aerial photos. It is judged that the unmanned aerial vehicle detection capability can be utilized as a method to improve the efficiency of investigation and supplement the data related to solar power generation facilities and vacant houses provided by public institutions. Although this study detected the status of solar power generation facilities and vacant houses through high-resolution aerial image analysis, as a follow-up study, automatic measurement methods using the temperature difference of solar power generation facilities and general characteristics of vacant houses that can be read from the air were investigated. If the deriving research is carried out, it is judged that it will be possible to contribute to the improvement of the accuracy of the detection result using the unmanned aerial vehicle and the expansion of the application range.