• Journal of the Korean Wood Science and Technology
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• v.49 no.2
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• pp.107-121
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• 2021

The purpose of this study is to optimize the bonding method of the plywood suitable for cross-laminated timber (CLT) with plywood as a core by analyzing the flexural performance and failure mode according to the lamina species, the method of bonding plywood in the longitudinal direction, and whether or not adhesive is applied to the joint. In the case of the Douglas fir lamina layer, the modulus of elasticity decreased by about 11.5% due to longitudinal bonding, and the modulus of rupture increased or decreased according to the adhesive application and bonding method. The optimal conditions were derived as the butt joint without adhesive, half lap joint with adhesive, and butt joint. In the case of the larch lamina layer, the modulus of rupture and the modulus of elasticity decreased by about 15% and 40%, respectively. When using the half lab joint and tongue & groove joint, it is believed that it reduces the load transmitted to the middle layer by primarily preventing the failure on flexure at the joint of the plywood layer. From the results of this study, the larch lamina layer used in the manufacturing process of Ply-lam CLT did not show any difference based on the bonding method. Butt joint and half lap joint bonding method are determined to be suitable when using Douglas fir lamina layer.

• Clean Technology
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• v.26 no.4
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• pp.293-303
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• 2020

According to the World Health Organization (WHO), air pollution is a typical health hazard, resulting in about 7 million premature deaths each year. Sulfur dioxide (SO2) is one of the major air pollutants, and the combustion process with sulfur-containing fuels generates it. Measuring SO2 generation in large combustion environments in real time and optimizing reduction facilities based on measured values are necessary to reduce the compound's presence. This paper describes the concentration measurement for SO2, a particulate matter precursor, using a wavelength modulation spectroscopy (WMS) of tunable diode laser absorption spectroscopy (TDLAS). This study employed a quantum cascade laser operating at 7.6 ㎛ as a light source. It demonstrated concentration measurement possibility using 64 multi-absorption lines between 7623.7 and 7626.0 nm. The experiments were conducted in a multi-pass cell with a total path length of 28 and 76 m at 1 atm, 296 K. The SO2 concentration was tested in two types: high concentration (1000 to 5000 ppm) and low concentration (10 ppm or less). Additionally, the effect of H2O interference in the atmosphere on the measurement of SO2 was confirmed by N2 purging the laser's path. The detection limit for SO2 was 3 ppm, and results were compared with the electronic chemical sensor and nondispersive infrared (NDIR) sensor.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.445-454
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• 2022

Ice accretion on the aircraft components, such as wings, fuselage, and empennage, can occur when the aircraft encounters a cloud zone with high humidity and low temperature. The prevention of ice accretion is important because it causes a decrease in the aerodynamic performance and flight stability, thus leading to fatal safety problems. In this study, a shape design optimization of a multi-element airfoil is performed to minimize the amount of ice accretion on the high-lift device including leading-edge slat, main element, and trailing-edge flap. The design optimization framework proposed in this paper consists of four major parts: air flow, droplet impingement and ice accretion simulations and gradient-free optimization algorithm. Reynolds-averaged Navier-Stokes (RANS) simulation is used to predict the aerodynamic performance and flow field around the multi-element airfoil at the angle of attack 8°. Droplet impingement and ice accretion simulations are conducted using the multi-physics computational analysis tool. The objective function is to minimize the total mass of ice accretion and the design variables are the deflection angle, gap, and overhang of the flap and slat. Kriging surrogate model is used to construct the response surface, providing rapid approximations of time-consuming function evaluation, and genetic algorithm is employed to find the optimal solution. As a result of optimization, the total mass of ice accretion on the optimized multielement airfoil is reduced by about 8% compared to the baseline configuration.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.471-478
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• 2022

This paper is about research and development of Korea Aerospace University's Solar-Powered UAV System that named of KAU-SPUAV, and describes the design process of the 4.2 m solar UAV that succeeded in a long flight of 32 hours and 19 minutes at June 2020. In order to improve the long-term flight performance of the KAU-SPUAV, For reduce drag, a circular cross-section of the fuselage was designed, and manufactured light and sturdy fuselage by applying a monocoque structure using a glass fiber composite material. In addition, a solar module optimized for the wing shape of a 4.2 m solar drone was constructed and arranged, and a propulsion system applied with the 23[in] × 23[in] propeller was constructed to improve charging and flight efficiency. The developed KAU-SPUAV consumes an average of 55W when cruising and can receive up to 165W of energy during the day, and its Long-term Endurance was verified through flight tests.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.38-43
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• 2022

Smart sewage treatment plant means creating a safe and clean water environment by establishing an ICT-based real-time monitoring, remote control management and intelligent system for the entire sewage treatment process. The core technology of such a smart sewage treatment plant can be operation control technology using measuring instruments. This research team analyzed and suggested the operation control technologies necessary for the establishment of the intelligent business by referring to the intelligent research projects of the sewage treatment plant in progress in Korea. As a result of the analysis, a total of six removal technologies were presented, including control by scale, reflow water control, linked treated water control, chemical quantity control, winter operation control, and total organic carbon control. By size, standards that can be classified into small and medium-sized large-scale are presented, and in the case of reflow water control, the location of water quality and flow sensors capable of managing reflow water is suggested. In the case of the linked treated water control, the influence and control points of the linked treated water on the sewage treatment plant were presented, and in the case of the chemical injection volume control, a system capable of optimizing the amount of chemical injection according to the introduction of an intelligent sewage treatment plant was presented. In the case of winter operation, the sensors and pumps to be controlled are suggested when considering the decrease in nitrification due to the decrease in water temperature. In the case of total organic carbon control, an interlocking system considering the total amount of pollution in the future was proposed. These operation control scenarios are expected to be used as basic data to be used in intelligent sewage treatment algorithms and scenarios in the future.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.955-968
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• 2021

A water resource plan is routinely based on a natural flow and can be estimated using observed streamflow data or a long-term continuous rainfall-runoff model. However, the watershed with the natural flow is very limited to the upstream area of the dam. In particular, for the ungauged watershed, a rainfall-runoff model is established for the gauged watershed, and the model is then applied to the ungauged watershed by transferring the associated parameters. In this study, the GR4J rainfall-runoff model is mainly used to regionalize the parameters that are estimated from the 14 dam watershed via an optimization process. In terms of optimizing the parameters, the Bayesian approach was applied to consider the uncertainty of parameters quantitatively, and a number of parameter samples obtained from the posterior distribution were used for the regionalization. Here, the relationship between the estimated parameters and the topographical factors was first identified, and the dependencies between them are effectively modeled by a Copula function approach to obtain the regionalized parameters. The predicted streamflow with the use of regionalized parameters showed a good agreement with that of the observed with a correlation of about 0.8. It was found that the proposed regionalized framework is able to effectively simulate streamflow for the ungauged watersheds by the use of the regionalized parameters, along with the associated uncertainty, informed by the basin characteristics.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.177-186
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• 2022

The performance of a surface plasmon resonance sensor is evaluated based on the sensitivity (nm/RIU) and sharpness from the full width at half maximum (FWHM) and the peak depth of a resonance peak. These factors are determined by the materials and conformational properties of the sensing structure. In this paper, we investigated an optimized insulator-metal-insulator (IMI) multilayer-based surface plasmon resonance sensor structure to simultaneously achieve high sensitivity, narrow FWHM, and deep peak depth while using gold for the metallic film layer which occurs peak broadening. By adopting the optimized structure, sensitivity of 8,390 nm/RIU, FWHM of 11.92 nm, and a resonance peak depth of 93.1% were achieved for 1.45-1.46 refractive index variation of the sensing layer. With the suggested structure conformation, high sensitivity and resolution of sensing performance can be achieved.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.