• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.203-214
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• 2022

As the global demand for small satellites weighing less than 500 kg increases, the development and operation of dedicated small launch vehicles increase significantly. The responsiveness of a launch vehicle that puts a small satellite into a target orbit at the desired time is attracting attention. As a result, interest in the air launch is increasing in the rapid establishment of a constellation. As the demand for small satellites in south Korea increases, this study performed analyses on the applicability of an air launch vehicle using a large civil aircraft considering the geographical environment. In terms of responsiveness, mission response times were compared and analyzed for air launch vehicles and ground small and large vehicles. In addition, an air vehicle and a small ground vehicle were quantitatively compared and analyzed for the orbital insertion performance. As a result of the analysis, the air launch vehicle has limited responsiveness in Korea regarding rapid satellite constellation establishment. However, it can be an effective alternative for low inclination angle orbit insertion with the benefit of a fast turnaround time. Furthermore, the performance of the orbital injection is close to that of the ground small launch vehicle, and the high efficiency in terms of the required propellant mass is possible, so air launch can be an effective launch means for putting small satellites into orbit in Korea.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.2
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• pp.95-102
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• 2023

Despite the easing of social distancing, demand for non-face-to-face services continues to rise. Recently, the EU is pursuing a comprehensive plastic use reduction by expanding the scope of plastic use regulations for packaging plastics according to the New Cyclical Economy Action Plan(NCEAP). In response to this trend, the packaging industry is moving away from conventional non-degradable/petroleum-based plastics and conducting research on packaging materials using biodegradable plastics such as PLA(Poly Lactic Acid), PBAT(Poly Butylene Adipate-co-butylene Terephthalate). On the other hand, ginkgo leaves occur in large quantities in Korea and act as a cause of slip accidents and flooding. In this study, a method to utilize ginkgo biloba leaf as a new alternative biomass resource was proposed by producing lactic acid through pretreatment, enzymatic saccharification, and fermentation processes. For the efficiency of lactic acid production, a comparative analysis of lignin content from before and after browning was performed. In addition, the degree of glucan extraction was evaluated by applying a pretreatment method using three catalysts: hot water, sulfuric acid, and sodium hydroxide. It is difficult to expect high production of lactic acid with single process. Therefore, an integrated process operation using both the pretreated hydrolyzate and the residual solid enzymatic saccharification solution must necessarily be applied.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.65-85
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• 2022

In this study, in order to derive the priority of indicators and sustainability areas of smart ports, which means ports in the digital era, previous studies and ESG, which have recently been indispensably introduced in all industries worldwide, were studied together. A hierarchical structure was established with upper evaluation items and 20 lower evaluation items in four areas (operational, environmental, social, and governance), and a relative evaluation method of weighting items among the AHP techniques was applied. The pairwise comparison questionnaire consisted of a 9-point scale proposed by Satty (1980). A survey was conducted targeting working-level workers who perform sustainability or ESG(Environmental, Social, Governance)-related work at four representative port authorities in Korea (Busan, Incheon, Ulsan, Yeosu Gwangyang). In order to increase the accuracy of the analysis results, AHP analysis was conducted on 17 questionnaires with a consistency ratio of 0.1 or less. As a result of the analysis, it was confirmed that among the four areas representing the sustainability of domestic smart ports, the operation area had the highest priority, followed by the environment area. In addition, looking at the overall priorities for the 20 detailed indicators, indicators such as operational efficiency, operational planning, energy management, and pollution measurement and management system were found to have high priority. On the other hand, it was confirmed that the social and the governance areas had relatively low importance compared to other areas.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Journal of Aerospace System Engineering
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• v.18 no.1
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• pp.53-63
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• 2024

Adopting an engine igniter with high efficiency and ignition performance is essential for reliable operation of liquid rocket engines. In this study, we developed a spark torch igniter for a 450 N-scale methane-oxygen liquid rocket engine by conducting numerical analyses, igniter manufacturing and validation. Specifically, we conducted a parametric study for maximizing the enthalpy at the igniter exit, specifically by adjusting the mass flow rate, nozzle area ratio, fuel-oxidizer mixture ratio, and the igniter length-to-diameter. The heat transferred via the igniter nozzle exit was computed using 3-dimensional numerical simulations. We also manufactured and tested the igniter based on a deduced design to confirm ignition performance of the designed spark torch igniter. The igniter developed through this study could contribute to the development of practical propulsion systems such as upper-stage engines of small launch vehicles.

• Membrane Journal
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• v.34 no.1
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• pp.38-49
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• 2024

Wastewater treatment using membrane bioreactors has been extensively used to alleviate water shortage and pollution by improving the quality of the treated water discharged into the environment. However, membrane biofouling persistently holds back an MBR process by reducing the process efficiency. Herein, we synthesized carbon nanospheres (CNSs) with many hydrophilic oxygen groups and utilized them as an additive to prepare high-performance ultrafiltration (UF) membranes with hydrophilicity and porous pore structure. CNSs were found to form crescent-shaped pores on the membrane surface, increasing the mean surface pore size by about 40% without causing significant defects larger than bubble points, as the CNS content increased by 4.6 wt%. In addition, the porous pore structure of CNS composite membranes was also attributable to the CNS's isotropic morphologies and relatively low particle number density because the aforementioned properties contributed to preventing the polymer solution viscosity from soaring with the loading of CNS. However, too porous structure compromised the mechanical properties, such that CNS2.3 was the best from a comprehensive consideration including the pore structure and mechanical properties. As a result, CNS2.3 showed not only 2 times higher water permeability than CNS0 but also 5 times longer operation duration until membrane cleaning was required.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.1-14
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• 2024

An image-reconstruction technology, involving the deployment of an unmanned mobility equipped with high-speed LiDAR (Light Detection And Ranging) has been proposed to reconstruct the shape of abandoned mine. Unmanned mobility operation is remarkably useful in abandoned mines fraught with operational difficulties including, but not limited to, obstacles, sludge, underwater and narrow tunnel with the diameter of 1.5 m or more. For cases of real abandoned mines, quadruped robots, quadcopter drones and underwater drones are respectively deployed on land, air, and water-filled sites. In addition to the advantage of scanning the abandoned mines with 2D solid-state lidar sensors, rotation of radiation at an inclination angle offers an increased efficiency for simultaneous reconstruction of mineshaft shapes and detecting obstacles. Sensor and robot posture were used for computing rotation matrices that helped compute geographical coordinates of the solid-state lidar data. Next, the quadruped robot scanned the actual site to reconstruct tunnel shape. Lastly, the optimal elements necessary to increase utility in actual fields were found and proposed.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.867-879
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• 2020

As the era of space technology utilization is approaching, the launch of CAS (Compact Advanced Satellite) 500-1/2 satellites is scheduled during 2021 for acquisition of high-resolution images. Accordingly, the increase of image usability and processing efficiency has been emphasized as key design concepts of the CAS 500-1/2 ground station. In this regard, "CAS 500-1/2 Image Acquisition and Utilization Technology Development" project has been carried out to develop core technologies and processing systems for CAS 500-1/2 data collecting, processing, managing and distributing. In this paper, we introduce the results of the above project. We developed an operation system to generate precision images automatically with GCP (Ground Control Point) chip DB (Database) and DEM (Digital Elevation Model) DB over the entire Korean peninsula. We also developed the system to produce ortho-rectified images indexed to 1:5,000 map grids, and hence set a foundation for ARD (Analysis Ready Data)system. In addition, we linked various application software to the operation system and systematically produce mosaic images, DSM (Digital Surface Model)/DTM (Digital Terrain Model), spatial feature thematic map, and change detection thematic map. The major contribution of the developed system and technologies includes that precision images are to be automatically generated using GCP chip DB for the first time in Korea and the various utilization product technologies incorporated into the operation system of a satellite ground station. The developed operation system has been installed on Korea Land Observation Satellite Information Center of the NGII (National Geographic Information Institute). We expect the system to contribute greatly to the center's work and provide a standard for future ground station systems of earth observation satellites.