• 한국항공운항학회지
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• 제29권4호
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• pp.67-77
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• 2021

Space development has been led by the state due to huge investment, risks of development failure, and restrictions on international cooperation on technology development and transfer. For this reason, it has been developed mainly by some advanced countries and industrial network formed among them. However, Korean space industry is being promoted by the successful launch of 'KSLV-I' and 'CAS 500-I' and the space launch vehicle "KSLV-II" under development. Recently, Korea and U.S. agreed to end bilateral missile guidelines. Therefore it is expected that development of Korean space industry will be accelerated due to the disappearance of the constraining factors for the development of space launch vehicles. Accordingly, this study examined the development and formation of the Korean space industry through the framework of network analysis. Based on this, the effect of structural position in the industrial network as a resource on the performance of Korean space companies was examined. Panel analysis was applied. Through this study, ideas for fostering the domestic space industry and implications for national policy related with building an industrial ecosystem are derived. It contributes to the development of space industry in Korea.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• 한국기계가공학회지
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• 제21권2호
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• pp.64-72
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• 2022

Gear designers need to select the proper tolerances for deviations in both the center distance and parallelism of axes because these deviations cause high stresses and lead to fatigue breakage of the teeth. In this study, a three-dimensional finite element analysis model was developed for a helical gear used in metro vehicles, and a bending stress analysis method for gear pairs was established according to the contact position change. Using this model, the effect of shaft misalignment due to the center distance and shaft parallelism deviations on the bending stress of the gear was analyzed. As a result, the magnitude of the bending stress changed nearly linearly with the change in the center distance deviation. The tooth contact of the helical gear is biased toward the end of the tooth width when the parallelism deviations of the shaft occur, and the tooth root bending stress increases.

• 한국항행학회논문지
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• 제27권1호
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• pp.50-56
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• 2023

관성항법장치는 내장된 관성센서만을 이용하여 외부의 도움 없이 항체의 가속도 및 각속도를 이용하여 항법 정보를 계산한다. 하지만, 장시간 운용 시 관성항법장치는 시간이 지남에 따라 오차가 누적되어 항법 성능이 저하된다. 이러한 관성항법장치의 누적 오차를 줄이기 위하여 관성센서조립체를 일정한 절차로 회전시켜 관성센서 오차가 회전을 통해 상쇄되도록 항법 성능을 개선 시키는 연구가 활발히 진행되고 있다. 본 논문에서는 2축 회전형 관성항법장치의 성능에 영향을 미칠 수 있는 오차 요소를 식별하고 각 오차가 항법 성능에 어떠한 영향을 미치는지 분석하였다. 오차 분석 수행 후 시뮬레이션을 통해 관성항법장치의 항법 성능분석 결과를 제시하였다.

• 로봇학회논문지
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• 제18권1호
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.337-342
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• 2023

Since the electric vehicle uses an electric motor, problems have arisen as the driver hears the inherent noise of the motor or external noise, which was not a problem in the past, due to the overall lower noise environment than when using an internal combustion engine. Therefore, the purpose of this paper is to reduce the noise and vibration of electric motors for electric vehicles, and recently, to increase the speed of high-power, high-efficiency electric motors in a small size, and to develop low-noise motors, IPM motors are applied to produce 35KW electric motors for electric vehicles. A motor for low noise was designed and implemented. N-T Curve and efficiency map were confirmed as the final result of developing a 35KW low-noise motor for electric vehicles by applying the IPM motor applied in this paper. Based on 3500 rpm, Max Torque [Nm]: 121.15, Max Power [KW]: 44.04, and Max Efficiency [%]: 97.65, showing high efficiency.

• 드라이브 ㆍ 컨트롤
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• 제19권2호
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• pp.17-26
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• 2022

Recently, automatic transmissions caused a good improvement in the shift quality of a forklift. An advanced shift control algorithm, which was based on TCU firmware, was applied with embedded control technology and microcontrollers. In the clutch-to-clutch shifting, one friction element is released and the other friction element is activated. During this process, if the release and application timings are not synchronized, an overrun or tie-up occurs and ultimately leads to a shift shock. The TCU, which measures only the speed of the forklift, inevitably applies the open-loop shift control. In this situation, the speed ratio does not change during the clutch fill. The torque phase occurs until the clutch is disengaged. In this study, an offline shift logic of the learning control was proposed. It induced a synchronous shift when the learning control progressed. During this process, the reference current trajectory of the release clutch was corrected and applied to the next upshift. We considered the results of the overrun/tie-up characteristics of the upshift performed immediately before. The vehicle test proved that the deviation in shift quality, which was caused by the difference in the mechanical characteristics of the clutch, could be improved by the learning control.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• Structural Engineering and Mechanics
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• 제86권6호
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• pp.751-764
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• 2023

In practical engineering such as aerial refueling pipes, the boundary of the fluid-conveying pipe is difficult to be completely immovable. Pipes under movable and immovable boundaries are controlled by different dominant nonlinear factors, where the boundary mobility will affect the nonlinear dynamic characteristics, which should be focused on for adopting different strategies for vibration suppression and control. The nonlinear dynamic instability characteristics of functionally graded fluid-conveying pipes lying on a viscoelastic foundation under movable and immovable boundary conditions are systematically studied for the first time. Nonlinear factors involving nonlinear inertia and nonlinear curvature for pipes with a movable boundary as well as tensile hardening and nonlinear curvature for pipes with an immovable boundary are comprehensively considered during the derivation of the governing equations of the principal parametric resonance. The stability boundary and amplitude-frequency bifurcation diagrams are obtained by employing the two-step perturbation- incremental harmonic balance method (TSP-IHBM). Results show that the movability of the boundary of the pipe has a great influence on the vibration amplitude, bifurcation topology, and the physical meanings of the stability boundary due to different dominant nonlinear factors. This research has guidance significance for nonlinear dynamic design of fluid-conveying pipe with avoiding in the instability regions.

• 한국해양공학회지
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• 제37권3호
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• pp.99-110
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• 2023

When a fire accident accompanied by an explosion occurs, the surrounding firewalls are affected by impact and thermal loads. Damaged firewalls due to accidental loads may not fully perform their essential function. Therefore, this paper proposes an advanced methodology for evaluating the fire resistance performance of firewalls damaged by explosions. The fragments were assumed to be scattered, and fire occurred as a vehicle exploded in a large compartment of a roll-on/roll-off (RO-RO) vessel. The impact velocity of the fragments was calculated based on the TNT equivalent mass corresponding to the explosion pressure. Damage and thermal-structural response analyses of the firewall were performed using Ansys LS-DYNA code. The fire resistance reduction was analyzed in terms of the temperature difference between fire-exposed and unexposed surfaces, temperature increase rate, and reference temperature arrival time. The degree of damage and the fire resistance performance of the firewalls varied significantly depending on impact loads. When naval ships and RO-RO vessels that carry various explosive substances are designed, it is reasonable to predict that the fire resistance performance will be degraded according to the explosion characteristics of the cargo.