• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.132-137
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• 2017

Drone is currently used for wide application areas in our real life. Also it performs more important functions. We propose a method of drone operation system for the prevention of industrial disaster. In normal operation of drone system the drone monitors the industrial sites according to the planned flight path with acquiring the monitored images and send the image information to the server. The server analyzes and compares the images to DB information by calculating the similarity based on the threshold. Then the system decides whether the industrial sites has problems or not. If the abnormal condition is occurred, the drone change the flight path to abnormal flight path and keep monitoring the industrial sites with measuring the air status by sensors and sends all information to server system on the ground. If the emergency case is occurred, drone approaches the closest position of accident points and acquiring the all information and send them to server and 119 center.

• Smart Structures and Systems
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• v.20 no.3
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• pp.385-396
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• 2017

The recent advancements in sensing technologies allow us to record measurements from target structures at multiple locations and with relatively high spatial resolution. Such measurements can be used to develop data-driven methodologies for condition assessment, control, and health monitoring of target structures. One of the state-of-the-art technologies, Fiber Optic Strain Sensors (FOSS), is developed at NASA Armstrong Flight Research Center, and is based on Fiber Bragg Grating (FBG) sensors. These strain sensors are accurate, lightweight, and can provide almost continuous strain-field measurements along the length of the fiber. The strain measurements can then be used for real-time shape-sensing and operational load-estimation of complex structural systems. While several works have demonstrated the successful implementation of FOSS on large-scale real-life aerospace structures (i.e., airplane wings), there is paucity of studies in the literature that have investigated the potential of extending the application of FOSS into civil structures (e.g., tall buildings, bridges, etc.). This work assesses the feasibility of using FOSS to predict operational loads (e.g., wind loads) on chain-like structures. A thorough investigation is performed using analytical, computational, and experimental models of a 4-story steel building test specimen, developed at the University of Southern California. This study provides guidelines on the implementation of the FOSS technology on building-like structures, addresses the associated technical challenges, and suggests potential modifications to a load-estimation algorithm, to achieve a robust methodology for predicting operational loads using strain-field measurements.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.173-181
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• 2008

Gas turbine engine for aircraft are usually operated at the altitude condition which is quite different from the ground condition. In order to measure the precise performance data at the altitude condition, the engine should be tested at the altitude condition by a real flight test or an altitude simulation test with an altitude test facility. In this paper describes the design of altitude test facility for turbo shaft engine. This facility will be located in test cell #2 at the Korea Aerospace Research Institute. Test Cell #2 will be used for altitude testing engines with mass flow rate up to 40kg/s and inlet temperatures in the range from $-65^{\circ}C$ to $200^{\circ}C$. The existing compressor/exhauster station with heater & cooler system will be used to simulate altitude conditions in Test Cell #2.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.4
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• pp.283-289
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• 2018

Launch Acceptability Region(LAR) means an area for successfully hitting the target. And LAR should be calculated in real time on aircraft so that LAR can be seen by pilot. LAR can be changed by the launch condition of the bomb, the impact condition of the target, and the atmospheric condition at the time of flight of the bomb. In this paper, we propose the calculation method of LAR using Artificial Neural Network(ANN). The learning data was generated by changing each condition from existing LAR model, and LAR model was derived through ANN learning. We confirmed the accuracy of the new LAR model by comparing the difference between the result data of existing LAR model and the new LAR model. And we confirmed the possibility of real time calculation of the LAR model on the aircraft by comparing the calculation time.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.93-101
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• 2022

It is essential to prevent LoC(Loss-of-Control) or upset situations caused by stall, icing or sensor malfunction in aircraft, because it may lead to the crash of the aircraft. With this regard, it is crucial to correctly identify the dynamic characteristics of aircraft in such upset conditions. In this paper, we present a SID(System IDentification) method utilizing the moving-window based least-square and the adaptive-order ZKF(Zonotopic Kalman Filter), which is more effective than the existing Kalman-filter based SID for the aircraft in upset condition at a high angle of attack with temporary sensor malfunction. The proposed method is then tested on real flight data and compared with the existing one.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.184-192
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• 2010

In this paper, an initial fine alignment algorithm, which is developed for the strap-down inertial navigation systems of satellite launch vehicles, is considered. For fast and accurate alignment, a simple closed-loop estimation algorithm using a proportional-integral controller is introduced. Through computer simulation for the sway condition in the launch pad, it is shown that a simple filter structure can guarantee fast computational speed that is adequate for real-time implementation as well as the required alignment accuracy and robustness. In addition, its implementation results are presented for the Naro-1 flight test.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.3
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• pp.60-67
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• 2008

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideration of the performance deterioration consist of the compressor, the gas generation turbine and the power turbine. Compared to the on-design point, the teaming data has been increased 200 times in case off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimal division has been proposed for learning time decrease as well as the high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been confirmed under 5 %.

• Aerospace Engineering and Technology
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• v.2 no.2
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• pp.105-114
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• 2003

It is conducted to analyze vibration loads on the 1st stage of KSR-III(KSR : Korea Sounding Rocket) during their ground transportation and various handling process. These loads may be different from the real flight environment. Inadequate assessment of these loads can cause not only local damages on the rocket system but also the critical problem like flight mission failure. Therefore, transportation and handling loads must be considered during design and attenuated to ensure that the rocket structural damage does not occur. This work is concerned with the generation of criteria and prediction of transportation and handling loads for KSR-III. The results show that the shipping container is well designed to satisfy the design requirements. The maximum vibration level recorded during whole transportation and handling for KSR-III is less than 2g, the criteria of KSR-III movement condition.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.350-353
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• 2007

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideriation of the performance deterioration is consist of the compressor, the gas generation turbine and the power turbine, repectively. Compared to the on-design point on the sea-level condition, the learning data has been increased 200 times in case of the off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimum division has been proposed to decrease learning time as well as to obtain high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been estimated under 5 %.

• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).