• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.285-288
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• 2006

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to teaming algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generation turbine and power turbine are considered for estimation for performance deterioration of a complex component at design point was conducted. As a result of that, complex defect diagnostics has been conducted. As a result, the accuracy of diagnostics were verified with its relative error with in 10% at each component.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.3
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• pp.238-247
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• 2007

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to learning algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generator turbine and power turbine are considered for engine performance deterioration and estimation for performance deterioration of a single component at design point was conducted. As a result of that, defect diagnostics has been conducted. The input criteria for the genetic algorithm to guarantee the high stability and reliability was discussed as increasing learning data sets. As a result, the accuracy of defect estimation and diagnostics were verified with its RMS error within 3%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.45-53
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• 2020

Conventional Convolutional Neural Network(CNN) algorithms have limitations in detecting small objects in large image. In this paper, we propose an improved model which is based on Region Of Interest(ROI) selection and image dividing technique. We prepared YOLOv3 / Faster R-CNN algorithms which are transfer-learned by airfield and aircraft datasets. Also we prepared large images for testing. In order to verify our model, we selected airfield area from large image as ROI first and divided it in two power n orders. Then we compared the aircraft detection rates by number of divisions. We could get the best size of divided image pieces for efficient small object detection derived from the comparison of aircraft detection rates. As a result, we could verify that the improved CNN algorithm can detect small object in large images.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.1
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• pp.30-40
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• 2015

Recently, the need of RPA(Remotely Piloted Aircraft) pilots is increasing rapidly with many requirements in order to be a beginner RPA pilot, including basic flight training, instrument flight qualification training, and aircraft type switching training. When RPA pilot gets disqualified, there will be generated much waste of efforts and expenses of trainees those pilots who are disqualified. Therefore, the methodology of pre-verifying those pilots who are not proper as RPA pilots through various scientific methods will save time and expenses with pre-reducing the pilots who will get disqualified later on. The methodology of aptitude test of RPA pilots is laid out as a consideration of pre-study of RPA pilots work analysis, and select types of aptitude test. A suitability of aptitude test is verified. In order to diagnose the flight aptitude precisely, it requires to be developed. Flight aptitude test tools might be connected with training program which could foster piloting aptitude with pre-diagnosing RPA pilot trainee selecting process. For that reason, we made an experiment in order to verify credibility and suitability of these selected programs with developing RPA pilot aptitude test tools. And also, we analyzed relationships among characteristics, analysis of data, and variables to verify the efficiency of data from prior experiment. Through this thesis, we expect to raise efficiency of flight training by providing pre-flight aptitude test information of RPA pilots.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.33-37
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• 2006

Since 1993, the civil aviation community through RTCA (Radio Technical Commission for Aeronautics) and the ICAO (International Civil Air Navigation Organization) have been working on the definition of GNSS augmentation systems that will provide improved levels of accuracy and integrity. These augmentation systems have been classified into three distinct groups: Aircraft Based Augmentation Systems (ABAS), Space Based Augmentation Systems (SBAS) and Ground Based Augmentation Systems (GBAS). The last one is an implemented system to support Air Navigation in CAT-I approaching operation. It consists of three primary subsystems: the GNSS Satellite subsystem that produces the ranging signals and navigation messages; the GBAS ground subsystem, which uses two or more GNSS receivers. It collects pseudo ranges for all GNSS satellites in view and computes and broadcasts differential corrections and integrity-related information; the Aircraft subsystem. Within the area of coverage of the ground station, aircraft subsystems may use the broadcast corrections to compute their own measurements in line with the differential principle. After selection of the desired FAS for the landing runway, the differentially corrected position is used to generate navigation guidance signals. Those are lateral and vertical deviations as well as distance to the threshold crossing point of the selected FAS and integrity flags. The Department of Applied Science in Naples has create for its study a virtual GBAS Ground station. Starting from three GPS double frequency receivers, we collect data of 24h measures session and in post processing we generate the GC (GBAS Correction). For this goal we use the software Pegasus V4.1 developed from EUROCONTROL. Generating the GC we have the possibility to study and monitor GBAS performance and integrity starting from a virtual functional architecture. The latter allows us to collect data without the necessity to found us authorization for the access to restricted area in airport where there is one GBAS installation.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3120-3137
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• 2021

Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.458-461
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• 2003

This paper presents a concept on seat assignment systems. The concept is based on the assumption that customers or passengers prefer to select seats at their own choosing and allowing them to do so would fragment remaining seats. This undesirable condition may make a group of people intending to be seated together unable to find a sufficient number of consecutive seats. The concept proposed is to set aside a number of seats when the map of available seats is shown for customer's selection. A number of functions are created to allot seats to be visible for choosing according to the number and locations of the remaining seats and the number in the group of the passengers. Passengers' preferences such as window or aisle seating, front seating are taken into accounts. A primitive example of seat assignment system of a Boeing 717 aircraft, assuming the number of passengers in a group being 1, 2 or 3, is given based on the concept.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.14 no.1
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• pp.63-69
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• 2006

The operational environment of helicopters extends from the civil air traffic control system to remote and hazardous areas and from day operations under visual flight conditions to night operations in adverse weather. Helicopters can move in any direction, remain stationary while airborne, climb and descend vertically, and take off and land almost anywhere. Thus their range of maneuvers and control requirements vary more widely than do those of fixed-wing aircraft. In this study, I analyzed the job of helicopter pilot through methods of observation, and classified the required ability of them into the domain of cognitive, perceptual/spatial, psychomotor. I expect that the result of this study will be used to aid training and selection of helicopter pilot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.729-737
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• 1996

Laminated composite plates have been applied to aircraft structures because their properties are superior to the conventional materials and the laminates have anisortropic elastic properties. However, it tis diffcult to determine stacking structures using actual design variables for the lack of searching capability of existing optimization technique. GA(generic algorithms) are robust search algorithms based on the mechanics of natural selection and natural genetics. Therefore, this study presents an application of IGA to stiffness and weight optimization design and gives the various stacking structures suitable to constraint conditions.