• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.2
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• pp.159-166
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• 2018

In this study, the internal structural design, dynamic characteristics and load analyses of the small scaled rotor blade required for LCH(Light Civil Helicopter) main rotor wind tunnel test were carried out. The test is performed to evaluate the aerodynamic performance and noise characteristics of the LCH main rotor system. Therefore, the Mach-scale technique was appled to design the small scaled blade to simulate the equivalent aerodynamic characteristics as the full scale rotor system. It is necessary to increase the rotor speed to maintain the same blade tip speed as the full scale blade. In addition, the blade weight, section stiffness, and natural frequency were scaled according to the Mach-type scaling factor(${\lambda}$). For the design of skin, spar, torsion box, which are the main components of the blade, carbon and glass fiber composite materials were adopted, and composite materials are prepreg types that can be supplied domestically. The KSec2D program was used to evaluate the section stiffness of the blade. Also, structural loads and dynamic characteristics of the Mach scale blade were investigated through the comprehensive rotorcraft analysis program CAMRADII.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.93-102
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• 2020

The design of large complex mechanical systems, such as automobile, aircraft, and ship, is a kind of Multidisciplinary Design Optimization (MDO) because it requires both experience and expertise in many areas. With the rapid development of technology and the demand to improve human convenience, the complexity of these systems is increasing further. The design of such a complex system requires an integrated system design, i.e., MDO, which can fuse not only domain-specific knowledge but also knowledge, experience, and perspectives in various fields. In the past, the MDO relied heavily on the designer's intuition and experience, making it less efficient in terms of accuracy and time efficiency. Process integration and the design optimization framework mainly support MDO owing to the evolution of IT technology. This paper examined the procedure and methods to implement an efficient MDO with reasonable effort and time using RCE, an open-source PIDO framework. As a benchmarking example, the authors applied the proposed MDO methodology to a bulk carrier's conceptual design synthesis model. The validity of this proposed MDO methodology was determined by visual analysis of the Pareto optimal solutions.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.1
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• pp.1-7
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• 2017

As a part of studies to lower the infrared (IR) emissivity from the surface of exhaust nozzle in the turbo jet engine, stainless steel plate was coated with copper particle/meta-aramid resin composites and the IR emissivity of the plate were investigated. Binders of filler particles based on synthetic polymers generally undergo thermal decomposition before $300^{\circ}C$. It was found that the meta aramid resin was thermally stable after the test at $320^{\circ}C$, confirming the excellent thermal stability. Contents of copper particles in the composites were varied from 0 to 70% by volume. It was observed that the copper particle/meta aramid resin composites showed good adhesion after the tests at $320^{\circ}C$. The specimen coated with the composite containing 50 vol% of copper particles showed the lowest IR emissivity, 0.6, at $320^{\circ}C$.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.1
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• pp.67-75
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• 2014

A full-scale static test for a composite structure small aircraft (KC-100) was conducted in the KARI. The test includes 15 full-scale test and 7 local test conditions. Test requirements with test schedule, test article with dummy structures, test load generation, test system, and equipment are introduced for the test. Test load data of the 1st test condition(U1) was analyzed to evaluate an accuracy of load control for the test. The analysis results show that load data obtained during test were within tolerance of Static Null Pacing Error(SNPE) and the error value of load control was 8.6N. The error of load controls for the full-scale static test using dozens of actuators was calculated by a method suggested by authors. Test data for all other test conditions is also shown in this paper. Finally, reactions measured from restraint system of the U1 test condition show that the reaction changes as load increment. The factors which may change the change of reactions for a full-scale static test are introduced in this study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of Digital Convergence
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• v.8 no.1
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• pp.41-53
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• 2010

In this paper, we discuss patent pool and a patent valuation scheme for fair royalty distribution among the patents in a pool. In the knowledge-based economy, intellectual capital-the accumulation of technology and know-how-is recognized as the most important source of company's competitive advantage and economic growth. By providing exclusive rights to patent holders, the patent system aims to encourage innovation-invention & commercialization of new technologies-in order to raise the standard of living. However, drawbacks of patent system, which occur as the number of patents issued increases rapidly and patent ownership is fragmented, may slow down the innovation efforts seriously. A promising solution is the patent pool approach, which was for instance employed by the U.S. congress during World War I to free the airplane manufacturers from the patent tangle by letting them license all the patents for a fee. It is necessary to figure out relative technological contribution of patent for fair distribution of royalty revenues among patent holders. The Rating/Ranking Method seems to fit to that valuation purpose. We examined technology valuation models from various organizations and selected a set of more influential valuation factors which can be incorporated as scoring criteria in the Rating/Ranking Method.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.314-324
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• 2014

Aerospace industry is a combination of high technologies which has several characteristics such as product reliability, precision, light weight, and energy efficiency. Nowadays, each country is trying to invigorating knowledge and information sharing between the companies for the synergy effect of aerospace industry. However, the research and empirical analysis on the vitalization of aerospace industry cluster are insufficient. Therefore, this study aims to firstly classify the supporting functions of government for aerospace industry cluster into five types by analyzing existing literatures and status reports issued by government. Secondly, companies are surveyed on the five classified types of supporting functions by questionnaire. Questionnaire survey responded by 30 aerospace companies in Gyeongnam aerospace industry cluster are analyzed. Quantitative analysis methods were used for statistical analysis. Based on the analysis, improvement directions of government supporting functions are suggested. The results of this study is expected to help policy making for invigorating the aerospace industry cluster.

• Journal of the Ergonomics Society of Korea
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• v.29 no.5
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• pp.751-762
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• 2010

The purpose of this study was to investigate the job stress factors of aircraft composite material part manufacturing workers using survey based on 'Job stress factors evaluation tool for Koreans' that was developed by KOSHA in 2003. Two hundred and fifty workers participated in this study, and among them 204 responses were analyzed for this study due to the unreliability and insincerity of responses. The eight job stress factors which are physical environment, job autonomy, job insecurity, organizational system, workplace culture, unfair compensation, relationship conflict, and job requirement were analyzed. The results showed that the stress level of the six job stress factors which are physical environment, job autonomy, job insecurity, organizational system, workplace culture, unfair compensation was relatively higher than that of other industry workers. Generally, all eight job stress factors showed higher stress with temporary workers than with permanent workers, and especially job autonomy, job insecurity, organizational system, and unfair compensation factors showed statistically significant differences (p<0.05). Since the temporary workers are insecure with their job, weak position in organization, having little self-control for the job and lower pay level than that of permanent workers though the job is as same as permanent workers', the stress level of above job stress factors would be much higher than that of the other factors. The group of unsatisfactory with workplace showed higher job stress than group of satisfactory with workplace in all job stress factors, as expected, at the statistically significance level (p<0.05). From the results of this study, the work loss due to the job stress could be prevented, and accurate stress factors could be removed at the workplace. Also the job stress management program can be implemented to improve the work efficiency and the workers' quality of life.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.189-196
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• 2017

This study investigates customer satisfaction with full service carriers (FSC) and low cost carriers (LCC) using social media sentiment evaluation. From 2008 to 2016, a total of 77,591 tweets about two FSC and six LCC were aggregated and classified as per airline choice factors. Sentiment evaluation was employed to assess customer satisfaction by three appraisers. The results showed that customer satisfaction with LCC was significantly higher (p<0.001) compared to FSC. Furthermore, overall customer satisfaction with both FSC and LCC has been facing a consistent downward trend since the last seven years. The results also highlighted low customer satisfaction with respect to booking and flight operation factors, and a steep decline in customer satisfaction across booking, onboard services, and marketing factors for FSC. The results of this study have practical implications for the airline industry, which can use this quantitative data to improve customer satisfaction with FSC and LCC.