• Composites Research
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• v.17 no.1
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• pp.29-37
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• 2004

Honeycomb sandwich composite(HSC) structures have been widely used in aircraft and military industry owing to their light weight and high stiffness. Mechanical properties of honeycomb core materials are needed for accurate analysis of the sandwich composites. In this study. theoretical formula for effective elastic modulus and Poisson's ratio of honeycomb core materials was established using an energy method considering the bending, axial and shear deformations of honeycomb core walls. Finite-element analysis results obtained by using commercial FEA code, ABAQUS 6.3 were comparable to the theoretical ones. In addition, we performed tensile test of HSC plates and analyzed deformation behaviors and interlaminar stresses through its FEA simulation. An increased shear stress along the interface between surface and honeycomb core layers was shown to be the main reason for interfacial delamination in HSC plate under tensile loading.

• Applied Chemistry for Engineering
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• v.27 no.3
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• pp.252-258
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• 2016

The weight reduction and improved mechanical property are one of the prime factors to develop new materials for the aerospace industry. Composite materials have thus become the most attractive candidate for aircraft and other means of transportations due to their excellent property and light weight. In particular, fiber reinforced polymer (FRP) composite materials have been used as an alternative to metals in the aircraft. The composite materials have shown improved properties compared to those of metal and polymeric materials, which made the composites being used as the skin structure of the airplane. This review introduces different types of materials which have been developed from the FRP composite material and also one of the most advantageous ways to employ the composites in aircraft.

• Korean Journal of Materials Research
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• v.28 no.12
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• pp.738-747
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• 2018

For the aerospace structural application of high-strength 2xxx series aluminum alloys, stress corrosion cracking(SCC) behavior in aggressive environments needs to be well understood. In this study, the SCC sensitivities of 2024-T62, 2124-T851 and 2050-T84 alloys in a 3.5 % NaCl solution are measured using a constant load testing method without polarization and a slow strain rate test(SSRT) method at a strain rate of 10-6 /sec under a cathodic applied potential. When the specimens are exposed to a 3.5 % NaCl solution under a constant load for 10 days, the decrease in tensile ductility is negligible for 2124-T851 and 2050-T84 specimens, proving that T8 heat treatment is beneficial in improving the SCC resistance of 2xxx series aluminum alloys. The specimens are also susceptible to SCC in a hydrogen-generating environment at a slow strain rate of $10^{-6}/sec$ in a 3.5 % NaCl solution under a cathodic applied potential. Regardless of the test method, low impurity 2124-T851 and high Cu/Mg ratio 2050-T84 alloys are found to have relatively lower SCC sensitivity than 2024-T62. The SCC behavior of 2xxx series aluminum alloys in the 3.5 % NaCl solution is discussed based on fractographic and micrographic observations.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.250-265
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• 2020

In the N-Screen environment, HTML5 standard-based content development is inevitable. However, it is still passive the development of HTML5 manipulation type contents due to high development cost and insufficient infrastructure. Therefor we propose an efficient contents development model by convergence multimedia contents (such as video and audio) with HTML5 documents that can implement dynamic manipulation for user interaction. The proposed model is designed to divide the multimedia and iframe areas in the HTML5 layout page included the player for integrated contents control. Interactive HTML5 documents are divided into screen units and provided through iframe. The integrated control player composed based on the HTML5

• Composites Research
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• v.21 no.6
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• pp.1-7
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• 2008

The main objective of this study is to investigate the effect of salt water on the mechanical properties of a high modulus carbon-epoxy composite. Specimens were made of a carbon-epoxy composite UPN139B of SK Chemical and tested under inplane tension and shear after 0, 1, 3, 6, 9, and 12 months immersion in 3.5% salt water. Acceleration technique such as temperature elevation was not used. The tensile strengths and modulli in fiber and matrix direction did not show any remarkable degradation until 12 months immersion. In contrast to the tensile properties, shear strength and modulus started to gradually decrease up to about 10% of values of dry specimens after 12 months immersion. It was confirmed through the test that the material UPN139B can be an effective material for the shell structures in salt water to resist against the external pressure buckling because of the high fiber directional modulus and corrosion resistance.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.275-286
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• 2024

Purpose: The spare part cost is one of the most important cost factors with which construct Life Cycle Cost. The LCSP(Life Cycle Sustainment Plan) Guidebook issued by Korea Ministry of Defense, however, suggests a simple equation to estimate the spare part cost using maintenance task frequencies and each part cost. Therefore, following the cost estimation method in the LCSP Guidebook may lead to an improper cost estimation result since both the hierarchical structure of the weapon system and the part discard rate are not considered. The purpose of this study is to develop a new life cycle cost estimation method for spare parts of weapon system during its life cycle. Methods: In this study, the detailed cost structure of spare parts is provided. Also a new spare part cost estimation methods for the each cost element are proposed, considering the hierarchical structure of weapon system and the part discard rate. And the proposed spare cost estimation methods are applied to K system for a case study. Results: Based on the case study of K system, the spare part cost estimation method, proposed by this study, shows that it can complement the estimation method suggested by the LCSP Guidebook. It also shows that it is applicable to the weapon systems for Korea armed forces. Conclusion: The proposed life cycle cost estimation method for spare parts has an advantage of estimating the spare part cost more accurately. It is expected to be useful in analyzing the procurement alternatives objectively and making up the Korea armed forces budget effectively.

• The Korean Journal of Air & Space Law and Policy
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• v.19 no.2
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• pp.97-136
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• 2004

The Northeast Asian air cargo market has expanded tremendously as a result of the opening up of the Chinese market. The importance of the Asia-Pacific region in the global air transport has also increased. The exchange of human and material resources, services, and information in Northeast Asia, which is expected to increase in the near future, requires that the airlines operating within this region adopt a more liberalized approach. This paper introduced alternatives which can be applied to the Northeast Asian airlines industry so as to bring about the integration of regional air transport: First, this paper found a need for individual Northeast Asian nations to alter their policies towards the airlines industry. Second, each country should further liberalize their respective domestic air transport. Third, there is a need for freer air service agreements to be signed between the nations of Northeast Asia. Fourth, the strategic alliances between the airlines operating in Northeast Asia should be further strengthened. Fifth, this liberalization process should be carried out in an incremental manner, beginning with more competitive airports and routes, or with less-in-demand routes. Sixth, there is a need for a shuttle system to be put into place between the main airports in China, Korea, and Japan. Seventh, these three nations jointly develop aviation safety and security systems that are in accordance with international standards. Eighth, the liberalization process of the aviation industry should be undertaken in conjunction with other related fields. Ninth, organizations linking together civil aviation organization in the Asia-Pacific area should be formed, as should each government linking together. By doing so, these countries will be able to establish regular venues through which to exchange opinions on the integration and liberalization of the air cargo market so as to induce the gradual liberalization of the actual market. The liberalization of the air transport in Northeast Asia will prove to be a daunting task in the short term. However, if the Chinese airlines continue to exhibit continuous growth and Japanese airlines are able to complete their move towards a low-cost structure, this process could be completed earlier than expected. Over the last twenty five years the air transport has undergone tremendous changes. The most important factor behind these changes has been the increased liberalization of the market. As a result, rates have decreased while demand has increased. This has resulted in turning the air transport industry, which was long perceived as an industry in decline, into a high-growth industry. The only method of increasing regional exchanges in the air transport is to pursue further liberalization. The country which implements this liberalization process at the earliest date may very well emerge as a leading force within the air transport industry.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.86-95
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• 2019

Recently, the applications of carbon fiber have been broader than ever when it comes to such industrials as automobiles, ships, aerospace, civil engineering and architecture because of their lightweight-ness and high mechanical properties. This study analyzed mechanical properties and flexural behavior of carbon fiber reinforced cement composites(CFRC) with different fiber contents and fiber lengths, and also impact resistance by natural drop test on mortar specimens was compared and examined. In addition, contents of carbon fiber(CF) were varied by 0.5%, 1.0%, 2.0% and 3.0%. Fiber lengths was used for 6 mm and 12 mm, respectively. As a result of the test, the flow value was very disadvantageous in terms of fluidity due to the carbon fiber ball phenomenon, and the unit weight was slightly reduced. In particular, the compressive strength was decreased with increasing carbon fiber contents. On the other hand, the flexural strength was the highest with 12 mm fiber length and 2% fiber content. As the results of the impact resistance test, the specimens of plain mortar takes about 2~3 times to final fracture, while the specimens of CFRC is somewhat different depending on the increase of the fiber contents. However, when the fiber length is 12 mm and the fiber content is 2%, the impact resistance was the highest.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.