• Journal of Digital Contents Society
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• v.15 no.4
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• pp.457-464
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• 2014

Recently, interests in space information data are increasing due to the initiation of spatial information open platform service by the Ministry of Land, Infrastructure and Transport. The purpose of this study is optimizing management and storing of the texture data, one kinds of 3D-spatial data. First, extract 3D-spatial data through the aerial triangulation and 3D-writing using raw image taken with the Multi-directional aerial camera and the vertical aerial camera. And develop the method to create single texture data and related technique by align and place corresponding 3D-spatial data to optimal storage space. Through experiment, the results show effect of 8 times of storage capacity reduction compared to existing single-file storage method, additionally, new method can improve file management efficiency in comparison with multiple file storage method. The results of this study can be cornerstone of three-dimensional space information management when dealing with bulk data, and utilizations will be enhanced through the further studies and algorithm improvement.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.7
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• pp.539-549
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• 2017

In this paper, experimental studies on the shroud separation were performed to investigate characteristics of the shroud separation at mach 3. Shroud separation tests were carried out in the vertical free-jet wind tunnel that is capable of testing separable structures. A shroud model was miniaturized to meet test objectives and test section dimensions of the wind tunnel. Pneumatic Locking and separation mechanisms were designed considering external force due to free stream. High speed cameras were used to record the shroud motion and unsteady shock patterns over the deploying shrouds during the shroud separation process. Also, unsteady pressures on the nose surface were measured by using the pressure sensors. Through the tests, the measurement data necessary for researches on the shroud separation technology were obtained. Shroud separation behaviors and characteristics of unsteady pressure on the nose surface for each external flow conditions were analyzed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.2
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• pp.35-42
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• 1992

The effectiveness of the support-isolation system for the equipment mounted on the primary structure is evaluated to reduce its responses under the earthquake load with considering the interaction between the primary structure and the internal equipment in this paper. A computer code (KBISAP) is developed to analyze the above system using the matrix condensation technique and constant average acceleration method. To evaluate the effectiveness of the support-isolation system, three systems are used in this study as follows: i) fixed-base structure with support-fixed equipment, ii) base-isolated structure with support-fixed equipment and iii) fixed-base structure with support-isolated equipment. The results of case study show that the acceleration of equipment with the support-isolation system is less than that of the support-fixed equipment in the base-isolated structure and significantly reduced the response compared with that of the support-fixed equipment in the fixed-base structure with the reduction factor of 8. The support-isolation system used in this study can reduce the response and also increase the safety margin of the important safety-related internal equipments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.4_1
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• pp.9-21
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• 1992

The effectiveness of the isolation system installed at the base of the primary structure and at the support of the substructure mounted on the primary structure is evaluated for reducing of structural responses under different earthquakes in this paper. The structural responses are analyzed to identify its behavior due to the input motion characteristics such as various peak acceleration and frequency content. Three analytical models are used to evaluate the effectiveness of the isolation system in this study as follows: fixed-base primary structure with support-fixed substructure, base-isolated primary structure with support-fixed substructure, and fixed-base primary structure with support-isolated substruciure. A computer code (KBISAP) is used for numerical integration of equation of motion considering the interaction between the primary structure and the secondary structure. The matrix condensation technique and constant average acceleration method are utilized in this program. And also, the effective stiffness of the base-isolator on reducing the structural response are evaluated for various earthquakes through the relationship of the acceleration - displacement.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.304-311
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• 2014

In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.2
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• pp.131-136
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• 2011

The furrow calculation and traffic measurement of sailing ship using computer vision are useful methods to prevent maritime accident by predicting the possibility of an accident occurrence in advance. In this paper, sailing ships are recognized using image erosion, differential operator and minimax value, which can be verified directly because the calculated coordinates are displayed on electronic navigation chart. The developed algorithm based on area information of this paper has the advantage which is compared to the conventional radar system focused on point information.

• Proceedings of KOSOMES biannual meeting
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• 2007.05a
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• pp.161-168
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• 2007

In this paper, the floating body with double barriers is introduced with a study on the flow patterns and characteristics in around the floating body by using 2 frame p article tracking method. This paper introduce an analisys method to predict the characteristics of flow around the neighbording fields of Floating Body with double barriers in order to investigate a high performance model. Flow visualization has conducted in a circulating water channel by a high speed camera and etc. Flow phenomena according to velocity distribution and flow separation around the floating body with double barriers were obtained by two-dimensional PIV system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.15-24
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• 2008

For high-dimensional classification tasks, such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in linear discriminant analysis-based methods for dimension reduction is what is known as the small sample size (SSS) problem. Recently, to solve the SSS problem, a way of employing a dissimilarity-based classification(DBC) has been investigated. In DBC, an object is represented based on the dissimilarity measures among representatives extracted from training samples instead of the feature vector itself. In this paper, we propose a new method of optimizing DBCs using multi-level fusion strategies(MFS), in which fusion strategies are employed to represent features as well as to design classifiers. Our experimental results for benchmark face databases demonstrate that the proposed scheme achieves further improved classification accuracies.