• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.113-119
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• 2013

The requirement of rehabilitation is increasing from the stroke, spinal cord injury. One of the most difficult part is the upper limb rehabilitation because of its nervous complexity. A rehabilitation has effectiveness when a professional therapist treats in work at facility, but it has problems of an accessibility, a constant availability, a self-participation and taking lots of cost and time. In this paper, we test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the upper limb function from the 3D trajectory information which is gathered from stereo vision sensor(Kinect). From the result, PCA, ICA have low accuracy, but LDA, SVM have good accuracy to use for physical rehabilitation on the upper limb function.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.3
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• pp.115-122
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• 2017

To achieve designed quality and reliability from R&D to force integration, an establishment of precise and distinct specifications and standards are required. However, the recent process of R&D standardization on defense acquisition system, has brought plenty of corrections on specifications and standards that may cause not only difficulties to production line and retardation to the military forces, but also a degradation of provided weapon systems. Therefore, a technical review should be performed by the developer, the producer, and the client, establishing the standard that include mass production requirements as well as clients' requirements. This paper propose a specified solution on process of R&D standardization, by applying a substantial FCA(Functional Configuration Audit) and PCA(Physical Configuration Audit) which implies participation of related agencies. By the improved PCA, 2,023 corrections on 74 types of QAR(Quality Assurance Requirement)s and 12,715 corrections on drawings are identified.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.836-850
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• 2009

Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5446-5451
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• 2014

Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1139-1145
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• 2014

Creation of a human skeleton model and characterization of the variation in the bone shape are fundamentally important in many applications of biomechanics. In this paper, we present a parametric shape modeling method for femurs that is based on extracting the main parameter of variations of the femur shape from a 3D model database by using statistical shape analysis. For this shape analysis, principal component analysis (PCA) is used. Application of the PCA to 3D data requires bringing all the models in correspondence to each other. For this reason, anatomical landmarks are used for guiding the deformation of the template model to fit the 3D model data. After subsequent application of PCA to a set of femur models, we calculate the correlation between the dominant components of shape variability for a target population and the anatomical parameters of the femur shape. Finally, we provide tools for visualizing and creating the femur shape using the main parameter of femur shape variation.

• 전자공학회논문지 IE
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• v.46 no.1
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• pp.37-43
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• 2009

In this paper, we propose a new algorithm for 3D model retrieval based on spherical coordinate system. We obtains sample points in a polygons on 3D model. We convert a point in cartesian coordinates(x, y, z) to it in spherical coordinate. 3D shape features are achieved by adopting distribution of zenith of sample point in spherical coordinate. We used Osada's method for obtaining sample points on 3D model and the PCA method for the pose standardization 3D model. Princeton university's benchmark data was used for this research. Experimental results numerically show the precision improvement of proposed algorithm 12.6% in comparison with Vranic's depth buffer-based feature vector algorithm.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.163-167
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• 2009

3D human body scan technique is known to be practically useful in industrial field as the technique becomes more precise and cheaper. Landmark extraction is essential for full utilization of the scan data. In this paper, we suggest an algorithm for automatic landmark extraction. For this purpose, we perform markerless matching to the target data using PCA analysis and quasi-Newton optimization. Landmarks are extracted from the topology of resulting body.

• Journal of Microbiology and Biotechnology
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• v.16 no.12
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• pp.1995-1999
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• 2006

Pseudomonas sp. K82 cultured in p-hydroxybenzoate induces protocatechuate 4,5-dioxygenase (PCD 4,5) for p-hydroxybenzoate degradation. In this study, a 6.0-kbp EcoR1 fragment containing p-hydroxybenzoate degradation genes was cloned from the genome of Pseudomonas sp. K82. Sequence analysis identified four genes, namely, pcaD, pcaA, pcaB, and pcaC genes known to be involved in p-hydroxybenzoate degradation. Two putative 4-hydroxyphenylpyruvate dioxygenases and one putative oxidoreductase were closely located by the p-hydroxybenzoate degradation genes. The gene arrangement and sequences of these p-hydroxybenzoate degradation genes were similar to those of Comamonas testosteroni and Pseudomonas ochraceae. PcaAB (PCD4,5) was overexpressed in the expression vector pGEX-4T-3, purified using a GST column, and confirmed to have protocatechuate 4,5-dioxygenase activity. The N-terminal amino acid sequences of overexpressed PCD4,5 were identical with those of purified PCD4,5 from Pseudomonas sp. K82.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.37-45
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• 2009

A method of conflict detection and resolution is described by using simple geometric approach. Two VAVs are dealt with and considered as point masses with constant velocity. This paper discusses en route aircraft which are assumed to be linked by real time data bases like ADS-B. With this data base, all DAVs share the information each other. Calculating PCA (Point of Closest Approach), we can evaluate the worst conflict condition between two VAVs. This paper proposes one resolution maneuvering logic, which can be called 'Vector Sharing Resolution'. In case of conflict, using miss distance vector in PCA, we can decide the directions for two VAVs to share the conflict region. With these directions, VAVs are going to maneuver cooperatively. First of all, this paper describes some '2-D' conflict scenarios and then extends to '3-D' conflict scenarios.