• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.209-212
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• 2003

In this paper, we proposed new speech feature parameter through parts-based feature extraction of speech spectrum using Non-Negative Matrix Factorization (NMF). NMF can effectively reduce dimension for multi-dimensional data through matrix factorization under the non-negativity constraints, and dimensionally reduced data should be presented parts-based features of input data. For speech feature extraction, we applied Mel-scaled filter bank outputs to inputs of NMF, than used outputs of NMF for inputs of speech recognizer. From recognition experiment result, we could confirm that proposed feature parameter is superior in recognition performance than mel frequency cepstral coefficient (MFCC) that is used generally.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.702-706
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• 1995

Integration of CAM and CAM information is important in the CIM era. For a CIM system, the feature representation can be a solution to the integration of product model data. These are geometry feature, functional feature, and manufacturing feature in the feature context. This paper proposes a framework to integrate the configuration design method, parametric modeling and the feature modeling method. The concept of design unit which is one level higher than functional feature and parametric modeling concept with functional features have been proposed.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.405-406
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• 2007

In this paper, we propose feature extraction and tracking algorithm using multi resolution in 2-dimensional wavelet domain. Feature extraction selects feature points using 2-level wavelet transform in interested region. Feature tracking estimates displacement between current frame and next frame based on feature point which is selected feature extraction algorithm. Experimental results show that the proposed algorithm confirmed a better performance than the existing other algorithms.

• Journal of Software Engineering Society
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• v.26 no.2
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• pp.31-44
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• 2013

Software product line (SPL) engineering is a reuse paradigm that helps organizations increase productivity and improve product quality by developing product from reusable core assets. In SPL, product configuration is the process of selecting the desired features and feature attributes for a given product from a feature model. In order to develop a successful product, feature and feature attribute selection that can achieve the product goal is important. There can be thousands of features and feature attributes resulting in myriads of configurations and finding the best configuration efficiently is a hard task. This paper proposes a systematic process for feature-based product configuration. To support development of a product that satisfys all product goals(business goals and quality goals), a model showing how feature and feature attribute combinations are related to product goals is included and a method for deriving an optimal product configuration using the model is proposed.

• Korean Journal of Computational Design and Engineering
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• v.18 no.1
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• pp.71-82
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• 2013

To modify product design easily, modern CAD systems adopt the feature-based model as their primary representation. On the other hand, the boundary representation (B-rep) model is used as their secondary representation. IGES and STEP AP203 edition 1 are the representative standard formats for the exchange of CAD files. Unfortunately, both of them only support the B-rep model. As a result, feature data are lost during the CAD file exchange based on these standards. Loss of feature data causes the difficulty of CAD model modification and prevents the transfer of design intent. To resolve this problem, a tool for recognizing design features from a B-rep model and then reconstructing a feature-based model with the recognized features should be developed. As the first part of this research, this paper presents a method for decomposing a B-rep model into simple volumes suitable for design feature recognition. The results of experiments with a prototype system are analyzed. From the analysis, future research issues are suggested.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1846-1856
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• 2016

This study proposes a novel feature point initialization method in order to improve the accuracy of feature point positions by fusing a vision sensor and a lidar. The initialization is a process that determines three dimensional positions of feature points through two dimensional image data, which has a direct influence on performance of a 6-DoF bearing-only SLAM. Prior to the initialization, an extrinsic calibration method which estimates rotational and translational relationships between a vision sensor and lidar using multiple calibration tools was employed, then the feature point initialization method based on the estimated extrinsic calibration parameters was presented. In this process, in order to improve performance of the accuracy of the initialized feature points, an iterative automatic scaling parameter tuning technique was presented. The validity of the proposed feature point initialization method was verified in a 6-DoF bearing-only SLAM framework through an indoor and outdoor tests that compare estimation performance with the previous initialization method.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.151-153
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• 2001

Feature Simplification is an essential method for multiple representations of spatial features in GIS. However, spatial features re various, complex and a alrge size. Among spatial features which describe spatial information. linear feature is the msot common. Therefore, an efficient linear feature simplification method is most critical for spatial feature simplification in GIS. This paper propose an original method, by which the problem of linear feature simplification is mapped into the signal processing field. This method avoids conventional geometric computing in existing methods and exploits the advantageous properties of wavelet transform. Experimental results are presented to show that the proposed method outperforms the existing methods and achieves the time complexity of O(n), where n is the number of points of a linear feature. Furthermore, this method is not bound to two-dimension but can be extended to high-dimension space.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.127-130
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• 2005

There are many methods in feature extraction have been developed. Recently, principal components analysis (PCA) and independent components analysis (ICA) is introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively In this paper, the feasibility of using nonlinear feature extraction will be studied. This method will employ the PCA and ICA procedure and adopt the kernel trick to nonlinearly map the data into a feature space. The goal of this study is to seek effectively useful feature for faults classification.

• Korean Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.142-149
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• 1997

This study is intended to develop a Feature based modeler. It is difficult to integrate CAD and CAM/CAPP with information that is given only by a conventional CAD system. Therefore a lot of studies have concentrated on a Feature based CAD system. But conventional Feature based modelers have had limitation on providing sufficient information related to Feature interaction. If a Feature based modeler is to be used in assembly simulation, a new Feature-based modeling method needs to be developed. Also to support collision detection between parts, we have to handle Feature interaction systematically. Therefore we suggest Cell data structure which handles interaction of Features by volume. The volume created by Feature interaction is saved as a Cell. With the Cell structure we solve problems involved with Feature interaction. This study shows how the Cell data structure can manage Feature interaction and give enough information in assembly simulation.

• ETRI Journal
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• v.24 no.6
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• pp.469-472
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• 2002

Speaker adaptation techniques are generally used to reduce speaker differences in speech recognition. In this work, we focus on the features fitted to a linear regression-based speaker adaptation. These are obtained by feature transformation based on independent component analysis (ICA), and the feature transformation matrices are estimated from the training data and adaptation data. Since the adaptation data is not sufficient to reliably estimate the ICA-based feature transformation matrix, it is necessary to adjust the ICA-based feature transformation matrix estimated from a new speaker utterance. To cope with this problem, we propose a smoothing method through a linear interpolation between the speaker-independent (SI) feature transformation matrix and the speaker-dependent (SD) feature transformation matrix. From our experiments, we observed that the proposed method is more effective in the mismatched case. In the mismatched case, the adaptation performance is improved because the smoothed feature transformation matrix makes speaker adaptation using noisy speech more robust.