• 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.

• Korean Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.242-256
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• 1996

Although feature-based design is a promising approach to fully integrating CAD/CAM, current feature-based design approaches seldom provide methodologies to easily define and design features. This paper proposes a new approach to integrating parametric design with feature-based design to overcome those limitations by globally decomposing a design into a set of features and locally defining and positioning each feature by geometric constraints. Each feature is defined as a parametric shape which consists of a feature section, attributes, and a set of constraints. The generalized sketching and sweeping techniques are used to simplify the process of designing features. The proposed approach is knowledge-based and its computational efficiency in geometric reasoning is improved greatly. Parametrically designed features not only have the advantage of allowing users to efficiently perform design changes, but also provide designers with a natural design environment in which they can do their work more naturally and creatively.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.269-272
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• 2006

현재 제품계열 공학에서 feature 중심의 공통성 및 가변성 분석을 통한 재사용성에 대한 연구가 활발히 이루어지고 있다. 지금까지는 도메인 전문가의 직관 및 경험에 의해 feature가 분석되어 그 개념의 불명확함으로 재사용 측면에서 제한점을 내포하고 있다. 본 논문에서는 개별 feature 속성 List 작성을 통해 feature간의 의미관계를 중심으로 한 Pattern 분석 방법을 제시하고, 의미 유사성 관계를 적용한 feature 온톨로지 그래프를 이용하여 S/W 제품계열 도메인 공학에서 사용자와 개발자간의 동일한 해석이 가능하고 재사용성을 위한 명시적 feature를 분석 및 추출하는 모델을 제안한다.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.189-201
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• 2011

Feature models are widely used to model commonalities and variabilities among products during software product line development. Feature configurations are generated by selecting the features to be included in individual products. Automated tools to identify errors or inconsistencies in the feature models and configurations are essential to successful software product line engineering. This paper proposes a verification technique and tool based on semantic web technologies such as OWL, SWRL and Protege API. This approach checks the feature model and configuration based on predefined rules and provides information on existence of errors as well as the kinds of those errors. This approach is extensible due to ease of rule modification and may be easily applied to other environments because semantic web technologies can be easily integrated with other programming environments. This paper demonstrates how various semantic web-related technologies can support automatic verification of one kind of software development artifact, the feature model.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1544-1548
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• 2011

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.128-134
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• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.581-586
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• 1996

This paper presents a feature recognition system for recognizing and extracting feature information needed for machining from design data contained in the CAD database of AutoCAD system. The developed system carries out feature recognition from an orthographic view of a press mold containing not only atomic features such as holes, pockets, and slots, but also compound features. Based on the result of feature recognition, it generates a 3-D modeling of the press mold. Especially, The feature recognition part is designed for detecting feature styles according to feature definition and classification, extracting parameters for various atomic features, and constructing necessary data structures for the recognized features.

• Korean Journal of Computational Design and Engineering
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• v.5 no.1
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• pp.12-22
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• 2000

Network and Internet technology opens up another domain for building future CAD/CAM environment. The environment will be global, network-centric, and spatially distributed. In this paper, we present an approach for network-centric feature-based modeling in a distributed design environment. The presented approach combines the current feature-based modeling technique with distributed computing and communication technology for supporting product modeling and collaborative design activities over the network. The approach is implemented in a client/server architecture, in which Web-enabled feature modeling clients, neutral feature model server, and other applications communicate with one another via a standard communication protocol. The paper discusses how the neutral feature model supports multiple views and maintains naming consistency between geometric entities of the server and clients. Moreover, it explains how to minimize the network delay between the server and client according to incremental feature modeling operations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.105-110
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• 2014

Feature-oriented software product line engineering is to develop various products by developing product line core assets in terms of features and composing those features. However, the developed product may not behave correctly if the feature interaction problem has not be properly taken into account during the feature composition. This paper proposes techniques for identifying and modularizing undesirable feature interactions effectively. The scientific calculator product line is used for evaluating the applicability of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.821-826
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• 1999

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.