• Korean Journal of Computational Design and Engineering
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• v.4 no.2
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• pp.139-152
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• 1999

This paper presents a feature-based approach to extracting machining features fro a feature-based design model. In the approach, a design feature to machining feature conversion process incrementally converts each added design feature into a machining feature or a set of machining features. The proposed approach an efficiently handle protrusion features and interacting features since it takes advantage of design feature information, design intent, and functional requirements during feature extraction. Protrusion features cannot be directly mapped into machining features so that the removal volumes surrounding protrusion features are extracted and converted it no machining features. By utilizing feature information as well as geometry information during feature extraction, the proposed approach can easily overcome inherent problems relating to feature recognition such as feature interactions and loss of design intent. In addition, a feature extraction process can be simplified, and a large set of complex part can be handled with ease.

• 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.

• ETRI Journal
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• v.34 no.6
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• pp.847-857
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• 2012

Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.

• Journal of electromagnetic engineering and science
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• v.18 no.3
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• pp.206-211
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• 2018

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.139-141
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• 2006

제품계열공학에서 feature diagram(FD)은 개발자의 직관이나 도메인 전문가의 경험에 근거하여 작성되어, feature간의 공통성 및 가변성분석 기준이 불명확하며 비정형적인 feature의 공통성 및 가변성 분석으로 인한 stakeholder의 공통된 이해가 부족한 문제점을 내포하고 있다. 따라서, 본 논문에서는 이를 해결하기 위하여 공통된 feature의 이해를 위해 feature 속성리스트에 기반한 메타 feature모델과 feature간의 의미유사성관계를 이용한 온톨로지를 적용한 공통성 및 가변성 분석모델을 제안한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.282-287
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• 2006

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.367-372
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• 2001

Feature restoration is that restore feature to 3D solid model using the feature information in STEP AP224. Feature is very important in CAPP, but feature information is defined very complicated in STEP AP224. This paper recommends the algorithm of extraction the feature information in physical STEP AP224file. This program import STEP AP224 file, parse the geometric and topological information, the tolerance data, and feature information line-by-line. After importation and parsing, store data into database. Feature restoration module analyze database including feature information, extract feature information, e.g. feature type, feature's parameter, etc., analyze the relationship and then restore feature to 3D solid model.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.53-61
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• 2017

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).

• ETRI Journal
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• v.30 no.1
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• pp.89-100
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• 2008

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.6
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• pp.176-184
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• 2000

Reverse engineering (RE) is a process to create computer aided design (CAD) models from the scanned data of an existing part acquired using 3D position scanners. This paper proposes a novel methodology of extracting geometric features directly from a set of 3D scanned points, which utilizes the concepts of feature-based technology and artificial neural networks (ANNs). The use of ANN has enabled the development of a flexible feature-based RE application that can be trained to deal with various features. The following four main tasks were mainly investigated and implemented: (1) Data reduction; (2) edge detection; (3) ANN-based feature recognition; (4) feature extraction. This approach was validated with a variety of real industrial components. The test results show that the developed feature-based RE application proved to be suitable for reconstructing prismatic features such as block, pocket, step, slot, hole, and boss, which are very common and crucial in mechanical engineering products.