• 대한전기학회논문지:시스템및제어부문D
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• 제52권12호
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• pp.702-707
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• 2003

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

• ETRI Journal
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• 제41권5호
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권4호
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• pp.840-855
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• 2011

In the context of additive noise steganography model, we propose a method to detect least significant bit (LSB) matching steganography in grayscale images. Images are decomposed into detail sub-bands with local linear transform (LLT) masks which are sensitive to embedding. Novel normalized characteristic function features weighted by a bank of band-pass filters are extracted from the detail sub-bands. A suboptimal feature set is searched by using a threshold selection algorithm. Extensive experiments are performed on four diverse uncompressed image databases. In comparison with other well-known feature sets, the proposed feature set performs the best under most circumstances.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1992년도 춘계학술대회
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• pp.175-178
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• 1992

This paper proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex. the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to set of three states pulse-cord train relative to the original ECG signal. The pulse-cord train has the advantage of easy implementation in digital hardware circuits to achive automated ECG diagnosis. The algorithm performs very well feature extraction in arrythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contration) detection has a at least 90 percent sensityvity for arrythmia data.

• 대한음성학회지:말소리
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• 제43호
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• pp.127-136
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• 2002

The speaker adaptation technique is generally used to reduce the speaker difference 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 transformation matrix is learned from a speaker independent training data. When the amount of data is small, however, it is necessary to adjust the ICA-based 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. We observed that the proposed technique is effective to adaptation performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.410-414
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• 1996

A visual servoing algorithm is proposed for a robot with a camera in hand. Specifically, novel image features are suggested by employing a viewing model of perspective projection to estimate relative pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a, commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• 한국지능시스템학회논문지
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• 제19권1호
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• pp.90-95
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• 2009

본 논문은 선형차원감소(Linear Dimensionality Reduction)을 위해 널리 이용되고 있는 특징추출 알고리듬인 선형판별분석(Linear Discriminant Analysis)의 문제점을 해결할 수 있는 새로운 특징추출 알고리듬을 제안한다. 선형판별분석에 포함되는 평균-자료 간 거리 및 평균-평균 간의 거리에 기반한 분산행렬은 역행렬 연산, 계수의 제한 등으로 인하여 계산상의 문제와 추출되는 특징의 수가 제한되는 한계를 가지고 있다. 또한 자료의 집단이 단일 모드의 정규 분포로부터 얻어진 것으로 가정되며 그렇지 않은 경우에 대해서는 적절한 결과를 얻을 수 없다. 본 논문에서는 자료-자료 간의 거리에 기반하고 적절하게 가중치가 추가된 새로운 행렬을 정의하였으며. 이에 기반하여 특징을 추출하는 방법을 제안하였다. 그럼으로써 앞서 선형판별분석의 여러 문제를 해결하고자 시도하였다. 제안된 방법의 성능을 실험을 통해 확인하였다.

• 대한의용생체공학회:의공학회지
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• 제37권2호
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• 한국통신학회논문지
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• 제39C권8호
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• pp.738-745
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• 2014

최근 모바일 사용자들이 증가하면서 모바일 어플리케이션 또한 계속적으로 증가하고 있다. 모바일 어플리케이션이 증가하면서 사용자들은 모바일 장치에 은행정보, 위치정보, 아이디, 패스워드 등의 민감한 정보들을 저장하고 있다. 따라서 최근에는 PC를 타겟으로 하는 악의적인 어플리케이션보다 모바일 장치를 타겟으로 하는 악의적인 어플리케이션들이 증가하고 있는 추세이다. 특히 안드로이드 플랫폼의 경우 오픈 플랫폼으로써 사용자들에게 악성 코드를 포함한 어플리케이션을 배포하기 유리한 환경을 가지고 있다. 본 논문에서는 안드로이드 환경에서 악성코드를 포함한 어플리케이션을 탐지하기 위해 선형 SVM(Support Vector Machine) 기계학습 분류기를 적용한 악성코드 탐지 시스템의 성능을 분석한다. 또한 모바일 악성코드의 탐지 성능 향상을 위한 feature를 제시하고, 의미있는 feature를 선정한다.

• 한국컴퓨터정보학회논문지
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• 제16권3호
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• pp.99-107
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• 2011

패턴을 이용한 불가사리 특징 검출은 불가사리의 오목 특징과 볼록 특징을 모두 검출하기 어려우며 또한, 오목과 볼록을 구분 할 수도 없다. 오목과 볼록은 아무르불가사리의 중요한 구조적 특징으로서 반드시 찾아야 할 특징이며 오목과 볼록을 분류함으로서 차후 불가사리 인식에서도 필요하다. 따라서 본 논문에서는 아무르불가사리의 주요 특징인 오목과 볼록 특징을 추출하는 기법을 제안한다. 이 기법은 다방향 선형 스캐닝을 이용하여 오목과 볼록의 특징점 후보군을 형성하고 이 후보군에서 특징점을 결정한 후 추출된 특징점에 컨벡스 헐 알고리즘을 적용하여 오목 특징과 볼록 특징을 구분한다. 제안한 기법은 불가사리의 주요 특징인 오목 특징과 볼록 특징을 구분하여 효과적으로 추출한다. 따라서 향후 불가사리 인식을 위한 연구에 기여할 것으로 기대한다.