• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.129-132
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• 2021

Two feature selection approaches are analyzed in this study. First Approach used in this paper is Filter Approach which comprises of correlation technique. It provides two reduced feature sets using positive and negative correlation. Secondly Approach used in this paper is the wrapper approach which comprises of Sequential Forward Selection technique. The reduced feature set obtained by positive correlation results comprises of Rate of Acceleration, Intensity and Formant. The reduced feature set obtained by positive correlation results comprises of Rasta PLP, Log energy, Log power and Zero Crossing Rate. Pitch, Rate of Acceleration, Log Power, MFCC, LPCC is the reduced feature set yield as a result of Sequential Forwarding Selection.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.503-511
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• 2004

In Korea, OCR-based techniques have been developed for digital library construction of historical documents. In this paper, we propose the nonlinear transform of contour directional feature (CDF) vectors using log it and power transforms with skewness criterion to enhance the discriminant power. Experiments were conducted using samples from Seung-jung-won diaries (Diaries of King's Secretaries). Our results show that proposed method outperforms the others like Box-Cox transform in this database.

• 융합정보논문지
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• 제11권10호
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• pp.10-19
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• 2021

본 논문은 뇌파 측정 및 분석을 통해 정신적 피로를 반영하는 특징 파라미터를 도출하고자 하였다. 이를 위해 30분간 눈을 감은 편안한 안정 상태와 뺄셈연산을 암산으로 수행하는 작업을 통해 정신적 피로를 유도하였다. 5명의 피험자가 실험에 참가하였으며, 피험자들은 모두 대학 재학 중인 오른손잡이 남학생들이며 평균 나이는 25.5세이다. 정신적 피로를 반영하는 특징 파라미터 도출을 위해 실험 처음과 마지막에서 수집된 뇌파에 대해 스펙트럼분석을 수행하였다. 분석 결과, 정신적으로 피로할수록 후두엽 및 측두엽 위치에서 알파대역의 절대파워는 증가한 반면 상대파워는 감소하였다. 또한 안정 상태와 작업 상태간 파워 차이는 절대파워에 비해 상대파워가 크게 나타났다. 이 결과는 후두엽 및 측두엽 위치에서의 알파 상대파워가 정신적 피로를 반영하는 특징 파라미터임을 나타낸다. 본 연구 결과는 운전 중 피로 및 졸음 판단과 같은 정신적 피로 판별을 위한 자동화시스템 개발을 위한 특징 파라미터로 활용될 수 있다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.127-128
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• 2007

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• 한국정보통신학회논문지
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• 제14권12호
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• pp.2761-2768
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• 2010

본 논문에서는 숫자의 전경특징과 배경특징을 이용하고 SVM 분류기를 사용하여 오프라인 필기체 숫자인식에서 인식률을 향상시키는 방안을 제시한다. 숫자의 전경특징은 숫자의 에지선을 추출한 Kirsch 방향특징과 숫자선 자체를 추출한 projection 방향특징으로 구성되며, 숫자의 배경특징은 숫자의 볼록외피로 부터 추출되는 오목특징이다. 여기서 오목특징은 방향특징에 대해 보완적인 특징으로 작용하여 분류 성능 향상에 기여한다. 인식기로는 RBF 커널을 이용한 SVM 분류기를 사용하고, CENPAMI 숫자특징 데이터베이스를 사용하여 제시된 방법의 성능을 검사하였다. 실험 결과 각기 다른 분류 성능을 갖는 이들 3종의 특징들이 상호 보완적으로 작용하여 인식률 향상에 기여함을 확인할 수 있었으며, 제시된 복합특징에 의해 98.90%의 인식률을 달성하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1436-1439
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• 1997

The objective of this paper is to present an efficient and practical approach to the automatic classification of power quality(PQ) disturbances, where and orthogonal polynomial approximation method is emloyed for the detection and localization of PQ disturbances, and a feature vector, newly extracted form the bispectra of the detected signal, is utilized for the automatic rectgnition of the various types of PQ disturbances. To demonstrae the performance and applicabiliyt of the proposed approach, some simulation results are provided.

• KEPCO Journal on Electric Power and Energy
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• 제4권2호
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• 전자공학회논문지C
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• 제34C권10호
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• pp.67-75
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• 1997

A design for a system to perform rapid recognition of three dimensional objects is presented, focusing on efficient indexing. In order to retrieve the best matched models without exploring all possible object matches, we have employed a bayesian framework. A decision-theoretic measure of the discriminatory power of a feature for a model object is defined in terms of posterior probability. Detectability of a featrue defined as a function of the feature itselt, viewpoint, sensor charcteristics, nd the feature detection algorithm(s) is also considered in the computation of discribminatory power. In order to speed up the indexing or selection of correct objects, we generate and verify the object hypotheses for rfeatures detected in a scene in the order of the discriminatory power of these features for model objects.