• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1030-1032
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• 1998

In this paper, an efficient feature vector extraction method and MLP neural network are utilized to automatically detect and classify power quality disturbances, where the proposed classification procedure consists of the following three parts: i.e., (i) PQ disturbance detection using discrete wavelet transform. (ii) feature vector extraction from the detected disturbance. using several methods, such as FFT, DWT, Fisher's criterion. etc.. and (iii) classification of the corresponding type of each PQ disturbance by recognizing the pattern of the extracted feature vector. To demonstrate the performance and, applicability of the proposed classification algorithm. some test results obtained by analyzing 10-class PQ disturbances are also provided.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.113-118
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• 2024

Pursuance Sentiment Analysis on Twitter is difficult then performance it's used for great review. The present be for the reason to the tweet is extremely small with mostly contain slang, emoticon, and hash tag with other tweet words. A feature extraction stands every technique concerning structure and aspect point beginning particular tweets. The subdivision in a aspect vector is an integer that has a commitment on ascribing a supposition class to a tweet. The cycle of feature extraction is to eradicate the exact quality to get better the accurateness of the classifications models. In this manuscript we proposed Term Frequency-Inverse Document Frequency (TF-IDF) method is to secure Principal Component Analysis (PCA) with Naïve Bayes Classifiers. As the classifications process, the work proposed can produce different aspects from wildly valued feature commencing a Twitter dataset.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.995-1001
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• 1995

The structure of a typical pattern recognition consists a pre-processing, a feature extraction(algorithm) and classification or recognition. In classification, when widely varying patterns exist in same category, we need the clustering which organize the similar patterns. Clustering algorithm is two approaches. Firs, statistical approaches which are k-means, ISODATA algorithm. Second, neural network approach which is T. Kohonen's LVQ(Learning Vector Quantization). Nikhil R. Palet al proposed the GLVQ(Generalized LVQ, 1993). This paper suggest the efficient feature extraction methods of handwritten numerals in GLVQ clustering network. We use the handwritten numeral data from 21's authors(ie, 200 patterns) and compare the proportion of misclassified patterns for each feature extraction methods. As results, when we use the projection combination method, the classification ratio is 98.5%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4473-4482
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• 2010

The examination of pulse, which is a typical palpation technique in the oriental medicine, has been used conventional analog system for discrimination of 28 pulses. However, the clipping phenomenon in the pulses, which used same feature extraction technique with ECG signals, has been occurred in analog system due to feature extraction method and over amplification from the input signals. It caused inaccurate to analyze the pulse signals. In this paper, we propose a digital filter design technique based on Prony's method for signal modeling and C-spline interpolation for feature extraction from pulse signal to compensate analog pulse detection system. In addition, we suggest a compensated electronic pulse detection system comprising new pulse analyzing algorithm and shape analysis technique for pulses, which were difficult to use in analog system. The feasibility for new proposed system has been confirmed comparing output signals between electronic pulse detection system having proposed filter design techniques with pulse analyzing algorithm and conventional analog system.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.135-143
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• 2005

RPC(Rational Polynomial Camera) models have become the replacement model of choice for a number of high resolution satellite imagery providers. RPCs(Rational Polynomial Coefficients) provide a compact accurate representation of the ground to image geometry, allowing users to perform full photogrammetric processing of satellite imagery including block adjustment, 3D feature extraction and orthorectification. This paper presents an algorithm for 3D feature extraction using downhill simpler method which requires only function evaluations, not derivatives. The algorithm was implemented as an executable software program and tested using stereo IKONOS images of Seoul city. The results showed that the proposed algorithm was fast and accurate enough to be used as a practical method for the 3D feature extraction from stereo images with RPCs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1307-1311
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• 2006

In this paper, we propose new speech feature parameter using the Matrix Factorization for appearance part-based features of speech spectrum. The proposed parameter represents effective dimensional reduced data from multi-dimensional feature data through matrix factorization procedure under all of the matrix elements are the non-negative constraint. Reduced feature data presents p art-based features of input data. We verify about usefulness of NMF(Non-Negative Matrix Factorization) algorithm for speech feature extraction applying feature parameter that is got using NMF in Mel-scaled filter bank output. According to recognition experiment results, we confirm that proposed feature parameter is superior to MFCC(Mel-Frequency Cepstral Coefficient) in recognition performance that is used generally.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.102-110
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• 1992

In this paper, a new method of character recognition is proposed. It uses density information, in addition to positional and directional information generally used, to recognize a character. Four directional feature primitives are extracted from the thinning templates on the observation that the output of the templates have directional property in general. A simple and fast feature extraction scheme is possible. Features are organized from recursive nonary tree(N-tree) that corresponds to normalized character area. Each node of the N-tree has four directional features that are sum of the features of it's nine sub-nodes. Every feature primitive from the templates are added to the corresponding leaf and then summed to the upper nodes successively. Recognition can be accomplished by using appropriate feature level of N-tree. Also, effectiveness of each node's feature vector was tested by experiment. A method to implement the proposed feature vector organization algorithm into hardware is proposed as well. The third generation node, which is 4$\times$4, is used as a unit processing element to extract features, and it was implemented in hardware. As a result, we could observe that it is possible to extract feature vector for real-time processing.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.486-491
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• 2000

The dicing process cuts a semiconductor wafer to lengthwise and crosswise direction by using a rotating circular diamond blade. But inferior goods are made under the influence of several parameters in dicing such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using neural network in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, five features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision, back-propagation neural network is adopted to classify the dicing process into normal and abnormal dicing, and normal and damaged blade. Experiments have been performed for GaAs semiconductor wafer in the case of normal/abnormal dicing and normal/damaged blade. Based upon observation of the experimental results, the proposed scheme shown has a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 6.5%.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.607-614
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• 1998

An automatic grading algorithm was developed to replace the manual trading of white ginseng. The algorithm consists of three consecutive stages, (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural network. Mathematical features such as area ratio, mean and standard deviation of graylevel, skewness of graylevel histogram, and the number of run segment are extracted from five equally divided parts of ginseng. An artificial neural network model was used to classify white ginsengs into three categories. The performance of the algorithm was evaluated using 120 ginseng samples and the rate of successful classification was 74%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.673-676
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• 2017

In this paper, we propose a day and night fog detection algorithm that is not affected by lighting conditions. First, we present the definitions and the extraction methods of fog features in daytime and nighttime environments, respectively. We then propose the fog detection algorithm using a neural network from the fog features. A set of experiments has been conducted with images taken at various environments, and the average recall of the proposed algorithm is 97.5%.