• Journal of the Korean Society for Precision Engineering
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• v.27 no.1
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• pp.64-75
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• 2010

This study introduces a complete feature map building method of sonar sensors for mobile robots. This method enhances the reality of feature maps by extracting even circle features as well as line and point features from sonar data. Edge features are, moreover, generated by combining line features close to circle features extracted around comer sites. The uncertainties of the specular reflection phenomenon and wide beam width of sonar data can be, therefore, reduced through this map building method. The experimental results demonstrate a practical validity of the proposed method in those environments.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.1
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• pp.119-127
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• 2000

In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

• Fisheries and Aquatic Sciences
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• v.4 no.1
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• pp.1-9
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• 2001

This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.179-185
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• 2010

In this paper, we present a performance evaluation for face recognition algorithm using feature feedback according to the Feature mask. In the face recognition method using feature feedback, important region is extracted from original data set by using the reverse mapping from the extracted features to the original space. In this paper, we evaluate the performance of feature feedback according to shape of Feature Mask for Yale data. Comparing the result using Important part and unimportant part, we show the validity and applicability of the pattern recognition method based on feature feedback.

• The Journal of Information Technology and Database
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• v.8 no.1
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• pp.117-128
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• 2001

Recently there are many researches of text mining to find interesting patterns or association rules from mass textual documents. However, the words extracted from informal documents are tend to be irregular and there are too many general words, so if we use pre-exist method, we would have difficulty in retrieving knowledge information effectively. In this paper, we propose a new feature extraction method to classify mass documents using association rule based on unsupervised learning technique. In experiment, we show the efficiency of suggested method by extracting features and classifying of documents.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.741-745
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• 1991

This paper describes a feature extraction in digitized chest X-ray image and CT head Image. There are Extraction, Thresholding, Region G rowing, Split-Merge and Relaxation in feature extraction technique. In this study, Region Growing System was realized and Fuzzy Set Theory was applied in order to extract the vague region which the conventional method has difficulties in extracting. The performance of proposed algorithm was proved by being applied to chest X-ray image and CT head image.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.207-211
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• 2000

For efficient massive image retrieval, an image retrieval requires that several important objectives are satisfied, namely: automated extraction of features, efficient indexing and effective retrieval. In this work, we present a technique for extracting the 4-dimension directional feature. By directional detail, we imply strong directional activity in the horizontal, vertical and diagonal direction present in region of the image texture. This directional information also present smoothness of region. The 4-dimension feature is only indexed in the 4-D space so that complex high-dimensional indexing can be avoided.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.81-84
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• 2000

In this paper, a new technology for extracting the feature of the speech signal of an isolated word by the analysis on the frequency domain is proposed. This technology can be applied efficiently for the limited speech domain. In order to extract the feature of speech signal, the number of peaks is calculated and the value of the frequency for a peak is used. Then the difference between the maximum peak and the second peak is also considered to identify the meanings among the words in the limited domain. By implementing this process hierarchically, the feature of speech signal can be extracted more quickly.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.237-242
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• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.