• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.111-120
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• 2017

In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.108-118
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• 1994

By using a concept of interrelation vector between line segments a new algorithm for partial shape recognition of two-dimensional objects is introduced. The interrelation vector which is invariant under translation rotation and scaling of a pair of line segments is used as a feature information for polygonal shape recognition. Several useful properties of the interrelation vector are also derived in relation to efficient partial shape recognition. The proposed algorithm requires only small space of storage and is shown to be computationally simple and efficient.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1129-1135
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• 2012

To employ NMF to transcribe music by extracting feature matrix and weight matrix at the same time, it is necessary to know in advance the dimension of the feature matrix, and to determine the pitch of each extracted feature vector. Another drawback of this approach is that it becomes more difficult to accurately extract the feature matrix as the number of pitches included in the target music increases. In this study, we prepare a feature matrix database, and apply the matrix to transcribe real music. Transcription experiments are conducted by applying the feature matrix to the music played on the same piano on which the feature matrix is extracted, as well as on the music played on another piano. These results are also compared to those of another experiment where the feature matrix and weight matrix are extracted simultaneously, without making use of the database. We could observe that the proposed method outperform the method in which the two matrices are extracted at the same time.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.167-179
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• 2001

This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query precessing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verily the superiority of our method, we perform extensive experiments. The results reseal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

• The Journal of the Korea Contents Association
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• v.3 no.1
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• pp.9-13
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• 2003

We propose a block motion estimation algorithm based on a statistical image feature for video sequences. The statistical feature of the reference block is obtained, then applied to select the candidate starting points (SPs) in the regular starting points pattern (SPP) by comparing the statistical feature of reference block with that of blocks which are spread ower regular SPP. The final SPs are obtained by their Mean Absolute Difference(MAD) value among the candidate SPs. Finally, one of conventional fast search algorithms, such as BRGDS, DS, and three-step search (TSS), has been applied to generate the motion vector of reference block using the final SPs as its starting points. The experimental results showed that the starting points from fine SPs were as dose as to the global minimum as we expected.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.79-87
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• 2015

Surface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1
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• pp.100-103
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• 1997

In this paper, we analyze the time delayed effect of speech feature. Here, the time delayed effect means that the current feature vector of speech is under the influence of the previous feature vectors. In this paper, we use a set of LPC driven cepstal coefficients and evaluate the time delayed effect of cepstrum with the performance of the speech recognition system. For the experiments, we used the speech database consisting of 22 words which uttered by 50 male speakers. The speech database uttered by 25 male speakers was used for training, and the other set was used for testing. The experimental results show that the time delayed effect is large in the lower orders of feature vector but small in the higher orders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.