• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.8-18
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• 2008

In this paper, we propose an efficient algorithm using wavelet transform for still image database retrieval. Especially, it uses only the lowest frequency sub-band in multi-level wavelet transform so that a retrieval system uses a smaller quantity of memory and takes a faster processing time. We extract different textured features, statistical information such as mean, variance and histogram, from low frequency sub-band. Then we measure the distances between the query image and the images in a database in terms of these features. To obtain good retrieval performance, we use the first feature (mean and variance of wavelet coefficients) to filter out most of the unlikely images. The rest of the images are considered to be candidate images. Then we apply the second feature (histogram of wavelet coefficient) to rank all the candidate images. To evaluate the algorithm, we create various distorted image databases using MIT VisTex texture images and PICS natural images. Through simulations, we demonstrate that our method can achieve performance satisfactorily in terms of the retrieval accuracy as well as the both memory requirement and computational complexity. Therefore it is expected to provide good retrieval solution for JPEG-2000 using wavelet transform.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.412-414
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• 2001

In this paper, we describe an real-time facial feature tracker. We only used a general USB PC Camera without a frame grabber. The system has achieved a rate of 8+ frames/second without any low-level library support. It tracks pupils, nostrils and corners of the lip. The signal from USB Camera is YUV 4:2:0 vertical Format. we converted the signal into RGB color model to display the image and We interpolated V channel of the signal to be used for extracting a facial region. and we analysis 2D blob features in the Y channel, the luminance of the image with geometric restriction to locate each facial feature within the detected facial region. Our method is so simple and intuitive that we can make the system work in real-time.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.167-174
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• 2022

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.281-285
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• 2001

This paper proposes a snubber circuit for flying capacitor multilevel inverter and converter. The proposed snubber circuit makes use of Undeland snubber as basic snubber unit. It has such an advantage of Undeland snubber used in the two-level inverter. Compared with conventional RLD/RCD snubber for multilevel inverter and converter, the proposed snubber keeps such good features as fewer number of components, reduction of voltage stress of main switching devices due to low overvoltage, and improved efficiency of system due to low snubber loss. In this paper. the proposed snubber is applied to three-level flying capacitor inverter and its feature is demonstrated by computer simulation and experimental result.

• Journal of KIISE
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• v.42 no.6
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• pp.699-706
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• 2015

In recent years, as near-duplicate image has been increasing explosively by the spread of Internet and image-editing technology that allows easy access to image contents, related research has been done briskly. However, BoF (Bag-of-Feature), the most frequently used method for near-duplicate image detection, can cause problems that distinguish the same features from different features or the different features from same features in the quantization process of approximating a high-level local features to low-level. Therefore, a post-verification method for BoF is required to overcome the limitation of vector quantization. In this paper, we proposed and analyzed the performance of a post-verification method for BoF, which converts SIFT (Scale Invariant Feature Transform) descriptors into 128 bits binary codes and compares binary distance regarding of a short ranked list by BoF using the codes. Through an experiment using 1500 original images, it was shown that the near-duplicate detection accuracy was improved by approximately 4% over the previous BoF method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1412-1422
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• 1993

As a part of development of process planning system for mold die manufaturing, a software system is developed, which recognizes features and extracts parameters of the shape from design data produced by solid modeller. The recognized feature date is fed to process planning and operation planning system. Low level geometry and topology data from commercial CAD system is transformed to high level machining feature data which used to be done by using a dedicated design system. The recognition algorithm is applied to the design data with boundary representation produced by a core modeller ACIS which has object oriented open architecture and is expected to become a common core modeller of next generation CAD system. The algoritm of recognition has been formulated for 21 features on prismatic components, but the feature set can be expanded by adding rules for the additional features.

• Korean Journal of Social Welfare
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• v.57 no.4
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• pp.67-90
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• 2005

The purpose of this study is to analyze factors to affect low fertility and to investigate its implications to social welfare. For the purpose, I surveyed 360 married women and men in Gyeongnam province, and employed multi-regression, logistic regression model to process the data. I analyzed factors to influence low fertility in three aspects: demographic feature, socio-economic status, and personal sense of value. The results of analysis can be summarized as follows: (1) the period of marriage in demographic feature, income level in social economic status, and the necessity of children in personal sense of value are important factors to affect the current fertility level, (2) period of marriage, total numbers of children, gender of the first child are determining the future childbirth in demographic feature. Secondly, income level is interrelated to the future childbirth in socio-economic status. Thirdly, in the aspect of personal values, how much one needs to get married, how much one prefers son to daughter, how much one relies on one's children to realize one's dream are interrelated to the future childbirth, (3) the cost of bringing up a child as well as he expense of private education, lacking of a day nursery, and economic difficulty are causes to make people to postpone or give up childbirth. These results suggest that development of population policy to promote women's social participation and to strengthen family welfare as well as social welfare is necessary. These also implicates that if we pursue integrated policies on women, childcare, and education, we can get much more effective population welfare policy.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.420-438
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• 2004

Artificially or naturally contained texts in the natural images have significant and detailed information about the scenes. If we develop a method that can extract and recognize those texts in real-time, the method can be applied to many important applications. In this paper, we suggest a new method that extracts the text areas in the natural images using the low-level image features of color continuity. gray-level variation and color valiance and that verifies the extracted candidate regions by using the high-level text feature such as stroke. And the two level features are combined hierarchically. The color continuity is used since most of the characters in the same text lesion have the same color, and the gray-level variation is used since the text strokes are distinctive in their gray-values to the background. Also, the color variance is used since the text strokes are distinctive in their gray-values to the background, and this value is more sensitive than the gray-level variations. The text level stroke features are extracted using a multi-resolution wavelet transforms on the local image areas and the feature vectors are input to a SVM(Support Vector Machine) classifier for the verification. We have tested the proposed method using various kinds of the natural images and have confirmed that the extraction rates are very high even in complex background images.