• The Journal of Information Technology
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• v.7 no.3
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• pp.113-118
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• 2004

Recently, diagnosis techniques have been investigated to detect a partial discharge associated with a dielectric material defect in a high voltage electrical apparatus. However, the properties of detection technique of PD aren't completely understood because the physical process of PD. Therefore, this paper analyzes the process on Surface Discharge of Polymer Insulator using Wavelet transform. Wavelet transform provides a direct quantitative measure of spectral content in the time frequency domain. As it is important to develop a non-contact method for detecting the Contamination Degree, this research analyzes the electromagnetic waves emitted from PD using Wavelet transform. This result experimentally shows the process of PD as a two-dimensional distribution in the time-frequency domain. The method is shown to be useful for detecting prediction of contamination degree.

• Annual Conference of KIPS
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• 2002.04a
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• pp.647-650
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.265-270
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• 2014

Existing speech recognition algorithm have a problem with not distinguish the order of vocabulary, and the voice detection is not the accurate of noise in accordance with recognized environmental changes, and retrieval system, mismatches to user's request are problems because of the various meanings of keywords. In this article, we proposed to event based semantic ontology inference model, and proposed system have a model to extract the speech recognition feature extract using ERB filter. The proposed model was used to evaluate the performance of the train station, train noise. Noise environment of the SNR-10dB, -5dB in the signal was performed to remove the noise. Distortion measure results confirmed the improved performance of 2.17dB, 1.31dB.

• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Journal of IKEEE
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• v.16 no.4
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• pp.356-363
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• 2012

Information technology improves convenience, safety, and performance of automobiles. Recently, a lot of algorithms are studied to provide safety and environment information for driving, and traffic sign recognition is one of them. It can provide important information for safety driving. In this paper, we propose a method for traffic sign detection and identification concentrating on reducing the computation time. First, potential traffic signs are segmented by color threshold, and a polygon approximation algorithm is used to detect appropriate polygons. The potential signs are compared with the template signs in the database using SURF and ORB feature matching method.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1831-1840
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• 2018

During a power swing, distance relays may mistakenly spread fault throughout the power grid, causing a great deal of damage. In some cases, such mistakes can cause global outages. For this reason, it is critical to make a distinction between power swings and faults in distance relays. In this paper, a new method is proposed based on RMS measurement to differentiate between faults and power swings. The proposed method was tested on two standard grids, demonstrating its capability in detecting a power swing and simultaneous fault with power swing. This method required no specific configurations, and was independent of grid type and zoning type of distance relays. This feature in practice allows the relay to be installed on any grid with any kind of coordination. In protective relays, the calculations applied to the microprocessor is of great importance. Distance relays are constantly calculating the current RMS values for protection purposes. This mitigates the computations in the microprocessor to detect power swings. The proposed method was able to differentiate between a fault and a power swing. Furthermore, it managed to detect faults occurring simultaneously with power swings.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.311-318
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• 2002

In this paper, we propose an efficient method for recognizing pupils and lip in a human face. Pupils are detected by a cost function, which uses features based on the eye's shape and a relation between pupil and eyebrow. The inner boundary of lip is detected by weighted vectors based on lip's shape and on the difference of gray level between lip and face skin. These vectors extract four feature points of lip : the top of the upper lip, the bottom of the lower lip, and the two corners. The experiments have been performed for many images and show very encouraging result.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2558-2562
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• 1999

This paper proposes on the methods of noise removal and edge extraction which is done by eliminating the ambiguities of the image using normalization and minimizing the entropy. Pre-existing methods have their own peculiarities and limitations, such as gray level distributions change very slowly or two regions which having similar gray level distribution are touched. This affects on the post processing such as feature extraction, as a result, this leads to false-recognition or no-recognition. Therefore, this paper proposes on the methods which overcome these problems. Finally, the effectiveness of this paper is demonstrated by several experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.