• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.551-558
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• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.329-336
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• 2007

The use of digital imaging technique and digital contents based on internet has grown rapidly for last several years, and the needs of digital image protection become more important. For the purpose of copyright protection on digital image, the verification of authentication techniques like content authentication, ownership authentication, illegal copy and etc are needed. Digital watermarking, the invisible encryption technique to insert digital watermark into image, the sophisticated perceptual information should be used for providing transparency and robustness of images on watermarking process. In this paper, we implement the digital watermarking algorithm for preventing forged attack, ownership protection and authentication by transforming the wavelet algorithms in frequency domain in terms of human visual system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1497-1504
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• 2009

This Paper Propose a method to reduce the calculation time in JPEG2000. That is, if a coefficient is estimate a upper-level subband and its descendents skip the scan process. There is a trade-off relationship between the calculation time and the image quality or the amount of output data, the calculation time and the amount of output data decreases, but the image degradation increases. The experimental results showed that in calculation time was 35% in average, which means that ls ge ses. The ein calculation time and output data can be obtls ed with a cost of an acceptlble image quality degradation.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2277-2287
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• 2015

In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

• Smart Structures and Systems
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• v.22 no.3
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• pp.315-326
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• 2018

This paper presents a novel time-domain method for the identification of plastic rotations and stiffness parameters of single-bay frames with nonlinear plastic hinges. Each plastic hinge is modelled as a pseudo-semi-rigid connection with nonlinear hysteretic moment-curvature characteristics at an element end. Through the comparison of the identified end rotations of members that are connected together, the plastic rotation that furnishes information of the locations and plasticity degrees of plastic hinges can be identified. The force consideration of the frame members may be used to relate the stiffness parameters to the elastic rotations and the excitation. The damped-least-squares method and damped-and-weighted-least-squares method are adopted to estimate the stiffness parameters of frames. A noise-removal strategy employing a de-noising technique based on wavelet packets with a smoothing process is used to filter out the noise for the parameter estimation. The numerical examples show that the proposed method can identify the plastic rotations and the stiffness parameters using measurements with reasonable level of noise. The unknown excitation can also be estimated with acceptable accuracy. The advantages and disadvantages of both parameter estimation methods are discussed.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.398-401
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• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2704-2712
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• 1998

In this paper, a MRI image compression technique, which allocates bits effectively by using lossless coding for region having important infommtion to decide disease and lossy coding for the rest, is proposed. In the proposed method, for MHI images needed to rccognize disk disease, we recognizc region having important objects by using the characteristics of c1isease. As the recognized region is imrxlrtant to decide whether disease exists or not, it is compressed by lossless coding and the rest is compressed by lossy coding, Also for the region compressed by lossy coding, we can obtain fine reconstructed images without blocking effect by adopting fractal coding in wavelet transform domain.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.65-76
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• 2001

In this paper, performance of frequency-based watermarking methods is compared, which are used to protect copyright of image data. For performance comparison of each transform method, a test image is transformed into frequency domain by a full- and block-discrete cosine transforms and wavelet transform, and after general image processing, robustness of each transform is evaluated with using the same watermark signals. Simulation results show that the watermarking method based on DCTs are more robust than WT. Especially, $8{\times}8$ BDCT is most robust for JPEG lossy compression and sharpening, while $8{\times}8$ FDCT is best. for smoothing and scaling. In addition, watermarking based on BDCT shows decreasing robustness for a larger block size, compared with general image processing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1540-1548
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• 1999

We propose a multi-resolution motion estimation algorithm using adaptive search region. It is constructed in wavelet domain that a binary plane which represents the potential motion areas(PMA's) based on the temporal redundancy between video frames, and motion estimation is processed in the PMA's. We reduce the PMA's gradually as the resolution level is higher, considering the distribution of the energy in subband layers and the complexity. As compared with EMRME(enhanced multi-resolution motion estimation) method[7], simulation results show that computational amount and bit rate reduced to about 33 ~46 % and 10 ~l8% respectively in active image with similar PSNR, and computational amount reduced to about 37 ~65 % in small notion image with similar PSNR and bit rate.