• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• 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 KIISE:Software and Applications
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• v.28 no.7
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• pp.501-510
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• 2001

Generally, it is essential to estimate exact disparity for the 3D modeling from stereo images. Because existing methods calculate disparities from a whole image, they require too much cimputational time and bring about the mismatching problem. In this article, using the characteristic that the disparity vectors in stereo images are distributed not equally in a whole image but only exist about the background and obhect, we do a wavelet transformation on stereo images and estimate coarse disparity fields from the reduced lowpass field using area-based method at first-step. From these coarse disparity vectors, we generate disparity histogram and then separate object from background area using it. Afterwards, we restore only object area to the original image and estimate dense and accurate disparity by our two-step pixel-based method which does not use pixel brightness but use second gradient. We also extract feature points from the separated object area and estimate depth information by applying disparity vectors and camera parameters. Finally, we generate 3D model using both feature points and their z coordinates. By using our proposed, we can considerably reduce the computation time and estimate the precise disparity through the additional pixel-based method using LOG filter. Furthermore, our proposed foreground/background method can solve the mismatching problem of existing Delaunay triangulation and generate accurate 3D model.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.277-284
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• 2013

The analysis of phase synchronization characteristics from EEG signals is important for the understanding of information processing functionality in the brain network. In this paper, wavelet transformation(WT), Hilbert tansformation (HT), complex demodulation (CD) methods having time localization characteristics were applied to real evoked potential data and noise added simulation data with center frequencies corresponding to EEG bands for the estimation performance analysis of phase offset, phase changing point, and interband crosstalk. The WT is the best both in ${\delta}$, ${\theta}$, and ${\alpha}$ band signal decomposition, and in analyzing phase synchronization performance. The CD can be efficiently used in changing point detection under tolerant noise condition because of its abrupt performance degradation over noise endurance level. From experimental observations, the WT is the most suitable in phase synchronization application of EEG signal, and the CD can be affordable in restricted application such as changing point detection for higher bands than ${\delta}$. Particularly, WT and CD can be used to detect the changing instant of brain function by indirectly estimating the phase changing point.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.102-109
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• 2008

In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.

• The Journal of the Acoustical Society of Korea
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• v.24 no.5
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• pp.271-281
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• 2005

An electromagnetic type shock wave generator suitable for extracorporeal shock wave therapy has been constructed by employing a solenoid coil. The Property of the shock waves produced by the shock wave generator was evaluated using a needle hydrophone. It was shown that, as the capacitor discharging voltage increased from 8 to 18 kV, the Positive Peak Pressure (P+) of the shock wave increased non-linearly from 10 to 77 Wa. In contrast. the negative peak Pressure (f) varied between -3.2 and -6.8 MPa. had its absolute maximum of -6.9 ma at 14 kV The peak amplitudes P+ measured repeatedly under the same voltage setting varied within $5\;\%$ from mean values and this is very small compared to about $50\;\%$ for electrohydraulic type shock wave generators. It could be observed, from the hydrophone signal recorded over 1 ms. several sequential acoustic impulses representing bubble collapses. namely. acoustic cavitation. induced by the shock wave. A technique based on wavelet transformation was used to accurately measure the time delay between the 1st and 2nd collapse known to be closely related to the shock strength. It was observed that the measured time delay increased almost linearly from 120 to $700\;{\mu}s$ with the shock wave Pressure P+ increasing from 10 to 77 MPa.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1069-1077
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• 2008

The railway abrasion measurement system have to satisfy two conditions to increase the measurement accuracy as follows. The laser region which is projected on the rail have to be extracted without the geometrical distortion. The mapping of the acquired laser region data on the rail profile have to be processed exactly. But, the conventional railway abrasion measurement system is deeply effected by the foreign substance( dust, rainwater, and so on ) on the railway or the sensitive response characteristic of the laser to the external measurement circumstance, and then the measurement errors arise from above factors. When the laser region is projected on the rail extracts from the acquired image, the interference of the light with the same frequency as the laser system occurs the serious problems. In the process of the mapping between the railway profile and the extracted laser region, the measurement accuracy is very highly effected by the geometrical distortion and the abnormal variation. In this Paper, we propose the novel method to increase the accuracy of the railway abrasion measurement dramatically. we designed and manufactured the high precision and fast image processing board with DSP Core and FPGA to measure the railway abrasion. The image processing board has the capability that the image of 1024X1280 from camera can be processed with the speed of 480 frame/sec. And, we apply the image processing algorithm base on the wavelet to extract the laser region is projected on the rail exactly. Finally, we developed high precision railway abrasion measurement system with the error range less than +/-0.5mm by which 2D image data is covered 3D data and mapped on the rail profile using the camera model and the perspective transform.