• 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.16 no.2
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• pp.713-725
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• 2022

Mostly, artificial intelligence does not show any definite change in emotions. For this reason, it is hard to demonstrate empathy in communication with humans. If frequency modification is applied to neutral emotions, or if a different emotional frequency is added to them, it is possible to develop artificial intelligence with emotions. This study proposes the emotion conversion using the Generative Adversarial Network (GAN) based voice frequency synthesis. The proposed method extracts a frequency from speech data of twenty-four actors and actresses. In other words, it extracts voice features of their different emotions, preserves linguistic features, and converts emotions only. After that, it generates a frequency in variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) in order to make prosody and preserve linguistic information. That makes it possible to learn speech features in parallel. Finally, it corrects a frequency by employing Amplitude Scaling. With the use of the spectral conversion of logarithmic scale, it is converted into a frequency in consideration of human hearing features. Accordingly, the proposed technique provides the emotion conversion of speeches in order to express emotions in line with artificially generated voices or speeches.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.49 no.2
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• pp.214-223
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• 2016

Joint time-frequency images of the broadband acoustic echoes of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The acoustic features were extracted by changing the sliced window widths and dividing the time window by a 0.02-ms interval and the frequency window by a 20-kHz bandwidth. The 22 spectrum amplitudes obtained in the time and frequency domains of the SPWVD images were fed as input parameters into an artificial neural network (ANN) to verify the effectiveness for species-dependent features related to fish species identification. The results showed that the time-frequency approach improves the extraction of species-specific features for species identification from broadband echoes, compare with time-only or frequency-only features. The ANN classifier based on these acoustic feature components was correct in approximately 74.5% of the test cases. In the future, the identification rate will be improved using time-frequency images with reduced dimensions of the broadband acoustic echoes as input for the ANN classifier.

• Phonetics and Speech Sciences
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• v.7 no.1
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• pp.3-10
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• 2015

In this paper, different frequency scales in cepstral feature extraction are evaluated for the text-independent speaker recognition. To this end, mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), and bilinear warped frequency cepstral coefficients (BWFCCs) are applied to the speaker recognition experiment. In addition, the spectro-temporal features extracted by the cepstral-time matrix (CTM) are examined as an alternative to the delta and delta-delta features. Experiments on the NIST speaker recognition evaluation (SRE) 2004 task are carried out using the Gaussian mixture model-universal background model (GMM-UBM) method and the joint factor analysis (JFA) method, both based on the ALIZE 3.0 toolkit. Experimental results using both the methods show that BWFCC with appropriate warping factor yields better performance than MFCC and LFCC. It is also shown that the feature set including the spectro-temporal information based on the CTM outperforms the conventional feature set including the delta and delta-delta features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Smart Structures and Systems
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• v.3 no.3
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• pp.263-280
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• 2007

In this paper, an improved GA-based damage detection algorithm using a set of combined modal features is proposed. Firstly, a new GA-based damage detection algorithm is formulated for beam-type structures. A schematic of the GA-based damage detection algorithm is designed and objective functions using several modal features are selected for the algorithm. Secondly, experimental modal tests are performed on free-free beams. Modal features such as natural frequency, mode shape, and modal strain energy are experimentally measured before and after damage in the test beams. Finally, damage detection exercises are performed on the test beam to evaluate the feasibility of the proposed method. Experimental results show that the damage detection is the most accurate when frequency changes combined with modal strain-energy changes are used as the modal features for the proposed method.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.77-80
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• 2001

In general, the informations of the inner image that user interested in are limited to a special domain. In this paper, as using Wavelet Transform for dividing image into high frequency and low frequency, We can separate foreground including many data. After calculating object boundary of separated part, We extract special features using Color Coherence Vector. According to results of this experiment, the method of comparing data extracting foreground features is more effective than comparing data extracting features of entire image when we extract the image user interested in.

• Speech Sciences
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• v.12 no.3
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• pp.59-77
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• 2005

The aim of the present study is to investigate how the phonemic characteristics influence on the disfluencies of children and adults who stutter. The participants were 10 children(9 boys and 1 girl) and 10 male adults. After having the participants to read out the Paradise-Fluency Assessment(Sim, Shin & Lee, 2004) passages, each of the productions were divided into syllables and words, and then the frequencies and the ratios of their disfluenceis were analyzed according to the specified phonemic features. In terms of the frequency of the disfluency, the participants stuttered more in the words which start with consonant than vowel. But they showed more disfluencies in the words initiated with vowel than consonant when the ratio of each phoneme's presences were considered. There found different tendencies among the phonemic features related with their disfluencies occuring with ralatively high frequency or ratio. It was difficult to find out the exact relationships among the order of the sound acquisition, phonemic complexity, and the disfluencies. To study the exact influence of the phonemic features upon the disfluencies, it comes important to consider the frequency of the stuttering itself together with the ratio of the disfluencies in which the opportunity of the specific sound's presence was considered. To compare the results of the different studies which has similar purposes, it seems important to consider the tasks and the methodologies in depth.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.71-76
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• 2005

Compared to normal speech, distant-talking speech is characterized by the acoustic effect due to interfering sound and echoes as well as articulatory changes resulting from the speaker's effort to be more intelligible. In this paper, the acoustic features for distant-talking speech due to the articulatory changes will be analyzed and compared with those of the Lombard effect. In order to examine the effect of different distances and articulatory changes, speech recognition experiments were conducted for normal speech as well as distant-talking speech at different distances using HTK. The speech data used in this study consist of 4500 distant-talking utterances and 4500 normal utterances of 90 speakers (56 males and 34 females). Acoustic features selected for the analysis were duration, formants (F1 and F2), fundamental frequency, total energy and energy distribution. The results show that the acoustic-phonetic features for distant-talking speech correspond mostly to those of Lombard speech, in that the main resulting acoustic changes between normal and distant-talking speech are the increase in vowel duration, the shift in first and second formant, the increase in fundamental frequency, the increase in total energy and the shift in energy from low frequency band to middle or high bands.

• Proceedings of the KIPE Conference
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• 2003.07a
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• pp.216-222
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• 2003

Today we have excellent motor drive system using high frequency carrier PWM control voltage source inverter in the other hand, we have met serious problems caused by high frequency switching. PWM Cyclo-converter called Matrix converter is expected as the new strategy Possible to improve these problems and add some more convenient features suitable for new drive system. in this Paper, we will introduce the background, features and outline of this converter, and additionally introduce some remarkable activity on this converter