• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2E
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• pp.56-62
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• 2007

This paper presents the automatic music emotion classification on acoustic data. A three-level structure is developed. The low-level extracts the timbre and rhythm features. The middle-level estimates the indication functions that represent the emotion probability of a single analysis unit. The high-level predicts the emotion result based on the indication function values. Experiments are carried out on 695 homogeneous music pieces labeled with four emotions, including pleasant, calm, sad, and excited. Three machine learning methods, GMM, MLP, and SVM, are compared on the high-level. The best result of 90.16% is obtained by MLP method.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.100-108
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• 1995

The In-process minitoring of the chatter vibration is necessarily required to an automatic manufacturing system. In this study, we constructed a multi-sensing system using tool dynamoneter, accelerometer and AE(Acoustic Emission) sensor for a more credible detection of chatter vibration. And a new approach using a multiple neural network to extract the features of multi-sensor for the recognition chatter vibration is proposed. With the Back-propagation training process, the neural network memorize and classify the features of multi-sensor signals. As a result, it is shown by multiple neural network that the chatter vibration can be monitored accurately, and it can be widely used in practical unmanned system.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.47.3-47.3
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• 2021

In this talk I will review recent progress that the SDSS-IV / eBOSS collaboration has made in constraining cosmology from the clustering of galaxies, quasars and the Lyman-alpha forest. The SDSS-IV / eBOSS collaboration has measured the baryon acoustic oscillation (BAO) and redshift space distortion (RSD) features in the correlation function in redshift bins from z~0.15 to z~2.33. These features constitute measurements of angular diameter distances, Hubble distances, and growth rate measurements. A number of consistency tests have been performed between the BAO and RSD datasets and additional cosmological datasets such as the Planck cosmic microwave background constraints, the Pantheon Type Ia supernova compilation, and the weak lensing results from the Dark Energy Survey. Taken together, these joint constraints all point to a broad consistency with the standard model of cosmology LCDM + GR, though they remain in tension with local measurements of the Hubble parameter.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.43-51
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• 2023

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

• Proceedings of the KSPS conference
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• 2000.07a
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• pp.353-363
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• 2000

The consonant system of English is relatively uniform throughout the English-speaking countries. Accents of English are mainly known to differ in terms of their vowel systems as well as in the phonetic realisations of vowel phonemes. The results of an acoustic study of vowel phonology of Japanese English, Singapore English and Indian English are presented, and an attempt is then made to compare the vowel phonology of these non-native varieties with that of Scottish English and RP. Various native varieties of English are thus shown to differ from each other in major ways, as much, perhaps, as the non-native varieties differ from the native varieties. Nevertheless, native speakers of English appear to be mutually intelligible to a degree that does not extend to non-native varieties. Obviously there are features that various native accents have in common which facilitate their mutual intelligibility, and these features are not shared by non-native accents. It is proposed that the foreign learner adopt certain core features of English in his pronunciation if he is to use English effectively as an international language. The common core that is significant in the communication process will be discussed. In conclusion, some pragmatic implications for the English language education in the new millennium will be articulated.

• Journal of Welding and Joining
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• v.28 no.2
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• pp.74-78
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• 2010

In this paper, a non-contact method of evaluating the thickness reduction in an aluminum sheet caused by corrosion and friction using SH-EMAT (shear horizontal, electromagnetic acoustic transducer) is described. Since this method is based on the measurement of the time-of-flight and amplitude change of guided waves caused from the thickness reduction, it provides information on the thinning defects. Information was obtained on the changes of the various wave features, such as their time-of-flight and amplitude, and their correlations with the thickness reduction were investigated. The interesting features in the dispersive behavior of selected guided modes were used for the detection of thinning defects. The measurements of these features using SH waves were performed on aluminum specimens with regions thinned by 7.2% to 29.5% of the total thickness. It is shown that the time-of-flight measurement provides an estimation of the thickness reduction and length of the thinning defects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2402-2409
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• 2011

The voice has distinguishable acoustic properties of speaker as well as transmitting information. The speaker recognition is the method to figures out who speaks the words through acoustic differences between speakers. The speaker recognition is roughly divided two kinds of categories: speaker verification and identification. The speaker verification is the method which verifies speaker himself based on only one's voice. Otherwise, the speaker identification is the method to find speaker by searching most similar model in the database previously consisted of multiple subordinate sentences. This paper composes feature vector from extracting MFCC coefficients and uses the dynamic time warping algorithm to compare the similarity between features. In order to describe common characteristic based on phonological features of spoken words, two subordinate sentences for each speaker are used as the training data. Thus, it is possible to identify the speaker who didn't say the same word which is previously stored in the database.

• Phonetics and Speech Sciences
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• v.4 no.4
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• pp.95-107
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• 2012

As BTX-A, which has been known to be the most effective treatment for ADSD, is not effective in treating vocal tremors, voice assessment must be employed to perform differential diagnosis of SD and vocal tremor in an accurate fashion. In this study, the characteristics of vocal changes after botulinum toxin injection were compared by analyzing the voice characteristics resulting from the presence of vocal tremors using objective analysis devices, with the aim of helping to provide prognoses and to determine remedial effects in clinical cases comprising patients with adductor spasmodic dysphonia accompanied by voice tremors. Respiratory function tests, aerodynamic analysis, electroglottography (EGG), acoustic analysis, auditory perception tests, and K-VHI had been conducted at intervals of four, eight, and twelve weeks before and after injection, targeting a group of 17 ADSD female patients (a ADSD group of four with vocal tremor and a ADSD group of 13 without voice tremor). For average FVC and FEV1, the T group showed statistically significant low averages compared with the NT group, whereas the T group showed statistically significant high average ATRI compared with the NT group. In addition, the T group showed a statistically significant Fatr, lower than that of the NT group. For the ADSD group of patients with voice tremor, their vocal tremor remained unchanged despite noticeable decrease in wringing voices. In other words, as the vocal tremor and wringing voices are two distinctive features, there is a need for the two features to be targeted separately for differential diagnosis.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.54-61
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• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.