Sound quality (SQ) is a perceptual or subjective reaction to a sound and its concept becomes one of the important factors that improve the competitive power of a product. Through the various studies related to SQ by psycho-acoustic researchers, models for objective measures that substitute subjective evaluation, called SQ metrics, have been proposed which consider human auditory characteristics. Representative SQ metrics are loudness, sharpness, roughness, and fluctuation strength. For other SQ metrics except loudness, however, the calculation algorithms have not been standardized yet. The purpose of this study is to investigate whether there is difference among the commercial software for the calculation of SQ metrics and if any, how much difference exists among them. For this, three kinds of popular commercial software and one self-coded program were chosen and by applying them to some sample sounds, four representative SQ metrics were calculated and compared. As a result, it was confirmed that there are considerable differences among the calculated results of SQ metrics including loudness. This means that it is necessary to standardize SQ metrics as soon as possible before everything else and in addition, to mention used SQ software when an index that can predict SQ is developed or SQ database for any kind of product is created.
The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated.
Regular inspection for the maintenance of the wind turbines is difficult because of their remote locations. For this reason, condition monitoring systems (CMSs) are typically installed to monitor their health condition. The purpose of this study is to propose a fault detection algorithm for the mechanical parts of the wind turbine. To this end, long-term vibration data were collected over two years by a CMS installed on a 3 MW wind turbine. The vibration distribution at a specific rotating speed of main shaft is approximated by the Weibull distribution and its cumulative distribution function is utilized for determining the threshold levels that indicate impending failure of mechanical parts. A Hidden Markov model (HMM) is employed to propose the statistical fault detection algorithm in the time domain and the method whereby the input sequence for HMM is extracted is also introduced by considering the threshold levels and the correlation between the signals. Finally, it was demonstrated that the proposed HMM algorithm achieved a greater than 95% detection success rate by using the long-term signals.
Condition Monitoring System (CMS) has been used to detect unexpected faults of wind turbine caused by the abrupt change of circumstances or the aging of its mechanical part. In fact, it is a very hard work to do regular inspection for its maintenance because wind turbine is located on the mountaintop or sea. The purpose of this study is to find out distribution patterns of vibration signals measured from the main mechanical parts of wind turbine according to its operation condition. To this end, acceleration signals of main bearing, gearbox, generator, wind speed, rotational speed, etc were measured through the long period more than 2 years and trend analyses on each signal were conducted as a function of the rotational speed. In addition, correlation analysis among the signals was done to grasp the relation between mechanical parts. As a result, the vibrations were dependent on the rotational speed of main shaft and whether power was generated or not, and their distributions at a specific rotational speed could be approximated to Weibull distribution. It was also investigated that the vibration at main bearing was correlated with vibration at gearbox each other, whereas vibration at generator should be dealt with individually because of generating mechanism. These results can be used for improving performance of CMS that early detects the mechanical abnormality of wind turbine.
In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in low open area plasma etch process is more difficult than before. For endpoint detection, various kinds of sensors are installed in many semiconductor manufacturing equipment, and sensor data sampled with predefined sampling rate. To solve this problem, a combination of Signal to Noise Ratio (SNR), Principal Component Analysis (PCA) and Expanded Hidden Markov model (eHMM) technique is applied to optical emission spectroscopy (OES) signals.
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