Local total and radiative heat-transfer coefficients during the heat treatment of a workpiece in a fluidised bed

The heat-transfer coefficients around a workpiece immersed in an electrically heated heat treatment fluidised bed were studied. A suspension probe designed to simulate a workpiece of complex geometry was developed to measure local total and radiative heat-transfer coefficients at a high bed temperature. The probe consisted of an energy-storage region separated by insulation from the fluidised bed, except for the measuring surface, and a multi-thermocouple measurement system. Experiments in the fluidised bed were performed for a fluidising medium of 120-mesh alumina, a wide temperature range of 110–1050 °C and a fluidising number range of 1.18–4.24. It was found that the workpiece surface temperature has a more significant effect on heat transfer than the bed temperature. The total heat-transfer coefficient at the upper surface of the workpiece sharply decreased at the start of heating, and then steadily increased as heating progressed, while a sharp decrease became a rapid increase and then a slow increase for the radiative heat-transfer coefficient. A great difference in the heat-transfer coefficients around the workpiece was observed.

Suppression of natural convection heat transfer coefficients in an attic shaped enclosure

The use of convection suppression devices has been widely discussed in the literature as a means of reducing natural convection heat loss from enclosed spaces. In this study the use of a single baffle was examined as a possible low cost means of suppressing heat loss by natural convection in an attic shaped enclosure.

Recently the authors experimentally demonstrated that the natural convection heat transfer for an attic shaped enclosure with Grashof Numbers in the range of 107 to 109 [1] could be predicted using a correlation in the literature developed for Grashof Numbers in the range 105 and 106 [2]. However, in this study the impact of adding a single baffle to the enclosure was examined. It was found that the heat transfer coefficient changes with increasing baffle length, and can be predicted using a modified form of the correlation presented in [1], as shown in Eq. (1).

Video genre categorization using audio wavelet coefficients

In this paper, we investigate the use of a wavelet transform-based analysis of audio tracks accompanying videos for the problem of automatic program genre detection. We compare the classification performance based on wavelet-based audio features to that using conventional features derived from Fourier and time analysis for the task of discriminating TV programs such as news, commercials, music shows, concerts, motor racing games, and animated cartoons. Three different classifiers namely the Decision Trees, SVMs, and k-Nearest Neighbours are studied to analyse the reliability of the performance of our wavelet features based approach. Further, we investigate the issue of an appropriate duration of an audio clip to be analyzed for this automatic genre determination. Our experimental results show that features derived from the wavelet transform of the audio signal can very well separate the six video genres studied. It is also found that there is no significant difference in performance with varying audio clip durations across the classifiers.

Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.