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Abstract

Thermoacoustic refrigerator uses acoustic power to transport heat from a low-temperature source to a high-temperature source. The increasing interest in thermoacoustic technology is caused due to its simplicity, reliability as well as application of environmentally friendly working fluids. A typical thermoacoustic refrigerator consists of a resonator, a stack of parallel plates, two heat exchangers and a source of acoustic wave. The article presents the influence of the stack position in the resonance tube and the acoustic frequency on the performance of thermoacoustic refrigerator with a standing wave driven by a loudspeaker, which is measured in terms of the temperature difference between the stack edges. The results from experiments, conducted for the stack with the plate spacing 0.3 mm and the length 50 mm, acoustic frequencies varying between 100 and 400 Hz and air as a working fluid are consistent with the theory presented in this paper. The experiments confirmed that the temperature difference for the stack with determined plate spacing depends on the acoustic frequency and the stack position. The maximum values were achieved for resonance frequencies and the stack position between the pressure and velocity node.
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Abstract

This work is focused on the automatic recognition of environmental noise sources that affect humans’ health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens’ daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding). To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.
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