sound

DETERMINATION OF HOPPER FULLNESS OF SMART SCREW PRESS USING MACHINE LEARNING

Problem statement. This research addresses the challenge of accurately determining the fullness of the hopper within a screw press for optimal oil extraction efficiency and quality. Existing weight or volume-based measurement methods can often struggle with determining the feed hopper fullness due to variable oil weights during extraction stages, material heterogeneity, environmental influences and imprecise instrument calibration. Purpose.

Analysis of artificial intelligence methods for rail transport traffic noise detection

Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency.