Study on the Perceived Quality of Hand-Operated Mechanical Controls in Passenger Cars Using the Example of Air Vents

Abstract

Existing techniques for monitoring and controlling the ventilation system in underground mines are limited; since they only detect areas of low oxygen level or use software to model systems based on standardized data, but not, they evaluate the factors and identify the causes that generate the deficiency in the system. For this reason, a predictive method of factors influencing the airflow of the ventilation system is proposed as a possible solution with the use of artificial neural networks (ANN) to strengthen the monitoring and control process. The methodology proposed in this research includes the analysis of air flow factors in critical mining areas to identify the study parameters. In the case study, a database of records of ventilation conditions of a mine was used. A test of 11 predictive neural networks was developed, with approximately a base of 250 standardized data.