Region-Dot Conversion Fusion Algorithm and Application

Abstract

The interior decoration materials and the new furniture using formaldehyde, ammonia, and other poisonous substances are known as the main sources of indoor air pollutions. However, it is still a big challenge to estimate accurately the overall air quality by using the current measuring tools. Accordingly, the region-dot fusion (RDF) algorithm is proposed to evaluate the air quality in this paper. For the conversion from a region to a dot, the region-dot function is firstly defined as the summation of the belief function and the weighted width of the belief interval. In the RDF algorithm, the belief intervals of the two sensors with the basic probability functions are calculated based on the measurements of formaldehyde sensor and ammonia sensor. Then, the belief intervals are converted to the specific values. After the computation of collision degree and combination, the pollution level represented by a belief interval with the maximum probability is selected as the outcome of fusion decision. Compared with the weighted fusion algorithm and D-S evidence reasoning method, it is experimentally proved that the RDF algorithm can improve the separability of the belief intervals of the belief functions. Also, the evidence collision degree is decreased dramatically.