An ontology aided gso optimized extreme learning for situation recognition in coal mining environment

Abstract

A wireless sensor network (WSN) is a wireless network comprising of spatially distributed autonomous devices utilizing sensors for monitoring the physical or environmental situations. WSN has been applied in many fields such as healthcare monitoring, coal mine safety monitoring system and also in military. To detect the seismic activities in the coal mining environment, several techniques such as Bord and Pillar model, Bayesian Decision method etc., were introduced and carried out. In this paper, we have proposed anOntology aided Fuzzy Cognitive Maps (FCM) based feature correlation extraction technique for the multi attribute sensor data.Further, the Galactic Swarm Optimization (GSO) algorithm optimized Extreme Learning Machine (ELM) is used. The correlation extraction technique gives the better solution to determine the similarity between the semantically related heterogeneous sensor reading data and resolves the semantic ambiguity problem of heterogeneous sensors present in the coal mining Environment. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved.