A fuzzy expert system for automatic seismic signal classification

Automatic classification of seismic events is of great importance due to the large amount of data received continuously. Seismic analysts classify events by visual inspection and calculation of event signal characteristics. This process is subjective and demands hard work as well as a significant amount of time and considerable experience. A reliable automatic classification task considerably reduces the effort required and makes classification faster and more objective. The aim of this study is to develop a fuzzy rule based expert classification system that is able to imitate human reasoning and incorporate the analyst's knowledge of seismic event classification. The fundamental idea behind using this approach was motivated by the way in which human analysts classify seismic events based on a set of experiential rules. Additionally, this approach was chosen due to its interpretability and adjustability, as well as its ability to manage the complexity of real data. Relevant discriminant features are extracted from event signal. Using these features, the classification system was built based on the vote by multiple rule fuzzy reasoning method with three types of rules. Comparison of this method with the single winner classical fuzzy reasoning model was carried out. Classification results on real seismic data showed the robustness of the classifier and its capability to operate in on-line classification

Forcespun metal oxide ultrafine tubes for hazardous gas monitoring

International audienceIn this paper, a simple home-made Forcespinning system was designed and successfully applied for the production of different metal oxide ultrafine tubes such as SnOx and ZnO. The morphology, purity, crystallinity and porosity of the forcespun samples were investigated by SEM, XRD, and EDS, TGA, and BET analysis. The samples were deposited unto a microsensing platform and their gas-sensing properties against different hazardous vapors such as ethanol, acetone and benzene were studied through preliminary tests at different detection temperatures. The results revealed different sensing behaviors of the prepared sensors towards the different vapors. Interestingly, the gas sensor based on SnO x exhibited a higher and faster sensor response towards ethanol at 180°C as operating temperature. On the other hand, ZnO presented a higher response to acetone at 250°C. The ability of the prepared sensors to discriminate between the different vapors was explored

Development and evaluation of a brine mining equipment monitoring and control system using wireless sensor network and fuzzy logic

The brine mining equipment failure can seriously affect the productivity of the salt lake chemical industry. Traditional monitoring and controlling method mainly depends on manned patrol that is offline and ineffective. With the rapid advancement of information and communication technologies, it is possible to develop more efficient online systems that can automatically monitor and control the mining equipment and to prevent equipment damage from mechanical failure and unexpected interruptions with severe consequences. This paper describes a Wireless Monitoring and feedback fuzzy logic-based Control System (WMCS) for monitoring and controlling the brine well mining equipment. Based on the field investigations and requirement analysis, the WMCS is designed as a Wireless Sensors Network module, a feedback fuzzy logic controller, and a remote communication module together with database platform. The system was deployed in existing brine wells at demonstration area without any physical modification. The system test and evaluation results show that WMCS enables to track equipment performance and collect real-time data from the spot, provides decision support to help workers overhaul the equipment and follows the deployment of fuzzy control in conjunction with remote data logging. It proved that WMCS acts as a tool to improve management efficiency for mining equipment and underground brine resources