RESUMO
Since the damage of the bridge structure may cause great disasters, it is necessary to monitor its health status, especially the bridge bearing, the important connecting component of the bridge's upper and lower structures. Nowadays, manual inspection is the main method to get the information of the bridge bearings' work status. However, occasional damage of bridge bearing may not be detected in time, and sometime the installation position of the bearing makes the manual inspection on bridge bearing difficult and even impossible. Therefore, in order to know the work status of the bridge bearings timely, an intelligent remote monitoring system for the bridge bearing is developed. A 32-channel real-time acquisition system is designed by using an AD7768-1 analog-to-digital converter and Xilinx Spartan-6 Field Programmable Gate Array for interface stress continuously acquired in the bridge bearing. To assure the good linearity and low noise performance of the monitoring system, the data acquisition card is meticulously designed to reduce noise from both hardware and software and realize high-precision acquisition. Through the establishment of the monitoring server, the compressive stress data can be displayed synchronously and the overpressure situation can be alarmed in real-time. The experimental results show that the accuracy of the calibrated sensor is within 1.6%, and the detection error of the acquisition board is less than 200 µV. The acquisition system is deemed to have considerable advantages in accuracy and applicability.
RESUMO
Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts' experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area.
