Study on scheme optimization of urban flood disaster prevention and reduction
The decision in flood disaster prevention and reduction will affect the evaluation of flood disaster directly. How to apply the natural, economic and social information, through anticipatory analysis, to ensure the minimum damage after practicing certain optimum scheme is a core problem in the flood disaster prevention and reduction decision-making. In this paper, the factors affecting this decision-making are analyzed, the fuzzy optimum selection and neural network are applied in scheme optimization of urban flood disaster prevention and reduction, and the model of scheme optimization is established.
Designing Early Warning Flood Detection and Monitoring System via IoT
Flooding is one of the biggest natural disasters that occurs frequently around the world. It can occur without warning and the after effect of it leaves great damage to the surrounding environment and exposes life threatening to citizen. Therefore, early flood detection and monitoring system with the implementation of Internet of Things and Global Positioning System is proposed in order to reduce the risks that may cause flooding. The aim of this project is to provide the information of a current water level in a drain. When water level increases to a certain level, the system will send a warning notification to users indicating three categories of water level, which are safe, warning and critical level. This system contains an ultrasonic sensor to detect the current water level and at the same time allows users to observe the period of the water level from their phone so that users are more aware of when flooding ought to happen. Moreover, the system consists of a flooding avoidance method that requires the usage of a solenoid as a shutter valve of the drain and water pump to pump out excessive water flow to a suitable place for water release purposes.
Review of Literature on Probability of Detection for Magnetic Particle Nondestructive Testing
Abstract : A critical review of the available literature concerning the minimum reliably detectable defect size aNDI for magnetic particle testing (MPT) in aerospace applications is presented. Four probability of detection (POD) studies relevant to detection of fatigue cracks in aircraft components were found over the period 1968 to 2011. As the statistical methods used in these four previous studies were either outdated or otherwise deficient, the original data were reanalysed using currently accepted techniques. A meta-analysis of the results is presented, with emphasis on statistical inferences for the defect size expected to be detected with 90% POD. It is shown that the minimum reliably detectable defect size aNDI = 2.0 mm currently specified by the Royal Australian Air Force for wet fluorescent magnetic particle inspection using the continuous method is consistent with estimates of the average performance of MPT derived from the reanalysis of the literature.