High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.
Pipeline vibration in aircraft hydraulic systems has drawn more attention in recent years. There are still a lot of problems unresolved in the field, due to the complex non-linearity of fluid–structure interaction. Two-dimensional stress analysis of pipeline in an aircraft hydraulic system has been carried out in this study. As the classical theory on the fluid-filled pipe is based on one-dimensional model, a two-dimensional model has been developed to study the stress distribution around the section vertical to the pipe axis, which affects the fatigue life of the pipeline. Influences of curvature and stress concentration have been included in the model. Three-dimensional finite element method model for a pipe section between the pump and the first pressure filter in backup system of the aircraft hydraulic system has been constructed to improve the one-dimensional theoretical model. Simulation and experimental results can match each other well in stress analysis. Some discussion about the parameters in the developed theory model is given.
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