Remote sensing technologies have been widely used in the detection of Land Use/Land Cover change (LUCC). In the past few decades, lots of methods have been proposed attempting to detect changes using multi-temporal satellite images, most of which are on the pixel level. In this paper, a new synthetic method based on objectoriented is proposed. Several customized difference features such as difference of band value, Normalized Difference Vegetation Index (NDVI), texture and so on are applied to the change detection, and also the fuzzy classification. The classified elements are image objects with the object-oriented approach which improve the salt-and-pepper problem effectively. Experiment results show that this method has a stronger advantage than the traditional method to high resolution remote sensing image change detection.
Reliability of motorized spindles has a great effect on the performance and productivity of computer numerical control (CNC) machine tools for intelligent manufacturing. Condition-based maintenance (CBM) is an efficient method to prevent serious failures, to improve system reliability, and to reduce management costs for motorized spindles. However, owing to various degradation features acquired during condition monitoring, the challenge is to propose an appropriate feature to evaluate the reliability level of motorized spindles and to set up optimal CBM policies. Based on the motivation, a three-stage approach is proposed in this paper. In the first stage, proportional hazard model (PHM) is developed to describe the reliability considering failure events together with multiple degradation features. Next, statistical process control (SPC) charts are constructed for condition monitoring and anomaly detection in order to achieve early detection of potential failures. At last, a CBM schedule is modeled in consideration of maintenance cost minimization; the maintenance plan is optimized by determining the optimal control limits of SPC charts.
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