自适应PCNN和改进C-V结合的遥感图像变化检测

为获取保留图像信息较完好的差异图并得到更好的变化检测结果,提出一种基于自适应脉冲耦合神经网络（PC‐NN）和改进Chan‐Vese （C‐V）模型的非监督的不同时相遥感图像的变化检测算法。用差值法、比值法对两幅遥感图像进行差异图获取;用自适应PCNN图像融合算法对两幅差异图进行融合,获取保留图像信息较好的差异图;用基于改进C‐V模型的分割算法对融合后的差异图进行分割,得到变化检测结果图。实验结果表明,该算法具有很好的变化检测效果,总检测精度较高。     

Mechanical Properties of Highly Deformable Elastomeric Gyroids for Multifunctional Capacitors

Triply periodic minimal surface lattices have mechanical properties that derive from the unit cell geometry and the base material. Through computation software like nTopology and Abaqus, these geometries are used to tune nonlinear stress–strain curves not readily achievable with solid materials alone and to change the compliance by two orders of magnitude compared to the constituent material. In this study, four elastomeric TPMS gyroids undergo large deformation compression and tension testing to investigate the impact of the structure's geometry on the mechanical properties. Among all the samples, the modulus at strain ε$=0.05$ varies by over one order of magnitude (7.7–293.4 kPa from FEA under compression). These lattices are promising candidates for designing multifunctional systems that can perform multiple tasks simultaneously by leveraging the geometry's large surface area to volume ratio. For example, the architectural functionality of the lattice to bear loads and store mechanical energy along with the larger surface area for energy storage is combined. A compliant double-gyroid capacitor that can simultaneously achieve three functions is demonstrated: load bearing, energy storage, and sensing.