Optimizing the adaptive circuit of piezoelectric sensors is important in accurately measuring blast waves. This study analyzed the impact of the bandpass cut-off frequencies and quality factor of adaptive circuits on shock wave measurements. We proposed a Multi-objective particle swarm optimization based on weighted Pareto-dominance sort (W-MOPSO) for optimizing the multi-objective function of an adaptive circuit in order to improve precision during shock wave measurements. W-MOPSO obtained a Pareto-optimal solution set through Pareto-dominant sorting of global optimal solutions to the weighted sum of a multi-objective function. The relative mean error in the optimized adaptive circuit mostly ranged between -2% and +2%. Simulations were used to analyze the impact of the optimized adaptive circuit on shock wave measurements. The effectiveness of W-MOPSO was verified through a real-world experiment using a TNT explosive and blast wave gauges. W-MOPSO provided a basis for adapter circuit design and optimization in shock wave measurement.

中文翻译：

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用于爆炸波测量的自适应电路中的 W-MOPSO


优化压电传感器的自适应电路对于精确测量冲击波具有重要意义。本研究分析了自适应电路的带通截止频率和品质因数对冲击波测量的影响。我们提出了一种基于加权帕累托优势排序（W-MOPSO）的多目标粒子群优化方法，用于优化自适应电路的多目标函数，以提高冲击波测量过程中的精度。 W-MOPSO通过对多目标函数的加权和的全局最优解进行帕累托显性排序，得到帕累托最优解集。优化后的自适应电路的相对平均误差大多在-2%和+2%之间。仿真用于分析优化的自适应电路对冲击波测量的影响。 W-MOPSO 的有效性通过使用 TNT 炸药和爆炸波计的实际实验得到了验证。 W-MOPSO为冲击波测量中的适配器电路设计和优化提供了基础。