单参考BSDF测量中的角度补偿装置设计与实验研究

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本文主要探讨了在测量双向散射分布函数(Bi-directional Scattering Distribution Function, BSDF)时,对于单一参考角度补偿装置的设计与实验研究。传统的BSDF测量方法要求测试样本和参考光源的入射顶角必须一致,以便获取样品表面的全空间散射特性。然而,由于实际应用中难以保持恒定的入射角度,通常采用旋转样本的方法来改变这个角度,这就需要一个角度补偿机制。 作者团队针对这个问题进行了数学建模,通过理论计算得出了当电机驱动样本旋转时,入射顶角变化对应的补偿角度表达式。这一补偿策略确保了测量过程中样本入射角的准确校准,从而减小了系统误差。经过补偿后,散射的顶角和方位角也随之调整,提高了测量结果的精度。 文章详细描述了实验装置的设计过程,包括电机驱动系统的精确控制和补偿算法的实现。在实验部分,研究者选择了铜样品作为测试对象,并在可见光条件下进行了不同温度下的散射测量,以此验证了补偿装置的有效性和系统的稳定性。整个实验过程中,系统的不确定度被控制在了0.75%,显示了该补偿装置在实际应用中的高精度性能。 这篇文章不仅提供了BSDF测量中角度补偿技术的关键理论支持,还展示了其在实际应用中的有效性和实用性。这对于提升光学测量设备的精度和效率具有重要意义,对于从事光学工程、材料科学以及相关领域的研究人员来说,是一篇有价值的参考文献。
2023-06-02 上传

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

2023-05-24 上传