一种多模态检测数据融合的增强推理方法

Bai Hua, Ping Bai
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引用次数: 0

摘要

多模态检测广泛应用于复杂工程系统的诊断与评价。在多传感器检测中,需要一种合理的推理方法来融合传统单模态传感器采集到的多数据。本文提出了一种有效结合不确定信息的增强证据推理算法。首先总结了模糊集和D-S理论的优缺点,然后讨论了它们之间的相似性,然后提出了一种将模糊集和D-S理论相结合的增强推理算法,实现了在各种来源的信息既不确定又不精确和模糊的情况下的诊断和评估。最后给出了一个例子来说明讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An enhanced reasoning approach for multimodal inspection data fusion
Multimodal inspection is widely used in the diagnosis and evaluation for complex engineering systems. By the use of multiple sensors, a reasonable reasoning approach is required to fuse multi-data acquired over conventional single modality sensors in inspection. In this paper, an enhanced evidential reasoning algorithm to effectively combine uncertain information is presented. We begin with a summary of the relative strength and weakness of vague sets and D-S theory, then the similarity between them is discussed, thereafter an enhanced reasoning algorithm is proposed by combining vague sets and D-S theory to realize the diagnosis and evaluation under the situation in which the presented information from various source is not only uncertain but also imprecise and vague. An example is finally given to illustrate the discussion.
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