Multisensory data fusion technique and its application to welding process monitoring

Zhang, Zhifen; Wen, Guangrui; Chen, Shanben

doi:10.1109/arso.2016.7736298

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Events that happen in the external environment are more simply perceived and localized when cross-modal stimuli being initiated from a similar location. The neuronal responses generated by SC are enhanced at the physiological level when stimuli coming from different modalities descent into the relevant receiving fields of overlapping unisensory neurons [9,29]. Similarly, the responses of SC and accuracy of localization should be depressed when the same stimuli are originated from different places or descent into receptive fields of neurons surrounding inhibitory regions [30,31].…”

Section: Related Workmentioning

confidence: 99%

“…When different sensors observe the same event, multiple questions arise that upon receiving multiple measurements of a single event, how consistent perception is generated by the system with higher accuracy [9][10][11]. Another important question that may arise is, that, how optimal decisions are made by the systems when various perceptions are measured through different sensors [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

Khan¹,

Abbas²,

Raza³

et al. 2022

Computers, Materials &Amp; Continua

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Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming signals. It is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place, the signal having more strength under the influence of emotions effects the response accordingly. Current research in multisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or depression. Therefore, there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or depression. In this study, two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or depression. It was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%