An Extended Base Belief Function in Dempster–Shafer Evidence Theory and Its Application in Conflict Data Fusion

The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time. In order to solve this problem, the base belief function is proposed to modify the basic probability assignment (BPA) in the exhaustive frame of discernment (FOD). However, in the non-exhaustive FOD, the mass function value of the empty set is nonzero, which makes the base belief function no longer applicable. In this paper, considering the influence of the size of the FOD and the mass function value of the empty set, a new belief function named the extended base belief function (EBBF) is proposed. This method can modify the BPA in the non-exhaustive FOD and obtain intuitive fusion results by taking into account the characteristics of the non-exhaustive FOD. In addition, the EBBF can degenerate into the base belief function in the exhaustive FOD. At the same time, by calculating the belief entropy of the modified BPA, we find that the value of belief entropy is higher than before. Belief entropy is used to measure the uncertainty of information, which can show the conflict more intuitively. The increase of the value of entropy belief is the consequence of conflict. This paper also designs an improved conflict data management method based on the EBBF to verify the rationality and effectiveness of the proposed method

Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified

Graphene oxide-deposited tilted fiber grating for ultrafast humidity sensing and human breath monitoring

We propose and experimentally demonstrate a high-performance relative humidity (RH) sensor by depositing graphene oxide (GO) onto tilted fiber grating (TFG). The largely tilted grating planes of the employed TFG can induce a set of polarization-dependent cladding modes and strong evanescent field to couple with the humidity-dependent dielectric of GO layer. The GO-deposited TFG presents the response sensitivities of 18.5 pm/%RH and 0.027 dB/%RH in the range of 30%˜80%RH by tracking the wavelength and intensity of a specific cladding mode resonance. By monitoring the human breath with different frequencies, the sensor exhibits an ultrafast response within ˜42 ms due to the thin GO film and unimpeded permeation of water molecules through GO interlayer. The easy fabrication, low hysteresis, fast response, and high repeatability and reliability of the proposed RH sensor may enable many potential applications including pharmaceutical processing, human health and environmental monitoring