Adaptive and robust evidence theory with applications in prediction of floor water inrush in coal mine

Abstract

The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sources of evidence. When the sources of evidence are reliable, the conjunctive rule plays a predominant role, whereas if the sources of evidence are in high conflict the disjunctive rule is critical. Our ART approach was compared with existing representative evidence theory methods through two examples, and was further applied in the prediction of floor water inrush in coal mines. The ART approach presented in this paper was demonstrated to behave better than the existing methods.

Coal mine personnel positioning algorithm based on improved adaptive unscented Kalman filter with wireless channel fading and unknown noise statistics

“…As for the standard UKF, it can be seen from equations (24) and (27) that the calculation of prediction covariance P k k − 1 , r and P zz is affected by the varying process noise covariance matrix Q k − 1 and the measurement noise covariance matrix R ¯ k , r . In light of this, a new IAUKF scheme is proposed to ensure that the process noise covariance matrix Q k − 1 and measurement noise covariance matrix R k , r − can be adaptively estimated accurately (Ge et al, 2019; Li et al, 2017a; Sun et al, 2016). For better online estimation, the q k , Q k , r ...…”

“…For example, the results obtained from the former SAWS (State Administration of Work Safety) annual accident survey and reports have revealed that there were about 495 major accidents and 10,546 fatalities occurring in the coal mine industry from 2001 to 2018 (Zhang et al, 2020). Allowing for this severe situation, it is urgent to develop an effective personnel monitoring system in the underground working environment, which is conducive to obtaining the position information of underground personnel timely and accurately, so as to facilitate the efficient rescue under abnormal conditions (Li et al, 2017a; Min et al, 2020; Yuan et al, 2015; Zhang et al, 2020). In the past, the personnel positioning in coal mine mainly used the wired communication, which has the defects of complicated wiring, high cost, strong line dependence, and so on.…”

Evidential Model Validation under Epistemic Uncertainty

“…Compared with the Bayesian probability model, the merits of D-S evidence theory have already been recognized in various fields. First, the D-S evidence theory can handle uncertainty or imprecision embedded in the evidence [54]. In contrast to the Bayesian probability model in which probability masses can be only assigned to singleton subsets, in D-S evidence theory, probability masses can be assigned to both singletons and compound sets.…”

Evidential Identification of New Target based on Residual

“…Among these efficient tools, Dempster Shafer evidence theory [5,44] has been paid greatly attention recently. Since Dempster-Shafer theory (DS theory) is proposed [5,44], it has been widely used in information fusion [9,28,32,51,60,61], control systems [10,31], uncertainty modelling [7,19,38,46,47,59] decision making [26,27,35,73], risk and reliability analysis [15,22,56,63,74] and other fields [6,50,68,69]. In DS theory , a basic probability assignment(BPA) is distributed to power sets of the frame of discernment and the sum of BPA is always one when supposed that elements in the frame of discernment are mutually exclusive and exhaustive.…”