Novel Paradigm for Constructing Masses in Dempster-Shafer Evidence Theory for Wireless Sensor Network’s Multisource Data Fusion

Zhang, Zhenjiang; Liu, Tonghuan; Zhang, Wenyu

doi:10.3390/s140407049

Cited by 13 publications

(9 citation statements)

References 23 publications

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“…In this experiment, we use the data collected from gas sensors to detect the types of states of the transformer [ 3 ]. There are many kinds of gases in the transformer’s inner space, such as H 2 , CH 4 , C 2 H 6 , and C 2 H 4 .…”

Section: Resultsmentioning

confidence: 99%

“…Recently, the Dempster–Shafer evidence theory (DST) has attracted extensive attention for its advantages in combining information from distinct sources into a unified source [ 1 , 2 ]. In wireless sensor networks (WSNs), evidence theory provides a flexible solution to decrease the uncertainty and imprecision of decisions when dealing with the data provided by sensors [ 3 ], and it has been widely used in many complex applications, such as object classification, environmental monitoring, disaster search, and information fusion [ 4 , 5 ]. In this paper, we mainly focus on how to classify the incomplete data collected from several sensor nodes in WSNs.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

Zhang

Liu

Chao

et al. 2018

Sensors

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In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data users. In this paper, a novel classification approach of incomplete data is proposed to reduce the misclassification errors. This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis of the distance between interval numbers. Finally, an evidential reasoning rule is adopted to fuse these classification results. The final decision is made according to the combined basic belief assignment. The experimental results show that this method has better performance than other traditional classification methods of incomplete data.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

Zhang

Liu

Chao

et al. 2018

Sensors

Self Cite

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“…e technology of multisensor data fusion has been extensively utilized in many practical applications, such as the risk analysis [16,17], decision-making [18,19], fault diagnosis [20][21][22][23], wireless sensor networks [24][25][26][27][28], health prognosis [29], image processing [30], target tracking [31,32], surveillance [33,34], and so forth [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Multisensor Data Fusion in Testability Evaluation of Equipment

Peng

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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The multisensor data fusion method has been extensively utilized in many practical applications involving testability evaluation. Due to the flexibility and effectiveness of Dempster–Shafer evidence theory in modeling and processing uncertain information, this theory has been widely used in various fields of multisensor data fusion method. However, it may lead to wrong results when fusing conflicting multisensor data. In order to deal with this problem, a testability evaluation method of equipment based on multisensor data fusion method is proposed. First, a novel multisensor data fusion method, based on the improvement of Dempster–Shafer evidence theory via the Lance distance and the belief entropy, is proposed. Next, based on the analysis of testability multisensor data, such as testability virtual test data, testability test data of replaceable unit, and testability growth test data, the corresponding prior distribution conversion schemes of testability multisensor data are formulated according to their different characteristics. Finally, the testability evaluation method of equipment based on the multisensor data fusion method is proposed. The result of experiment illustrated that the proposed method is feasible and effective in handling the conflicting evidence; besides, the accuracy of fusion of the proposed method is higher and the result of evaluation is more reliable than other testability evaluation methods, which shows that the basic probability assignment of the true target is 94.71%.

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“…After many years of development of information fusion technology, Dempster-Shafer (DS) theory is now commonly known and used. In [ 26 ], a novel and easily implemented method was presented to fuse the multisource data in wireless sensor networks through the DS evidence theory. In [ 27 ], a novel information fusion approach using the DS evidence theory and neural networks was proposed to forecast the distribution of coal seam terrain.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

Wang

Liu

et al. 2015

Sensors

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In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

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Novel Paradigm for Constructing Masses in Dempster-Shafer Evidence Theory for Wireless Sensor Network’s Multisource Data Fusion

Cited by 13 publications

References 23 publications

Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

Multisensor Data Fusion in Testability Evaluation of Equipment

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

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