Bayesian-Wavelet-Based Multisource Decision Fusion

Yang, Wangzhuo; Chen, Bin

doi:10.1109/tim.2021.3094829

Cited by 10 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, the data collected from a multisensory system are incomplete or overlapping which may cause improper decision making. Therefore, data fusion is an essential step that improves the overall performance of a multisensory system [49][50][51].…”

Section: Decision Fusion Using Weighted Majority Voting Methodsmentioning

confidence: 99%

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Biran

Jeremić

2023

DMHT

View full text Add to dashboard Cite

Electrocardiography (ECG) has been a subject of research interest in human identification because it is a promising biometric trait that is believed to have discriminatory characteristics. However, features of ECGs that are recorded at different times are often likely to vary significantly. To address the variability of ECG features over multiple records, we propose a new methodology for human identification using ECGs recorded on different days. To demonstrate the applicability of our method, we use the publicly available ECG ID dataset. The main goal of this work is to extract the most significant and discriminative wavelet components of the ECG signal, followed by utilizing the ECG spectral change for human identification using multi-level filtering technique. Our proposed multi-channel identification system is based on using the Maximal Overlap Discrete Wavelet Transform (MODWT) and its inverse (the IMODWT) to create multiple filtered ECG signals. The discriminative feature that we utilize for human identification is based on modeling the dynamic change of the frequency components in these multiple filtered signals. To reach the best possible identification performance, we use the Weighted Majority Voting Method (WMVM) for ECG classification. We evaluated the robustness of our proposed method over several random experiments and obtained 92.29% average identification accuracy, 0.9495 precision, 0.9229 recall, 0.0771 FRR and 0.0013 FAR. These results indicate that filtering some of the ECG wavelet components along with performing data fusion technique can be utilized for human identification.

show abstract

Section: Decision Fusion Using Weighted Majority Voting Methodsmentioning

confidence: 99%

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Biran

Jeremić

2023

DMHT

View full text Add to dashboard Cite

show abstract

“…Finally, the final extraction result was obtained by decision fusion. Decision fusion is generally achieved through algebraic operations such as maximum, mean and majority vote (MV) [23][24][25]. We adopt the MV method for decision fusion.…”

Section: Methodsmentioning

confidence: 99%

Monitoring of Discolored Trees Caused by Pine Wilt Disease Based on Unsupervised Learning with Decision Fusion Using UAV Images

Wan

Zhang

et al. 2022

Forests

View full text Add to dashboard Cite

Pine wilt disease (PWD) has caused severe damage to ecosystems worldwide. Monitoring PWD is urgent due to its rapid spread. Unsupervised methods are more suitable for the monitoring needs of PWD, as they have the advantages of being fast and not limited by samples. We propose an unsupervised method with decision fusion that combines adaptive threshold and Lab spatial clustering. The method avoids the sample problem, and fuses the strengths of different algorithms. First, the modified ExG-ExR index is proposed for adaptive threshold segmentation to obtain an initial result. Then, k-means and Fuzzy C-means in Lab color space are established for an iterative calculation to achieve two initial results. The final result is obtained from the three initial extraction results by the majority voting rule. Experimental results on unmanned aerial vehicle images in the Laoshan area of Qingdao show that this method has high accuracy and strong robustness, with the average accuracy and F1-score reaching 91.35% and 0.8373, respectively. The method can help provide helpful information for effective control and tactical management of PWD.

show abstract

“…With more and more experts and scholars delving into the issue, mature theories have emerged to handle such uncertain information, among which the most commonly used are Bayesian theory [9,10] and Dempster-Shafer evidence theory. However, Bayesian theory requires prior probability to be obtained before obtaining new probability, which is not suitable for the real-time data fusion in comprehensive mining works.…”

Section: Introductionmentioning

confidence: 99%

The Multisensor Data Fusion Method Based on Improved Fuzzy Evidence Theory in the Coal Mine Environment

Wang,

Fu,

2024

Journal of Sensors

View full text Add to dashboard Cite

An enhanced evidence theory-based multisensor data fusion technique is presented to address the problem of poor data fusion caused by an unknown interference in the fully automated mining face multisensor system of a coal mine. Initially, the set of all measurement values is considered as the identification framework, and the principles of fuzzy mathematics are applied to introduce the membership function. This leads to the proposal of a novel method for calculating mutual support among multiple sensors. Furthermore, the basic belief assignment (BBA) in evidence theory is determined by measuring the confidence distance between sensors. Subsequently, a divergence measure is employed to assess the level of conflict and difference between BBA functions, which serves as an indicator of their credibility. The credibility of BBA functions is further adjusted by calculating their information volume using Shannon entropy. This adjustment aims to increase the weight of BBA functions that exhibit less conflict with other BBA functions. Ultimately, the fusion result is obtained through an evidence combination rule based on a conflict allocation. The numerical experimental results demonstrate that the proposed approach achieves higher accuracy, better robustness, and generality compared to the existing methods.

show abstract

Bayesian-Wavelet-Based Multisource Decision Fusion

Cited by 10 publications

References 32 publications

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Monitoring of Discolored Trees Caused by Pine Wilt Disease Based on Unsupervised Learning with Decision Fusion Using UAV Images

The Multisensor Data Fusion Method Based on Improved Fuzzy Evidence Theory in the Coal Mine Environment

Contact Info

Product

Resources

About