Fractional Deng Entropy and Extropy and Some Applications [PDF]
Entropy, 2021 Deng entropy and extropy are two measures useful in the Dempster–Shafer evidence theory (DST) to study uncertainty, following the idea that extropy is the dual concept of entropy.
Mohammad Reza Kazemi +3 more
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Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion [PDF]
Entropy, 2021 Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data.
Yutong Chen, Yongchuan Tang
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Efficient Deep Wavelet Gaussian Markov Dempster–Shafer Network-Based Spectrum Sensing at Very Low SNR in Cognitive Radio Networks [PDF]
SensorsCognitive radio networks (CRNs) rely heavily on spectral sensing to detect primary user (PU) activity, yet detection at low signal-to-noise ratios (SNRs) remains a major challenge.
Sunil Jatti, Anshul Tyagi
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Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system. [PDF]
PLoS ONE, 2022 In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of ...
Liming Gou +5 more
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Probabilistic Hesitant Fuzzy Evidence Theory and Its Application in Capability Evaluation of a Satellite Communication System [PDF]
EntropyEvaluating the capabilities of a satellite communication system (SCS) is challenging due to its complexity and ambiguity. It is difficult to accurately analyze uncertain situations, making it difficult for experts to determine appropriate evaluation ...
Jiahuan Liu +3 more
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A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion [PDF]
Entropy, 2021 Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results.
Fanghui Huang +3 more
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A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy [PDF]
Entropy, 2019 Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST ...
Qian Pan +4 more
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Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis [PDF]
Entropy, 2020 Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S),
Haixia Zheng, Yongchuan Tang
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Synthetic database of space objects encounter events subject to epistemic uncertainty [PDF]
Data in Brief, 2020 The databases included on this article refers to variables and parameters belonging to the Space Traffic Management (STM), Evidence Theory and Machine Learning (ML) fields.
Luis Sánchez, Massimiliano Vasile
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Evidence Theory in Picture Fuzzy Set Environment
Journal of Mathematics, 2021 Picture fuzzy set is the most widely used tool to handle the uncertainty with the account of three membership degrees, namely, positive, negative, and neutral such that their sum is bound up to 1.
Harish Garg +4 more
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