A novel complex evidential distance with its application in pattern recognition

Zhang, Zhanhao; Xiao, Fuyuan

doi:10.1016/j.engappai.2021.104312

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…Moreover, the DOFS system used for pipelines needs a longer measurement range and usually needs to continuously monitor tens to hundreds of kilometers, so the spatial resolution is low. In the PSEW field (Zhu et al 2023c;Yang and Zhao 2022), it is hoped that the DOFS system has both a high accuracy rate and a low false alarm rate to reduce the frequency of false alarms, so as to reduce the operation and maintenance cost of the measures taken after each early warning. However, due to the complex environment of long-distance pipeline (Zhu et al 2023d;Lyu et al 2023), it is still a challenging problem in practical applications to classify and identify various vibrations with a high recognition rate.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

Zhu,

Yang,

Yang

et al. 2024

AAAI

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The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

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Section: Related Workmentioning

confidence: 99%

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

Zhu,

Yang,

Yang

et al. 2024

AAAI

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“…In the past few years, related scholars put forward complex evidence distance (CED) 62 to measure the difference between CBBAs. At the same time, Zhang and Xiao 63 also proposed a new measurement method. Here we call it improved complex evidence distance (ICED).…”

Section: Introductionmentioning

confidence: 99%

Complex belief interval‐based distance measure with its application in pattern recognition

Zhang

Xiao

2022

Int J of Intelligent Sys

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The complex evidence theory is an effective methodology for multiattribute decision-making. Since difference measure between multiattribute plays an important role for conflict management in the process of multiattribute decision-making, how to measure discrepancy between complex basic belief assignments (CBBAs) in complex evidence theory is still an open issue. In this context, a new distance measurement (complex belief distance-CBD) is proposed in this paper by taking advantages of complex belief function and complex plausibility function, called complex belief interval-based distance. In addition, we compare the proposed CBD with the related work to illustrate its superiority. Next, based on CBD, we devise a novel multiattribute decision-making algorithm for pattern recognition. Finally, we apply the method to problems of medical diagnosis to verify the effectiveness of the proposed method.

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Generalized quantum evidence theory

Xiao

2022

Appl Intell

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A novel complex evidential distance with its application in pattern recognition

Cited by 5 publications

References 53 publications

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

Complex belief interval‐based distance measure with its application in pattern recognition

Generalized quantum evidence theory

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