IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i12p1550147719894550.html
   My bibliography  Save this article

An evidential evaluation of nuclear safeguards

Author

Listed:
  • Shang Gao
  • Yong Deng

Abstract

Nuclear safeguards evaluation is a complicated issue with many missing values and uncertainties. By invoking Dempster–Shafer theory of evidence, the missing values are assigned to a subset of a set of multiple objects, at the same time, by combining different evaluation values, and the effect of uncertainty will be decreased. In this way, both the missing values and uncertainties are considered in the final evaluations. This method has been used in considering the International Atomic Energy Agency experts’ evaluation for nuclear safeguards. The result shows that ( s 2 , 0.1897) is the biggest belief degree.

Suggested Citation

  • Shang Gao & Yong Deng, 2019. "An evidential evaluation of nuclear safeguards," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:12:p:1550147719894550
    DOI: 10.1177/1550147719894550
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719894550
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147719894550?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Piero Baraldi & Enrico Zio, 2010. "A Comparison Between Probabilistic and Dempster‐Shafer Theory Approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories," Risk Analysis, John Wiley & Sons, vol. 30(7), pages 1139-1156, July.
    2. Yutong Song & Yong Deng, 2019. "A new method to measure the divergence in evidential sensor data fusion," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    3. Xiaoyan Su & Sankaran Mahadevan & Peida Xu & Yong Deng, 2015. "Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1296-1316, July.
    4. Deng, Xinyang & Jiang, Wen & Wang, Zhen, 2019. "Zero-sum polymatrix games with link uncertainty: A Dempster-Shafer theory solution," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 101-112.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Yige Xue & Yong Deng, 2020. "Refined Expected Value Decision Rules under Orthopair Fuzzy Environment," Mathematics, MDPI, vol. 8(3), pages 1-14, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liguo Fei & Jun Xia & Yuqiang Feng & Luning Liu, 2019. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.
    2. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Wu, Yu’e & Zhang, Zhipeng & Yang, Guoli & Liu, Haixin & Zhang, Qingfeng, 2022. "Evolution of cooperation driven by diversity on a double-layer square lattice," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    4. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    5. Shijun Xu & Yi Hou & Xinpu Deng & Peibo Chen & Kewei Ouyang & Ye Zhang, 2021. "A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.
    6. Matteo Vagnoli & Francesco Di Maio & Enrico Zio, 2018. "Ensembles of climate change models for risk assessment of nuclear power plants," Journal of Risk and Reliability, , vol. 232(2), pages 185-200, April.
    7. Yu Zhang & Wen Jiang & Xinyang Deng, 2019. "Fault diagnosis method based on time domain weighted data aggregation and information fusion," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    8. Deyun Zhou & Yongchuan Tang & Wen Jiang, 2017. "An Improved Belief Entropy and Its Application in Decision-Making," Complexity, Hindawi, vol. 2017, pages 1-15, March.
    9. Rong Yuan & Debiao Meng & Haiqing Li, 2016. "Multidisciplinary reliability design optimization using an enhanced saddlepoint approximation in the framework of sequential optimization and reliability analysis," Journal of Risk and Reliability, , vol. 230(6), pages 570-578, December.
    10. Shengwen Yin & Keliang Jin & Yu Bai & Wei Zhou & Zhonggang Wang, 2023. "Solution-Space-Reduction-Based Evidence Theory Method for Stiffness Evaluation of Air Springs with Epistemic Uncertainty," Mathematics, MDPI, vol. 11(5), pages 1-19, March.
    11. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    12. Saad Muslet Albogami & Mohd Khairol Anuar Bin Mohd Ariffin & Eris Elianddy Bin Supeni & Kamarul Arifin Ahmad, 2021. "A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    13. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.
    14. Jin-Ling Yan & Yong-Jie Xue & Muhammad Mohsin, 2022. "Accessing Occupational Health Risks Posed by Fishermen Based on Fuzzy AHP and IPA Methods: Management and Performance Perspectives," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    15. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    16. Michael Greenberg & Charles Haas & Anthony Cox & Karen Lowrie & Katherine McComas & Warner North, 2012. "Ten Most Important Accomplishments in Risk Analysis, 1980–2010," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 771-781, May.
    17. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    18. Yutong Chen & Yongchuan Tang, 2021. "An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis," Mathematics, MDPI, vol. 9(11), pages 1-16, June.
    19. Yu, Hui & Chen, LuYuan & Yao, JingTao & Wang, XingNan, 2019. "A three-way clustering method based on an improved DBSCAN algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    20. Sarat Sivaprasad & Cameron A. MacKenzie, 2018. "The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility," Decision Analysis, INFORMS, vol. 15(3), pages 139-153, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:15:y:2019:i:12:p:1550147719894550. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.