An optimized method for variational autoencoders based on Gaussian cloud model

Dai, Jingmin; Guo, Qing; Wang, Guoyin; Liu, Xiao; Zheng, Zhifang

doi:10.1016/j.ins.2023.119358

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…The Gaussian cloud model [10] is an model that integrates information randomness and fuzziness, and achieves uncertainty transformation between qualitative and quantitative information, represented by (Ex, En, He). Among them, the expected Ex represents the distribution center of the cloud, which is the point value that best reflects the concept of attribute; entropy En is a measure of the uncertainty of attribute concept, reflecting the acceptable numerical range of concept; super entropy He is a measure of entropy uncertainty, reflecting the degree of dispersion of cloud droplets.…”

Section: Establishment Of Extension Cloud Modelmentioning

confidence: 99%

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State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index

Xu,

Zhang,

Zhang

et al. 2024

Preprint

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Aiming at the problem of overmaintenance and undermaintenance caused by lack of equipment running state, a method of state evaluation and tripping probability prediction of large power transformer based on health index is proposed. The paper constructs a transformer health evaluation system with a four layer deep architecture, uses the extension cloud theory to evaluate the deterioration of state indicators, combines the Analytic Hierarchy Process and Entropy Weight Method to weight the indicators in indicator level, introduces the improved Dezert-Smarandache (DSmT) theory to effectively integrate the evaluation results of each layer, and reconciles the contradictions and conflicts between conclusions. Using the health index to represent the health state of transformers, constructing a tripping probability prediction model with the health index as input, and obtaining the tripping probability of transformers. The research results indicate that the method proposed in this paper can accurately and effectively evaluate the health state of transformers and their functional components, providing information for the management and maintenance strategy of equipment.

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Section: Establishment Of Extension Cloud Modelmentioning

confidence: 99%

“…Step 3: To verify the rationality and effectiveness of 123 weight allocation, a consistency test needs to be performed on the judgment matrix, and the test equations are shown in equations ( 9) and (10). When CR<0.1, it indicates a successful consistency test.…”

Section: )mentioning

confidence: 99%

State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index

Xu,

Zhang,

Zhang

et al. 2024

Preprint

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Evaluating the Condition of Large Power Transformers Through the Use of a Health Index and an Enhanced DSmT Evidence Theory Method

Xu,

Zhang,

Wang

et al. 2024

Lecture Notes in Electrical Engineering

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Restructuring the Landscape of Generative AI Research

Edam

2024

Advances in Educational Technologies and Instructional Design

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This Chapter delves into the impact of generative AI on academic research and publishing, discussing various architectures such as Mixture of Experts (MoE), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Generative Pre-trained Transformers (GPT). The research explores the increase of AI-centered preprints, their effects on peer review, and the ethical considerations linked to them. The peer-review system's integrity is under examination, focusing on challenges related to AI, misuse, and redefining plagiarism. The chapter explores the potential of AI tools to improve peer review processes and stresses the importance of academic institutions creating ethical frameworks for AI utilization. The article concludes by evaluating the advantages and drawbacks of generative AI in research, with the goal of presenting a fair viewpoint on its revolutionary capabilities while upholding ethical principles.

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An optimized method for variational autoencoders based on Gaussian cloud model

Cited by 3 publications

References 35 publications

State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index

State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index

Evaluating the Condition of Large Power Transformers Through the Use of a Health Index and an Enhanced DSmT Evidence Theory Method

Restructuring the Landscape of Generative AI Research

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