Data-knowledge driven: a new learning strategy for iris recognition

Liu, Shuai; Liu, Yuanning; Zhu, Xiaodong; Zhang, Shaoqiang

doi:10.1007/s11042-023-16567-4

Cited by 1 publication

(1 citation statement)

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“…However, in real systems, models based on data-driven approaches may be inconsistent or unfair across different datasets due to external factors, such as data limitations and system complexity. The knowledge-driven approach is a modeling methodology based on a priori knowledge and the experience of domain experts, which is aimed at better understanding and describing the behavior and relationships of complex systems [2][3][4]. The knowledge-driven modeling approach aims to enhance the performance and explanatory power of the model by leveraging the knowledge and experience of domain experts in the modeling process.…”

Section: Introductionmentioning

confidence: 99%

A Lithium Battery Health Evaluation Method Based on Considering Disturbance Belief Rule Base

Zhang,

Gong,

et al. 2024

Batteries

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Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm.

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Section: Introductionmentioning

confidence: 99%