A Robust Prognostic Indicator for Renewable Energy Technologies: A Novel Error Correction Grey Prediction Model

Zhou, Daming; Al‐Durra, Ahmed; Zhang, Ke; Ravey, Alexandre; Gao, Fei

doi:10.1109/tie.2019.2893867

Cited by 81 publications

(37 citation statements)

References 49 publications

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“…Additionally, an improved algorithm using PFSDM distance is proposed in medical diagnosis, which can avoid producing counter-intuitive results especially when a data conflict exists. The proposed method and the magnified algorithm are both demonstrated to be rational and practical in applications.2 of 20 applications, such as strategy selection [30][31][32][33][34], decision-making [35][36][37][38][39], prediction [40][41][42], and fault diagnosis [43,44]. In addition, Zadeh presented a useful theory in 1965, the fuzzy set (FS) theory [45], which drives a big step forward in decision theory.…”

mentioning

confidence: 99%

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

2020

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As the extension of the fuzzy sets (FSs) theory, the intuitionistic fuzzy sets (IFSs) play an important role in handling the uncertainty under the uncertain environments. The Pythagoreanfuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Pythagorean fuzzy sets, which is based on the belief function in Dempster–Shafer evidence theory, and is called PFSDM distance. It describes the Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of PFSs, which is the step in establishing a link between the PFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods. Additionally, an improved algorithm using PFSDM distance is proposed in medical diagnosis, which can avoid producing counter-intuitive results especially when a data conflict exists. The proposed method and the magnified algorithm are both demonstrated to be rational and practical in applications.

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mentioning

confidence: 99%

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

2020

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“…Local feature-based gated recurrent unit (LFGRU) networks have been proposed in [11] to predict machine condition by further processing of handcrafted features that aggregate time series information, using a gated recurrent unit network to learn richer feature representations. In order to predict defects and model degradation phenomena in renewable energy storages, the work of [25] introduces an error correction factor that enhances the grey model (GM) without increasing complexity. A predictive method for remaining useful life (RUL) estimation is proposed in [26], that utilizes support vector regression to directly model the correlation between sensor values or health indicators and estimate RUL of equipment.…”

Section: Predictive Algorithms In Industrial Processesmentioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

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The prediction of upcoming events in industrial processes has been a longstanding research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published "PCB scans" dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

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“…[50][51][52]. Among the existing math tools [53][54][55], evidence theory is widely used due to its efficiency [56,57]. The identification framework is the most basic concept in evidence theory, originating from what people can know and what they want to know [58].…”

Section: Preliminariesmentioning

confidence: 99%

A New Conflict Management in Evidence Theory Based on DEMATEL Method

Fan

Xiao

2019

Journal of Sensors

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D-S evidence theory is widely used in data fusion. However, the result of Dempster’s combination rule is not efficient and in highly conflicting situation. Though the existing methods have been proved efficient to deal with conflict in some applications, the indirect conflict among evidence is neglected to some degree. To solve this problem, a new method is proposed based on decision-making trial and evaluation laboratory (DEMATEL) and the belief correlation coefficient in this paper. The application in target recognition illustrates the efficiency of the proposed method. Compared with Dempster’s rule, averaging method and weighted averaging method, etc., the results obtained by the proposed method have better performance. The main reason is that the indirect conflict is well addressed in the proposed method.

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A Robust Prognostic Indicator for Renewable Energy Technologies: A Novel Error Correction Grey Prediction Model

Cited by 81 publications

References 49 publications

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

A Deep Learning framework for simulation and defect prediction applied in microelectronics

A New Conflict Management in Evidence Theory Based on DEMATEL Method

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