A Novel Stochastic Cloud Model for Statistical Characterization of Wind Turbine Output

Chen, Shaonan; Guo, Xiaoxuan; Han, Shuai; Xiao, Jing; Wu, Ning

doi:10.1109/access.2020.3038450

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Cloud model aims to accurately present amounts of decision information by considering both the subjective components of natural language evaluation and their objective distribution, which is more reasonable and practical in describing the questionnaire data. It has been successfully applied to solve pattern recognition [ 11 – 13 ], decision making [ 14 , 15 ], and other related problems.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Xie

Honghui

et al. 2022

Appl Intell

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During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Secondly, the cloud model is used to quantitatively describe the natural language and uncertainty in a large amount of assessment information. The cloud model generator is used for sub-indicators and achieves an effective and flexible conversion between linguistic information and quantitative values. The cloud model of indicators are presented by integrating the corresponding sub-indicators. The weights of indicators are determined by the entropy method based on the cloud model and possibility degree matrix, which eliminates the judgment of decision-makers and has great power for handling practical problems with unknown weight information. Finally, a fuzzy TOPSIS method based on the cloud model is proposed to rank the satisfaction of online education of different colleges. The proposed method is compared with other existing methods to shown its merits. The experimental result is consistent with the proportion of students who accept online education in the post-epidemic era. According to the second questionnaire, as the qualitative evaluation of the cloud model of indicators increases, the qualitative evaluation of satisfaction of different types of colleges will also increase. It indicates that the method proposed in this paper is practical.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Xie

Honghui

et al. 2022

Appl Intell

View full text Add to dashboard Cite

show abstract

“…Based on probability theory and fuzzy mathematics, Academician Li Deyi innovatively proposed a cloud model theory to deal with uncertain decision-making problems [48]. It uses membership cloud, digital features, cloud generators, and other means to realize the uncertainty conversion between qualitative language values and quantitative representations, effectively deal with the problems of randomness and ambiguity, and solve the shortcomings of traditional evaluation methods in this respect [49,50]. At the same time, compared with the traditional evaluation methods, it is more consistent with objective facts.…”

Section: Cloud Modelmentioning

confidence: 99%

“…(2) Determine the standard cloud. This paper uses the golden section idea to determine the standard cloud model for evaluation [49]. B min and B max are the minima and maximum boundaries respectively.…”

Section: Evaluation Stepsmentioning

confidence: 99%

Evaluation on the implementation effect of public participation in the decision-making of NIMBY facilities

Zhao

Zhang

2022

PLoS ONE

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The construction of NIMBY (Not in my backyard) facilities has caused many conflicts but is struggling to reduce it in China. With the background of public participation in social governance in the future, effective public participation is extremely helpful to solve this issue. Promoting public participation and scientifically evaluating the implementation effect of public participation are urgent problems to be solved at present. This study aims to analysis the factors hindering public participation and improve the implementation effect. Therefore, an evaluation system with 16 factors is established based on literature review and questionnaire survey, namely the basis of participation, participation process, external support, and cost-effectiveness. Interactions among the 16 factors are further evaluated by expert opinions. The objective and subjective weights of indicators are determined and combined by introducing Decision-Making Trial and Evaluation Laboratory (DEMATEL) and entropy weight method (EWM). Considering the uncertainty and randomness of subjective judgment, cloud model is introduced to evaluate the implementation effect of public participation. Finally, this framework is applied to evaluate the project of Jiu Feng waste-to-energy (WTE) plant in Hangzhou, China, which verifies the applicability of the evaluation framework for the implementation effect of public participation in NIMBY facilities. The results indicate that the implementation of public participation is between "average" and "good", with significant room for improvement in the involvement of NGOs and the influence of public opinion on decision-making. Additionally, the participation process has a significant impact on the whole system. The framework can provide government departments with guidance in implementing public participation.

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“…After several years of exploration and development, the cloud model has become completer and more universal ( Wang, Li & Yang, 2019 ). The cloud model has been successfully applied and developed in many fields, such as in the statistical representation of engineering parameters ( Chen et al, 2020 ; Luo et al, 2022 ), system evaluation and decision-making ( Tong & Srivastava, 2022 ; Su & Yu, 2020 ; Wu et al, 2020 ), data mining ( Shehab, Badawy & Ali, 2022 ; Zarinbal, Zarandi & Turksen, 2014 ), image processing ( Li, Li & Du, 2018 ; Tversky & Kahneman, 1992 ), and decision making problems ( Yu et al, 2021 ; Wang, Huang & Cai, 2020 ; Zhou, Chen & Ming, 2022 ). It should be noted that the practical applications of cloud model theory (such as data mining and decision analysis) all involve the similarity measurement ( Zhang, Zhao & Li, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

Yang

Han

Wan

et al. 2023

PeerJ Computer Science

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It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

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A Novel Stochastic Cloud Model for Statistical Characterization of Wind Turbine Output

Cited by 9 publications

References 26 publications

RETRACTED ARTICLE: Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

RETRACTED ARTICLE: Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Evaluation on the implementation effect of public participation in the decision-making of NIMBY facilities

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

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