Optimization of reservoir operation scheme based on fuzzy optimization and convolutional neural network

Hu, He-xuan; Miao, Yue-qing; Hu, Qiang; Zhang, Ye; Zhen-yun, HU; Ma, Rui

doi:10.1117/12.2601054

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…This paper refers to Hu et al (2021), uses CNN as the benchmark model for reservoir operation scheme selection, and uses fuzzy optimization theory to construct CNN training samples. Firstly, the evaluation index system is constructed according to the fuzzy optimization theory; Secondly, the weight of each evaluation index is determined, the subjective weight is determined by analytic hierarchy process, the objective weight is determined by entropy weight method, and the comprehensive weight is obtained by coupling the subjective and objective weights by game theory; Finally, the comprehensive evaluation value of the scheme is calculated by fuzzy comprehensive evaluation, and the initial sample of CNN model is obtained.…”

Section: Overall Research Methodologymentioning

confidence: 99%

“…In this paper, the Xidayang reservoir (Hu et al, 2021) is selected as the research object, and many reservoir operation schemes generated by the reservoir multi-objective operation model are used as the experimental data set, including the flood data of the Xidayang reservoir with 10-year return period (data set 1), 20-year return period (data set 2) and 50-year return period (data set 3). Each data has six characteristic columns, which are water resources conversion, water level recovery level, peak shaving amplitude, drawdown depth, recovery time and maximum water level variation.…”

Section: Data Preprocessingmentioning

confidence: 99%

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A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

Lin

et al. 2023

Front. Plant Sci.

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Reservoir operation is an important part of basin water resources management. The rational use of reservoir operation scheme can not only enhance the capacity of flood control and disaster reduction in the basin, but also improve the efficiency of water use and give full play to the comprehensive role the reservoir. The conventional decision-making method of reservoir operation scheme is computationally large, subjectivity and difficult to capture the nonlinear relationship. To solve these problems, this paper proposes a reservoir operation scheme decision-making model IWGAN-IWOA-CNN based on artificial intelligence and deep learning technology. In view of the lack of data in the original reservoir operation scheme and the limited improvement of data characteristics by the traditional data augmentation algorithm, an improved generative adversarial network algorithm (IWGAN) is proposed. IWGAN uses the loss function which integrates Wasserstein distance, gradient penalty and difference item, and dynamically adds random noise in the process of model training. The whale optimization algorithm is improved by introducing Logistic chaotic mapping to initialize population, non-linear convergence factor and adaptive weights, and Levy flight perturbation strategy. The improved whale optimization algorithm (IWOA) is used to optimize hyperparameters of convolutional neural networks (CNN), so as to obtain the best parameters for model prediction. The experimental results show that the data generated by IWGAN has certain representation ability and high quality; IWOA has faster convergence speed, higher convergence accuracy and better stability; IWGAN-IWOA-CNN model has higher prediction accuracy and reliability of scheme selection.

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Section: Overall Research Methodologymentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

Lin

et al. 2023

Front. Plant Sci.

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Collaboration effectiveness-based complex operations allocation strategy towards to human–robot interaction

Zhang

2022

Auton. Intell. Syst.

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Under the background of the fourth industrial revolution driven by the new generation information technology and artificial intelligence, human–robot collaboration has become an important part of smart manufacturing. The new “human–robot–environment” relationship conducts industrial robots to collaborate with workers to adapt to environmental changes harmoniously. How to determine a reasonable human–robot interaction operations allocation strategy is the primary problem, by comprehensively considering the workers’ flexibility and industrial robots’ automation. In this paper, a human–robot collaborative operation framework based on CNC (Computer Number Control) machine tool was proposed, which divided into three stages: pre-machining, machining and post-machining. Then, an action-based granularity decomposition method was used to construct the human–robot interaction hierarchical model. Further, a collaboration effectiveness-based operations allocation function was established through normalizing the time, cost, efficiency, accuracy and complexity of human–robot interaction. Finally, a simulated annealing algorithm was adopted to solve preferable collaboration scheme; a case was used to verify the feasibility and effectiveness of the proposed method. It is expected that this study can provide useful guidance for human–robot interaction operations allocation on CNC machine tools.

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Optimization of reservoir operation scheme based on fuzzy optimization and convolutional neural network

Cited by 2 publications

References 5 publications

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

Collaboration effectiveness-based complex operations allocation strategy towards to human–robot interaction

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