In the multivariable fault isolation of industrial process,the effectiveness of fault isolation is affected by the "smearing" effect because of the correlation between *** order to isolate all the key fault ...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In the multivariable fault isolation of industrial process,the effectiveness of fault isolation is affected by the "smearing" effect because of the correlation between *** order to isolate all the key fault variables correctly,the fault isolation problem is regarded as a variable selection ***,LASSO is applied to the sparse contribution vector based on k-nearest ***,the LASSO-KNN model based on sliding window is established,and the sparse representation of contribution vector is obtained by least angle regression(LAR) algorithm and cumulative weight contribution percent criterion,thus the main fault variables are *** an extended EWMA algorithm combined with sliding window mechanism is introduced to achieve on-line fault ***,the effectiveness of the proposed algorithm is verified by a simulation example of TE process.
The intelligent bearing diagnosis with big data has been widely *** high performance diagnosis models are constantly being proposed,these models are difficult to apply in some practical application with limited collec...
详细信息
ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
The intelligent bearing diagnosis with big data has been widely *** high performance diagnosis models are constantly being proposed,these models are difficult to apply in some practical application with limited collectable *** this paper,an Improved Metric Space Support Vector Machine with a two-stage learning strategy is proposed to overcome the challenge of few-shot scenarios under target domain ***,an improved triplet loss is designed in the metric network to select features by utilizing the similarity of ***,kernel method is used to model in few-shot scenarios by maximizing the inter-class *** on CWRU dataset verify the superiority and robustness of IMSSVM comparing with popular baseline ***,the effectiveness of IMSSVM is verified with visualizing the feature embeddings by t-distributed stochastic neighbor embedding.
This paper focuses on the sliding mode fault-tolerant control (SMFTC) problem for a class of delayed nonlinear systems in presence of actuator fault and nonlinear input. Firstly, the augmented strategy involving syste...
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With the advancement of modern electronic integration technology, the complexity of circuit board composition has significantly increased, making fault detection more challenging. This study proposes a fault diagnosis...
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Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise ...
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Murals are an important part of China's cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, ...
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This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppres...
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In this paper, the problem of adaptive detection of signals in the compound Gaussian clutter when the signal mismatch occurs is discussed. The texture follows the generalized inverse Gaussian distribution which includ...
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In recent years, multi-agent systems are becoming more complex, scalable, adaptive, and integrating machine learning for enhanced capabilities. A popular approach for addressing this challenge involves the utilisation...
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Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient t...
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Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
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