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Hierarchical Bayesian Calibration and On-line updating Method for Influence Coefficient of Automatic Dynamic Balancing Machine

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Chinese Journal of Mechanical Engineering 2009年第6期22卷 876-882页

作者： ZHANG Jian WU Jianwei MA Zhiyong Mechanical Design Institute Zhejiang University Hangzhou 310027 China

Measurement error of unbalance＇s vibration response plays a crucial role in calibration and on-line updating of influence coefficient（IC）. Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process, an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed. During calibration process, for the repeatedly-measured data obtained from experiments with different trial weights, according to the fact that measurement error of each sensor had the same statistical characteristics, the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established. During the updating process, information obtained from calibration was regarded as prior information, which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating. Moreover, Gibbs sampling method of Markov Chain Monte Carlo（MCMC） was adopted to obtain the maximum posterior estimation of parameters to be estimated. On the independent developed dynamic balancing testbed, prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively, the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy; the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process, and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.

关键词： influence coefficient hierarchical Bayesian calibration online updating dynamic balancing Markov Chain Monte Carlo(MCMC)

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Two-Stage online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming Data

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Journal of Systems Science & Complexity 2024年第3期37卷 1251-1270页

作者： PENG Yanjin WANG Lei School of Statistics and Data Science KLMDASRLEBPS and LPMCNankai UniversityTianjin 300071China

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming *** the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative *** estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming *** the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be *** authors conduct extensive numerical experiments to evaluate the performance of the proposed *** experiments demonstrate the effectiveness of the proposed method and support the theoretical *** application to the Beijing PM2.5 Dataset is also presented.

关键词： Adaptive tuning asymptotic normality debiased lasso online updating quantile regres-sion

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An Adaptive Deep Learning Method Integrating Transfer Learning and Concept Drift Detection for Wind Power Forecasting

An Adaptive Deep Learning Method Integrating Transfer Learni...

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第43届中国控制会议

作者： Jingdi Wang Yundong Cao Huaikang Jin Shouyuan Fan Bin Wang Huaiping Jin Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Huaneng Renewables Co. Ltd.Yunnan Branch China Energy Engineering Group Yunnan Electric Power Design Institute

With the continuous advancement of new energy technologies,wind power is becoming an increasingly crucial source of clean *** to the stochastic and fluctuating nature of wind power data,there is a significant challenge known as concept drift,where the data distribution changes over *** poses a substantial obstacle to accurate prediction,leading to performance degradation of *** address this issue,an adaptive deep-learning wind power prediction method combining transfer learning and concept drift detection is *** method incorporates the concept of online transfer learning,transferring offline-trained model parameters to adapt quickly to new data distributions during the online ***,a combination of an online moving window and Kullback-Leibler(KL) divergence is employed to capture and analyze changes in the online predictor's output layer network *** facilitates a more precise detection of concept drift in wind power prediction,determining whether model updates are *** dynamically updating model parameters and effectively handling concept drift,the approach can maintain high accuracy and stability in wind power *** effectiveness and superiority of the proposed method are validated through the analysis of real-world wind power field data.

关键词： Wind Power Forecasting Concept Drift Transfer Learning online updating Gated Recurrent Unit

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