Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph

Li, Linhan; Yang, Guanci; Li, Yang; Zhu, Da; He, Ling

doi:10.1016/j.engappai.2023.106374

Cited by 32 publications

(8 citation statements)

References 48 publications

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“…Some factors such as temperature and aging exert notable effects on SOC values and battery performance, and subsequent research should be accounted for corresponding variations. The presented methods in this work can be extended to estimate the parameters, orders and delays of other stochastic systems from observation data [119][120][121][122][123][124][125][126] and can be applied to process control systems [127][128][129][130][131][132][133] and industrial production systems and so on.…”

Section: Discussionmentioning

confidence: 99%

Parameter and order estimation algorithms and convergence analysis for lithium‐ion batteries

Hu,

Liu,

2023

Intl J Robust & Nonlinear

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The fractional‐order equivalent circuit model can reflect the internal reaction mechanism of a lithium‐ion battery well. This article aims to design an effective model and optimization method to describe and analyze the operating characteristics of the lithium‐ion battery based on online measurement data. The fractional‐order controlled autoregressive model is derived by exploiting the memory superiorities of the fractional‐order, which comprises the electrochemical impedance spectroscopy and the ‐RC equivalent circuit model as the special cases. The utilization of polynomial properties reduces the difficulty of identification while preserving the ability of the controlled autoregressive model to fit the characteristics of the battery. To realize simultaneous parameter and the order estimation, a weighted gradient descent algorithm is proposed. The approach designs a new gradient direction and fully utilizes the data from the lithium‐ion battery by adding a suitable weighted factor. In addition, the forgetting factor is introduced to speed up convergence and produce more accurate parameter estimation. Furthermore, in order to analyze the convergence of the proposed algorithms, the convergence properties are proved by using martingale convergence theory and stochastic principle. Finally, the experimental simulation result shows the performance of the proposed algorithms.

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Section: Discussionmentioning

confidence: 99%

Parameter and order estimation algorithms and convergence analysis for lithium‐ion batteries

Hu,

Liu,

2023

Intl J Robust & Nonlinear

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“…Moreover, the convergence analysis proclaims that the AM-HLS parameter estimates converge to their true values. The algorithm proposed in this article can combine with other identification methods and be applied to MIMO systems with colored noise and other field [99][100][101][102][103][104] such as process control systems [105][106][107][108][109][110][111] and information processing systems.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Xing,

Ding,

Pan

et al. 2023

Adaptive Control & Signal

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SummaryMultiple‐input multiple‐output (MIMO) models are widely used in practical engineering. This article derives a new identification model of the MIMO system by decomposing the MIMO system into several multiple‐input single‐output subsystems. By means of the auxiliary model identification idea, an auxiliary model‐based recursive least squares (AM‐RLS) algorithm is derived for identifying the MIMO systems. In order to reduce the computational burden for identifying MIMO systems, this article presents a hierarchical identification model for the MIMO systems. By applying the hierarchical identification principle, an auxiliary model‐based hierarchical least squares (AM‐HLS) algorithm is proposed for improving the computational efficiency. The computational efficiency analysis indicates that the AM‐HLS algorithm is effective in reducing the calculation amount compared with the AM‐RLS algorithm. Moreover, this article analyzes the convergence of the AM‐HLS algorithm. The simulation example shows that the AM‐RLS and AM‐HLS algorithms studied in this article are effective.

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“…In summary, Equations ( 28)-( 43) consist of the O-AM-HGI algorithm based on the mean value. The O-AM-HGI algorithms can combine some mathematical tools [72][73][74][75] and identification approaches [76][77][78][79][80][81][82] to explore parameter estimation methods of different dynamic stochastic systems [83][84][85][86][87][88][89] and can be applied to signal processing and chemical process control.…”

Section: The O-am-hgi Algorithmmentioning

confidence: 99%

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

Ding,

Xu,

Zhang

et al. 2023

Intl J Robust & Nonlinear

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This article investigates the parameter identification problems of the stochastic systems described by the input‐nonlinear output‐error (IN‐OE) model. This IN‐OE model consists of two submodels, one is an input nonlinear model and the other is a linear output‐error model. The difficulty in the parameter identification of the IN‐OE model is that the information vector contains the unknown variables, which are the noise‐free (true) outputs of the system, the approach taken here is to replace the unknown terms with the outputs of the auxiliary model. Based on the over‐parameterization model and the hierarchical identification principle, an over‐parameterization auxiliary model hierarchical gradient‐based iterative algorithm and an over‐parameterization auxiliary model hierarchical least‐squares‐based iterative algorithm are proposed to estimate the unknown parameters of the IN‐OE systems. Finally, two numerical simulation examples are given to demonstrate the effectiveness of the proposed algorithms.

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Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph

Cited by 32 publications

References 48 publications

Parameter and order estimation algorithms and convergence analysis for lithium‐ion batteries

Parameter and order estimation algorithms and convergence analysis for lithium‐ion batteries

Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

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