Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network

Bai, Lei; Zhang, Zhi-Tong; Guan, Huanhuan; Liu, Wenjian; Chen, Li; Yuan, Dongping; Chen, Pan; Xue, Mei; Yan, Guojun

doi:10.1016/j.talanta.2024.126098

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…(1) Determination of optimization dimension and range: The optimization range for hyperparameters is derived from the optimal ranges suggested in the present study [27,28]. The learning rate is set within the range of [0.001, 0.1], striking a balance between the training speed and the quality of convergence to avoid oscillation or sluggish convergence.…”

Section: Ltg-sabo-gru-based Soc Estimation Approachmentioning

confidence: 99%

Estimation of lithium battery state of charge using the LTG-SABO-GRU model

Xiao,

Song,

Liu

et al. 2024

Meas. Sci. Technol.

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Accurate estimation of the state of charge (SOC) in lithium batteries is crucial for optimizing energy utilization and ensuring battery safety within Battery Management Systems (BMS). While deep learning techniques have made significant progress, time-series models based on the Gate Recurrent Unit (GRU) have gained widespread application in SOC estimation. However, their performance heavily hinges on the initial hyperparameter settings, impacting both precision and application range. To address this challenge, we propose a novel algorithm—the Logistic-tent-Gold Subtraction Average-based Optimizer (LTG-SABO)—which combines composite chaotic mapping with the golden sine algorithm. The LTG-SABO algorithm aims to optimize key hyperparameters of the GRU model, thereby enhancing precision and robustness in SOC estimation. By leveraging the Logistic-tent composite chaotic mapping for population initialization, our approach not only expands the search space but also effectively prevents algorithm convergence to local optima. Additionally, integrating the Gold-SA strategy further enhances the global search capability of the SABO algorithm, significantly reducing convergence time. The computational results reveal that the proposed LTG-SABO-GRU model outperforms the traditional GRU model in estimating SOC precision under both normal and extreme temperature conditions. Specifically, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) show a substantial improvement, increasing by over 50% compared to the traditional model. Moreover, the LTG-SABO-GRU model exhibits significantly fewer convergence iterations than existing typical population optimization algorithms. This study introduces a novel, efficient, and practical approach for SOC estimation in BMS applications.

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Section: Ltg-sabo-gru-based Soc Estimation Approachmentioning

confidence: 99%

Estimation of lithium battery state of charge using the LTG-SABO-GRU model

Xiao,

Song,

Liu

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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Multi-spectra combined with Bayesian optimized machine learning algorithms for rapid and non-destructive detection of adulterated functional food Panax notoginseng powder

Guan,

Zhang,

Bai

et al. 2024

Journal of Food Composition and Analysis

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Deep Learning for Accurate Diagnosis of Benign Paroxysmal Positional Vertigo

Dong,

Li,

Milanov

et al. 2024

Advances in Medical Technologies and Clinical Practice

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Benign paroxysmal positional vertigo (BPPV) is characterized by paroxysms of vertigo and nystagmus triggered by head position changes. The diagnosis of BPPV can be objectively determined through the objective analysis of nystagmus, making it a promising approach towards artificial intelligence (AI) -assisted diagnosis. The diagnostic criteria for BPPV have been clearly defined, and standardized protocols for data collection have been established. Video-oculography utilizing infrared cameras has been employed for the quantification of nystagmus. These objective data can be used to train AI algorithms. Utilizing deep learning models allows for accurate tracking of pupil movement trajectories, facilitating the identification of nystagmus types, and making automated diagnosis of BPPV possible. This chapter summarizes the recent advances in AI-assisted diagnosis of BPPV and discusses the limitations and challenges in clinical practice.

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Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network

Cited by 4 publications

References 39 publications

Estimation of lithium battery state of charge using the LTG-SABO-GRU model

Estimation of lithium battery state of charge using the LTG-SABO-GRU model

Multi-spectra combined with Bayesian optimized machine learning algorithms for rapid and non-destructive detection of adulterated functional food Panax notoginseng powder

Deep Learning for Accurate Diagnosis of Benign Paroxysmal Positional Vertigo

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