Satellite battery sensor values prediction using Bayesian ridge regression models

Galal, Mohamed Ahmed; Hussein, Walid; Abdelkawy, Ezz Eldin

doi:10.1088/1757-899x/610/1/012012

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…An anomaly in the flowmeter occurred, resulting in a congestion of flows. The proposed method is applied to identify the anomaly and used root mean squared error (RMSE) and mean absolute error (MAE) [17] values as indicators to compared with Lasso [18] and Ridge [19,20]. Details of the dataset and experimental setup are provided in the methodology section.…”

Section: Application Results and Discussionmentioning

confidence: 99%

Online Monitoring of Flowmeter Anomaly in Tobacco Production Process Using Sliding Window Recursive Lasso

Guan

Liu

et al. 2023

Processes

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Ensuring the accuracy of flow measurement is crucial to promoting high-quality cigarette production. In order to monitor the working status of flowmeters, this paper proposes an anomaly detection method based on the sliding-window recursive Lasso (Least absolute shrinkage and selection operator), which is able to track the changes in flowmeter operating conditions by self-adapting model parameters based on observed measurements. Due to the frequent mode switch and high sampling frequency of flow data, this paper introduces the sliding-window strategy to remove the effect of outdated data and accelerate the optimization. The tracking errors are used as a measure of anomaly and different thresholds are introduced based on the operating manual of cigarette production, which are used to distinguish between mode switch and flowmeter anomalies. The method’s effectiveness is verified by detecting flowmeter anomalies in a real cigarette production line. The mean absolute error (MAE) is 8.1479 and the root mean squared error (RMSE) is 2.8544, which outperforms methods such as Lasso and the ridge regression.

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Section: Application Results and Discussionmentioning

confidence: 99%

Online Monitoring of Flowmeter Anomaly in Tobacco Production Process Using Sliding Window Recursive Lasso

Guan

Liu

et al. 2023

Processes

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“…The optimization methods prevent these three models from overfitting. Thus, they are excellent in practical applications [29,60,61]. The XGBoost, MARS, and BRR algorithms were implemented with the xgboost, earth, and monomvn packages, respectively, in the R language environment (version 3.6.3).…”

Section: Mfgwml Downscaling Methods 251 Principles Of Base Learnersmentioning

confidence: 99%

“…BRR refers to ridge regression implemented based on Bayesian statistical inference [61]. Ridge regression is a technique that imposes L 2 regularization on ordinary least squares regression (Equation ( 14)) to mitigate the problem of multicollinearity.…”

Section: Brrmentioning

confidence: 99%

Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model

Zhao

Yin

et al. 2021

Remote Sensing

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Land surface temperature (LST) is a critical parameter of surface energy fluxes and has become the focus of numerous studies. LST downscaling is an effective technique for supplementing the limitations of the coarse-resolution LST data. However, the relationship between LST and other land surface parameters tends to be nonlinear and spatially nonstationary, due to spatial heterogeneity. Nonlinearity and spatial nonstationarity have not been considered simultaneously in previous studies. To address this issue, we propose a multi-factor geographically weighted machine learning (MFGWML) algorithm. MFGWML utilizes three excellent machine learning (ML) algorithms, namely extreme gradient boosting (XGBoost), multivariate adaptive regression splines (MARS), and Bayesian ridge regression (BRR), as base learners to capture the nonlinear relationships. MFGWML uses geographically weighted regression (GWR), which allows for spatial nonstationarity, to fuse the three base learners’ predictions. This paper downscales the 30 m LST data retrieved from Landsat 8 images to 10 m LST data mainly based on Sentinel-2A images. The results show that MFGWML outperforms two classic algorithms, namely thermal image sharpening (TsHARP) and the high-resolution urban thermal sharpener (HUTS). We conclude that MFGWML combines the advantages of multiple regression, ML, and GWR, to capture the local heterogeneity and obtain reliable and robust downscaled LST data.

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Enrichment Track Recommendation Based On Soft Skill Test and Personality Test Result Using Machine Learning

Artajaya,

Julieta,

Giancarlos

et al. 2023

2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter)

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Satellite battery sensor values prediction using Bayesian ridge regression models

Cited by 3 publications

References 1 publication

Online Monitoring of Flowmeter Anomaly in Tobacco Production Process Using Sliding Window Recursive Lasso

Online Monitoring of Flowmeter Anomaly in Tobacco Production Process Using Sliding Window Recursive Lasso

Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model

Enrichment Track Recommendation Based On Soft Skill Test and Personality Test Result Using Machine Learning

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