An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Article Chemistry, Analytical

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Seungmin Lee et al.

Summary: This study aims to obtain similar particle count results in a single-sensing device replicated from a multi-sensing device. Through filtering and simplifying the calculation algorithm, the proposed method reduced the search time by 87% and the error by 58.5% compared to existing methods.

SENSORS (2023)

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Article Chemistry, Analytical

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

Sadiqa Jafari et al.

Summary: The instability and variable lifetime of lithium-ion batteries contribute to their benefits in terms of high efficiency and low-cost issues. Accurate prediction of the remaining useful life of these batteries is crucial for requirement-based maintenance and cost reduction. However, assessing the working capacity of a battery is challenging, and existing prediction methods fail to account for uncertainty. This paper presents a novel prediction technique combining particle filters with extreme gradient boosting for lithium-ion battery RUL estimation, which demonstrates improved accuracy compared to other methods.

SENSORS (2022)

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Article Environmental Sciences

A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

Ning Zhou et al.

Summary: This paper proposes an improved genetic optimization based resampling method in indoor target tracking based on wireless sensor networks, which aims to mitigate particle impoverishment problem and improve positioning accuracy. The method demonstrates better robustness against particle impoverishment and achieves better performance compared to existing algorithms through testing.

REMOTE SENSING (2021)

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Article Engineering, Electrical & Electronic

Fault Diagnosis for Electro-Mechanical Actuators Based on STL-HSTA-GRU and SM

Xiaoyu Zhang et al.

Summary: In this study, a novel algorithm combining HSTA-GRU and STL is proposed to predict multiple time-series data for enhanced fault diagnosis of EMAs. By extracting seasonal factors and capturing spatio-temporal relationships among multivariate EMA sensors, the algorithm improves time series prediction and fault classification efficiency.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic