A concept of IIR filters with time-varying coefficients and equalised group delay response

Okoniewski, Piotr; Piskorowski, Jacek

doi:10.1016/j.measurement.2014.09.077

Cited by 7 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is inappropriate to assign the same weigh for each sampling point for the nonstable process of WIM; the lateral and longitudinal acceleration are uneven. The polynomial and exponential models minimize the fitting error by employing the least squares method [ 23 ]. It shows better performance than the traditional LR or the averaging model without adding too much operational complexity and is widely used in low-cost embedded devices.…”

Section: Introductionmentioning

confidence: 99%

Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm

Shi

Jiang

Bao

et al. 2022

Sensors

View full text Add to dashboard Cite

A weigh-in-motion (WIM) system continuously and automatically detects an object’s weight during transmission. The WIM system is used widely in logistics and industry due to increasing labor and time costs. However, the accuracy and stability of WIM system measurements could be affected by shock and vibration under high speed and heavy load. A novel six degrees-of-freedom (DOF), mass–spring damping-based Kalman filter with time scale (KFTS) algorithm was proposed to filter noise due to the multiple-input noise and its frequency that is highly coupled with the basic sensor signal. Additionally, an attention-based long short-term memory (LSTM) model was built to predict the object’s mass by using multiple time-series sensor signals. The results showed that the model has superior performance compared to support vector machine (SVM), fully connected network (FCN) and extreme gradient boosting (XGBoost) models. Experiments showed this improved deep learning model can provide remarkable accuracy under different loads, speed and working situations, which can be applied to the high-precision logistics industry.

show abstract

Section: Introductionmentioning

confidence: 99%