Autocorrelation model-based identification method for ARMA systems in noise

Hasan, Md. Kamrul; Hossain, Nazia; Naylor, Patrick A.

doi:10.1049/ip-vis:20045042

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Therefore, the sequence needs to be analysed. In the model identification process, the characteristics of the autocorrelation and partial correlation graphs of the sample were used to estimate the self-correlation order [31]. As shown in Figure 6, the autocorrelation coefficient attenuation speed of sequence after difference after a delay is very fast, and the clear majority falls within a scope of two times the standard deviation.…”

Section: Combined Model Based On Regression and Time-series Methodsmentioning

confidence: 99%

Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Wang

Sun

et al. 2018

Energies

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Battery electric vehicles (BEVs) reduce energy consumption and air pollution as compared with conventional vehicles. However, the limited driving range and potential long charging time of BEVs create new problems. Accurate charging time prediction of BEVs helps drivers determine travel plans and alleviate their range anxiety during trips. This study proposed a combined model for charging time prediction based on regression and time-series methods according to the actual data from BEVs operating in Beijing, China. After data analysis, a regression model was established by considering the charged amount for charging time prediction. Furthermore, a time-series method was adopted to calibrate the regression model, which significantly improved the fitting accuracy of the model. The parameters of the model were determined by using the actual data. Verification results confirmed the accuracy of the model and showed that the model errors were small. The proposed model can accurately depict the charging time characteristics of BEVs in Beijing.

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Section: Combined Model Based On Regression and Time-series Methodsmentioning

confidence: 99%

Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Wang

Sun

et al. 2018

Energies

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“…As an alternative, higher-order YW equations, iterative bias compensation schemes such as those proposed by [14], Davila's method [15] or errors-in-variables (EIV) approaches [16] [17] can be considered. In [18] and [19], the estimations of the ARMA parameters are addressed from noisy observations. More particularly, in [18], Fattah et.…”

Section: B Parameter Estimation Methodsmentioning

confidence: 99%

Jeffrey's divergence between autoregressive moving-average processes

Legrand¹,

Grivel²,

Giremus³

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Various works have been carried out about the Jeffrey's divergence (JD) which is the symmetric version of the Kullback-Leibler (KL) divergence. An expression of the JD for Gaussian processes can be deduced from the definition of the KL divergence and the expression of the Gaussian-multivariate distributions of k-dimensional random vectors. It depends on the k × k Toeplitz covariance matrices of the stationary processes. However, the resulting computational cost may be high as these matrices must be inverted and it is all the higher as k increases. To circumvent this problem, a recursive expression can be obtained for real 1 st -order autoregressive (AR) processes. When they are disturbed by additive uncorrelated white noises, we showed that when k becomes large, the derivative of the JD with respect to k tends to be constant. This constant is sufficient to compare the noisy AR processes. In this paper, we propose to extend our work to AR moving-average (MA) processes with one AR term and one MA term. Some examples illustrate the theoretical analysis.

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“…Articles based on vision‐based gesture recognition (VGR) and hybrid‐based techniques are excluded in this study as these have been the topic of recent reviews. [ 11–14 ] The remainder of this review is organized as follows. Section 2 investigates noninvasive wearable sensing technology, along with multisensing fusion principles.…”

Section: Introductionmentioning

confidence: 99%

“…[ 8,9 ] However, despite the evident achievements of ML/DL, estimation accuracy normally degrades in practical scenarios. [ 10–14 ] Researchers have recently developed various upper‐limb wearable devices: wearable data gloves and wrist/armbands. [ 15 ] Wrist‐or‐arm‐worn devices may include several sensors to continuously monitor various biosignals about important indications (e.g., muscle volume change, skin vibration, blood pressure, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

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Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

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Autocorrelation model-based identification method for ARMA systems in noise

Cited by 11 publications

References 18 publications

Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Jeffrey's divergence between autoregressive moving-average processes

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

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