An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

Bhatt, Deepak L.; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay

doi:10.3390/s120709448

Cited by 52 publications

(35 citation statements)

References 24 publications

(38 reference statements)

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“…To approximate the nonlinear regression function, the parameters w and b need to be estimated, such that the function f ( x ) should be as close as possible to desired output y and should be as flat as possible to hold back the problem of over‐fitting. The approximation of the function f ( x ) and its mathematical expressions can be referred to in . The approximated prediction function is given as:

f true(x true) = {true \sum}_{i = 1}^{n} true(α_{i} - α_{i}^{*} true) . K true(x_{i}, x_{j} true) + b

…”

Section: Support Vector Regressionmentioning

confidence: 99%

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Gummadi

Bhatt

Adusumilli

et al. 2014

Env Prog and Sustain Energy

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Radon‐222 and its parent, radium‐226, is a naturally occurring radioactive decay product of uranium‐238. The US Environmental Protection Agency (USEPA) states that 10% of total lung cancer cases in the United States are directly attributable to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e., the average indoor radon screening level is greater than 4 pCi/L). To implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This article presents a comparison between recently developed interpolation techniques such as neural network techniques, support vector regression (SVR), random forest regression (RFR), and the conventional interpolation techniques. The performance of all the techniques has been assessed using 10‐fold cross‐validation data. This study confirmed that the correction‐based artificial neural networks have performed better over SVR and RFR with least validation error of 3.63. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 169–177, 2015

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f true(x true) = {true \sum}_{i = 1}^{n} true(α_{i} - α_{i}^{*} true) . K true(x_{i}, x_{j} true) + b

…”

Section: Support Vector Regressionmentioning

confidence: 99%

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Gummadi

Bhatt

Adusumilli

et al. 2014

Env Prog and Sustain Energy

Self Cite

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“…Neglecting the linear accelerations sensitivity and cross-axis coupling the measured yaw rate z can be modelled as [13]:

z = (1 + S) Ω + b + n

where Ω is the actual yaw rate, S is the scale factor of the sensor, b is the gyroscope bias and n is the gyroscope noise. Both the scale factor and bias are temperature dependent [14]; the ADXRS-614 includes a temperature sensor allowing the thermal compensation of these errors. In our case, since the rail track is accurately known, both gyroscope scale factor and bias can be frequently estimated and compensated for example at curves with constant radius of curvature (scale factor) and at straight track sections (bias).…”

Section: Performance Of a Simple Turn Rate Threshold Detector Using Amentioning

confidence: 99%

Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors

Broquetas

Comerón

Gelonch

et al. 2012

Sensors

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The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns.

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“…Firstly, inspired by the time sequence processing in data science community, Allan variance (AV) was employed to analyze the MEMS IMU error components, and then ARMA models are employed for modeling and representing the noise [22][23][24][25][26][27][28][29][30][31]. After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

Zhu

Jiang

2019

Mathematical Problems in Engineering

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Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a selfcontained inertial navigation system (INS). IMU manufactured through the Micromechanics Electronics Manufacturing System (MEMS) technology becomes more popular, due to its smaller column, lower cost, and gradually improved accuracy. However, limited by the manufacturing technology, the MEMS IMU raw measurement signals experience complicated noises, which cause the INS navigation solution errors diverge dramatically over time. For addressing this problem, an advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed in the MEMS gyroscope noise suppressing. NAS-RNN was the recently invented artificial intelligence method for time series problems in data science community. Different from conventional method, NAS-RNN was able to search a more feasible architecture for selected application. In this paper, a popular MEMS IMU STIM300 was employed in the testing experiment, and the sampling frequency was 125 Hz. e experiment results showed that the NAS-RNN was effective for MEMS gyroscope denoising; the standard deviation values of denoised three-axis gyroscope measurements decreased by 44.0%, 34.1%, and 39.3%, respectively. Compared with the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), the NAS-RNN obtained further decreases by 28.6%, 3.7%, and 8.8% in standard deviation (STD) values of the signals. In addition, the attitude errors decreased by 26.5%, 20.8%, and 16.4% while substituting the LSTM-RNN with the NAS-RNN.

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An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

Cited by 52 publications

References 24 publications

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

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