Data validation of multifunctional sensors using independent and related variables

Yang, Jingli; Lin, Lianlei; Sun, Zhen; Chen, Yinsheng; Jiang, Shouda

doi:10.1016/j.sna.2017.05.015

Cited by 10 publications

(6 citation statements)

References 36 publications

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“…This type of error would usually require calibration to subtract the offset from the observed reading to get its true value. Drifts are readings that deviate from its true value over time due to the degradation of sensing material which is an irreversible chemical reaction [60] whereas constant values are readings with a constant value over time, though it might belong to a normal range. It is usually caused by a faulty sensor or transmission problems [84].…”

Section: Types Of Errors In Sensor Datamentioning

confidence: 99%

“…where a and b are parameters and z (1) (u) is the adjacent mean generating operation. The papers [30,52,60] provide detailed explanation on how to derive the differential…”

Section: Univariate Autoregressive Modelsmentioning

confidence: 99%

“…Then, Kalman filter is applied to estimate the sensor drift value and the drift value is subtracted from the sensor reading to give a better estimate of the true value. Yang et al [60] suggested a data validation technique to detect, identify and correct faults such as bias, drifts, and impacts in a multifunctional sensor. The technique separates (using Maximal Information Coefficient) the variables into independent and dependent variables.…”

Section: Bayesian Network Dereszynski and Dietterichmentioning

confidence: 99%

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Sensor data quality: a systematic review

2020

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Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are rendered useless if the data quality is bad. This systematic review aims to provide an introduction and guide for researchers who are interested in quality-related issues of physical sensor data. The process and results of the systematic review are presented which aims to answer the following research questions: what are the different types of physical sensor data errors, how to quantify or detect those errors, how to correct them and what domains are the solutions in. Out of 6970 literatures obtained from three databases (ACM Digital Library, IEEE Xplore and ScienceDirect) using the search string refined via topic modelling, 57 publications were selected and examined. Results show that the different types of sensor data errors addressed by those papers are mostly missing data and faults e.g. outliers, bias and drift. The most common solutions for error detection are based on principal component analysis (PCA) and artificial neural network (ANN) which accounts for about 40% of all error detection papers found in the study. Similarly, for fault correction, PCA and ANN are among the most common, along with Bayesian Networks. Missing values on the other hand, are mostly imputed using Association Rule Mining. Other techniques include hybrid solutions that combine several data science methods to detect and correct the errors. Through this systematic review, it is found that the methods proposed to solve physical sensor data errors cannot be directly compared due to the non-uniform evaluation process and the high use of non-publicly available datasets. Bayesian data analysis done on the 57 selected publications also suggests that publications using publicly available datasets for method evaluation have higher citation rates.

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Section: Types Of Errors In Sensor Datamentioning

confidence: 99%

“…where a and b are parameters and z (1) (u) is the adjacent mean generating operation. The papers [30,52,60] provide detailed explanation on how to derive the differential…”

Section: Univariate Autoregressive Modelsmentioning

confidence: 99%

Section: Bayesian Network Dereszynski and Dietterichmentioning

confidence: 99%

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Sensor data quality: a systematic review

2020

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“…Medicine and genetics explore the associations of genes [28] and diseases [26], [27] based on MIC. MIC is mainly used in feature extraction [29], prediction [30], and estimation [31] based on energy datasets. MIC is also used in the fault diagnosis [32], [33] and prediction [34] of complex systems and equipment.…”

Section: Introductionmentioning

confidence: 99%

Detecting Associations Based on the Multi-Variable Maximum Information Coefficient

Guo

et al. 2021

IEEE Access

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The maximum information coefficient (MIC) is a novel and widely-using measure of association detection in large datasets. The most outstanding feature of MIC is that it has both generality and equability. However, MIC can only deal with two variables and cannot precisely estimate coupling associations of multiple variables. In this paper, we propose an extension of MIC to deal with multi-variable datasets, called the multi-variable maximum information coefficient (MMIC). Some inherited and novel properties of MMIC are proved, including generality, equability, monotonicity, and subadditivity. We design an algorithm based on greedy stepwise strategy and upper confidence bound (UCB) for an approximate calculation of MMIC. The tests of MMIC on generated datasets and examples on real datasets are carried out to detect known and novel associations.

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“…Based on Henry and Clarke's definition, researchers make appropriate adjustments on these five output parameters for specific problems, so as to design suitable self-validation sensors for corresponding application scenarios. Regarding to multifunctional self-validating sensors, failure detection, isolation, and data recovery are of high concern [13][14][15]. In the field of self-validating multifunctional sensors, Yang et al [15] develop an efficient approach by upgrading traditional contribution plots method for self-validating multifunctional sensors, which consist of metal-oxide sensor, temperature sensor, and humidity sensor.…”

Section: Introductionmentioning

confidence: 99%

Self-validating high-g accelerometers through data-driven methods

Wen¹,

Yao²,

Ji³

et al. 2021

Sensors and Actuators A: Physical

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Data validation of multifunctional sensors using independent and related variables

Cited by 10 publications

References 36 publications

Sensor data quality: a systematic review

Sensor data quality: a systematic review

Detecting Associations Based on the Multi-Variable Maximum Information Coefficient

Self-validating high-g accelerometers through data-driven methods

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