Approach to the validation function of intelligent sensors based on error's predictors

Rivera-Mejia, Jose; Arzabala-Contreras, E.; Léon-Rubio, A.G.

doi:10.1109/imtc.2010.5488250

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The authors of [30] also agreed with Staroswiecki in the separation of the data validation in two types, presenting a real time algorithm based on probabilistic methods. Other studies have been researched and developed, including the data validation techniques using intelligent sensor systems [31].…”

Section: Data Validation Methodsmentioning

confidence: 99%

Validation Techniques for Sensor Data in Mobile Health Applications

et al. 2016

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Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output.

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Section: Data Validation Methodsmentioning

confidence: 99%

Validation Techniques for Sensor Data in Mobile Health Applications

et al. 2016

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“…This implies that these sensors compensate to eliminate the systematic errors and to ease the calibration. Previous calibration algorithms for intelligent sensors have been implemented [ 11 , 20 , 21 , 22 , 23 , 24 ]. In detail, researchers have different options for algorithms, some of which are recursive algorithms [ 24 , 25 ] or artificial neural networks [ 10 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network

Almassri

Hasan

Ahmad

et al. 2018

Sensors

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This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

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Performance evaluation of two altimeters intended for euler angles measurement

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2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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Approach to the validation function of intelligent sensors based on error's predictors

Cited by 4 publications

References 23 publications

Validation Techniques for Sensor Data in Mobile Health Applications

Validation Techniques for Sensor Data in Mobile Health Applications

Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network

Performance evaluation of two altimeters intended for euler angles measurement

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