Scilit | Article - Design and technology of the self-validating sensor Scilit | Article - Design and technology of the self-validating sensor

A review of self-validating sensor technology collingwood, article versions

In addition, on real-time systems, these problems should be verified constantly to prevent the existence of failures in the data captured.

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The health state belongs to the health evaluating criterion which has the maximum of CGAVs. Setup of multifunctional self-validating sensor experimental system Fig. Therefore, the data validation methods with verified reliability during the conception should be also used to validate the data automatically during the execution time.

To simplify the health evaluation problem, the decrease is assumed to be linear. However, more detailed health information could not be obtained in this way, and a quantitative health evaluation may emerge as it can directly manifest the health level [ 101415 ].

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However, only a minor part of studies is related to the use of mobile devices, smart sensors, and other devices used daily. This process consists of the validation of the external conditions of the data and the validity of the data for specific purpose, in order to obtain accurate and reliable results.

The selection of the best technique for sensor data astrogirl horoscopes gemini flirting also depends on the type of data collected, the purpose of its application, and the computational platform where the algorithm will be run.

Following the existence of missing values at random instants of time, the causes may be the mechanical problems or power failures of sensors.

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The use of mobile devices allows the data acquisition anywhere and at anytime, but these devices have several constraints, such as low memory, processing power, and battery resources, but data validation may help for increasing of the performance of the measurements, reducing the resources needed [ 46 — 48 ].

The other assisting techniques or tools mainly consist of the validation of the working state of the sensors, and this validation may be executed at the same time of the execution of faulty data detection and data correction methods, because these types of failures invalidated the results of the algorithms.

Firstly, for faulty data detection methods, the ANNs are the most used methods for the training of the data and for the detection of the inconsistent values.

A review of self‐validating sensor technology

In order to verify the feasibility of the proposed strategy, a health evaluating experimental system for multifunctional self-validating sensors was designed.

By using the established whitening functions under different evaluating criterions, the GSE matrix can be obtained. Applications and Reviews, vol.

Most of sensitive units are faulty and the measurements have completely deviated from their true values, so it is totally unreliable.

Choosing a Humidity Sensor: A Review of Three Technologies | Sensors Magazine

In fact the SVM without kernel is a single neural network neuron with a different cost function. The feature parameters BRDs are acquired by using the proposed grey evaluation method.

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Despite the fact that there is a wide array and types of data validation algorithms, there is also a lack of published information on the validity of many mobile applications. The HRDs under different health situations is analyzed thoroughly and experimental results conclude that the HRD could be used to indicate the quantitative health level.

Commonly, the correlations of multiple measured parameters are not fully used. The HRD is a comprehensive variable as a quantitative index, which indicates the degree of reliability of the multifunctional sensor and each sensitive unit.

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Once faults occur, major industrial accidents could happen, so their health evaluation is extremely important. The health evaluation of multifunctional self-validating sensor is generalized as evaluating the reliability.

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The experimental results are consistent with the normal operational condition which validates the proposed method. These states are defined based on expert experience; however, it is absence of a set of universal theory.

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To achieve a minimum degree of error, statistical methods need to be applied to ensure that the output of the mobile application is to maximum extent similar to the output given by the relevant golden standard, if and when this is possible.

Commonly, most situations are in HS or SH.

Validation Techniques for Sensor Data in Mobile Health Applications

The CGAV represents the current health distribution under the above four grey health evaluating criterion sets and they are exactly the feature attached parameters in Eq.

Further, some potential faults could happen too quickly for humans to detect them before they become catastrophic [ 10 ]. This experiment has also been done in the same gas chamber on the same day and the heating voltage of gas sensitive unit 3 and gas sensitive unit 4 would be both removed to simulate that both of heater strips are broken.

The state 0 indicates that the multifunction sensor or certain sensitive unit is in catastrophe failure mode, while state 1 is complete health. By using historical HRD information, health forecasting for multifunctional self-validating sensors can be done and this will play a more important role in industrial production.

In [ 37 ], the authors proposed an approach of sensor data validation using self-reporting, including the measurement based on the data quality, that is, validating the data loss measured by periodic sensors, the timing of data collection, and the accuracy of the detection of changes.