A review of self-validating sensor technology magazine, validation techniques for sensor data in mobile health applications
Finally, Section 5 offers some concluding remarks and future directions. More detailed health level information of overall multifunctional sensor itself should also be known by users.
This study make such an improvement from qualitative health diagnosis to quantitative health level evaluation to develop the traditional self-validating function which would also provide more detailed health information for users.
The grey evaluation theory fluoridating public drinking water with multivariable data fusion method can be adopted to implement this task.
More sensitive units may cause more potential faults. The validation of the data is important, but, for critical systems, for example, clinical systems, not only should the input data be validated, but also the results should be validated to guarantee the reliability, accuracy and, consequently, acceptance of the system.
Above self-validating functions has been studied in our previous achievements and the novel health evaluation is illustrated and verified here.
A novel conception of health reliability degree HRD is defined to indicate a quantitative health level, which is different from traditional so-called qualitative fault diagnosis. To avoid further performance degradation of multifunctional sensor, the research on health evaluation is extremely important.
The characteristics of the sensors are also important for the selection of the best techniques, which may be separated in three large groups, which are sensor performance characteristics, pervasive metrics, and environmental characteristics [ 1 ]. Detailed steps are as follows: Using these parameters, a minimum number of calibration experiments need to be performed to allow the fine tuning of the algorithm.
Computing grey sample evaluating GSE matrix: The process is done by humans and it is very labor intensive. From a quantitative view, however, the problem will become far compressor portatil xas 420 dating difficult.
The data acquisition assignment was done by using PCI-data acquisition board PCI made by National Instrument Corporation which included 16 analog inputs maximum up to kHz sampling rate and supported 16 bits sampling accuracy. The multifunctional self-validating sensor reserves above advantages and provides the MVS for every sensitive unit.
As an example, the authors of [ 23 ] proposed the use of the Kalman filter for the validation of the GPS data.
A review of self‐validating sensor technology
The multi-sensor data fusion method has been studied by many researchers. Each sensitive unit is also healthy and their measured data are nearly close to the true value. Health levels of a single sensitive unit are different at different time points, so data fusion of multiple time points is needed to achieve the HRD of each sensitive unit as shown in Figure 2.
Time From Submission to Publication: Sometimes, the invisible changes of the structure are exactly the main reason why its health performance degrades.
Choosing a Humidity Sensor: A Review of Three Technologies | Sensors Magazine
By using historical HRDs, the further health forecast can be done as our next study which plays a more important role in industrial production. Being different from the traditional static uncertainty evaluating method, the self-validating sensor is built on the online uncertainty evaluation and dynamic process.
The correlation among multiple parameters has been fully considered for the weights distribution of different sensitive units which is different from the traditional evaluating methods.
Traditionally, some discrete Measurement Value Status MVS has been defined to indicate the working state of certain single-sensitive component.
A Novel Health Evaluation Strategy for Multifunctional Self-Validating Sensors
Professors from Oxford University present five qualitative MVSs to make users understand the current health state in In this way, more detailed health information can be provided by using the proposed HRD, which benefits the understanding for users.
To evaluate the health condition from both local and global perspectives, the HRD of a single sensitive component at multiple time points and the overall multifunctional sensor at a single time point are defined, respectively. From a qualitative view, health state assessment could provide two or more health status typically, health and fault which is essentially a fault diagnosis.
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.
The HRD methodology is emphasized by using multi-variable data fusion technology coupled with a grey comprehensive evaluation method.
The feature parameters BRDs are acquired by using the proposed grey evaluation method. The HRD of different time points can be treated as a tool for fault detection, and it should have fast response to faults. Furthermore, a classification of these methods in accordance with its functionalities was provided.
Depending on the types of the data, for some complex data acquired, such as images, videos, GPS signal, and other complex types of data, the validation of the data should be accomplished by other auxiliary systems working at the same time, validating the data at the server-side, but a constant network connection must be available.
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. Forecasting the future health level is necessary in order to remind users to take precautionary measures to improve its reliability.
To get the weights distribution of all sensitive units, the information entropy way is used, in which the correlation of multiple parameters has fully considered.
From a quantitative point of view, the problem will become far more difficult and the quantitative health level analysis of multifunctional sensor not only involves the health level of each sensitive unit itself, but relates to their distinct weight distribution [ 16 ].
In [ 15 ], PCA is used for the compression of linearly correlated data. As one of the most promising technologies in computing, the back-propagation neural network BPNN is suitable to solve the health level problem. As the emphasis of this article, the HRD methodology is implemented by using the multi-variable data fusion technology coupled with grey evaluation algorithm.
Therefore, more detailed health information can be acquired through HRD. According to [ 49 ], the data validation methods may be classified in several types of methods, which are presented in Figure 2.
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Detailed self-validating functions are presented, especially the proposed health evaluation emphasized in this study. Once FDIR is accomplished, detailed fault information can be provided such as what type of, when and where faults occur.
Coupled with above HRD computation, the novel strategy can be implemented by providing the quantitative health level. It is mainly used in incomplete information. The gas chamber was sealed and made by the organic glass material, whose capacity was 50x20x10 cm.
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The multifunctional sensor is nearly a failure. 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.
In this paper, we extend the traditional qualitative fault detection to quantitative health level evaluation by using multi-variable data fusion coupled with a grey evaluating algorithm [ 1718 ]. The experimental results are consistent with the normal operational condition which validates the proposed method.