# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 6 Discussion
### Q2: What Are the Advantages of AI-Based Solutions Compared to State-of-the-Art Solutions?
 } \\ \cline{1-1}  & MEMS IMU consists of a triaxial accelerometer sensor, a triaxial gyroscope sensor, a triaxial magnetometer, and a temperature sensor. The data points were obtained at different temperature ranges for gyroscopes. \\ \cline{1-1}  & MEMS IMU consists of a triaxial accelerometer sensor, a triaxial gyroscope sensor, a triaxial magnetometer, and a temperature sensor. \\ \cline{1-1}  & MEMS IMU consists of a triaxial accelerometer sensor, a triaxial magnetometer, and a temperature sensor. \\ \cline{1-1}  & MEMS IMU consists of a triaxial accelerometer sensor, a triaxial magnetometer, and a temperature sensor. \\ \cline{1-1}  & MEMS IMU consists of a triaxial magnetometer sensor, a triaxial magnetometer, and a temperature sensor. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref. ** & **Data Features and Attributes** & **Data Availability** & **Performance Metrics** & **Implemented AI Algorithm Advantage** \\ \hline \hline \multirow{9}{*}{[48]} & The aim was to have the mass proof & & & To account for the impact of external disturbances and model errors, an adaptive, stable, and fully tuned RBF neural network controller was used as it provides non-linear approximation and adaptive nominal control. Further, it enhanced the MEMS gyroscope’s dynamic properties and robustness. \\ \cline{1-1}  & Three-axis MEMS IMU was used, and the X-axis gyroscope was analyzed. Wavelet filtering was used to remove the noise. The data type was chaotic time series. The dimension of the data was improved one-dimensional time series into an auxiliary phase space using PSR. \\ \hline \multirow{9}{*}{[49]} & Creating a temperature compensation model that fits the function is the key challenge for MEMS gyroscope temperature correction due to the non-linear characteristics. The three-axis gyroscope data were collected within a temperature range of \(-30\)\(\mathrm{\SIUnitSymbolMicro C}\), with seven temperature points. \\ \cline{1-1}  & Six-axis MEMS accelerometer, high-precision rotary table, a thermostat, a resonant accelerometer, and the testing circuit were used to build the thermal calibration system. The sensor chip was mounted on a side-brazed ceramic package through the silver conductive epoxy adhesive to collect the data. The model’s inertial temperature was 293.15 K, and it was assumed that the structure had no internal tension at this temperature. The materials expanded or contracted as the temperature changed, putting the six DETFs under uneven thermal stress. \\ \hline \multirow{9}{*}{[52]} & Temperature signal data were collected from a MEMS gyroscope. IPSO-VMD decomposed the gyro signal and obtained the ideal VMD parameters. Using SE, the sequence complexity was calculated and divided into three categories: noise, mixed, and feature. \\ \cline{1-1}  & The authors used both synthetic [98] and real ground vibration data [99] with two different labels assigned to it as peak acceleration and earthquake magnitude, respectively. For the synthetic data, artificial noise was introduced in a controlled manner. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & &  
* [69] _Data Features and Attributes_ **Data Availability** **Performance Metrics** **Implemented AI Algorithm** _Advantage_
* [70] _Gyroscope and accelerometer data were synthetically generated with two label classes, such as faulty and nominal flight conditions. The training was performed offline, but the prediction was made online._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. ** & **Data Features and Attributes** & **Data Availability** & **Performance Metrics** & **Implemented AI Algorithm Advantage** \\ \hline \multirow{9}{*}{[69]} & Gyroscope and accelerometer data were synthetically generated with two label classes, such as faulty and nominal flight conditions. The training was performed offline, but the prediction was made online.