| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系 === 103 === The aim of this study is to develop a hydrogen sensing system by using Kalman filter algorithm. In this study, the Kalman filter was used to filter the environmental noise at the sending end as well as recovery the reduced signal data at the receiver end. In order to monitor and record the hydrogen sensing situation, a large number of hydrogen sensors must be set up in the work space. However, large sensing data makes data transmission becomes very difficult.
In this study, we have successfully established a wireless hydrogen sensing system. At the sending end, the Kalman filter and reduce redundant sensing data based on first order differential concept were adapted to dealing with the sensing data. The Kalman filter was used to suppress the environmental noise. The reduced redundant algorithm can effectively reduce the number of sensing data, which are conducive for wireless transmission. At the receiver end, the Kalman filter was used to restore the reduced sensing data. It’s clear that 85% of the redundant data were reduced by using the reduced redundant algorithm. It’s worth to note that the total average error compared original sensing data and restored data is less than 0.71%.
According to the simulation results, the studied algorithms were implanted on the Arduino microcontroller. After the signal filtering and reduced unrequired data, these data transfer to the receiver end by Bluetooth. Then, these data will be restored and upload to the database. In addition, our study also designed to monitor hydrogen concentration and humidity and working temperature simultaneously.
Finally, user can query the status of hydrogen concentration, sensing current, temperature and humidity on the website.
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