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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
132

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Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System
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Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in

Seungmin Lee1, Jisu Kwon1, Daejin Park1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

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|July 8, 2023
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Summary

This study simplifies particle counting for low-cost embedded devices. A new algorithm reduces processing time by 87% and error by 58.5%, enabling accurate single-sensor replication of multi-sensor particle count data.

Keywords:
ADC filterdigital twindust sensingembedded deviceparticle count

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Area of Science:

  • Embedded Systems
  • Sensor Technology
  • Digital Twins

Background:

  • Digital twin applications are expanding, driving cost optimization research.
  • Low-power embedded devices are explored for cost-effective digital twin implementations.
  • Replicating multi-sensing device performance with single-sensing devices is a key challenge.

Purpose of the Study:

  • To achieve similar particle count results from a single-sensing device as a multi-sensing device.
  • To develop a simplified particle count algorithm without prior knowledge of the multi-sensing acquisition method.
  • To reduce computational complexity and improve efficiency in particle counting.

Main Methods:

  • Applied data filtering to suppress noise and baseline drift in raw sensor data.
  • Simplified the multi-threshold particle count determination algorithm for look-up table utilization.
  • Developed a novel, simplified particle count calculation algorithm.

Main Results:

  • Reduced optimal multi-threshold search time by an average of 87%.
  • Decreased root mean square error by 58.5% compared to existing methods.
  • Confirmed similar particle count distribution shapes between single and multi-sensing devices.

Conclusions:

  • The simplified algorithm effectively replicates multi-sensing particle count data using a single device.
  • The method offers significant improvements in speed and accuracy for embedded digital twin applications.
  • This approach enables cost-effective, high-performance particle sensing solutions.