Haoda Yu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Haoda Yu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ben Wang
Hangzhou Normal University
2 shared publications - Keyong Hu
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China
2 shared publications - Shuifa Sun
Hangzhou Normal University
2 shared publications - Wenbo Liu
School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
2 shared publications - Yang Feng
School of Information Science and Engineering (SISE), Hangzhou Normal University, Hangzhou 311121, China
2 shared publications - Zhen Yang
School of electronic information, Huzhou College, Huzhou, China
1 shared publication
Latest Publications
- Open Access
- 9 May 2024
- Published by MDPI AG in Micromachines
- Vol. 15 (5) , 637
Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors
Improving measurement accuracy is the core issue with surface acoustic wave (SAW) micro-force sensors. An electrode transducer can stimulate not only the SAW but also the bulk acoustic wave (BAW). A portion of the BAW can be picked up by the receiving transducer, leading to an unwanted or spurious signal. This can harm the device’s frequency response characteristics, thereby potentially reducing the precision of the micro-force sensor’s measurements. This paper examines the influence of anisotropy on wave propagation, and it also performs a phase-matching analysis between interdigital transducers (IDTs) and bulk waves. Two solutions are shown to reduce the influence of BAW for SAW micro sensors, which are arranged with acoustic absorbers at the ends of the substrate and in grooving in the piezoelectric substrate. Three different types of sensors were manufactured, and the test results showed that the sidelobes of the SAW micro-force sensor could be effectively inhibited (3.32 dB), thereby enhancing the sensitivity and performance of sensor detection. The SAW micro-force sensor manufactured using the new process was tested and the following results were obtained: the center frequency was 59.83 MHz, the fractional bandwidth was 1.33%, the range was 0–1000 mN, the linearity was 1.02%, the hysteresis was 0.59%, the repeatability was 1.11%, and the accuracy was 1.34%.
- 9 May 2024
- Published by MDPI AG in Micromachines
- Vol. 15 (5) , 637
Open AccessGrooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors
Improving measurement accuracy is the core issue with surface acoustic wave (SAW) micro-force sensors. An electrode transducer can stimulate not only the SAW but also the bulk acoustic wave (BAW). A portion of the BAW can be picked up by the receiving transducer, leading to an unwanted or spurious signal. This can harm the device’s frequency response characteristics, thereby potentially reducing the precision of the micro-force sensor’s measurements. This paper examines the influence of anisotropy on wave propagation, and it also performs a phase-matching analysis between interdigital transducers (IDTs) and bulk waves. Two solutions are shown to reduce the influence of BAW for SAW micro sensors, which are arranged with acoustic absorbers at the ends of the substrate and in grooving in the piezoelectric substrate. Three different types of sensors were manufactured, and the test results showed that the sidelobes of the SAW micro-force sensor could be effectively inhibited (3.32 dB), thereby enhancing the sensitivity and performance of sensor detection. The SAW micro-force sensor manufactured using the new process was tested and the following results were obtained: the center frequency was 59.83 MHz, the fractional bandwidth was 1.33%, the range was 0–1000 mN, the linearity was 1.02%, the hysteresis was 0.59%, the repeatability was 1.11%, and the accuracy was 1.34%.
- Open Access
- 12 November 2023
- Published by MDPI AG in Micromachines
- Vol. 14 (11) , 2093
Temperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm
In this paper, a SAW winding tension sensor is designed and data fusion technology is used to improve its measurement accuracy. To design a high-measurement precision SAW winding tension sensor, the unbalanced split-electrode interdigital transducers (IDTs) were used to design the input IDTs and output IDTs, and the electrode-overlap envelope was adopted to design the input IDT. To improve the measurement accuracy of the sensor, the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm was used to compensate for the temperature error. After temperature compensation, the sensitivity temperature coefficient αs of the SAW winding tension sensor was decreased by an order of magnitude, thus significantly improving its measurement accuracy. Finally, the error with actually applied tension was calculated, the same in the LSSVM and PSO-LSSVM. By multiple comparisons of the same sample data set overall, as well as the local accuracy of the forecasted results, which is , it is easy to confirm that the output error predicted by the PSO-LSSVM model is , much smaller relative to the LSSVM’s . As a result, a new way for performing data analysis of the SAW winding tension sensor is provided.- 12 November 2023
- Published by MDPI AG in Micromachines
- Vol. 14 (11) , 2093
Open AccessTemperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm
In this paper, a SAW winding tension sensor is designed and data fusion technology is used to improve its measurement accuracy. To design a high-measurement precision SAW winding tension sensor, the unbalanced split-electrode interdigital transducers (IDTs) were used to design the input IDTs and output IDTs, and the electrode-overlap envelope was adopted to design the input IDT. To improve the measurement accuracy of the sensor, the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm was used to compensate for the temperature error. After temperature compensation, the sensitivity temperature coefficient αs of the SAW winding tension sensor was decreased by an order of magnitude, thus significantly improving its measurement accuracy. Finally, the error with actually applied tension was calculated, the same in the LSSVM and PSO-LSSVM. By multiple comparisons of the same sample data set overall, as well as the local accuracy of the forecasted results, which is , it is easy to confirm that the output error predicted by the PSO-LSSVM model is , much smaller relative to the LSSVM’s . As a result, a new way for performing data analysis of the SAW winding tension sensor is provided.