This paper presents and verifies a new idea for constructing an ultrasonic motor (USM). The stator contains several vibrators fabricated by bonding piezoel

This paper aims to present a proof-of-concept for a novel piezoelectric motor using a single-phase driving signal. The stator is constructed by bonding a Terfenol-D plate and a piezoelectric ceramic (PZT) plate onto the outer surface of a metal base. The PZT plate based on d 31 effect is used to simultaneously excite two in-plane bending modes of the stator. Similarly, the Terfenol-D plate can also be used to excite the two bending modes. By superposition of the two bending modes, elliptical motions of the stator surface points are generated. The working modes and vibration characteristics of the motor are analyzed by finite element analysis software. The resonance frequencies of the two bending modes are tuned to be close by additional mass. The dimensions of the stator are optimized to maximize the output displacement. To confirm the feasibility of the proposed design, a prototype motor is fabricated and tested. The size of the prototype motor is about 11.2×10.7×28.0 mm ³ . The tested resonance frequencies of the two bending modes are 16.0820 kHz and 16.3476 kHz respectively. The maximum no-load speed is 85 rpm. The applied driving voltage is 100 V _o-p (peak voltage) at a frequency of 16.34 kHz.

Algae are vital to aquatic ecosystems, with their structure and abundance influencing ecological health. However, automated detection in real water samples is hindered by complex backgrounds, species diversity, and size variations. Traditional methods are deemed costly and species-specific, leading to deep learning adoption. Current studies rely on CNN-based models and limited datasets. To improve the detection accuracy of multiple algal species in real, complex backgrounds, this study collected multi-species algae samples from actual water environments and implemented an integrated Transformer-based framework for automated localization and recognition of small, medium, and large algae species. Specifically, algae samples from five different regions were collected to construct a comprehensive dataset containing 25 algal species with diverse backgrounds and rich category diversity. To address dataset imbalances in minority species, a segmentation-fusion data augmentation method was proposed, which enhanced performance across YOLO, Faster R-CNN, and Deformable DETR models, with YOLO achieving a 7.1% precision increase and a 1.5% mAP improvement. Model optimization focused on an improved Deformable DETR, incorporating multi-scale feature extraction, deformable attention mechanisms, and the normalized Wasserstein distance loss function. This improvement enhanced small target and overlapping object detection, achieving a 10.4% mAP increase at an intersection over union (IoU) threshold of 0.5 and outperforming unmodified Deformable DETR.