This paper designs an experimental scheme to monitor the tool wear state by extracting the vibration signal of tool wear. The data acquisition and signal processing hardware includes a T-type cutting tool, a vibration sensor, an amplifier, a data acquisition card, and a computer. In the time domain, the vibration signal is analyzed by statistical analysis method, and it is concluded that the variance of vibration signal of X-axis wear is positively correlated with the degree of tool wear. Moreover, the vibration signal is converted from time domain to frequency domain by Fourier transform, and the characteristic frequency bands of vibration signal are 2–4 kHz and 7–9 kHz in frequency domain. In addition, in time-frequency domain, the vibration signal is decomposed by wavelet packet decomposition technology, and the energy statistics of the decomposed frequency band is carried out. It is further judged that the energy percentage of 2.5–3.75 kHz and 7.5–8.25 kHz is closely related to tool wear, so the energy percentage of the two characteristic frequency bands is selected as the characteristic value of tool wear monitoring.

Amidst the rapid expansion of e-commerce and the consequential surge in industries such as transportation, logistics, and supply chain management, there has been remarkable growth. Radio Frequency Identification (RFID) technology, along with its associated tags, plays a pivotal role in the transmission and tracking of crucial information within these sectors. However, the presence of metallic components within RFID systems poses a significant obstacle to efficient antenna transmission. Consequently, there is a pressing need for the development of RFID tag antennas endowed with metal-resistant properties. This paper undertakes a comparative analysis of existing designs and structures for metal-resistant RFID tag antennas, providing valuable insights into their prospective developmental trajectories and potential applications. Such analysis serves as a pertinent reference for research and engineering endeavors across related domains.

Being abundant and non-polluted, solar energy is an increasingly desirable energy source. Growing research is concerned about maximum solar energy acquisition from solar panels, especially in the automatic sun-tracking of the solar panels. This paper presents a modified algorithm based on the “PSA Algorithm” proposed in the 2001. And presents a new automatic solar tracking system with a two-axis is designed based on the algorithm. The PSA modified algorithm is used to calculate the sun’s horizontal azimuth and zenith angle combined with time, longitude, latitude, and other information. We designed a prototype mainly composed of a microcontroller, a Global Positioning System (GPS) module, a digital compass, a tilt sensor, a 555 timer, and gates, optical couples, an H-bridge and two stepper motors. Then using the proposed prototype, a series of experiments are conducted in Tianjin City. Measured zenith angle values were collected and recorded based on the PSA modified algorithm and the original PSA algorithm, compared with the actual Zenith Angle of the Sun in Tianjin City. The comparison curves of Zenith Angle show that the measurement accuracy of the modified PSA algorithm is higher than that of the PSA original algorithm.

Currently, the detection of defects in solar cells faces several challenges, including the difficulty in identifying small-sized defects, high sensitivity to complex backgrounds, and the large size of existing models. To overcome these limitations, this study proposes Laser Doppler Velocimetry-You Only Look Once (LDV-YOLO), a lightweight model for efficient defect detection. Initially, the backbone feature extraction network of YOLOv11n was reconstructed using a lightweight LCNet-G design to minimize both the model's parameters and computational cost. Furthermore, we replaced YOLOv11n's original C3k2 neck module with a VoVGSCSP module, which reduces computation while improving detection accuracy. Ultimately, we substituted YOLOv11n's original Complete-IoU (CIoU) loss with the Weighted Intersection over Union (WIoU) variant, improving the localization precision of bounding boxes. Quantitative results obtained from the PV Evolution Labs (PVEL)-AD-2021 dataset indicate that LDV-YOLO achieves 1,783,282 parameters, 4.7 Giga Floating Point Operations Per second (GFLOPs), and 84.1% mAP. Compared to YOLOv11n, the proposed model reduces the parameter count by 31%, decreases GFLOPs by 25.40%, and improves mean average precision by 3.7%. The improved algorithm exhibits superior performance in PV cell defect detection while maintaining a lightweight architecture suitable for deployment on embedded devices.