In heterogeneous polymers and emulsions, the volume fraction of the discrete phase and the frequency of electromagnetic waves affect the accuracy of the dielectric model. The integral method was used to modify the Maxwell–Wagner (M–W) heterogeneous dielectric theory, and a new model for the complex dielectric constant of polymers and emulsions was established. The experimental data were compared with the results of the M–W heterogeneous dielectric integral modification model and other theoretical models for different frequencies and volume fractions of the discrete phase. We discovered that with a decreasing volume fraction of the discrete phase, the dominant frequency range of the integral modification model expanded. When the volume fraction of the discrete phase is 10%, the dominant frequency range reaches 3 GHz. When the volume fraction of the discrete phase is 1%, the dominant frequency range reaches 4 GHz. When the volume fraction of the discrete phase is 0.06%, the dominant frequency range of the real part reaches 9.6 GHz, and the dominant frequency range of the imaginary part reaches 7.2 GHz. These results verify the advantages of the M–W modification model, which provides a theoretical basis to study the dielectric properties of polymers and emulsions, as well as for microwave measurement.

The dielectric change law of aqueous insulating oil has an important impact on the safe operation of transformers. In this paper, the integration method is used to modify the Maxwell-wanger non-uniform dielectric model to make it suitable for aqueous insulating oil, and the reliability of the new model is verified. The experimental results show that when the water content is 20 ppm, the maximum error of the real part of the complex dielectric constant of the insulating oil does not exceed 0.0541%, and the maximum error of the imaginary part does not exceed 1.076%. Finally, based on this model, the dielectric spectrum of the aqueous insulating oil is obtained, the variation law of the real part and the imaginary part of the aqueous insulating oil under different conditions are revealed, and the internal reasons for the dielectric properties of the aqueous insulating oil with temperature, frequency, and moisture content are analyzed, which provides an important reference data for the evaluation and online monitoring of the insulation performance of the aqueous insulating oil.

In cold and highly humid regions, wind turbine blades (WTB) are susceptible to icing, which can have a significant impact on the security and economic operation of turbines. Therefore, precise and prompt icing status detection is pivotal for maintaining wind turbine operational normalcy. In this research, an improved PP-YOLOE network is developed for classifying and detecting the icing state of WTB. First, a dataset of WTB icing is constructed based on a wind tunnel laboratory and expanded to improve the generalization of the model. To enhance feature representation, the network architecture was improved by embedding a coordinate attention (CA) mechanism and integrating atrous spatial pyramid pooling (ASPP) to better capture multi-scale contextual information. Moreover, a key innovation of this work is the novel application of a particle swarm optimization (PSO) algorithm to systematically automate hyperparameter tuning. Through ablation experiments and comparative tests, the improved PP-YOLOE network demonstrates superior overall performance on this dataset, achieving a multiple average precision of 0.94. It surpasses the original model across multiple evaluation metrics, indicating a robust and meaningful enhancement. The improved PP-YOLOE network proposed in this study provides a promising and effective method for WTB icing detection. This work provides a paradigm for applying advanced deep learning techniques to enhance intelligent industrial inspection tasks.

Relative humidity (RH) as an important physical parameter, requires real-time and accurate monitoring of its levels, which is crucial for manufacturing, environmental monitoring, and the medical industry. However, temperature fluctuations in the measurement environment have a significant impact on sensor sensitivity. To this end, this study proposes a λ/2 microwave coaxial resonant cavity with temperature compensation and applies it as a sensing device for RH measurement. First, based on the effective medium theory, a dielectric constant calculation model for humid air was established, and its dielectric properties were analyzed. Subsequently, a comprehensive theoretical measurement model was developed by integrating microwave coaxial resonant cavity theory. Through parameter scanning, the simulation modeling of the resonant cavity was optimized, determining the characteristic dimensions that enable the cavity to achieve high energy absorption efficiency and significant modal isolation characteristics, thereby verifying the global optimality of the structural parameters. Finally, temperature characteristic experiments were conducted to correct measurement errors caused by thermal expansion effects. RH measurement experiments were performed to validate the performance of the designed resonant cavity. The experimental results demonstrate that the resonant cavity achieves a maximum sensitivity of 234 kHz/%RH within the 20%RH–90%RH range, with an overall average sensitivity of 53.34 kHz/%RH. Additionally, it exhibits advantages such as fast response and stable measurement, enabling online monitoring of RH.