Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the need for accurate detection methods. This paper proposes an enhanced defect detection approach based on a lightweight neural network derived from the YOLOv11n architecture. Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. Experimental results demonstrate that the proposed method outperforms existing models like YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP), while maintaining low computational complexity. This approach provides a promising solution for real-time, high-accuracy insulator defect detection, enhancing the safety and reliability of power transmission systems.

A plasmonic refractive index nanosensor structure consisting of a metal-insulator-metal (MIM) waveguide with two symmetrical rectangle baffles coupled with a connected-concentric-double rings resonator (CCDRR) is presented. In this study, its transmission characteristics were investigated using the finite element method (FEM). The consequences, studied via simulation, revealed that the transmission spectrum of the system presents a sharp asymmetric Fano profile due to the destructive interference between the wide-band mode of two rectangle baffles on the bus waveguide and the narrow-band mode of the CCDRR. The effects of the geometric parameters of the structure on the transmission characteristics were investigated comprehensively. A sensitivity of 2260 nm/RIU and figure of merit (FOM) of 56.5 were the best levels of performance that the designed structure could achieve. In addition, the system could act as a sensor for use for temperature sensing, with a sensitivity that could reach 1.48 nm/°C. The designed structure advances with technology with new detection positions and has good application prospects in other high-sensitivity nanosensor fields, for example, acting as a biosensor to detect the hemoglobin level in the blood.

At present, it is difficult to detect the photovoltaic (PV) hot spots and the recognition efficiency is low. In this paper, an improved Single Shot MultiBox Detector (SSD) algorithm was designed for PV hot spot detection. The algorithm used the MobileNet network to replace the VGG16 convolutional neural network structure in the original SSD. This network is a depthwise separable convolution structure. Using it for feature extraction can reduce the number of parameters in the structure and achieve the purpose of speeding up the network. The experimental results show that the improved algorithm can detect the hot spots of PV array with good confidence, low detection rate and good robustness. Compared with the You Only Look Once(YOLO) algorithm and the original SSD algorithm, the detection speed is significantly improved, which verifies the effectiveness of the algorithm.

In this study, a novel refractive index sensor structure was designed consisting of a metal-insulator-metal (MIM) waveguide with two rectangular baffles and a U-Shaped Ring Resonator (USRR). The finite element method was used to theoretically investigate the sensor’s transmission characteristics. The simulation results show that Fano resonance is a sharp asymmetric resonance generated by the interaction between the discrete narrow-band mode and the successive wide-band mode. Next, the formation of broadband and narrowband is further studied, and finally the key factors affecting the performance of the sensor are obtained. The best sensitivity of this refractive-index sensor is 2020 nm/RIU and the figure of merit (FOM) is 53.16. The presented sensor has the potential to be useful in nanophotonic sensing applications.

With the continuous progress of artificial intelligence and other manufacturing technologies, there is promising potential for wearable piezoresistive sensors in human physiological signal detection and bionic robots. Here, we present a facile solution-mixing process to fabricate a multiwalled carbon nanotube/graphite powder (MWCNT@Gp) film, which has high sensitivity and great linearity and is more oriented to flexible piezoresistive sensors. The sensor consists of two parts: a spinosum microstructure shaped by a sandpaper template and polydimethylsiloxane (PDMS) as the top substrate and interdigital electrodes as the bottom substrate. The experiments we have conducted show that these two parts provide good protection to the MWCNTs@Gp film and improve sensor sensitivity. Additionally, the sensitivity of the optimal ratio of multiwalled carbon nanotubes and graphite powder is analyzed. The 5%MWCNT@5%Gp composites were found to have relatively good conductivity, which is convenient for the fabrication of conductive films of piezoresistive sensors. Finally, we conducted application experiments and found that the flexible piezoresistive sensor can detect minute signals of human motion and different pressure points. This indicates the feasibility of portable sensors in electronic skin and smart devices.

This paper proposes a nanosensor structure consisting of a metal–insulator–metal (MIM) waveguide with a rectangular root and a double-ring (SRRDR) with a rectangular cavity. In this paper, the cause and internal mechanism of Fano resonance are investigated by the finite element method (FEM), and the transport characteristics are optimized by changing various parameters of the structure. The results show that the structure can achieve double Fano resonance. Due to the destructive disturbance between the wideband mode of the inverted rectangle on the bus waveguide and the narrowband mode of the SRRDR, the output spectrum of the system shows an obvious asymmetric Fano diagram, and the structural parameters of the sensor have a great influence on the Fano resonance. By changing the sensitive parameters, the optimal sensitivity of the refractive index nanosensor is 2280 nm/RIU, and the coefficient of excellence (FOM) is 76.7. In addition, the proposed high-sensitivity nanosensor will be used to detect hemoglobin concentration in blood, which has positive applications for biosensors and has great potential for future nanosensing and optical integration systems.