The knitting needle cylinder is one of the core parts of a hosiery machine. The operation of its needles can directly affect the production quality and efficiency of the hosiery machine. To reduce the production loss of a hosiery machine caused by knitting needle faults, a knitting needle fault detection system for hosiery machines based on a synergistic combination of laser detection and machine vision is proposed in this paper. When the system was operating normally, a photoelectric detector collected the laser signal reflected by the knitting needle and the system monitored the operation of the knitting needle using the ratio of adjacent peak-to-peak distances of the signals. When a fault signal was detected, the hosiery machine was stopped by the system immediately, and a charge-coupled device camera was used to take an image of the faulty knitting needle. After image preprocessing, the faulty knitting needle could be identified quickly and accurately using an image region size classifier based on a decision tree. The experimental results showed that a single image classification by the classifier could be performed in as little as 0.002 s.

Problems with knitting needles are one of the main causes of production loss of fabric. In order to detect problems with knitting needles quickly and accurately, this paper proposes a hosiery knitting needle detection system based on machine vision. Meanwhile, according to the working condition of real hosiery knitting needles, a simulated needle cylinder rotary platform is built. The system can detect knitting needle problems, and the needle causing the issue can be identified as the beginning of the fabric defect appears. The production losses caused by the bending and fracture of knitting needles are reduced at the source. In the image processing, the vertical projection algorithm is introduced in the system, and the defect of image binarization is improved. At the same time, the multi-classification support vector machine based on the decision tree is used to realize the image classification. The experimental results show that the classification accuracy of the two levels classifiers in the system can reach 100% and 98%, and the best time of system detection is 0.348 s.

The growing demand for marine resource development and in-depth exploration of the marine environment has positioned soft biomimetic underwater vehicles (SBUVs) as a research hotspot in the fields of underwater equipment and soft robotics. SBUVs are characterized by bodies made of flexible and extensible materials, integrating the dual advantages of softness and biomimetics. They can achieve muscle-like continuous deformation to efficiently absorb collision energy, while mimicking the propulsion mechanisms of marine organisms—such as fish and jellyfish—through undulating body movements or cavity contraction and relaxation. Such biomimetic propulsion is highly compatible with the flexible actuation of soft materials, enabling excellent environmental adaptability while maintaining favorable propulsion efficiency. Compared with traditional rigid underwater vehicles, SBUVs offer higher degrees of freedom, superior environmental adaptability, enhanced impact resistance and greater motion flexibility. This review systematically summarizes typical actuation methods for SBUVs—including fluid-powered actuation, shape memory alloy actuation, and electroactive polymer actuation—elaborating on their working principles, key technological advances, and representative application cases on SBUVs. These actuation mechanisms each offer distinct advantages. Fluid-powered systems are valued for high power density and precise motion control through direct fluidic force transmission. Shape memory alloys provide high force output and accurate positional recovery via controlled thermal phase changes. Meanwhile, electroactive polymers stand out for their rapid (often millisecond-scale) dynamic response, low hysteresis, and fine, muscle-like deformation under electrical stimuli. Current challenges are also analyzed, such as limited actuation efficiency, material durability issues, and system integration difficulties. Despite these constraints, SBUVs show broad application prospects in marine resource exploration, ecological monitoring, and underwater engineering operations. Future research should prioritize the development of novel materials, coordinated optimization of actuation and control systems, and breakthroughs in core technologies to accelerate the practical implementation and industrialization of SBUVs.

The dielectric elastomer (DE) is a new kind of functional polymer that can be used as a smart actuator due to the large deformation induced by voltage excitation. Dielectric elastomer actuators (DEAs) are usually excited by dynamic voltages to generate alternating motions. DEAs are prone to premature breakdown failure during the dynamic excitation, while the research on the breakdown of DEAs under cyclic voltage excitation is still not fully revealed. In this paper, the dynamic breakdown behaviors of DEAs made from VHB4910 film were experimentally investigated. The factors affecting the breakdown behavior of DEAs under dynamic voltages were determined, and the relevant changing laws were summarized accordingly. The experimental results show that under dynamic voltage excitation, the critical breakdown voltage of DEAs were augmented slowly with voltage frequency and showed a substantial dispersion. In addition, the maximum cycle numbers before breakdown were significantly affected by voltage parameters (such as frequency, amplitude, waveform). Finally, the underlying mechanisms of breakdown under cyclic voltages were discussed qualitatively, a power-law equation was proposed to characterize the maximum cycle number for the dynamic breakdown of DEAs, and related parameters were fitted. This study provides a new path to predict the service life of DEAs under dynamic voltage.