A novel polymer stabilized liquid crystal (PSLC) film doped with antimony doped tin oxide (ATO) nanoparticles enable the widest waveband modulation to date, covering the visible and infrared regions from 380 to 5500 nm.
With the development of optical technologies, transparent materials that provide protection from light have received considerable attention from scholars. As important channels for external light, windows play a vital role in the regulation of light in buildings, vehicles, and aircrafts. There is a need for windows with switchable optical properties to prevent or attenuate damage or interference to the human eye and light-sensitive instruments by inappropriate optical radiation. In this context, liquid crystals (LCs), owing to their rich responsiveness and unique optical properties, have been considered among the best candidates for advanced light protection materials. In this review, we provide an overview of advances in research on LC-based methods for protection against light. First, we introduce the characteristics of different light sources and their protection requirements. Second, we introduce several classes of light modulation principles based on liquid crystal materials and demonstrate the feasibility of using them for light protection. In addition, we discuss current light protection strategies based on liquid crystal materials for different applications. Finally, we discuss the problems and shortcomings of current strategies. We propose several suggestions for the development of liquid crystal materials in the field of light protection.
The deep learning model has gradually matured in the detection of mechanical faults. However, due to the changes in the mechanical operating environment and the application of new sensors in real work, the effect of the training model is not ideal in field applications. The key of this problem is the deviation of feature space mapping between training source domain and application target domain. This paper proposes an unsupervised adversarial domain adaptive fault diagnosis transfer learning model based on the minimum domain spacing to reduce the deviation. In adversarial network training, by training the weight parameters of the classifier, some features extracted by the composed classifier are added to the feature distribution of the target domain through weight changes, which reduces the feature distribution difference between the source domain and the target domain. It is reflected on the reduction of the maximum mean difference distance (MMD) between the two domains, and the fitting features of the data distribution are improved. Finally, through two experimental platforms of rolling bearing and planetary gearbox dataset, the results of six diagnostic tasks show that the new model reduces the amount of parameters by 33.66% and keeps accuracy more than 99% compared with the DANN model under the condition.
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