In this paper, piezoelectric sensing elements are used to assist in the study and analysis of ceramic art process optimization and visual quantization characteristics. A series piezoelectric element impedance sensor is designed based on the resonant frequency characteristics of the series piezoelectric element. Combining the resonant frequency characteristics of the series piezoelectric element and the basic principle of the impedance method, a multisensing impedance method based on the series piezoelectric element impedance sensor is proposed. The feasibility of the multisensing impedance method for monitoring the grout compactness was verified experimentally, and the basic principle of the method was further investigated by finite element simulation. The vase-type porcelain vessels were classified according to symmetry elements to find the characteristic points, the abdominal morphology was used as the basis for classification, and the screened samples were extracted from the contours to exclude the influence of other factors on the vessel shape. By the symmetrical elements of each type of ware, the classification principle of the ware type was designed and divided into six types, and each type was further subdivided into various types to establish a typological map of Qing dynasty bottle porcelain. The information entropy redundancy that describes the uniformity of the code appearance probability and the visual redundancy that describes the human eye’s sensitivity to image content or details are all entry points that can be considered for image coding. The experimental results show that the LBP-HOG fusion features can digitally express the information of ancient ceramic ornamentation and dig and verify the evolution of ceramic ornamentation with the times from the digital quantity. The GRNN model has an excellent performance in processing small samples of ancient ceramic data.
Piezoelectric Sensing Element-Assisted Ceramic Art Process Optimization and Visual Quantitative Characterization
