Feature Optimization Method of Material Identification for Loose Particles Inside Sealed Relays

Abstract

Existing material identification for loose particles inside sealed relays focuses on the selection and optimization of classification algorithms, which ignores the features in the material dataset. In this paper, we propose a feature optimization method of material identification for loose particles inside sealed relays. First, for the missing value problem, multiple methods were used to process the material dataset. By comparing the identification accuracy achieved by a Random-Forest-based classifier (RF classifier) on the different processed datasets, the optimal direct-discarding method was obtained. Second, for the uneven data distribution problem, multiple methods were used to process the material dataset. By comparing the achieved identification accuracy, the optimal min–max standardization method was obtained. Then, for the feature selection problem, an innovative multi-index–fusion feature selection method was designed, and its superiority was verified through several tests. Test results show that the identification accuracy achieved by RF classifier on the dataset was improved from 59.63% to 63.60%. Test results of ten material verification datasets show that the identification accuracies achieved by RF classifier were greatly improved, with an average improvement of 3.01%. This strongly promotes research progress in loose particle material identification and is an important supplement to existing loose particle detection research. This is also the highest loose particle material identification accuracy achieved to in aerospace engineering, which has important practical value for improving the reliability of aerospace systems. Theoretically, it can be applied to feature optimization in machine learning.

Fast and Selective Luminescent Sensing by Langmuir–Schaeffer Films Based on Controlled Assembly of Perylene Bisimide Modified with A Cyclometalated AuIII Complex

“…The sensing dynamics parameters were considered as the dependent variables (Tables S1–S8). Parameters such as the RPI, RPT, RLT, and RCT were extracted (Figure 3a) from response traces, and the hierarchical GRA method was applied to analyse various relationships (Figures 3b, S3, and S4) [32–35] …”

“…Parameters such as the RPI, RPT, RLT, and RCT were extracted (Figure 3a) from response traces, and the hierarchical GRA method was applied to analyse various relationships (Figures 3b, S3, and S4). [32][33][34][35] The adlayer thickness and porosity is related to the packing models of the luminophores. The values in Table S6 suggest that thickness and porosity are the most crucial parameters, with high grey relational degrees ( � 0.83 and 0.74).…”