Application of WAM Data Set and Classification Method of Electromagnetic Wave Absorbing Materials

YUAN Yuyang; ZHANG Junhan; LI Dandan; SHA Jian jun

doi:10.11999/JEIT250166

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YUAN Yuyang, ZHANG Junhan, LI Dandan, SHA Jian jun. Application of WAM Data Set and Classification Method of Electromagnetic Wave Absorbing Materials[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250166

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YUAN Yuyang, ZHANG Junhan, LI Dandan, SHA Jian jun. Application of WAM Data Set and Classification Method of Electromagnetic Wave Absorbing Materials[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250166

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Application of WAM Data Set and Classification Method of Electromagnetic Wave Absorbing Materials

doi: 10.11999/JEIT250166 cstr: 32379.14.JEIT250166

YUAN Yuyang^{1, 2},
ZHANG Junhan^{1, 2},
LI Dandan^{1, 2},
SHA Jian jun^{1, 2, 3
,
,}

1.
College of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 110624
2.
State Key Laboratory of Industrial Equipment and Structural Analysis, Dalian University of Technology, Dalian 116024
3.
Key Laboratory of Advanced Technology for Aerospace Vehicles, Liaoning Province, Dalian 116024

Received Date: 2025-03-17
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-13

Abstract

Abstract

The performance of electromagnetic radiation shielding and absorbing materials is mainly determined by thickness, maximum reflection loss, and effective absorption bandwidth. Research focuses on metal-organic frameworks, carbon-based, and ceramic absorbing materials, and weak artificial intelligence is used to analyze the WAM (Wave Absorption Materials) dataset. After dividing the dataset into training and testing sets, data augmentation and correlation and principal component analysis are conducted. The decision tree algorithm is used to establish classification indicators, and it is found that the reflection loss of MOFs (Metal Organic Frameworks) materials is better than that of carbon-based materials, and MOFs materials are more likely to meet the maximum reflection loss value of less than –45 dB. The generalization performance of the random forest algorithm is better than that of the decision tree algorithm, and the ROC-AUC value is higher. The neural network is used for classification research, and the results show that the self-organizing mapping neural network performs better in classification, while the probabilistic neural network has a poor effect. After extending the binary classification problem to a three-class classification problem, nonlinear classification, clustering, and Boosting algorithms are used, and it is found that the maximum reflection loss is a key indicator. Further analysis shows that the WAM dataset is nonlinearly separable, and the fuzzy clustering effect is better.Artificial intelligence helps to reveal the relationship between material properties and absorbing performance, accelerate the development of new materials, and support the construction of the knowledge graph and knowledge base of absorbing materials. Objective Computational materials science, high-throughput experimentation, and the Materials Genome Initiative (MGI) have become prominent research frontiers in materials science. The Materials Genome Initiative serves as a strategic framework and developmental roadmap aimed at advancing materials research through artificial intelligence. Similar to gene sequencing in bioinformatics, its primary goal is to facilitate the discovery of novel material compositions and structures. Extracting valuable insights from large-scale datasets contributes significantly to cost reduction, efficiency improvement, interdisciplinary integration, and leapfrog advancements in materials development. Big data analytics, high-performance computing, and advanced algorithms constitute the foundational pillars of this initiative, providing critical support for the research and development of new materials. However, a prerequisite for discovering new material compositions and structures lies in the effective screening of candidate materials to identify those with outstanding properties that satisfy engineering application requirements. This necessitates the construction of comprehensive datasets, the development of robust classification algorithms, further enhancement of model generalization capabilities, and the advancement of associated application software. Methods This study was conducted using pattern recognition methods. First, a self-developed Wave-Absorbing Materials (WAM) dataset was constructed, comprising a test set and a validation set. Data preprocessing was carried out initially, which included data augmentation, data merging, and principal component analysis. Decision trees and random forests were employed to establish classification indicators and define the basis for classification. Self-Organizing Maps (SOM) and Probabilistic Neural Networks (PNN) were utilized for the classification task. Finally, the accuracy rates of different clustering algorithms were compared, revealing that the fuzzy clustering algorithm demonstrated relatively superior performance and was capable of achieving satisfactory results. Results and Discussions It was found that the reflection loss of MOFs (Metal Organic Frameworks) materials is superior to that of carbon-based materials. Semantic segmentation algorithms are not applicable to the classification of the WAM dataset. The classification accuracy of SOM is better than that of PNN. The WAM dataset is not linearly separable, and the classification results depend on the data distribution characteristics of the dataset itself. The maximum reflection loss is the key indicator for classification. Conclusions For the construction of the dataset of absorbing materials, a self-created WAM dataset was first built, which solved the problem that there was no dataset for the study of absorbing materials using pattern recognition reported in the known literature. The performance of various algorithms was compared and studied, and the optimal algorithm was determined based on the characteristics of the dataset. The traditional binary classification problem was extended to three classifications, preparing for the next step of multi-classification problem research. The use of artificial intelligence algorithms is conducive to improving the credibility and reliability of the research, and is beneficial to saving time costs and human resources. This method can explore the relationship between material properties and absorbing performance. It is conducive to shortening the research and development cycle, providing assistance for the screening of new materials, and providing support for the construction of the knowledge base of absorbing materials. The useful knowledge extracted from WAM is troubled by the problem of data sparsity, so there are certain limitations to artificial intelligence.
- Electromagnetic wave absorbing materials,
- pattern recognition,
- neural networks,
- data mining,
- artificial intelligence.

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