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, China
2.
State Key Laboratory of Industrial Equipment and Structural Analysis, Dalian University of Technology, Dalian 116024, China
3.
Key Laboratory of Advanced Technology for Aerospace Vehicles, Liaoning Province, Dalian 116024, China

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 depends primarily on thickness, maximum reflection loss, and effective absorption bandwidth. Current research focuses on Metal–Organic Frameworks (MOFs), carbon-based, and ceramic absorbing materials, analyzed using weak artificial intelligence techniques applied to the Wave-Absorbing Materials (WAM) dataset. After dividing the dataset into training and testing subsets, data augmentation, correlation analysis, and principal component analysis are performed. A decision tree algorithm is then applied to establish classification indicators, revealing that the reflection loss of MOF materials exceeds that of carbon-based materials. MOFs are more likely to achieve a maximum reflection loss below –45 dB. The random forest algorithm demonstrates stronger generalization ability than the decision tree algorithm, with a higher ROC–AUC value. Neural network classification shows that the self-organizing map neural network yields superior classification performance, whereas the probabilistic neural network performs poorly. When the binary classification problem is extended to a three-class problem, nonlinear classification, clustering, and Boosting algorithms indicate that maximum reflection loss serves as a key discriminative feature. Further analysis confirms that the WAM dataset is nonlinearly separable and that fuzzy clustering achieves better results. Artificial intelligence facilitates the identification of relationships between material properties and absorption performance, accelerates the development of new Wave-Absorbing Materials (WAM), and supports the construction of a knowledge graph and database for absorbing materials. Objective Computational materials science, high-throughput experimentation, and the Materials Genome Initiative (MGI) have emerged as key frontiers in modern materials research. The MGI provides a strategic framework and developmental roadmap for advancing materials discovery through artificial intelligence. Analogous to gene sequencing in bioinformatics, its central objective is to accelerate the identification of novel material compositions and structures. Extracting valuable information from large-scale datasets substantially reduces costs, enhances efficiency, fosters interdisciplinary integration, and promotes transformative progress in materials development. Big data analytics, high-performance computing, and advanced algorithms form the core pillars of this initiative, supplying essential support for new materials research and development. Nevertheless, the discovery of new compositions and structures depends on the effective screening of candidate materials to identify those exhibiting superior properties suitable for engineering applications. Achieving this goal requires the establishment of comprehensive datasets, the development of reliable classification algorithms, the improvement of model generalization performance, and the advancement of application-oriented software tools. Methods Pattern recognition techniques are employed in this study. A self-developed WAM dataset is first constructed, comprising a test set and a validation set. Data preprocessing is performed initially, including data augmentation, data integration, and principal component analysis. Decision tree and random forest algorithms are applied to establish classification indicators and define classification criteria. Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN) models are subsequently utilized for material classification. Finally, the accuracy of various clustering algorithms is evaluated, and the fuzzy clustering algorithm is found to achieve relatively superior performance and satisfactory classification results. Results and Discussions It is found that the reflection loss of MOF materials is superior to that of carbon-based materials. Semantic segmentation algorithms are identified as unsuitable for classifying the WAM dataset. Among the neural network approaches, the SOM achieves higher classification accuracy than the PNN. The WAM dataset is determined to be nonlinearly separable, indicating that classification performance depends strongly on the intrinsic data distribution characteristics. The maximum reflection loss is identified as the key indicator for effective classification. Conclusions A self-developed WAM dataset is constructed to address the lack of publicly available datasets for applying pattern recognition methods to electromagnetic WAM. The performance of multiple algorithms is evaluated, and the optimal algorithm is identified according to the dataset characteristics. The conventional binary classification problem is extended to a three-class framework, providing the foundation for further research on multi-class classification. The application of artificial intelligence algorithms is found to enhance the credibility and reliability of the research, reduce time and labor costs, and facilitate the exploration of relationships between material properties and absorption performance. This approach shortens the research and development cycle, supports the screening of new materials, and contributes to the establishment of a knowledge base for absorbing materials. However, the knowledge extracted from the WAM dataset remains limited by data sparsity, which constrains the effectiveness of artificial intelligence methods.
- Electromagnetic wave absorbing materials,
- Pattern recognition,
- Neural networks,
- Data mining,
- Artificial intelligence.

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