AUTOMATION, ІОТ, ROBOTICS AND INFORMATION-MEASUREMENT SYSTEMS

Hybrid Algorithm for Automated Primary Analysis of Broadband Dielectric Spectra of Heterogeneous Materials

Authors

A. Ye. Shcherbak ORCID 0009-0007-0166-430X
Oles Honchar Dnipro National University ROR
I. V. Gomilko ORCID 0000-0003-3256-9771
Oles Honchar Dnipro National University ROR
I. A. Skuratovsky ORCID 0000-0002-9444-5988
Oles Honchar Dnipro National University ROR
DOI https://doi.org/10.31649/1997-9266-2026-186-3-87-91

Keywords

broadband dielectric spectroscopy primary analysis distribution of relaxation times Tikhonov regularization convolutional neural network noise suppression heterogeneous materials Maxwell–Wagner polarization

Abstract

Broadband dielectric spectroscopy (BDS) provides the measurement of frequency-dependent complex permittivity ε*(ω) that encodes multiple relaxation mechanisms and charge-transport phenomena in heterogeneous materials. However, the superposition of dipolar relaxation, interfacial (Maxwell–Wagner) polarization, electrode polarization and measurement noise makes the primary analysis of BDS data non-trivial and labor-intensive. The paper proposes a hybrid pipeline that combines physics-aware preprocessing, distribution-of-relaxation-times (DRT) estimation via Tikhonov regularization, and lightweight learning-based interpretation using a one-dimensional convolutional neural network (1D-CNN) trained on physics-informed synthetic spectra. First, singular spectrum analysis (SSA) and baseline correction are employed to suppress noise and low-frequency artifacts while preserving the overall spectral shape. Then a non-negative DRT profile is recovered from ε′(ω) and ε″(ω) by solving a discretized Fredholm equation of the first kind with smoothness regularization. The resulting DRT summarizes latent relaxation modes without explicit model-order selection. Next, physically interpretable features such as the number of peaks, their positions and widths, low-frequency conduction indicators and heuristics for Maxwell–Wagner polarization are extracted from both spectra and DRT. These features, together with the standardized spectra, are fed into a compact 1D-CNN that infers high-level meta-parameters including the number of overlapping relaxations, the presence of interfacial polarization and the presence of conduction tails. Numerical experiments covering a wide range of signal-to-noise ratios demonstrate robust denoising (ΔSNR ≈ 8...12 dB), accurate DRT recovery (median Wasserstein distance < 0.08) and reliable meta-parameter prediction (macro-F1 ≈ 0.91). A reference Python implementation is suggested, providing a command-line interface and a REST microservice suitable for integration into laboratory workflows. The proposed approach minimizes a priori model assumptions while preserving interpretability, which makes it a suitable primary-analysis engine for BDS studies of composites, porous media, soils, polymer electrolytes and other heterogeneous systems.

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How to Cite

[1]
“Hybrid Algorithm for Automated Primary Analysis of Broadband Dielectric Spectra of Heterogeneous Materials”, Вісник ВПІ, no. 3, pp. 87–91, Jul. 2026, doi: 10.31649/1997-9266-2026-186-3-87-91.

Author Biographies

A. Ye. Shcherbak, Oles Honchar Dnipro National University

Post-Graduate Student the Chair of Electronic Computing Machines

I. V. Gomilko, Oles Honchar Dnipro National University

Dean of the Department of Physics, Electronics and Computer Systems

I. A. Skuratovsky, Oles Honchar Dnipro National University

Cand. Sc. (Phys.-Math.), Associate Professor of the Chair of Electronic Computing Machines

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