arXiv subjects

Zhiyong Liang

Publications and source records attributed to Zhiyong Liang.

4 recordsLinked to original sources

arXiv2025

WVEmbs with its Masking: A Method For Radar Signal Sorting

Our study proposes a novel embedding method, Wide-Value-Embeddings (WVEmbs), for processing Pulse Descriptor Words (PDWs) as normalized inputs to neural networks. This method adapts to the distribution of interleaved radar signals, ranking original signal features from trivial to useful and stabilizing the learning process. To address the imbalance in radar signal interleaving, we introduce a value dimension masking method on WVEmbs, which automatically and efficiently generates challenging samples, and constructs interleaving scenarios, thereby compelling the model to learn robust features. Experimental results demonstrate that our method is an efficient end-to-end approach, achieving high-granularity, sample-level pulse sorting for high-density interleaved radar pulse sequences in complex and non-ideal environments.

eess.SP↗

arXiv2020

Real-time Data-driven Quality Assessment for Continuous Manufacturing of Carbon Nanotube Buckypaper

Carbon nanotube (CNT) thin sheet, or buckypaper, has shown great potential as a multifunctional platform material due to its desirable properties, including its lightweight nature, high mechanical properties, and good conductivity. However, their mass adoption and applications by industry have run into significant bottlenecks because of large variability and uncertainty in quality during fabrication. There is an urgent demand to produce high-quality, high-performance buckypaper at an industrial scale. Raman spectroscopy provides detailed nanostructure information within seconds, and the obtained spectra can be decomposed into multiple effects associated with diverse quality characteristics of buckypaper. However, the decomposed effects are high-dimensional, and a systematic quantification method for buckypaper quality assessment has been lacking. In this paper, we propose a real-time data-driven quality assessment method, which fills in the blank of quantifying the quality for continuous manufacturing processes of CNT buckypaper. The composite indices derived from the proposed method are developed by analyzing in-line Raman spectroscopy sensing data. Weighted cross-correlation and maximum margin clustering are used to fuse the fixed effects into an inconsistency index to monitor the long-term mean shift of the process and to fuse the normal effects into a uniformity index to monitor the within-sample normality. Those individual quality indices are then combined into a composite index to reflect the overall quality of buckypaper. A case study indicates that our proposed approach can determine the quality rank for ten samples, and can provide quantitative quality indices for single-walled carbon nanotube buckypaper after acid processing or functionalization. The quality assessment results are consistent with evaluations from the experienced engineers.

eess.SP↗

arXiv2019

Identification of a monoclinic metallic state in VO2 from a modified first-principles approach

Metal-insulator transition underlies many remarkable and technologically important phenomena in VO2. Even though its monoclinic structure had before been the reserve of the insulating state, recent experiments have observed an unexpected monoclinic metallic state. Here we use a modified approach combining first-principles calculations with orbital-biased potentials to reproduce the correct stability ordering and electronic structure of different phases of VO2. We identify a ferromagnetic monoclinic metal that is likely to be the experimentally observed mysterious metastable state. Furthermore, our calculations show that an isostructural insulator-metal electronic transition is followed by the lattice distortion from the monoclinic structure to the rutile structure. These results not only explain the experimental observations of the monoclinic metallic state and the decoupled structural and electronic transitions of VO2, but also provide a understanding for the metal-insulator transition in other strongly correlated d electron systems.

cond-mat.str-el↗

arXiv2019

Tensor Mixed Effects Model with Applications in Nanomanufacturing Inspection

Raman mapping technique has been used to perform in-line quality inspections of nanomanufacturing processes. In such an application, massive high-dimensional Raman mapping data with mixed effects is generated. In general, fixed effects and random effects in the multi-array Raman data are associated with different quality characteristics such as fabrication consistency, uniformity, defects, et al. The existing tensor decomposition methods cannot separate mixed effects, and existing mixed effects model can only handle matrix data but not high-dimensional multi-array data. In this paper, we propose a tensor mixed effects (TME) model to analyze massive high-dimensional Raman mapping data with complex structure. The proposed TME model can (i) separate fixed effects and random effects in a tensor domain; (ii) explore the correlations along different dimensions; and (iii) realize efficient parameter estimation by a proposed iterative double Flip-Flop algorithm. We also investigate the properties of the TME model, existence and identifiability of parameter estimation. The numerical analysis demonstrates the efficiency and accuracy of the parameter estimation in the TME model. Convergence and asymptotic properties are discussed in the simulation and surrogate data analysis. The case study shows an application of the TME model in quantifying the influence of alignment on carbon nanotubes buckypaper. Moreover, the TME model can be applied to provide potential solutions for a family of tensor data analytics problems with mixed effects.

stat.ME↗