Dr. Long Yu Profile

Dr. Long Yu

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Proceedings Article | 24 November 2023 Paper

Study on neural network setting proper samples in dynamic light scattering particle size analysis

Yinan Wu, Long Yu, Zipei Zhang, Wei Li

Proceedings Volume 12935, 129352S (2023) https://doi.org/10.1117/12.3007686

KEYWORDS: Particles, Neural networks, Autocorrelation, Transformers, Dynamic light scattering, Statistical analysis, Scattered light, Error analysis

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Dynamic light scattering is often used to detect small particles, such as in industry and medicine. A typical ill-posed problem needs to be solved to recover PSD by inverting ACF data , which is a difficult problem of DLS. when DLS is used for detecting small particles in transformer oil, it is difficult to accurately recover PSD using traditional algorithms. Generalized regression neural network(GRNN) has been proved to be applicable to solving ill conditioned equations in Dynamic light scattering method. However, accurate inversion relies on proper training sets closely matching measured particle size to avoid large errors. Generating numerous samples for multimodal distributions is time-consuming. This study investigates how sample setting range affects GRNN inversion accuracy during training.The experimental system was self-built, using 362.2nm and 806.9nm polystyrene mixed diluted lotion as selected samples. The training sets were centered around theoretical particle sizes, with range variations of 10nm, 30nm, 50nm, and 100nm. The GRNN was trained using these sets, and the experiment’s light intensity autocorrelation data was input into the neural networks to obtain particle size distributions and bimodal peak particle sizes. All the sample set were achieved by measuring ACF of 362.2nm and 806.9nm polystyrene suspension on a self-built DLS experiment system. These findings indicate that closer proximity between the sample range used in neural network training and the actual situation leads to more accurate inversion results, demonstrating the network’s ability to accurately invert bimodal samples. Furthermore, the accuracy improves with more realistic training set settings. In practical measurements, combining regularization methods with this approach can enhance particle size analysis accuracy.

Proceedings Article | 24 November 2023 Paper

Particle size analysis of transformer oil pollutants using dynamic light scattering technique

Zipei Zhang, Long Yu, Yinan Wu, Wei Li

Proceedings Volume 12935, 129350H (2023) https://doi.org/10.1117/12.3000911

KEYWORDS: Particles, Transformers, Statistical analysis, Light scattering, Dynamic light scattering, Standards development, Autocorrelation

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Existing particle size analysis methods are difficult to detect micron and submicron sized particles in transformer oil which generated during the early stages of transformer oil aging. As a supplement to the existing analysis methods, we introduce dynamic light scattering (DLS) to extended the lower limit of particle size detection to the nanoscale. In this work, we designed a novel multi-angle DLS system which utilizes optical fibers and microfluidic chips to achieve accurate measurement in high viscosity media. We use the self-developed system to analyze the particle size distribution in multiple aging transformer oil samples, the experimental results show that the relative errors of measurement for simulated aging samples are within 7%, and the measurement results for real aging samples are consistent with the microscopic observations.

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