arXiv subjects

Yanhua Chen

Publications and source records attributed to Yanhua Chen.

3 recordsLinked to original sources

arXiv2026

Evaluating Synthetic Images as Effective Substitutes for Experimental Data in Surface Roughness Classification

Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performance. However, deploying artificial intelligence (AI) for surface roughness classification is often constrained by the need for large labeled datasets and costly high-resolution imaging equipment. In this study, we explore the use of synthetic images, generated with Stable Diffusion XL, as an efficient alternative or supplement to experimentally acquired data for classifying ceramic surface roughness. We show that augmenting authentic datasets with generative images yields test accuracies comparable to those obtained using exclusively experimental images, demonstrating that synthetic images effectively reproduce the structural features necessary for classification. We further assess method robustness by systematically varying key training hyperparameters (epoch count, batch size, and learning rate), and identify configurations that preserve performance while reducing data requirements. Our results indicate that generative AI can substantially improve data efficiency and reliability in materials-image classification workflows, offering a practical route to lower experimental cost, accelerate model development, and expand AI applicability in materials engineering.

cs.CV↗

arXiv2020

A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors

With the development of research on novel memristor model and device, neural networks by integrating various memristor models have become a hot research topic recently. However, state-of-the-art works still build such neural networks using drift memristor only. Furthermore, some other related works are only applied to a few individual applications including pattern recognition and edge detection. In this paper, a novel kind of multilayer neural network is proposed, in which diffusion and drift memristor models are applied to construct a system merging image preprocessing and pattern recognition. Specifically, the entire network consists of two diffusion memristive cellular layers for image preprocessing and one drift memristive feedforward layer for pattern recognition. Experimental results show that good recognition accuracy of noisy MNIST is obtained due to the fusion of image preprocessing and pattern recognition. Moreover, owing to high-efficiency in-memory computing and brief spiking encoding methods, high processing speed, high throughput, and few hardware resources of the entire network are achieved.

cs.ET↗

arXiv2020

Fully Memristive Spiking-Neuron Learning Framework and its Applications on Pattern Recognition and Edge Detection

Fully memristive spiking-neuron learning framework, which uses drift and diffusion memristor models as axon and dendrite respectively, becomes a hot topic recently with the development of memristor devices. Normally, some other devices like resistor or capacitor are still necessary on recent works of fully memristive learning framework. However, theoretically, one neuron needs axon and dendrite only, which makes technique process simpler and learning framework more similar to biologic brain. In this paper, a fully memristive spiking-neuron learning framework is introduced, in which a neuron structure is just built of one drift and one diffusion memristive models. To verify it merits, a feedforward neural network for pattern recognition and a cellular neural network for edge detection are designed. Experiment results show that compared to other memristive neural networks, our framework's the processing speed is much faster and the hardware resource is saved in pattern recognition due to its simple structure. Further due to the dynamic filtering function of diffusion memristor model in our learning framework, its peak signal noise ratio (PSNR) is much higher than traditional algorithms in edge detection.

cs.ET↗