This paper presents a novel approach utilizing piezoelectric plate structures with random electrode distribution patterns for energy harvesting applications across various vibration modes. For the first time, leveraging electromechanical Finite Element Analysis (eFEA) and data extraction techniques, we investigate the integration of conditional Generative Adversarial Networks (cGAN)-based dynamic models. The cGAN offers an effective technique for generating realistic synthetic data conditioned on input parameters, thereby enabling the creation of diverse and representative datasets for training energy harvesting systems. The integration of eFEA with cGAN opens up new possibilities for optimizing the design and performance of piezoelectric energy harvesters across various applications. Specifically, we explore four distinct cGAN models-based mechanics of energy harvesters by deploying distribution patterns. These models include training data generated by stacking simultaneously mode images, utilizing separate cGAN models for each mode, labeling images by mode, and concatenating all mode images into one. Our study focuses on assessing the effectiveness of these models in minimizing loss in cGAN-based power generation and predicting Structural Similarity Index Measure (SSIM) values, and more importantly, identifying the predicted data point outputs from the generated pixel image extractions. By analyzing the generated data from numerical model and its application in deep learning, we aim to enhance the understanding of the effects of distribution patterns and image processing techniques for optimal power generation and the effectiveness of piezoelectric energy harvesting systems across different vibration modes. The studies explore how different distribution patterns affect the power harvesting efficiency and frequency bandwidth, utilizing the generated datasets.

中文翻译：

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压电板能量收集分布模式的深度对抗学习模型


本文提出了一种利用具有随机电极分布模式的压电板结构在各种振动模式下的能量收集应用的新方法。我们首次利用机电有限元分析 （eFEA） 和数据提取技术，研究了基于条件生成对抗网络 （cGAN） 的动态模型的集成。cGAN 提供了一种有效的技术，可以生成以输入参数为条件的真实合成数据，从而能够为训练能量收集系统创建多样化且具有代表性的数据集。eFEA 与 cGAN 的集成为优化各种应用中压电能量采集器的设计和性能开辟了新的可能性。具体来说，我们通过部署分布模式探索了四种不同的基于 cGAN 模型的能量收集器机制。这些模型包括通过同时堆叠模式图像生成的训练数据，为每种模式使用单独的 cGAN 模型，按模式标记图像，以及将所有模式图像连接成一个。我们的研究重点是评估这些模型在最大限度地减少基于 cGAN 的发电中的损失和预测结构相似性指数测量 （SSIM） 值方面的有效性，更重要的是，确定从生成的像素图像提取中预测的数据点输出。通过分析数值模型生成的数据及其在深度学习中的应用，我们旨在增强对分布模式和图像处理技术对最佳发电的影响以及压电能量收集系统在不同振动模式下的有效性的理解。 这些研究利用生成的数据集探讨了不同的分布模式如何影响功率收集效率和频率带宽。