Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost by HEI Xinhong, GAO Miao, ZHANG Kuan, FEI Rong, QIU Yuan, JI Wenjiang

Subjects: “…TimeGAN;Nadam;XGBoost;fault diagnosis;data augmentation…”
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Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost by HEI Xinhong, GAO Miao, ZHANG Kuan, FEI Rong, QIU Yuan, JI Wenjiang

Subjects: “…TimeGAN…”
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Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN by Peihao Tang, Zhen Li, Xuanlin Wang, Xueping Liu, Peng Mou

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Fault Diagnosis of Tractor Transmission System Based on Time GAN and Transformer by Liyou Xu, Guodong Zhang, Sixia Zhao, Yiwei Wu, Zhiqiang Xi

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Metrics for Evaluating Synthetic Time-Series Data of Battery by Sujin Seol, Jaewoo Yoon, Jungeun Lee, Byeongwoo Kim

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Assessing Deep Generative Models on Time Series Network Data by Muhammad Haris Naveed, Umair Sajid Hashmi, Nayab Tajved, Neha Sultan, Ali Imran

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Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework by Dong Ha Choi, Wei Li, Albert Y. Zomaya

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Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network by Yuefei Sun, Xianbo Sun, Tao Hu, Li Zhu

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Predicting Ultra-Short-Term Wind Power Combinations Under Extreme Weather Conditions by Wanting Li, Tongguang Yang, Jingyu Yang, Li Peng

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Multi-Scale Price Forecasting Based on Data Augmentation by Ting Yue, Yahui Liu

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Synthetic demand data generation for individual electricity consumers : Generative Adversarial Networks (GANs) by Bilgi Yilmaz, Ralf Korn

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Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network by Yantao Ma, Jie Xue, Xinlong Feng, Jianping Zhao, Junhu Tang, Huaiwei Sun, Jingjing Chang, Longke Yan

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Hybrid Network Model Based on Data Enhancement for Short-Term Power Prediction of New PV Plants by Shangpeng Zhong, Xiaoming Wang, Bin Xu, Hongbin Wu, Ming Ding

Subjects: “…time-series generative adversarial network (TimeGAN)…”
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