Search Results - "semi-supervised autoencoder"

Discriminant autoencoder for feature extraction in fault diagnosis by Luo, Xiaoyi, Li, Xianmin, Wang, Ziyang, Liang, Jun

“…Nowadays, some traditional autoencoders and their extensions have been widely applied in data-driven fault diagnosis for feature extraction. However, because…”
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scSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder by Zhao, Jian-Ping, Hou, Tong-Shuai, Su, Yansen, Zheng, Chun-Hou

“…•In this study, We proposed to use semi-supervised autoencoder to reduce the dimension of data, because it makes good use of some existing label…”
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Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum by Jang, Hee-Deok, Kwon, Seokjoon, Nam, Hyunwoo, Chang, Dong Eui

“…Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses…”
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Ironmaking process modeling uncertainty quantification via conformal prediction based on random vector functional link networks by Zhou, Ping, Wen, Chaoyao, Zhao, Peng, Li, Mingjie

“…For real-world industrial system modeling, dynamic stochastic errors inevitably exist in data-driven deterministic predictions (i.e., point predictions). The…”
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Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace by Zhou, Ping, Zhao, Peng, Ou, Zihui, Chai, Tianyou

“…This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten…”
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A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis by Kim, Sunuwe, Jo, Soo-Ho, Kim, Wongon, Park, Jongmin, Jeong, Jingyo, Han, Yeongmin, Kim, Daeil, Youn, Byeng Dong

“…This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract a health feature space for power transformer fault diagnosis using…”
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Semi-Supervised Autoencoder: A Joint Approach of Representation and Classification by Haiyan, Wu, Haomin, Yang, Xueming, Li, Haijun, Ren

“…Recent years have witnessed the significant success of representation learning and deep learning in various prediction and recognition applications. Most of…”
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