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conditional variational autoencoder and generative adversarial network-based for fault for the motor

MEASUREMENT 2025年 242卷

作者： Huang, Mei Sheng, Chenxing Rao, Xiang Reliabil Engn Inst Natl Engn Res Ctr Water Transportat Safety Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China

This study addresses the challenge of diagnosing motor faults in long-tailed data distributions, characterized by dominant healthy states and rare fault types. We propose the LT-CVAE-GAN model, which integrates a conditional variational autoencoder (CVAE) with a conditional Generative Adversarial Network (CGAN) to enhance long-tailed fault diagnosis. Initially, we train the CVAE-GAN model using traditional CVAE and CGAN losses such as Kullback-Leibler (KL) divergence loss, reconstruction loss, and adversarial loss. Additionally, we introduce mean feature matching loss and pairwise feature matching loss to mitigate mode collapse and improve model stability, thereby enhancing the generation ability of less frequent fault samples under long-tail conditions. Subsequently, the pre-trained Generator is used to produce infrequent fault mode data to rebalance the dataset. Classifier parameters are fine-tuned in this step to improve fault diagnosis accuracy. Experimental results demonstrate that our LT-CVAE-GAN surpasses state-of-the-art models in diverse long-tailed conditions.

关键词： Fault diagnosis Long-tailed distribution Motor conditional variational autoencoder Generative adversarial networks

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A generative design method of airfoil based on conditional variational autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Wang, Xu Qian, Weiqi Zhao, Tun Chen, Hai He, Lei Sun, Haisheng Tian, Yuan China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang 621000 Peoples R China Chengdu Aeronaut Polytech Chengdu 610100 Peoples R China China Aerodynam Res & Dev Ctr Low Speed Aerodynam Inst Mianyang 621000 Peoples R China

The challenges in multi-objective and multi-dimensional optimization design of airfoils, marked by prolonged optimization cycles and low accuracy, call for an efficient solution to expedite airfoil design. This study presents an innovative airfoil generative design model based on a conditional variational autoencoder (CVAE). Initially, to overcome the limitation of insufficient training data, the model leverages the variational autoencoder (VAE) to learn the spatial distribution of University of Illinois at Urbana-Champaign (UIUC) airfoils, enabling the generation of a diverse set of airfoils with similar distributions. Subsequently, two CVAE-based airfoil generation models, the airfoil freedom design model and the airfoil precision design model, are proposed, which can realize diverse airfoil design under different conditions, such as shape and aerodynamic conditions. Furthermore, two measurements of roughness and diversity are introduced to evaluate the quality of the generated airfoils. The impact of different conditions and network parameters on the model's generation performance is thoroughly analyzed. Results indicate that our proposed model achieves a 65% lower error compared to physics-guided conditional Wasserstein generative adversarial networks (PG-cWGAN) when generating airfoils that satisfy a specific lift coefficient and a 99.99% lower error compared to airfoil pressure distributions generative adversarial networks (Airfoil-Cp-GAN) when generating airfoils that satisfy specific pressure distributions. This method introduces amore creative and accurate approach for aircraft designers in the realm of airfoil design. The code used for this paper is available at https://***/liujun39/airfoilvae.

关键词： Deep learning Generative model variational autoencoder conditional variational autoencoder Airfoil design

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Remaining useful-life prediction of lithium battery based on neural-network ensemble via conditional variational autoencoder

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APPLIED INTELLIGENCE 2025年第1期55卷 1-15页

作者： Zhang, Hengshan Guo, Kaijie Chen, Yanping Sun, Jiaze Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian Peoples R China Shanxi Key Lab Network Data Anal & Intelligent Pro Xian Peoples R China Xian Key Lab Big Data & Intelligent Comp Xian Peoples R China

Ensemble learning using deep neural networks has become prevalent in predicting the Remaining Useful Life (RUL) of Lithium Batteries (LiBs). However, owing to the predominant linearity of ensemble learning, capturing nonlinear relationships among base learners remains a persistent challenge. This study presents an RUL-prediction method for LiBs based on a neural-network ensemble via a conditional variational autoencoder (CVAE). The proposed method serves as a nonlinear ensemble learning method and promises enhanced prediction performance while maintaining ease of implementation. The methodology entails several key steps. First, data smoothing is conducted via local weighted linear regression. Subsequently, a preliminary linear-ensemble phase is executed through an attention mechanism, which filters out extraneous information in the features and bolsters the importance of valid features. Subsequently, a nonlinear ensemble is accomplished by utilizing the CVAE, with truth labels serving as conditions. Finally, the efficacy of the proposed method is substantiated through experimentation, demonstrating its superior performance compared to the candidate methods.

关键词： Remaining useful-life prediction Locally weighted linear regression conditional variational autoencoder Attention mechanism

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conditional variational autoencoder Networks for Autonomous Vehicle Path Prediction

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NEURAL PROCESSING LETTERS 2022年第5期54卷 3965-3978页

作者： Jagadish, D. N. Chauhan, Arun Mahto, Lakshman Indian Inst Informat Technol Dharwad Dharwad 580009 Karnataka India Graph Era Univ Dehra Dun 248002 Uttarakhand India

Mobility of autonomous vehicles is a challenging task to implement. Under the given traffic circumstances, all agent vehicles' behavior is to be understood and their paths for a short future needs to be predicted to decide upon the maneuver of the ego vehicle. We explore variational autoencoder networks to get multimodal predictions of agents. In our work, we condition the network on past trajectories of agents and traffic scenes as well. The latent space representation of traffic scenes is achieved by using another variational autoencoder network. The proposed networks are trained for varied prediction horizon. The performance of a network is compared with other networks trained on the dataset.

关键词： Autonomous vehicle path conditional variational autoencoder Deep learning

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Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data

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MEDICAL PHYSICS 2023年第12期50卷 7548-7557页

作者： Matsuo, Hidetoshi Kitajima, Kazuhiro Kono, Atsushi K. Kuribayashi, Kozo Kijima, Takashi Hashimoto, Masaki Hasegawa, Seiki Yamakado, Koichiro Murakami, Takamichi Kobe Univ Grad Sch Med Dept Radiol Kobe Japan Hyogo Med Univ Sch Med Dept Radiol Nishinomiya Hyogo Japan Hyogo Med Univ Sch Med Dept Resp Med & Hematol Nishinomiya Hyogo Japan Hyogo Med Univ Sch Med Dept Thorac Surg Nishinomiya Hyogo Japan Kobe Univ Grad Sch Med Dept Radiol 7-5-2 Kusunoki ChoChuo Ku Kobe Japan

BackgroundDeep learning (DL) has been widely used for diagnosis and prognosis prediction of numerous frequently occurring diseases. Generally, DL models require large datasets to perform accurate and reliable prognosis prediction and avoid overlearning. However, prognosis prediction of rare diseases is still limited owing to the small number of cases, resulting in small *** paper proposes a multimodal DL method to predict the prognosis of patients with malignant pleural mesothelioma (MPM) with a small number of 3D positron emission tomography-computed tomography (PET/CT) images and clinical *** 3D convolutional conditional variational autoencoder (3D-CCVAE), which adds a 3D-convolutional layer and conditional VAE to process 3D images, was used for dimensionality reduction of PET images. We developed a two-step model that performs dimensionality reduction using the 3D-CCVAE, which is resistant to overlearning. In the first step, clinical data were input to condition the model and perform dimensionality reduction of PET images, resulting in more efficient dimension reduction. In the second step, a subset of the dimensionally reduced features and clinical data were combined to predict 1-year survival of patients using the random forest classifier. To demonstrate the usefulness of the 3D-CCVAE, we created a model without the conditional mechanism (3D-CVAE), one without the variational mechanism (3D-CCAE), and one without an autoencoder (without AE), and compared their prediction results. We used PET images and clinical data of 520 patients with histologically proven MPM. The data were randomly split in a 2:1 ratio (train : test) and three-fold cross-validation was performed. The models were trained on the training set and evaluated based on the test set results. The area under the receiver operating characteristic curve (AUC) for all models was calculated using their 1-year survival predictions, and the results were *** obtained

关键词： autoencoder conditional variational autoencoder deep learning PET imaging semi-supervised learning

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A boosting resampling method for regression based on a conditional variational autoencoder

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INFORMATION SCIENCES 2022年 590卷 90-105页

作者： Huang, Yang Liu, Duen-Ren Lee, Shin-Jye Hsu, Chia-Hao Liu, Yang-Guang Natl Yang Ming Chiao Tung Univ Inst Informat Management Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Management Technol Hsinchu Taiwan Ind Technol Res Inst Hsinchu Taiwan

Resampling is the most commonly used method for dealing with imbalanced data, in addition to modifying the algorithm mechanism, it can, for example, generate new minority samples or reduce majority samples to adjust the data distribution. However, to date, related research has predominantly focused on solving the classification problem, while the issue of imbalanced regression data has rarely been discussed. In real-world applications, predicting regression data is a common and valuable issue in decision making, especially in regard to those rare samples with extremely high or low values, such as those encountered in the fields of signal processing, finance, or meteorology. This study therefore divided its regression data into rare samples and normal samples, with self-defined relevance functions and, in addition, proposed a boosting resampling method based on a conditional variational autoencoder. The experimental results showed that when using the proposed resampling method was employed, the prediction performance of the whole testing data set was slightly increased, while the performance for the rare samples was significantly improved. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Boosting resampling conditional variational autoencoder Regression problem

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Data-driven topology optimization using a multitask conditional variational autoencoder with persistent homology

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2024年第7期67卷 1-12页

作者： Sugai, Tomotaka Shintani, Kohei Yamada, Takayuki Univ Tokyo Grad Sch Engn 2-11-6 YayoiBunkyo Ku Tokyo 1138656 Japan

Topology optimization is crucial for the mechanical design of vehicles and aircraft, allowing changes in the shape of structures and the placement of features. Recent advances have integrated deep generative models, particularly convolutional neural networks, to streamline this *** streamline this process. However, these models struggle to preserve subtle structural features. To overcome these limitations, this study introduced a generative model adept at identifying the topological features inherent in real shapes, such as connectivity and holes, to enhance the effectiveness of topology optimization. A conditional variational autoencoder (CVAE) was employed to predict both the shape and compliance simultaneously. This model, CVAE with persistent homology, generates optimal material distributions by considering topological properties. The learning process introduced a term that minimizes the difference in topological features between true and reconstructed shapes. The proposed model can generate optimal material distributions by considering topological properties, eliminating the need for iterative calculations. This approach was validated using two numerical examples. The accuracy of the generated material distributions was compared with conventional methods using the mean-squared error. An average improvement in accuracy of approximately 36.85% was observed across the two results. This confirms that shapes considering compliance and connectivity can be accurately predicted.

关键词： Topology optimization conditional variational autoencoder Persistent homology Topological features

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conditional variational autoencoder with Gaussian process regression recognition for parametric models

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2024年 438卷

作者： Zhang, Xuehan Jiang, Lijian Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China

In this article, we present a data-driven method for parametric models with noisy observation data. Gaussian process regression based reduced order modeling (GPRbased ROM) can realize fast online predictions without using equations in the offline stage. However, GPR-based ROM does not perform well for complex systems since POD projection are naturally linear. conditional variational autoencoder (CVAE) can address this issue via nonlinear neural networks but it has more model complexity, which poses challenges for training and tuning hyperparameters. To this end, we propose a framework of CVAE with Gaussian process regression recognition (CVAEGPRR). The proposed method consists of a recognition model and a likelihood model. In the recognition model, we first extract low-dimensional features from data by POD to filter the redundant information with high frequency. And then a non-parametric model GPR is used to learn the map from parameters to POD latent variables, which can also alleviate the impact of noise. CVAE-GPRR can achieve the similar accuracy to CVAE but with fewer parameters. In the likelihood model, neural networks are used to reconstruct data. Besides the samples of POD latent variables and input parameters, physical variables are also added as the inputs to make predictions in the whole physical space. This cannot be achieved by either GPR-based ROM or CVAE. Moreover, the numerical results show that CVAE-GPRR may alleviate the overfitting issue in CVAE. (c) 2023 Published by Elsevier B.V.

关键词： Parametric models conditional variational autoencoder Proper orthogonal decomposition Gaussian process regression

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conditional variational autoencoder for query expansion in ad-hoc information retrieval

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INFORMATION SCIENCES 2024年 652卷

作者： Ou, Wei Huynh, Van-Nam Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou Zhejiang Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Ishikawa Japan

Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR research, neural QE models have emerged in recent years. Many of these models focus on learning embeddings by leveraging query document relevance. These embedding models allow computing semantic similarities between queries and documents to generate expansion terms. However, existing models often ignore query-document interactions. This research aims to address that gap by proposing a QE model using a conditional variational autoencoder. It first maps a query-document pair into a latent space based on their interaction, then estimates an expansion model from that latent space. The proposed model is trained on relevance feedback data and generates expansions using pseudo relevance feedback at test time. The proposed model is evaluated on three standard TREC collections for document ranking: AP and Robust 04 and GOV02, and the MS MARCO dataset for passage ranking. Results show the model outperforms state-of-the-art traditional and neural QE models. It also demonstrates higher additivity with neural matching than baselines.

关键词： Information retrieval Query expansion conditional variational autoencoder Relevance language model

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methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder

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BMC BIOINFORMATICS 2020年第1期21卷 1-10页

作者： Choi, Joungmin Chae, Heejoon Sookmyung Womens Univ Div Comp Sci Seoul South Korea

BackgroundRecently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylation signatures in cancer, demand for utilizing publicly available methylome dataset has been increased. To satisfy this, large-scale projects were launched to discover biological insights into cancer, providing a collection of the dataset. However, public cancer data, especially for certain cancer types, is still limited to be used in research. Several simulation tools for producing epigenetic dataset have been introduced in order to alleviate the issue, still, to date, generation for user-specified cancer type dataset has not been *** this paper, we present methCancer-gen, a tool for generating DNA methylome dataset considering type for cancer. Employing conditional variational autoencoder, a neural network-based generative model, it estimates the conditional distribution with latent variables and data, and generates samples for specified cancer *** evaluate the simulation performance of methCancer-gen for the user-specified cancer type, our proposed model was compared to a benchmark method and it could successfully reproduce cancer type-wise data with high accuracy helping to alleviate the lack of condition-specific data issue. methCancer-gen is publicly available at https://***/cbi-bioinfo/methCancer-gen.

关键词： DNA methylation Cancer Generator conditional variational autoencoder Simulator

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