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Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data 37th

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37th International Modal Analysis Conference and Exposition (IMAC) on Structural Dynamics

作者： Mylonas, C. Abdallah, I Chatzi, E. N. Swiss Fed Inst Technol Inst Struct Engn Zurich Switzerland

ISBN: (纸本)9783030120757;9783030120740

In this work we are addressing the problem of statistical modeling of the joint distribution of data collected from wind turbines interacting due to collective effect of their placement in a wind-farm, the wind characteristics (speed/orientation) and the turbine control. Operating wind turbines extract energy from the wind and at the same time produce wakes on the down-wind turbines in a park, causing reduced power production and increased vibrations, potentially contributing in a detrimental manner to fatigue life. This work presents a variational Auto-Encoder (VAE) Neural Network architecture capable of mapping the high dimensional correlated stochastic variables over the wind-farm, such as power production and wind speed, to a parametric probability distribution of much lower dimensionality. We demonstrate how a trained VAE can be used in order to quantify levels of statistical deviation on condition monitoring data. Moreover, we demonstrate how the VAE can be used for pre-training an inference model, capable of predicting the power production of the farm together with bounds on the uncertainty of the predictions. Examples employing simulated wind-farm Supervisory Control And Data Acquisition (SCADA) data are presented. The simulated farm data are acquired from a Dynamic Wake Meandering (DWM) simulation of a small wind farm comprised of nine 5MW turbines in close spacing using OpenFAST. The contribution of this work lies in the introduction of state-of-the-art machine learning techniques in the general context of condition monitoring and uncertainty quantification. We show how the high dimensional joint probability distribution of condition monitoring parameters can be analyzed by exploiting the underlying lower dimensional structure of the data imposed by the physics of the problem. The process of making use of the trained joint distribution for the purposes of inference under uncertainty and condition monitoring is clearly exposed.

关键词： Uncertainty quantification Deep learning variational autoencoder Windfarm SCADA DWM

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Collaborative Filtering Based on a variational Gaussian Mixture Model

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FUTURE INTERNET 2021年第2期13卷 37-37页

作者： Yang, FengLei Liu, Fei Liu, ShanShan Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For example, variational autoencoders (VAEs) have been extensively used in CF, and have achieved excellent results. Aiming at the problem of the prior distribution for the latent codes of VAEs in traditional CF is too simple, which makes the implicit variable representations of users and items too poor. This paper proposes a variational autoencoder that uses a Gaussian mixture model for latent factors distribution for CF, GVAE-CF. On this basis, an optimization function suitable for GVAE-CF is proposed. In our experimental evaluation, we show that the recommendation performance of GVAE-CF outperforms the previously proposed VAE-based models on several popular benchmark datasets in terms of recall and normalized discounted cumulative gain (NDCG), thus proving the effectiveness of the algorithm.

关键词： recommendation system variational autoencoder collaborative filtering Gaussian mixture model

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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation

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JOURNAL OF CHEMINFORMATICS 2019年第1期11卷 70-70页

作者： Kwon, Youngchun Yoo, Jiho Choi, Youn-Suk Son, Won-Joon Lee, Dongseon Kang, Seokho Samsung Elect Co Ltd Samsung Adv Inst Technol 130 Samsung Ro Suwon South Korea Seoul Natl Univ Dept Comp Sci & Engn 1 Gwanak Ro Seoul South Korea Sungkyunkwan Univ Dept Syst Management Engn 2066 Seobu Ro Suwon South Korea

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions.

关键词： Molecular graph variational autoencoder Graph neural network Deep learning

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Generative chemistry: drug discovery with deep learning generative models

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JOURNAL OF MOLECULAR MODELING 2021年第3期27卷 71-71页

作者： Bian, Yuemin Xie, Xiang-Qun Univ Pittsburgh Dept Pharmaceut Sci Sch Pharm Pittsburgh PA 15261 USA Univ Pittsburgh Computat Chem Genom Screening Ctr Sch Pharm Pittsburgh PA 15261 USA Univ Pittsburgh NIH Natl Ctr Excellence Computat Drug Abuse Res Pittsburgh PA 15261 USA Univ Pittsburgh Drug Discovery Inst 335 Sutherland Dr206 Salk Pavilion Pittsburgh PA 15261 USA Univ Pittsburgh Sch Med Dept Computat Biol Pittsburgh PA 15261 USA Univ Pittsburgh Sch Med Dept Struct Biol Pittsburgh PA 15261 USA

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

关键词： Drug discovery Deep learning Generative model Recurrent neural network variational autoencoder Adversarial autoencoder Generative adversarial network

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Multimodal anomaly detection in complex environments using video and audio fusion

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Scientific Reports 2025年第1期15卷 1-22页

作者： Wang, Yuanyuan Zhao, Yijie Huo, Yanhua Lu, Yiping Library Hebei North University Hebei Zhangjiakou 075000 China Educational Technology and Information Center Hebei North University Hebei Zhangjiakou 075000 China

Due to complex environmental conditions and varying noise levels, traditional models are limited in their effectiveness for detecting anomalies in video sequences. Aiming at the challenges of accuracy, robustness, and real-time processing requirements in the field of image and video processing, this study proposes an anomaly detection and recognition algorithm for video image data based on deep learning. The algorithm combines the innovative methods of spatio-temporal feature extraction and noise suppression, and aims to improve the processing performance, especially in complex environments, by introducing an improved Variable Auto Encoder (VAE) structure. The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. This approach improves the accuracy of anomaly detection. Multi-stream network architecture and cross-attention fusion mechanism are also adopted to comprehensively consider different factors such as color, texture, and motion, and further improve the robustness and generalization ability of the model. The experimental results show that compared with the existing models, the new model has obvious advantages in performance stability and real-time processing under different noise levels. Specifically, the AUC value of the proposed model is 0.95 on UCSD Ped2 dataset, which is about 10% higher than other models, and the AUC value on Avenue dataset is 0.93, which is about 12% higher. This study not only proposes an effective image and video processing scheme but also demonstrates wide practical potential, providing a new perspective and methodological basis for future research and application in related fields. © The Author(s) 2025.

关键词： Deep learning Image processing Spatiotemporal feature extraction variational autoencoder Video processing

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VKAD: A novel fault detection and isolation model for uncertainty-aware industrial processes

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Neural networks : the official journal of the International Neural Network Society 2025年 189卷 107664页

作者： Shengbin Zheng Dechang Pi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. Electronic address: binnuaacs@***. College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. Electronic address: pinuaa@***.

Fault detection and isolation (FDI) are essential for effective monitoring of industrial processes. Modern industrial processes involve dynamic systems characterized by complex, high-dimensional nonlinearities, posing significant challenges for accurate modeling and analysis. Recent studies have employed deep learning methods to capture and model these complexities in dynamic systems. In contrast, Koopman operator theory offers an alternative perspective, as the Koopman operator describes the linear evolution of observables in nonlinear systems within a high-dimensional space. This linearization simplifies complex nonlinear dynamics, making them easier to analyze and interpret in higher-dimensional settings. However, the Koopman operator theory does not inherently incorporate uncertainties in dynamical systems, which can hinder its performance in process monitoring. To tackle this issue, we integrate Koopman operator theory with variational autoencoders to propose a novel fault detection and isolation model called the variational Koopman Anomaly Detector (VKAD). VKAD is capable of inferring the distribution of observables from time series data of dynamical systems. By advancing the distribution through the Koopman operator over time, VKAD can capture the uncertainty in the evolution of dynamic systems. The uncertainty estimates yielded by VKAD are applicable for both fault detection and isolation in industrial processes. The effectiveness of the proposed VKAD were illustrated using the Tennessee Eastman Process (TEP) and a real satellite on-orbit telemetry dataset (SAT). The experimental results demonstrate that the Fault Detection Rate (FDR) of VKAD achieves superior performance on both the TEP and SAT datasets compared to advanced methods, while the Fault Alarm Rate (FAR) is also highly competitive.

关键词： Fault detection Fault isolation Industrial process Koopman operator theory variational autoencoder

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Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation

Training Reject-Classifiers for Out-of-distribution Detectio...

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作者： Vernekar, Sachin University of Waterloo

学位级别：硕士

Discriminatively trained neural classifiers can be trusted only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distribution (OOD) samples is very important to avoid classification errors. In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called "confident-classifier" by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KL divergence between the predictive distribution of OOD samples in the low-density "boundary" of in-distribution and the uniform distribution (maximizing the entropy of the outputs). Thus, the samples could be detected as OOD if they have low confidence or high entropy. In this work, we analyze this setting both theoretically and experimentally. We also propose a novel algorithm to generate the "boundary" OOD samples to train a classifier with an explicit "reject" class for OOD samples. We show that this approach is effective in reducing high-confident miss-predictions on OOD samples while maintaining the test-error and high-confidence on the in-distribution samples compared to standard training. We compare our approach against several recent classifier-based OOD detectors including the confident-classifiers on MNIST and FashionMNIST datasets. Overall the proposed approach consistently performs better than others across most of the experiments.

关键词： Out-of-distribution detection Reject-Classifier variational autoencoder Manifold

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Distributed unsupervised meta-learning algorithm over multi-agent systems

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Digital Communications and Networks 2024年

作者： Zhenzhen Wang Bing He Zixin Jiang Xianyang Zhang Haidi Dong Di Ye Department of Electronic Engineering PLA Rocket Force University of Engineering Xi'an 710025 China College of Weaponry Engineering PLA Naval University of Engineering Wuhan 430033 China

Multi-Agent Systems (MAS), which consist of multiple interacting agents, are crucial in Cyber-Physical Systems (CPS), because they improve system adaptability, efficiency, and robustness through parallel processing and collaboration. However, most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents. Meta-GMVAE, based on variational autoencoder (VAE) and set-level variational inference, represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks, increasing adaptability and reducing sample requirements. Inspired by these advancements, we propose a novel Distributed Unsupervised Meta-Learning (DUML) framework based on Meta-GMVAE and a fusion strategy. Furthermore, we present a DUML algorithm based on Gaussian Mixture Model (DUMLGMM), where the parameters of the Gaussian-mixture is solved by an Expectation-Maximization algorithm. Simulations on Omniglot and MiniImageNet datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.

关键词： Unsupervised meta-learning Multi-agent systems variational autoencoder Gaussian mixture model

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Integrating multi-tube flow cytometry data via deep generative modelling

Integrating multi-tube flow cytometry data via deep generati...

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作者： Hiltunen, Saara Aalto University

学位级别：硕士

Flow cytometry has been used for several decades to quantitatively analyse single cells in a high-throughput manner. This has resulted in a wide range of medical and biological applications. For example in immunology, flow cytometry data analysis identifies populations of immune cells based on cellular marker expression. As a consequence, flow cytometry has established itself as one of the main instruments in the diagnosis, monitoring and classification of leukemias, human immunodeficiency virus (HIV), and other diseases. State-of-the-art flow cytometers allow the detection of more than 20 cellular parameters, but the instruments used in clinical practice usually have much more limited capabilities. Thus, flow cytometry samples are often split into separate tubes with varying marker combinations to increase the number of measurable markers. However, this poses challenges to the analysis of flow cytometry data because the data from multiple tubes must be integrated while preserving the original biological information. Currently, most of the computational analysis techniques are not able to handle this kind of multi-tube flow cytometry data. In this work, we develop a deep generative modelling framework to enable simultaneous integration, clustering, and visualization of such data. We show that the model, named fcmVI, successfully discovers a latent representation of the cell types from flow cytometry data. Furthermore, we show that the fcmVI model can be used to align multiple tubes originating from the same sample in the latent space. The model is applied to two different data sets from mouse immune cells and human acute myeloid leukemia (AML) samples. In addition, the model enables the imputation of missing marker values for each tube, which is demonstrated on both data sets and the results are compared to nearest neighbor imputation.

关键词： flow cytometry neural networks generative modelling variational autoencoder adversarial learning

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An Interpretable and Domain-Informed Real-Time Hybrid Earthquake Early Warning for Ground Shaking Intensity Prediction

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Engineering 2025年

作者： Jawad Fayaz Rodrigo Astroza Sergio Ruiz Department of Computer Science University of Exeter Exeter EX4 4RN UK Department of Civil Environmental and Geomatic Engineering University College London (UCL) London WC1E 6BT UK Faculty of Engineering and Applied Sciences University of the Andes Santiago 7620001 Chile Department of Geophysics University of Chile Santiago 8330111 Chile

In the face of the unrelenting challenge posed by earthquakes—a natural hazard of unpredictable nature with a legacy of significant loss of life, destruction of infrastructure, and profound economic and social impacts—the scientific community has pursued advancements in earthquake early warning systems (EEWS). These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure. This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra (HEWFERS), which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time, aligning with the United Nations’ disaster risk reduction goals. HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable (LV) extraction, a feed-forward neural network for on-site prediction, and Gaussian process regression for spatial prediction. Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms, ensuring stakeholder-informed decisions. By conducting an extensive analysis of the proposed framework under a large database of approximately 14 000 recorded ground motions, this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response, thus paving the way for a safer and more resilient future.

关键词： Domain-informed neural networks Physics-informed neural networks Earthquake early warning variational autoencoder Bayesian updating Spatial regression Interpretable artificial intelligence

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