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5th IFAC Workshop on Mining, Mineral and Metal Processing (MMM)

作者： McCoy, John T. Kroon, Steve Auret, Lidia Stellenbosch Univ Dept Proc Engn Private Bag X1 ZA-7602 Matieland South Africa Stellenbosch Univ Div Comp Sci Private Bag X1 ZA-7602 Matieland South Africa

Missing data values and differing sampling rates, particularly for important parameters such as particle size and stream composition, are a common problem in minerals processing plants. Missing data imputation is used to avoid information loss (due to downsampling or discarding incomplete records). A recent deep -learning technique, variational autoencoders (VAEs), has been used for missing data imputation in image data, and was compared here to imputation by mean replacement and by principal component analysis (PCA) imputation. The techniques were compared using a synthetic, nonlinear dataset, and a simulated milling circuit dataset, which included process disturbances, measurement noise, and feedback control. Each dataset was corrupted with missing values in 20% of records (lightly corrupted) and in 90% of records (heavily corrupted). For both lightly and heavily corrupted datasets, the root mean squared error of prediction for VAE imputation was lower than the traditional methods. Possibilities for the extension of missing data imputation to inferential sensing are discussed. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Missing Data Imputation Machine Learning variational autoencoder

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Robustness Analysis of Deep Learning Models for Population Synthesis

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Transportation Research Procedia 2025年 82卷 3790-3806页

作者： Daniel Opoku Mensah Godwin Badu-Marfo Bilal Farooq Laboratory of Innovations in Transportation (LiTrans) Toronto Metropolitan University Toronto Canada

Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been proposed, but their performance is only tested on a single cross-sectional sample. The implementation of population synthesis on single datasets is seen as a drawback that needs further studies to explore the robustness of the models on multiple datasets. While comparing with the real data can increase trust and interpretability of the models, techniques to evaluate deep generative models’ robustness for population synthesis remain underexplored. In this study, we present bootstrap confidence interval for the deep generative models, an approach that computes efficient confidence intervals for mean errors predictions to evaluate the robustness of the models to multiple datasets. Specifically, we adopt the tabular-based Composite Travel Generative Adversarial Network (CTGAN) and variational autoencoder (VAE), to estimate the distribution of the population, by generating agents that have tabular data using several samples over time from the same study area. The models are implemented on multiple travel diaries of Montréal Origin-Destination Survey of 2008, 2013, and 2018 and compare the predictive performance under varying sample sizes from multiple surveys. Results show that the predictive errors of CTGAN have narrower confidence intervals indicating its robustness to multiple datasets of the varying sample sizes when compared to VAE. Again, the evaluation of model robustness against varying sample size shows a minimal decrease in model performance with decrease in sample size. This study directly supports agent-based modelling by enabling finer synthetic generation of populations in a reliable environment.

关键词： Generative Adversarial Networks variational autoencoder population synthesis robustness

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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment

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RESULTS IN ENGINEERING 2025年 25卷

作者： Vijai, Praveen Sivakumar, P. Bagavathi Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Coimbatore India

Anomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. While traditional anomaly detection approaches face limitations such as scalability issues, high false alarm rates, and reliance on skilled expertise, this study proposes novel approach using a BiLSTM-variational autoencoder (BiLSTM-VAE) model with a dynamic loss function. The proposed model addresses key challenges, including data imbalance, interpretability issues, and computational complexity. By leveraging the bidirectional capability of BiLSTM in the encoder and decoder, the model captures comprehensive temporal dependencies, enabling more effective anomaly detection. The innovative dynamic loss function integrates a tempering index mechanism with tuneable parameters (alpha and gamma), which assigns higher weights to underrepresented classes and down-weights easily classified samples. This improves reconstruction and enhances detection accuracy, particularly for minority class anomalies. Experimental evaluations on the SKAB and TEP datasets demonstrate the superiority of the proposed framework. The model achieved an accuracy of 98% and an F1 score of 96% for binary classification on the SKAB dataset and a multiclass classification accuracy of 92% with an F1 score of 85% on the TEP dataset. These results significantly outperform state-ofthe-art models, including traditional VAE, LSTM, and transformer-based approaches. The proposed BiLSTM-VAE model not only demonstrates robust anomaly detection capabilities across diverse datasets but also effectively handles data imbalance and reduces false positives, making it a scalable and reliable solution for industrial anomaly detection in the context of Industry 4.0 and IoT environments.

关键词： Anomaly detection Deep learning variational autoencoder Bidirectional long short term memory model Dynamic loss function

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Advanced temporal deep learning framework for enhanced predictive modeling in industrial treatment systems

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RESULTS IN ENGINEERING 2025年 25卷

作者： Ramya, S. Srinath, S. Tuppad, Pushpa JSS Sci & Technol Univ Dept Comp Sci & Engn Mysuru India JSS Sci & Technol Univ Dept Environm Engn Mysuru India

This research introduces an innovative hybrid modeling framework tailored for interval prediction, aimed at forecasting water quality parameters in industrial sewage treatment plants (STPs). It tackles key challenges in the field, including limited data availability, detecting anomalies, and selecting relevant features with precision. By addressing these critical gaps, the study advances predictive analytics for wastewater treatment systems. The main goal was to create a scalable and resilient model that consistently provides accurate forecasts for essential water quality indicators. To accomplish this, variational autoencoders (VAEs) were employed to generate synthetic datasets that mimic real-world patterns, improving data availability and enhancing the model's generalization capabilities. autoencoder paired with a Self-Organizing Map (SOM) was leveraged for anomaly detection and efficient feature selection. The study evaluated advanced architectures, including a Temporal Convolutional Network (TCN), TCN integrated with bidirectional Long Short-Term Memory (BiLSTM), and refined TCN_BiLSTM models featuring preand post-soft attention layers. The final model incorporated Multi- Head Attention mechanisms in both preand post-processing stages (TCN_BiLSTM_MultiHead_Attention), delivering a substantial performance improvement. The TCN_BiLSTM_MultiHead_Attention model proved to be the top performer, delivering state-of-the-art results with R2 scores of 0.9732, 0.9567, and 0.9638 for the training, validation, and test datasets, respectively. On the test set, it achieved an impressive MSE of 0.0008 and an MAE of 0.0198. These results underscore the model's exceptional accuracy in predicting key parameters, including BOD, COD, and AmmoniaNitrogen. The results highlight the significant potential of hybrid deep learning frameworks in capturing temporal patterns and complex dynamics within STP data. By integrating temporal pattern recognition, long-term dependency modeling, and

关键词： Treatment plant variational autoencoder Self-organizing map Temporal convolutional network Bidirectional long short-term memory Gated recurrent units Attention mechanism

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Achieving deep clustering through the use of variational autoencoders and similarity-based loss

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第10期19卷 10344-10360页

作者： Ma, He Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150000 Peoples R China

Clustering is an important and challenging research topic in many fields. Although various clustering algorithms have been developed in the past, traditional shallow clustering algorithms cannot mine the underlying structural information of the data. Recent advances have shown that deep clustering can achieve excellent performance on clustering tasks. In this work, a novel variational autoencoder-based deep clustering algorithm is proposed. It treats the Gaussian mixture model as the prior latent space and uses an additional classifier to distinguish different clusters in the latent space accurately. A similarity-based loss function is proposed consisting specifically of the cross-entropy of the predicted transition probabilities of clusters and the Wasserstein distance of the predicted posterior distributions. The new loss encourages the model to learn meaningful cluster-oriented representations to facilitate clustering tasks. The experimental results show that our method consistently achieves competitive results on various data sets.

关键词： clustering deep learning data representation variational autoencoder network architecture

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Semi-supervised soft sensor method for fermentation processes based on physical monotonicity and variational autoencoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期137卷

作者： Cheng, Xinyue Yu, Zhenhua Wang, Guan Jiang, Qingchao Cao, Zhixing East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol State Key Lab Bioreactor Engn Shanghai 200237 Peoples R China

Data-driven models have shown broad application prospects in soft sensor modeling. However, numerous challenges persist. On the one hand, data-driven soft sensor methods have high requirements on data quality. On the other hand, models relying on limited experimental data often lack physical interpretability. To tackle these challenges, a semi-supervised soft sensor method (PMVAER) for fermentation processes based on physical monotonicity and variational autoencoders (VAEs) is introduced. First, physical monotonicity constraint is incorporated into the loss function of VAEs for regression to ensure that the model's predictions adhere to physical feasibility. Next, considering the disparate sampling frequencies for process and quality variables, this approach is extended to learn from unlabeled data, creating a semi-supervised soft sensor model. The proposed model is validated on simulation and real cases of penicillin fermentation. Comparisons with five other methods verify that the proposed method exhibits exceptional predictive accuracy along with enhanced generalization ability.

关键词： Soft sensor variational autoencoder Fermentation process Semi-supervised learning

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Toward a better monitoring statistic for profile monitoring via variational autoencoders

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JOURNAL OF QUALITY TECHNOLOGY 2021年第5期53卷 454-473页

作者： Sergin, Nurettin Dorukhan Yan, Hao Arizona State Univ Ind Engineer Program Tempe AZ USA Arizona State Univ Tempe AZ 85287 USA

variational autoencoders have been recently proposed for the problem of process monitoring. While these works show impressive results over classical methods, the proposed monitoring statistics often ignore the inconsistencies in learned lower-dimensional representations and computational limitations in high-dimensional approximations. In this work, we first manifest these issues and then overcome them with a novel statistic formulation that increases out-of-control detection accuracy without compromising computational efficiency. We demonstrate our results on a simulation study with explicit control over latent variations, and a real-life example of image profiles obtained from a hot steel rolling process.

关键词： Deep learning high-dimensional nonlinear profile latent variable model profile monitoring variational autoencoder

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Scalable variational quantum circuits for autoencoder-based drug discovery 22

Scalable variational quantum circuits for autoencoder-based ...

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Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe

作者： Junde Li Swaroop Ghosh The Pennsylvania State University

ISBN: (纸本)9783981926361

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. variational autoencoder is one of the computer-aided design methods which explores the chemical space based on an existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited number of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corresponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period.

关键词： drug discovery quantum machine learning variational autoencoder

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Securing VANETs: Multi-Objective Intrusion Detection With variational autoencoders

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3867-3874页

作者： Nissar, Nabil Naja, Najib Jamali, Abdellah Inst Natl Postes Telecommun RAISS Lab Rabat 10000 Morocco Inst Natl Postes Telecommun Rabat 10000 Morocco Hassan I Univ Settat 26002 Morocco

The rising prevalence of connected vehicles in Vehicular Ad-hoc Networks (VANETs) within Intelligent Transportation Systems (ITS) has introduced a heightened susceptibility to various cyber threats, particularly zero-day attacks. The magnitude of intercommunicating vehicles compounds the difficulty of identifying dynamic and spatially-distant anomalies that elude static rules. Existing solutions, often rule-bound and inflexible, face challenges in dealing with novel threats. Hence, the diverse and evolving nature of potential threats underscores the necessity for more adaptive and robust detection frameworks, especially when considering the end consumer's safety and privacy *** paper introduces an innovative anomaly detection framework designed for VANETs. It employs a variational autoencoder (VAE) and optimizes multiple objectives: divergence, KL-divergence, and reconstruction error, using the AGE-MOEA and R-NSGA-III algorithms. Deployable within Roadside Units, it captures and analyzes broadcast vehicular data sequences, classifying messages as anomalous or normal to address not only the technological intricacies but also the paramount concern of consumer safety within the vehicular ecosystem. Experiments involve exploring various hyperparameters, with performance assessed using key metrics. The proposed framework undergoes comprehensive benchmarking against prior research, considering accuracy, precision, recall, and ROC-AUC. This underscores the efficacy of fully unsupervised learning and multi-objective optimization in enhancing VANETs security against emerging threats.

关键词： Consumer electronics VANETs security intrusion detection systems anomaly detection NSL-KDD artificial intelligence deep learning unsupervised learning variational autoencoder AGE-MOEA R-NSGA-III

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Multilevel Anomaly Detection Through variational autoencoders and Bayesian Models for Self-Aware Embodied Agents

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 1399-1414页

作者： Slavic, Giulia Baydoun, Mohamad Campo, Damian Marcenaro, Lucio Regazzoni, Carlo Univ Genoa DITEN Fac Engn I-16145 Genoa Italy Univ Genoa DITEN I-16145 Genoa Italy

Anomaly detection constitutes a fundamental step in developing self-aware autonomous agents capable of continuously learning from new situations, as it enables to distinguish novel experiences from already encountered ones. This paper combines Dynamic Bayesian Networks (DBNs) and Neural Networks (NNs) and proposes a method for detecting anomalies in video data at different abstraction levels. We use a variational autoencoder (VAE) to reduce the dimensionality of video frames, and Optical Flows between subsequent images, generating a latent space that captures both visual and dynamical information and that is comparable to low-dimensional sensory data (e.g., positioning, steering angle). An Adapted Markov Jump Particle Filter is employed to predict the following frames and detect anomalies in video data. Our method's evaluation is executed using different video data from a semi-autonomous vehicle performing different tasks in a closed environment. Tests on benchmark anomaly detection datasets have additionally been conducted.

关键词： Feature extraction Anomaly detection Predictive models Image reconstruction Self-aware Probabilistic logic Data models Anomaly detection kalman filtering particle filtering variational autoencoder

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