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Online nonlinear data reconciliation to enhance nonlinear dynamic process monitoring using conditional dynamic variational autoencoder networks with particle filters

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 253卷

作者： Chiu, Kuanhsuan Chen, Junghui Zhang, Zhengjiang Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China

In the chemical plants, data-driven process monitoring serves as a vital tool to ensure product quality and maintain production line safety. However, the accuracy of monitoring hinges directly upon the quality of process data. Given the inherently slow and complex nature of chemical processes, coupled with the potential for gross errors in process data leading to inaccuracies in model predictions, this paper proposes a method called Conditional Dynamic variational autoencoder combined with a Particle Filter (CDVAE-PF) for data reconciliation and subsequent process monitoring. CDVAE-PF leverages the capabilities of Conditional Dynamic variational autoencoder (CDVAE) to effectively model chemical process data in the presence of noise. This probabilistic model serves as the foundation for the Particle Filter (PF), which is employed for data reconciliation. Moreover, CDVAE-PF incorporates mechanisms to detect and rectify gross errors in process data, further enhancing its efficacy in data reconciliation. Subsequently, monitoring indices based on CDVAE are established to facilitate process monitoring. Through numerical simulations of a two-to-one variables Continuous Stirred Tank Reactor (CSTR) example and a fifteen-to-one variables dichloroethane distillation process from an actual chemical plant, CDVAE-PF demonstrates its effectiveness by reducing mean absolute error to 7.8 % and 12.8 % respectively in gross error data reconciliation. Moreover, in terms of monitoring performance, CDVAE-PF successfully mitigates misjudgments caused by gross errors, thereby significantly enhancing the reliability of process monitoring in chemical plants.

关键词： Data reconciliation variational autoencoder Particle filter Process monitoring

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Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised variational autoencoder Model

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ENERGIES 2023年第12期16卷 4544页

作者： Oliveira-Filho, Adaiton Zemouri, Ryad Cambron, Philippe Tahan, Antoine Ecole Technol Super Dept Mech Engn 1100 Rue Notre Dame Montreal PQ H3C 1K3 Canada Inst Rech Hydroquebec 1800 Bd Lionel Boulet Varennes PQ J3X 1S1 Canada Power Factors 7005 Blvd Taschereau Brossard PQ J4Z 1A7 Canada

The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretability limits their effective industrial implementation. The present work introduces a new condition-monitoring approach for wind turbines featuring a built-in visualization tool that confers interpretability upon the model outcomes. The proposed approach is based on a supervised implementation of the variational autoencoder model, which allows the projection of the wind turbine system onto a low-dimensional representation space. Three outcomes follow from such representation: a health indicator for the early detection of abnormal conditions, a classifier providing the diagnosis status, and a visualization tool depicting the wind turbine condition as a trajectory in a 2D plot. The approach is implemented with a vast database. Two case studies demonstrate the potential of the proposed approach. The proposed health indicator detects the main bearing overtemperature 11 days before the control system alarm, one week earlier than a competing approach. Study cases illustrate that the built-in visualization tool enhances the interpretability and trust in the model outcomes, thus supporting wind turbine operation and maintenance.

关键词： wind turbine condition monitoring variational autoencoder SCADA data early detection diagnosis model interpretability

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An interpretable image classification model Combining a fuzzy neural network with a variational autoencoder inspired by the human brain

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INFORMATION SCIENCES 2024年 661卷

作者： Zhang, Ke Hao, Wenning Yu, Xiaohan Shao, Tianhao Natl Univ Def Technol Res Inst 63 Changsha Peoples R China Army Engn Univ PLA Coll Command & Control Engn Nanjing Peoples R China

Fuzzy neural networks (FNNs) have gained attention for their interpretability and self -learning ability. However, they struggle with interpreting high -dimensional unstructured data and the problem of "rule explosion". To address this, a model called VAE-FNN is proposed, which combines a FNN with a variational autoencoder (VAE). The VAE-FNN simulates the image perception, feature extraction, inductive reasoning, and adjustment learning processes in the human brain. An encoder is used to simulate the visual cortex for extracting features from complex images, reducing the dimensionality, and mitigating the rule explosion problem. The fuzzy neural network classifier (FNNC) simulates the reasoning functions of the parietal and prefrontal cortex in the human brain and achieves interpretable classification based on the encoder's output features. A training algorithm is designed to improve the stability of the FNNC. The VAE-FNN's training method adjusts the feature extraction process based on reconstruction and classification effects, enabling the model to obtain advanced and semantic classification features. Detailed experimental results on two image datasets demonstrate that the proposed model can extract high-level classification features and provide explanations consistent with human intuition while achieving high -precision classification. The experimental results on the other two datasets further validate the effectiveness of the proposed model.

关键词： Fuzzy neural network Image classification Interpretability variational autoencoder

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Towards automatic feature extraction and sample generation of grain structure by variational autoencoder

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COMPUTATIONAL MATERIALS SCIENCE 2024年 232卷

作者： Ji, Ye Koeppe, Arnd Altschuh, Patrick Rajagopal, Deepalaxmi Zhao, Yinghan Chen, Weijin Zhang, Yi Zheng, Yue Nestler, Britta Sun Yat sen Univ Ctr Phys Mech & Biophys Sch Phys Xingang West Rd 135 Guangzhou 510275 Guangdong Peoples R China Sch Phys Guangdong Prov Key Lab Magnetoelectr Phys & Device Xingang West Rd 135 Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sch Phys & Engn State Key Lab Optoelect Mat & Technol Xingang West Rd 135 Guangzhou 510275 Guangdong Peoples R China Karlsruhe Inst Technol Kaiserstrasse 12 D-76131 Karlsruhe Germany Karlsruhe Inst Technol Inst Appl Mat IAM MMS Kaiserstr 12 D-76131 Karlsruhe Baden Wurttembe Germany Karlsruhe Univ Appl Sci Inst Digital Mat Sci Moltkestr 30 D-76133 Karlsruhe Baden Wurttembe Germany

Granularity is one of the most predominant structures for polycrystalline materials, from geological compounds to technical high-performance alloys. In particular, grain structures in metal alloys enable custom-tailored material properties if their formation can be modeled and simulated accurately to analyze, characterize, and generate granular materials. Despite significant advances in the field, established physics-based grain-growth models still need to improve to match real-life experimental data accurately. Instead of relying on physical knowledge, data-driven models provide a complementary way to study grain structures using only the information in the data. However, many machine learning methods require supervised learning on information that is challenging to measure or numerically expensive to compute. In this work, we propose a new strategy that uses unsupervised deep learning on synthetic physics-based data to generate realistic granular structures with customizable features and properties (grain size/number). A variational autoencoder learns to compress grain structures into a low-dimensional latent space, derive data-driven features that characterize grain structures, and use these features to generate new structures with representative grain size distributions in a computationally efficient way. These data-driven features can complement the physics-based features derived from classical materials research and help identify new characterizing properties. Thus, this work represents a prototype application of bridging machine learning methods to material characterization and generation.

关键词： Grain structure Machine learning variational autoencoder

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Innovative IoT Threat Detection: Weighted variational autoencoder-Based Hunter Prey Search Algorithm for Strengthening Cybersecurity

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IETE JOURNAL OF RESEARCH 2024年第10期70卷 7687-7700页

作者： Alshmrany, Sami Islamic Univ Madinah Fac Comp & Informat Syst Al Madinah Al Munawarah 42351 Saudi Arabia

Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted variational autoencoder-based Hunter Prey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted variational autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The Hunter Prey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment.

关键词： Cybersecurity Hunter prey optimization algorithm Internet of Things Threat detection variational autoencoder Weight average ensemble

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Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

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MEDICAL IMAGE ANALYSIS 2023年 90卷 102961页

作者： Park, Hyojoon Li, Bin Liu, Yuming Nelson, Michael S. Wilson, Helen M. Sifakis, Eftychios Eliceiri, Kevin W. Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Madison Dept Biomed Engn Madison WI 53706 USA Univ Wisconsin Madison Lab Opt & Computat Instrumentat Madison WI 53706 USA Morgridge Inst Res Madison WI 53706 USA

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://***/uw-loci/collagen-fiber-metrics.

关键词： Digital pathology variational autoencoder Collagen fiber Deep learning Generative model

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Development of a diagnostic algorithm for abnormal situations using long short-term memory and variational autoencoder

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ANNALS OF NUCLEAR ENERGY 2021年 153卷 108077-108077页

作者： Kim, Hyojin Arigi, Awwal Mohammed Kim, Jonghyun Chosun Univ Dept Nucl Engn 309 Pilmun Daero Gwangju 501709 South Korea

It is recognized that an abnormal situation diagnosis is a challenging task for nuclear power plant (NPP) operators because of the excessive information and high workload in such situations. To help operators, several studies have proposed operator support systems using artificial intelligence techniques. However, those methods could neither assess anonymous cases as an unknown situation nor confirm whether its outputs are reliable or not. In this study, an algorithm that can confirm the diagnosis results and determine unknown situations using long short-term memory (LSTM) and variational autoencoder (VAE) is proposed. LSTM was adopted as the primary network for diagnosing abnormal situations. Meanwhile, VAE-based assistance networks were added to the algorithm to ensure that the credibility of the diagnosis is estimated via the anomaly score-based negative log-likelihood. The algorithm was tested and implemented using the compact nuclear simulator for the Westinghouse 900 MWe NPP. Furthermore, the simulation also considered noise-added data. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Event diagnosis algorithm Abnormal situation Long short-term memory variational autoencoder

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A Hybrid Cluster variational autoencoder Model for Monitoring the Multimode Blast Furnace System

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PROCESSES 2023年第9期11卷 2580页

作者： Chen, Chenyu Cai, Jinhui China Jiliang Univ Coll Metrol & Measurement Engn Hangzhou 310018 Peoples R China

Efficient monitoring of the blast furnace system is crucial for maintaining high production efficiency and ensuring product quality. This article introduces a hybrid cluster variational autoencoder model for monitoring the blast furnace ironmaking process which exhibits multimode behaviors. In contrast to traditional approaches, this method utilizes neural networks to learn data features and effectively handles the diverse feature types observed in different production modes. Through the utilization of a clustering process within the hidden layer of the variational autoencoder, the proposed technique facilitates efficient fault detection in the context of multimodal blast furnace data. Based on the variational autoencoder model, this study further establishes a unified monitoring index and defines a method for computing the control limits. The application of the model to real blast furnace data reveals its proficiency in accurately identifying faults across diverse modes;compared with the probabilistic principal component analysis based on the local nearest neighbor standardization method and the recursive probabilistic principal component analysis, the model shows a reduction in false positives by up to 10.3% and a substantial reduction of 19.2% in the missed detection rate. This method achieves a remarkable false detection rate of only 0.2% and 0 instances of missed detection.

关键词： multimode blast furnace system process monitoring variational autoencoder Gaussian mixture model

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Attention-Based variational autoencoder Models for Human-Human Interaction Recognition via Generation

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SENSORS 2024年第12期24卷 3922页

作者： Banerjee, Bonny Baruah, Murchana Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

The remarkable human ability to predict others' intent during physical interactions develops at a very early age and is crucial for development. Intent prediction, defined as the simultaneous recognition and generation of human-human interactions, has many applications such as in assistive robotics, human-robot interaction, video and robotic surveillance, and autonomous driving. However, models for solving the problem are scarce. This paper proposes two attention-based agent models to predict the intent of interacting 3D skeletons by sampling them via a sequence of glimpses. The novelty of these agent models is that they are inherently multimodal, consisting of perceptual and proprioceptive pathways. The action (attention) is driven by the agent's generation error, and not by reinforcement. At each sampling instant, the agent completes the partially observed skeletal motion and infers the interaction class. It learns where and what to sample by minimizing the generation and classification errors. Extensive evaluation of our models is carried out on benchmark datasets and in comparison to a state-of-the-art model for intent prediction, which reveals that classification and generation accuracies of one of the proposed models are comparable to those of the state of the art even though our model contains fewer trainable parameters. The insights gained from our model designs can inform the development of efficient agents, the future of artificial intelligence (AI).

关键词： embodied AI agent intent prediction human-human interaction recognition human-human interaction generation attention perception proprioception multimodal variational autoencoder recurrent neural network (RNN) long-short term memory (LSTM)

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Decoupled variational autoencoder with interactive attention for affective text generation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期123卷

作者： Chen, Ruijun Wang, Jin Yu, Liang-Chih Zhang, Xuejie Yunnan Univ Sch Informat Sci & Engn Kunming Peoples R China Yuan Ze Univ Dept Informat Management Taoyuan Taiwan

Artificial emotional intelligence (AEI) is an important ability for intelligent systems. Representing emotional states as a continuous sentiment intensity could achieve a more fine-grained sentiment application than traditional categorical approaches. By using sentiment intensity, the main challenge of the existing variational autoencoder methods lies in distinguishing emotional information from semantic information. Practically, it is difficult to ensure that the input training texts do not contain any sentiment. If the disentangled latent variable for text generation contains sentiment features that conflict with the assigned sentiment, the generated texts will be messy. Therefore, we propose a decoupled variational autoencoder (VAE) with interactive attention to solve this problem. The proposed method applies a sentiment decoupler to extract the sentiment from the text. Then, sentiment embeddings are applied to map the intensities into the latent space. To enhance the representation ability of sentiment embeddings and the performance of the decoder, we consider affective text generation as a process of denoising, design a noisy sampling strategy in training, and then continuously update the emotional information with variational attention through a dynamic update mechanism. Extensive experiments are conducted on the Yelp dataset and the Amazon dataset, and the experimental results show that our method outperforms other VAE-based methods in fine-grained affective text generation.

关键词： Affective text generation Sentiment embeddings Interactive attention variational autoencoder Denoising

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