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IEEE 21st International Conference on Industrial Informatics (INDIN)

作者： Yang, Chao Zhang, Congcong Wang, Junxiang Liu, Qiang Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9781665493130

With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults are quality-relevant. To address this issue, an orthogonal feature separation autoencoder (OFSAE) method is developed for quality-relevant fault monitoring. The proposed OFSAE mainly consists of the quality-relevant encoder network, quality-irrelevant encoder network, decoder network, and regression network. Through parallel learning and orthogonal projection for process variables, quality-relevant and quality-irrelevant variations can be isolated while maintaining good prediction performance. Finally, in comparison with conventional monitoring methods, the superiority of OFSAE is validated by the Tennessee Eastman process.

关键词： process monitoring autoencoder supervised autoencoder orthogonal projection information separation

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Capsule-based autoencoder Network for Pointcloud Reconstruction 21

Capsule-based Autoencoder Network for Pointcloud Reconstruct...

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IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Hollosi, Janos Ballagi, Aron Pozna, Claudiu Radu Szechenyi Istvan Univ Dept Informat Technol Gyor Hungary Szechenyi Istvan Univ Dept Automat Gyor Hungary

ISBN: (纸本)9798350319866

The theory of capsule networks and the dynamic routing mechanism for capsules was introduced by Geoffrey Hinton and his research team. In this new approach, they tried to solve typical problems of classical convolutional neural networks. For example, that the efficiency of neural networks degrades when a geometric transformation is applied on the input image, or when the data is far away from the training dataset. It became clear early on that capsule networks are state-of-the-art solutions for visual data classification tasks. For other tasks their use is less common and in many cases difficult to apply. For example image segmentation or object detection and localization. The efficiency of the capsule networks theory in the field of pointcloud processing is also an open question. In this work we investigated the pointcloud reconstruction capability of capsule networks. In this approach, three different complexity autoencoder networks was selected. We created a decoder network based on capsules theory, which was fitted to the existing autoencoder networks. The efficiency of the networks was tested using four different datasets. As a result of our work, we show the effectiveness of capsule networks in the field of pointcloud reconstruction compared with the selected autoencoder networks.

关键词： capsule network pointcloud autoencoder reconstruction

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A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery

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NEURAL COMPUTING & APPLICATIONS 2022年第18期34卷 15997-16017页

作者： Xu, Zhaoyi Guo, Yanjie Saleh, Joseph Homer Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

Lithium-ion batteries (LIBs) are currently the standard for energy storage in portable consumer electronic devices. They are also used in electric vehicles and in some large industrial settings and for grid power storage. The adverse consequences of a dramatic battery failure can be significant compared with the cost of timely replacement or maintenance. Consequently, accurate state-of-health (SOH) prediction is important to inform maintenance or replacement decisions. In this work, we address current challenges related to accuracy and interpretability in data-driven SOH prediction for LIBs by devising a novel physics-informed machine learning prognostic model, termed PIDDA. PIDDA includes three elements: an autoencoder, a physics-informed model training, and a physics-based prediction adjustment. We examine and benchmark our model against alternative data-driven SOH prediction models using the NASA battery prognostic dataset. The computational experiments demonstrate that PIDDA (1) provides significantly higher prediction accuracy;(2) requires less prior data for its predictions;(3) produces more informative and interpretable predictions than alternative models. We conclude with an ablation study of PIDDA to analyze the relative effectiveness of two of its elements, the physics equations in the model training and the physics-based prediction adjustment. The results show that the former (training) provides the heavy lifting in accuracy improvement, roughly two-thirds, and the latter (adjustment) the remaining incremental improvement.

关键词： Lithium-ion battery State-of-health Deep learning Prognostic and health management Physics-informed machine learning autoencoder

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Griffin: Real-Time Network Intrusion Detection System via Ensemble of autoencoder in SDN

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第3期19卷 2269-2281页

作者： Yang, Liyan Song, Yubo Gao, Shang Hu, Aiqun Xiao, Bin Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211189 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Many efforts have been devoted to the development of efficient Network Intrusion Detection System (NIDS) using machine learning approaches in Software-defined Network (SDN). Unfortunately, existing solutions failed to detect real-time and zero-day attacks due to their limited throughput and prior knowledge-based detection. To this end, we propose Griffin, a NIDS that uses unsupervised machine learning expertise to detect both known and zero-day intrusion attacks in real-time with high accuracy. Specifically, Griffin uses an efficient feature extraction framework to capture the sequential features of the traffic packets. Then, it utilizes cluster analysis to reduce the feature scale to achieve low throughput. Moreover, an ensemble autoencoder is built automatically to further extract features with low complexity and high precision to train the model. We evaluate the accuracy, robustness, and complexity of the system using open datasets. The result shows that Griffin's complexity is about 40% lower, and its accuracy is at most 19% higher than existing ***, even in the situation with evasion, the Griffin has at most 9% decrease of AUC, which is a good performance compared with other solutions. Furthermore, this paper also utilizes the differential privacy framework during training autoencoders to protect datasets' privacy which is inherent in machine learning approaches.

关键词： Feature extraction Real-time systems Privacy Differential privacy Training Network intrusion detection Complexity theory Software-defined network (SDN) network intrusion detection system (NIDS) autoencoder ensemble learning differential privacy

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Human Motion Enhancement via Tobit Kalman Filter-Assisted autoencoder

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IEEE ACCESS 2022年 10卷 29233-29251页

作者： Lannan, Nate Zhou, Le Fan, Guoliang Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

We present a novel approach to enhance the quality of human motion data collected by low-cost depth sensors, namely D-Mocap, which suffers from low accuracy and poor stability due to occlusion, interference, and algorithmic limitations. Our approach takes advantage of a large set of high-quality and diverse Mocap data by learning a general motion manifold via the convolutional autoencoder. In addition, the Tobit Kalman filter (TKF) is used to capture the kinematics of each body joint and handle censored measurement distribution. The TKF is incorporated with the autoencoder via latent space optimization, maintaining adherence to the motion manifold while preserving the kinematic nature of the original motion data. Furthermore, due to the lack of an open source benchmark dataset for this research, we have developed an extension of the Berkeley Multimodal Human Action Database (MHAD) by generating D-Mocap data from RGB-D images. The newly extended MHAD dataset is skeleton-matched and time-synced to the corresponding Mocap data and is publicly available. Along with simulated D-Mocap data generated from the CMU Mocap dataset and our self-collected D-Mocap dataset, the proposed algorithm is thoroughly evaluated and compared with different settings. Experimental results show that our approach can improve the accuracy of joint positions and angles as well as skeletal bone lengths by over 50%.

关键词： Manifolds Kinematics Filtering Kalman filters Bones Optimization Deep learning autoencoder human motion manifold depth sensors motion capture Tobit Kalman filter

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Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients

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ISA TRANSACTIONS 2022年 125卷 371-383页

作者： Peng, Peng Zhang, Yi Wang, Hongwei Zhang, Heming Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Zhejiang Univ ZJU UIUC Inst Haining 314400 Peoples R China

Industrial applications of fault detection and diagnosis face great challenges as they require not only accurate identification of faulty statuses but also the effective understandability of the results. In this paper, a two-step robust and understandable fault detection and diagnosis framework is developed to address this challenge by exploiting denoising sparse autoencoder and smooth integrated gradients. Specifically, denoising sparse autoencoder(DSAE) is utilized to detect faults in the first step. DSAE is more robust to noise corruption and has better generalization performance compared to the existing autoencoder-based methods. In the second step, smooth integrated gradients(SIG) is used to diagnose the root-cause variables of the faults detected. Smooth integrated gradients can provide a denoising effect on the feature importance. The proposed framework is evaluated through an application to the Tennessee Eastman process. As proved in the experiments, the presented DSAE-SIG method not only achieves higher diagnosis accuracy but also successfully identifies the potential root-cause variables of process disturbances. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fault detection and diagnosis Deep learning Explainable artificial intelligence Root cause diagnosis autoencoder

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autoencoder-Driven Training Data Selection Based on Hidden Features for Improved Accuracy of ANN Short-Term Load Forecasting in ADMS

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ENERGIES 2024年第20期17卷 5183页

作者： Pajic, Zoran Jankovic, Zoran Selakov, Aleksandar Univ Novi Sad Fac Tech Sci Dept Power Elect & Telecommun Engn Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Schneider Elect Novi Sad Ind 3G Novi Sad 21000 Serbia

This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the Similar Days Method (SDM) and Artificial Neural Network (ANN), introduces several novelties: (1) selection of similar days based on hidden representations of day data using autoencoder (AE);(2) enhancement of model generalization by utilizing a broader set of training examples;(3) incorporating the relative importance of training examples derived from the similarity measure during training;and (4) mitigation of the influence of outliers by applying an ensemble of ANN models trained with different data splits. The presented AE configuration and procedure for selecting similar days generated a higher-quality training dataset, which led to more robust predictions by the ANN model for days with unexpected deviations. Experiments were conducted on actual load data from a Serbian electrical power system, and the results were compared to predictions obtained by the field-proven STLF tool. The experiments demonstrated an improved performance of the presented solution on test days when the existing STLF tool had poor predictions over the past year.

关键词： short-term load forecast autoencoder artificial neural network similar days Advanced Distribution Management System (ADMS) Distributed Energy Resources (DERs) prosumer

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IS autoencoder TRULY APPLICABLE FOR 3D CT SUPER-RESOLUTION? 20

IS AUTOENCODER TRULY APPLICABLE FOR 3D CT SUPER-RESOLUTION?

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20th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Luo, Weixun Xing, Xiaodan Yang, Guang Imperial Coll London Dept Bioengn London England Imperial Coll London Natl Heart & Lung Inst London England Royal Brompton Hosp Cardiovasc Res Ctr London England

ISBN: (纸本)9781665473583

Featured by a bottleneck structure, autoencoder (AE) and its variants have been largely applied in various medical image analysis tasks, such as segmentation, reconstruction and de-noising. Despite of their promising performances in afore-mentioned tasks, in this paper, we claim that AE models are not applicable to single image super-resolution (SISR) for 3D CT data. Our hypothesis is that the bottleneck architecture that resizes feature maps in AE models degrades the details of input images, thus can sabotage the performance of super-resolution. Although U-Net proposed skip connections that merge information from different levels, we claim that the degrading impact of feature resizing operations could hardly be removed by skip connections. By conducting large-scale ablation experiments and comparing the performance between models with and without the bottleneck design on a public CT lung dataset, we have discovered that AE models, including U-Net, have failed to achieve a compatible SISR result (p < 0.05 by Student's t-test) compared to the baseline model. Our work is the first comparative study investigating the suitability of AE architecture for 3D CT SISR tasks and brings a rationale for researchers to re-think the choice of model architectures especially for 3D CT SISR tasks. The full implementation and trained models can be found at: https://***/Roldbach/autoencoder-3D-CT-SISR

关键词： autoencoder super-resolution CT

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A Novel Multi-dimensional Time-series Data Anomaly Detection Model Based on Generative Adversarial Network Aided autoencoder 22

A Novel Multi-dimensional Time-series Data Anomaly Detection...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Gong, Zejun Liu, Qiang Ding, Jinliang Wang, Xiaobo Wang, Peng Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781713872344

With increasing amount and easiness of access the data in industrial processes, data-driven technologies have become more prevalent in process monitoring. Anomaly detection is an indispensable part of process monitoring. However, most industrial data are closely related to time, and classical anomaly detection algorithms mostly focus on learning the features of static data, ignoring the dynamic features of industrial data. This paper proposes a multi-dimensional time-series data anomaly detection model based on generative adversarial network aided autoencoder. By extracting the features of the normal time-series data, feature representation is established in latent space. Meanwhile, we introduce generated adversarial network (GAN) into the autoencoder (AE) training to enhance the feature learning ability of the autoencoder, so that the normal time-series data can be well represented in the latent space. Gated recurrent unit (GRU) is used as the main network of the autoencoder to learn the dynamic features between different time steps in the sequence data and detect fault data through the value of the reconstruction error. We verify the validity of the proposed model in simulation data and apply it to the real anomaly detection of steel plate production. Compared with k-nearest neighbor, linear discriminant analysis, principal component analysis, one-class support vector machine, data-enhanced method and the traditional dynamic autoencoder, the proposed method performs the best.

关键词： anomaly detection multi-dimensional time series time-series generation adversarial network autoencoder

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Lazy Node-Dropping autoencoder

Lazy Node-Dropping Autoencoder

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Lee, Jaewon Kim, Myung Jun Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon South Korea INRIA Saclay Soda Palaiseau France Ajou Univ Dept Ind Engn Dept Artificial Intelligence Suwon South Korea

ISBN: (纸本)9781665475785

autoencoders are widely used for dimensionality reduction nonlinearly. However, determining the number of nodes in the autoencoder embedding space is still a challenging task. The number of nodes in the bottleneck layer, which is an encoded representation, is estimated and determined by users. Therefore, to maintain embedding performance and reduce the complexity of the model, an indicator that automatically selects the number of bottleneck nodes is needed. This study proposes a method for automatically estimating the adequate number of nodes in the bottleneck layer while training the model. The basic idea of the proposed method is to eliminate lazy nodes which rarely affect the model performance based on the weight distribution of the bottleneck layer. Since the proposed method takes place in the learning process of the autoencoder, it has the advantage of accelerating the training speed. The proposed method showed better or similar performances in classification accuracy.

关键词： Neural Networks autoencoder Dimensionality Reduction Dimension Estimation

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