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 ...
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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.
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 convolutio...
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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.
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 stor...
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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.
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...
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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.
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 algor...
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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%.
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...
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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.
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 ...
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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.
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 ...
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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
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 monitori...
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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.
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 lay...
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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.
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