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Near-Real-Time Identification of Seismic Damage Using Unsupervised Deep Neural Network

JOURNAL OF ENGINEERING MECHANICS 2022年第3期148卷

作者： Kim, Minkyu Song, Junho Seoul Natl Univ Dept Civil & Environm Engn Seoul 08826 South Korea

Prompt identification of structural damage is essential for effective postdisaster responses. To this end, this paper proposes a deep neural network (DNN)-based framework to identify seismic damage based on structural response data recorded during an earthquake event. The DNN in the proposed framework is constructed by variational autoencoder, which is one of the self-supervised DNNs that can construct the continuous latent space of the input data by learning probabilistic characteristics. The DNN is trained using the flexibility matrices obtained by operational modal analysis (OMA) of simulated structural responses of the target structure under the undamaged state. To consider the load-dependency of OMA results, the undamaged state of the structure is represented by the flexibility matrix, which is closest to that obtained from the measured seismic response in the latent space. The seismic damage of each member is then estimated based on the difference between the two matrices using the flexibility disassembly method. As a numerical example, the proposed method is applied to a 5-story, 5-bay steel frame structure for which structural analyses are first performed under artificial ground motions to create train and test datasets. The proposed framework is verified with the near-real-time simulation using ground motions of El Centro and Kobe earthquakes. The example demonstrates that the proposed DNN-based method can identify seismic damage accurately in near-real-time.

关键词： Seismic response Deep neural network (DNN) variational autoencoder Near-real-time Damage identification Operational modal analysis (OMA) Flexibility matrix Flexibility disassembly method

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Handling information loss of graph convolutional networks in collaborative filtering

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INFORMATION SYSTEMS 2022年 109卷

作者： Xiong, Xin Li, XunKai Hu, YouPeng Wu, YiXuan Yin, Jian Nanjing Univ Sch Artificial Intelligence 163 Xianlin Ave Nanjing Jiangsu Peoples R China Shandong Univ Sch Mech Elect & Informat Engn 180 Wenhua West Rd Weihai Shandong Peoples R China Zhejiang Univ Polytech Inst 269 Shixiang Rd Hangzhou Zhejiang Peoples R China

Collaborative filtering (CF) methods based on graph convolutional network (GCN) and autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer from information loss problems, which are caused by information lossy initialization and using low-order Chebyshev Polynomial to fit the graph convolution kernel. And the AE-based CF methods obtain the prediction results by reconstructing the user-item interaction matrix, which does not conduct deep excavation of the behavior patterns, resulting in the limited-expression ability. To solve the above problems, we propose variational autoencoder-Enhanced Graph Convolutional Network (VE-GCN) for CF. Specifically, we use a variational autoencoder (VAE) to compress the interactive behavior patterns as the prior information of GCN to achieve sufficient learning, thus alleviating the information lossy initialization problem. And then the generalized graph Laplacian convolution kernel is proposed in GCN to handle the high-frequency information loss problem caused by Chebyshev Polynomial fitting in the GCN-based CF. To the best of our knowledge, VE-GCN is a feasible method to handle the information loss problems mentioned above in GCN-based CF for the first time. Meanwhile, the structure of GCN is optimized by removing redundant feature transformation and nonlinear activation function, and using DenseGCN to complete multi-level information interaction. Experiments on four real-world datasets show that the VE-GCN achieves state-of-the-art performance. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Collaborative filtering Graph convolutional network Recommender system variational autoencoder

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Partial Discharge Data Matching Method for GIS Case-Based Reasoning

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ENERGIES 2019年第19期12卷 3677页

作者： Dai, Jiejie Teng, Yingbing Zhang, Zhaoqi Yu, Zhongmin Sheng, Gehao Jiang, Xiuchen State Grid Shanghai Shinan Elect Power Supply Co Xinbei Rd 268 Shanghai 201199 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Dongchuan Rd 800 Shanghai 200240 Peoples R China State Grid Shanghai Elect Power Co Yuanshen Rd 1122 Shanghai 200120 Peoples R China

With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods.

关键词： partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder

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Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

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SENSORS 2024年第10期24卷 2992页

作者： Yang, Lei Jiang, Yibo Zeng, Kang Peng, Tao Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China Jiaxing Shutuo Technol Co Ltd Jiaxing 314031 Peoples R China Zhejiang Loong Airlines Co Ltd Hangzhou 311243 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power Components & Mechatron S Hangzhou Peoples R China

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time-frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

关键词： remaining useful life variational autoencoder bi-directional long short-term memory

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An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets

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IEEE ACCESS 2021年 9卷 135210-135223页

作者： Amarbayasgalan, Tsatsral Pham, Van-Huy Theera-Umpon, Nipon Piao, Yongjun Ryu, Keun Ho Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chiang Mai Univ Biomed Engn Inst Chiang Mai 50200 Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Nankai Univ Sch Med Tianjin 300071 Peoples R China Tianjin Cent Hosp Gynecol Obstet Tianjin Key Lab Human Dev & Reprod Regulat Tianjin 300199 Peoples R China

This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned from the whole or randomly sampled training datasets, our suggested method draws up training datasets by separating regular and highly biased subsets to build accurate prediction models. We use a two-step approach to prepare the training dataset: (1) divide the initial training dataset into two groups, commonly distributed and highly biased using Principal Component Analysis, (2) enrich the highly biased group by variational autoencoders. Then, two deep neural network classifiers learn from the isolated training groups separately. The well-organized training groups enable a chance to build more accurate prediction models. When predicting the risk of coronary heart disease from the given input, only one appropriate model is selected based on the reconstruction error on the Principal Component Analysis model. Dataset used in this study was collected from the Korean National Health and Nutritional Examination Survey. We have conducted two types of experiments on the dataset. The first one proved how Principal Component Analysis and variational autoencoder models of the proposed method improves the performance of a single deep neural network. The second experiment compared the proposed method with existing machine learning algorithms, including Naive Bayes, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Adaptive Boosting. The experimental results show that the proposed method outperformed conventional machine learning algorithms by giving the accuracy of 0.892, specificity of 0.840, precision of 0.911, recall of 0.920, f-measure of 0.915, and AUC of 0.882.

关键词： Training Heart Principal component analysis Predictive models Diseases Deep learning Support vector machines Coronary heart disease deep neural network machine learning principal component analysis reconstruction error variational autoencoder

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Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Bucher, Martin Juan Jose Kraus, Michael Anton Rust, Romana Tang, Siyu Swiss Fed Inst Technol Stefano Franscini Pl CH-8093 Zurich Switzerland

Parametric Modeling, Generative Design, and Performance-Based Design have gained increasing attention in the AEC field as a way to create a wide range of design variants while focusing on performance attributes rather than building codes. However, the relationships between design parameters and performance attributes are often very complex, resulting in a highly iterative and unguided process. In this paper, we argue that a more goal-oriented design process is enabled by an inverse formulation that starts with performance attributes instead of design parameters. A Deep Conditional Generative Design workflow is proposed that takes a set of performance attributes and partially defined design features as input and produces a complete set of design parameters as output. A model architecture based on a Conditional variational autoencoder is presented along with different approximate posteriors, and evaluated on four different case studies. Compared to Genetic Algorithms, our method proves superior when utilizing a pre-trained model.

关键词： Deep generative modeling Performance-based design Generative design variational autoencoder Deep generative design Artificial intelligence

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Prediction of rare feature combinations in population synthesis: Application of deep generative modelling

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 120卷 102787-102787页

作者： Garrido, Sergio Borysov, Stanislav S. Pereira, Francisco C. Rich, Jeppe Tech Univ Denmark Dept Technol Management & Econ DTU DK-2800 Lyngby Denmark

Population synthesis is concerned with the generation of agents for agent-based modelling in many fields, such as economics, transportation, ecology and epidemiology. When the number of attributes describing the agents and/or their level of detail becomes large, survey data cannot densely support the joint distribution of the attributes in the population due to the curse of dimensionality. It leads to a situation where many attribute combinations are missing from the sample data while such combinations exist in the real population. In this case, it becomes essential to consider methods that are able to impute such missing information effectively. In this paper, we propose to use deep generative latent models. These models are able to learn a compressed representation of the data space, which when projected back to the original space, leads to an effective way of imputing information in the observed data space. Specifically, we employ the Wasserstein Generative Adversarial Network (WGAN) and the variational autoencoder (VAE) for a large-scale population synthesis application. The models are applied to a Danish travel survey with a feature-space of more than 60 variables and trained and tested using cross validation. A new metric that applies to the evaluation of generative models in an unsupervised setting is proposed. It is based on the ability to generate diverse yet valid synthetic attribute combinations by comparing if the models can recover missing combinations (sampling zeros) while keeping truly impossible combinations (structural zeros) models at a minimum. For a low dimensional experiment, the VAE, the marginal sampler and the fully random sampler generate 5%, 21% and 26% more structural zeros per sampling zero when compared to the WGAN. For a high dimensional case, these figures increase to 44%, 2217% and 170440% respectively. This research directly supports the development of agent-based systems and in particular cases where detailed socio-economic or geog

关键词： Population synthesis Generative modeling Deep learning Generative Adversarial Networks variational autoencoder Transportation modeling Agent-based modeling

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Prediction of the microstructure of cold-compacted Astaloy 85Mo with deep generative models

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COMPUTATIONAL MATERIALS SCIENCE 2024年 242卷

作者： Schenk, Oliver Becker, Marco Deng, Yuanbin Niemietz, Philipp Bergs, Thomas Broeckmann, Christoph Chair & Inst Mat Applicat Mech Engn Augustinerbach 4 D-52062 Aachen Germany Rhein Westfal TH Aachen Mfg Technol Inst MTI Campus Blvd 30 D-52074 Aachen Germany Fraunhofer Inst Prod Technol IPT Steinbachstr 17 D-52074 Aachen Germany

In powder metallurgy, iron -based powders are compacted to so-called green bodies, which undergo a subsequent sintering process. The microstructure of the green bodies affects the sintering process and the resulting properties of the produced component. Assessing the microstructure experimentally is time consuming and costly, while simulation approaches rely on simplifications that significantly affect relevant microstructural information. This work investigates the suitability of deep generative models to synthetically generate realistic micrographs of green bodies as an alternative to experiments and simulations. For that purpose experiments comparing Generative Adversarial Neural Networks and variational autoencoders were conducted. The two deep learning frameworks were used to train models which produce synthetic micrographs in dependence of two parameters, namely powder particle size and green body porosity. The data utilized to train and evaluate the models was acquired from compacted water -atomized Astaloy 85Mo via scanning electron microscopy. Besides visual inspection, the trained models were evaluated via quantitative metrics. Distributions of relevant microstructural parameters such as pore perimeter or minimum and maximum pore feret diameter of the synthetically generated micrographs were compared to those of experimentally acquired micrographs. The results indicate that Generative Adversarial Networks represent a promising approach to train models, which produce synthetic micrographs that capture relevant properties of green body micrographs in dependence of given parameters such as powder particle size. In contrast, the variational autoencoders trained for this study did not capture microstructural properties well.

关键词： Generative models Generative Adversarial Network variational autoencoder Micrograph Powder metallurgy Microstructure

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A Lightweight Unsupervised Intrusion Detection Model Based on variational Auto-Encoder

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SENSORS 2023年第20期23卷 8407页

作者： Ren, Yi Feng, Kanghui Hu, Fei Chen, Liangyin Chen, Yanru Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu 610065 Peoples R China

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data;we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.

关键词： industrial control systems intrusion detection variational autoencoder

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Layer-constrained variational autoencoding kernel density estimation model for anomaly detection

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KNOWLEDGE-BASED SYSTEMS 2020年 196卷 105753-105753页

作者： Lv, Peng Yu, Yanwei Fan, Yangyang Tang, Xianfeng Tong, Xiangrong Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Shandong Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Shandong Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Adv High Temp Mat & Precis Formi Shanghai 200240 Peoples R China Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

Unsupervised techniques typically rely on the probability density distribution of the data to detect anomalies, where objects with low probability density are considered to be abnormal. However, modeling the density distribution of high dimensional data is known to be hard, making the problem of detecting anomalies from high-dimensional data challenging. The state-of-the-art methods solve this problem by first applying dimension reduction techniques to the data and then detecting anomalies in the low dimensional space. Unfortunately, the low dimensional space does not necessarily preserve the density distribution of the original high dimensional data. This jeopardizes the effectiveness of anomaly detection. In this work, we propose a novel high dimensional anomaly detection method called LAKE. The key idea of LAKE is to unify the representation learning capacity of layer-constrained variational autoencoder with the density estimation power of kernel density estimation (KDE). Then a probability density distribution of the high dimensional data can be learned, which is able to effectively separate the anomalies out. LAKE successfully consolidates the merits of the two worlds, namely layer-constrained variational autoencoder and KDE by using a probability density-aware strategy in the training process of the autoencoder. Extensive experiments on six public benchmark datasets demonstrate that our method significantly outperforms the state-of-the-art methods in detecting anomalies and achieves up to 37% improvement in F-1 score. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Anomaly detection variational autoencoder Kernel density estimation Layer constraint Deep learning

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