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GEOENERGY SCIENCE AND ENGINEERING 2023年 229卷

作者： Yousefzadeh, Reza Ahmadi, Mohammad Amirkabir Univ Technol Dept Petr Engn Tehran Iran

Most of the geological parametrization techniques used in history matching of sub-surface formations including the deep learning-based methods could not capture the non-linear and non-Gaussian dependencies and were limited to facies realizations. This paper proposes a novel method for parametrization of permeability realizations using ConvLSTM layers to build a variational autoencoder, named ConvLSTM-VAE, for the first time in this study. Since geological layers of the reservoir models are deposited during different periods, reservoir properties have a spatial distribution along the third dimension that can be regarded as a temporal distribution. To evaluate the proposed method, its parametrization results were compared with those of 3D convolutional VAE (3DCNN-VAE), which was recently used in the literature. Quantitative and visual results showed that the proposed method was far better than the 3DCNN-VAE, where the train, validation, and test losses obtained by the ConvLSTM-VAE was 0.3663, 0.3695, and 0.3710, and by the 3DCNN-VAE was 0.3980, 0.4086, 0.4095, respectively. In addition, history matching with parametrized realizations by the ConvLSTM-VAE could preserve the geologic realism in the conditioned realizations with significantly low data mismatch errors in the historical and prediction phases;however, history matching with 3DCNN-VAE parametrization and without any parametrization could not preserve the geologic realism. Finally, this paper can guide reservoir engineers to select a proper parametrization method for highly channelized, 3D, complex reservoirs with non-linear and non-Gaussian distribution of the uncertain parameters.

关键词： Data assimilation variational autoencoder History matching Deep learning Geological parametrization

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Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期191卷

作者： Zhou, Haoxuan Lei, Zihao Zio, Enrico Wen, Guangrui Liu, Zimin Su, Yu Chen, Xuefeng Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China MINES ParisPSL Univ Ctr Rech Risques & Crises CRC Sophia Antipolis France Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Politecn Milan Energy Dept Via Masa 34 I-20156 Milan Italy

Anomaly detection(AD) is an important task of machines' condition monitoring(CM). Data-driven policies can be used in a more intelligent way to achieve anomaly detection and effectively avoid the introduction of expert experience, thus having a broader scope of application. However, Machines like wind turbines often work under time-varying operating conditions(TVOCs), and the performance of traditional data-driven AD methods is significantly degraded because TVOCs can lead to "false alarms" and "missed alarms" in the implementation due to the monitoring data shifting caused by variation of operating conditions(OCs). To address this problem, this paper proposes a novel conditional feature disentanglement learning framework to solve the distur-bance in AD on account of entanglement between OCs and health states. The proposed approach performs conditional self-supervised AD by utilizing the variational autoencoder(VAE) and OCs information. Then, a feature disentanglement conditional VAE(FDCVAE) network is developed to realize the disentanglement of OCs and health states. Subsequently, An anomaly indicator(ANI) is constructed by the dimension reduction of the disentangled health state-related feature and combined with the statistics anomaly threshold for AD. Experiments on accelerated fatigue degradation of bearings under TVOCs validate the effectiveness of the proposed method and further demonstrate the superiority of the constructed ANI in eliminating TVOC interference, compared with common fault mechanisms-based and data-driven ANI. The proposed method not only achieves anomaly detection under TVOCs but also provides a new way for representation learning under variable working conditions in machine health management applications in the foreseeable future.

关键词： Time-varying operating conditions Anomaly detection Machines Feature disentanglement learning variational autoencoder Bearing

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VAE-Based Latent Representations Learning for Botnet Detection in IoT Networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2023年第1期31卷 4-4页

作者： Snoussi, Ramzi Youssef, Habib Univ Sousse Prince Lab ISITCOM Hammam Sousse Sousse Tunisia

Botnets pose significant threats to cybersecurity. The infected Internet of Things (IoT) devices are used to launch unsupported malicious activities on target entities to disrupt their operations and services. To address this danger, we propose a machine learning-based method, for detecting botnets by analyzing network traffic data flow including various types of botnet attacks. Our method uses a hybrid model where a variational autoencoder (VAE) is trained in an unsupervised manner to learn latent representations that describe the benign traffic data, and one-class classifier (OCC) for detecting anomaly (also called novelty detection). The main aim of this research is to learn the discriminating representations of the normal data in low dimensional latent space generated by VAE, and thus improve the predictive power of the OCC to detect malicious traffic. We have evaluated the performance of our model, and compared it against baseline models using a real network based dataset, containing popular IoT devices, and presenting a wide variety of attacks from two recent botnet families Mirai and Bashlite. Tests showed that our model can detect botnets with a satisfactory performance.

关键词： Anomaly detection Botnet detection variational autoencoder One-class classification Latent representations learning

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Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035018页

作者： Templin, Thomas Memarzadeh, Milad Vinci, Walter Lott, P. Aaron Asanjan, Ata Akbari Armenakas, Anthony Alexiades Rieffel, Eleanor NASA Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA NASA Univ Space Res Assoc Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA HP SCDS Leon 24009 Spain NASA Univ Space Res Assoc Quantum Artificial Intelligence Lab Ames Res Ctr Moffett Field CA 94035 USA Harvard Univ Dept Phys Cambridge MA 02138 USA NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA

Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models-variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors-in detecting anomalies in multivariate time series of commercial-flight operations. We created two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) with novel positive-phase architecture as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. To the best of our knowledge, our work is the first that applies DVAE models to anomaly-detection tasks in the aerospace field. The DVAE with RBM prior, using a relatively simple-and classically or quantum-mechanically enhanceable-sampling technique for the evolution of the RBM's negative phase, performed better in detecting anomalies than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. The transfer of a model to an unseen dataset with the same anomaly but without re-tuning of hyperparameters or re-training noticeably impaired anomaly-detection performance, but performance could be improved by post-training on the new dataset. The RBM model was robust to change of anomaly type and phase of flight during which the anomaly occurred. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection problems. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum anneale

关键词： generative modeling deep learning variational autoencoder anomaly detection restricted Boltzmann machine Gibbs sampling quantum-assisted machine learning

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Disentangling the correlated continuous and discrete generative factors of data

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PATTERN RECOGNITION 2023年 133卷

作者： Choi, Jaewoong Hwang, Geonho Kang, Myungjoo Seoul Natl Univ Dept Math Sci 1 Gwanak Ro Seoul 08826 South Korea Korea Inst Adv Study KIAS Ctr Artificial Intelligence & Nat Sci Seoul South Korea

Real-world data typically include discrete generative factors, such as category labels and the existence of objects, as well as continuous generative factors. Continuous generative factors may be dependent on or independent of discrete generative factors. For instance, an intra-class variation of a category is dependent on the discrete generative factor, whereas a common variation of all categories is not. Most previous attempts to integrate discrete generative factors into disentanglement assumed statistical independence between the continuous and discrete variables. In this paper, we propose a variational autoencoder(VAE) model capable of disentangling both continuous generative factors. To represent these generative factors, we introduce two sets of continuous latent variables: a private variable and a public variable . The private and public variables represent the intra-class variations and common variations in categories, respectively. Our proposed framework models the private variable as a Gaussian mixture and the public variable as a Gaussian. Each mode of the private variable is responsible for a class of discrete variables. Our proposed model, called Discond-VAE, DISentangles the class-dependent CONtinuous factors from the Discrete factors by introducing private variables. The experiments showed that Discond-VAE could discover private and public factors from the data. Moreover, even under the dataset with only public factors, Discond-VAE does not fail and adapts private variables to represent public factors.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： variational autoencoder Disentanglement Generative model Representation learning

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Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications

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JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2024年第1期32卷

作者： Huang, Anzhong Zhuang, Jianming Ren, Yuheng Rao, Yun Tsai, Sangbing Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang Peoples R China Univ Hamburg Sch Business Adm Hamburg Germany Univ Cambridge Cambridge England Jianpan Kunlu Internet Things Res Inst Xiamen Co Xiamen Peoples R China Hangzhou Vocat & Tech Coll Coll Entrepreneurship Hangzhou Peoples R China Int Engn & Technol Inst Hong Kong Peoples R China

Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates variational autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

关键词： Supply Chain Management Deep Learning variational autoencoder Graph Neural Network Deep Reinforcement Learning

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Synthesizing affective neurophysiological signals using generative models: A review paper

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JOURNAL OF NEUROSCIENCE METHODS 2024年 406卷 110129页

作者： Nia, Alireza F. Tang, Vanessa Talou, Gonzalo Maso Billinghurst, Mark Bioengn Inst 70 Symonds St Auckland 1010 New Zealand

The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end -to -end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near -Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.

关键词： Generative Adversarial Network variational autoencoder Augmentation Data scarcity Human-computer interaction Emotion recognition EEG fNIRS

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Predicting steel column stability with uncertain initial defects using bayesian deep learning

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Zhao, Haoyang Wang, Chen Fan, Jiansheng Tsinghua Univ Dept Civil Engn China Educ Minist Key Lab Civil Engn Safety & Durabil Beijing 100084 Peoples R China

The stability of steel columns is difficult to predict accurately due to uncertain initial defects such as geometric imperfections and residual stress. To address this issue, we propose a probabilistic model that uses variational autoencoder (VAE) and transfer learning to estimate the loading capacities of steel columns. Our model can predict the confidence intervals of buckling loads without knowing the exact distribution of initial defects, providing more comprehensive information for engineering applications than traditional deterministic strength index. We establish a dataset of 1500 load-displacement curves of steel columns using four data augmentation approaches, and analyze the data distribution to validate the model's assumptions. Various criterions, including the mean squared error (MSE), the prediction interval coverage probability (PICP), and the prediction interval normalized average width (PINAW), are adopted to comprehensively measure the performance of confidence interval prediction. The numerical experiment validates that the trained model accurately predicts the confidence intervals for load-displacement responses, which perfectly cover the true curves with reasonable PINAW. Finally, we conduct a case study with a practical experiment to illustrate the model's potential application in failure probability calculation and reliability design. Our proposed model provides a promising probabilistic solution for quantifying the impact of uncertain parameters on structural analysis and significantly simplifies probability-based reliability design and optimal design processes.

关键词： Stability prediction variational autoencoder Initial defects Probabilistic computing Uncertainty quantification

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Scene text detection using structured information and an end-to-end trainable generative adversarial networks

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PATTERN ANALYSIS AND APPLICATIONS 2024年第2期27卷 33-33页

作者： Naveen, Palanichamy Hassaballah, Mahmoud KPR Inst Engn & Technol Dept Elect & Elect Engn Coimbatore India Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci AlKharj 16278 Saudi Arabia South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt

Scene text detection poses a considerable challenge due to the diverse nature of text appearance, backgrounds, and orientations. Enhancing robustness, accuracy, and efficiency in this context is vital for several applications, such as optical character recognition, image understanding, and autonomous vehicles. This paper explores the integration of generative adversarial network (GAN) and network variational autoencoder (VAE) to create a robust and potent text detection network. The proposed architecture comprises three interconnected modules: the VAE module, the GAN module, and the text detection module. In this framework, the VAE module plays a pivotal role in generating diverse and variable text regions. Subsequently, the GAN module refines and enhances these regions, ensuring heightened realism and accuracy. Then, the text detection module takes charge of identifying text regions in the input image via assigning confidence scores to each region. The comprehensive training of the entire network involves minimizing a joint loss function that encompasses the VAE loss, the GAN loss, and the text detection loss. The VAE loss ensures diversity in generated text regions and the GAN loss guarantees realism and accuracy, while the text detection loss ensures high-precision identification of text regions. The proposed method employs an encoder-decoder structure within the VAE module and a generator-discriminator structure in the GAN module. Rigorous testing on diverse datasets including Total-Text, CTW1500, ICDAR 2015, ICDAR 2017, ReCTS, TD500, COCO-Text, SynthText, Street View Text, and KIAST Scene Text demonstrates the superior performance of the proposed method compared to existing approaches.

关键词： Text detection Generative adversarial network variational autoencoder GAN loss VAE loss

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Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kwon, Han Jo Heo, Jun Park, Su Hwan Park, Sung Who Byon, Iksoo Pusan Natl Univ Sch Med Biomed Res Inst Pusan Natl Univ HospDept Ophthalmol Gudeok Ro 179 Busan 49241 South Korea Pusan Natl Univ Yangsan Hosp Res Inst Convergence Biomed Sci & Technol Dept Ophthalmol Geumo Ro 20 Yangsan Si 50612 Gyeongsangnam D South Korea

This study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperative swept-source OCT data were collected from 150 patients with successfully closed FTMH using 6 x 6 mm2 macular volume scan datasets. Randomly selected and augmented 120,000 training and 5000 validation pairs of OCT images were used to train the GDLM. We assessed the accuracy and F1 score of concordance for neurosensory retinal areas, performed Bland-Altman analysis of foveolar height (FH) and mean foveal thickness (MFT), and predicted postoperative external limiting membrane (ELM) and ellipsoid zone (EZ) restoration accuracy between artificial intelligence (AI)-OCT and ground truth (GT)-OCT images. Accuracy and F1 scores were 94.7% and 0.891, respectively. Average FH (228.2 vs. 233.4 mu m, P = 0.587) and MFT (271.4 vs. 273.3 mu m, P = 0.819) were similar between AI- and GT-OCT images, within 30.0% differences of 95% limits of agreement. ELM and EZ recovery prediction accuracy was 88.0% and 92.0%, respectively. The proposed GDLM accurately predicted macular OCT images following FTMH surgery, aiding patient and surgeon understanding of postoperative macular features.

关键词： Deep learning Generative deep learning model Macular hole Optical coherence tomography variational autoencoder

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