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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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GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies

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IEEE ACCESS 2021年 9卷 148994-149042页

作者： Ditzel, Carsten Dietmayer, Klaus Inst Measurement Control & Microtechnol D-89081 Ulm Germany

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream.

关键词： Radar Cameras Radar imaging Robot sensing systems Probabilistic logic Task analysis Signal processing algorithms Radar signal processing computer vision sensor fusion deep learning machine learning variational autoencoder self-supervised learning annotation-free learning scene understanding

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Nonlinear blind source separation exploiting spatial nonstationarity

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INFORMATION SCIENCES 2024年 665卷

作者： Sipila, Mika Nordhausen, Klaus Taskinen, Sara Univ Jyvaskyla Dept Math & Stat Jyvaskyla Finland

In spatial blind source separation the observed multivariate random fields are assumed to be mixtures of latent spatially dependent random fields. The objective is to recover latent random fields by estimating the unmixing transformation. Currently, the algorithms for spatial blind source separation can only estimate linear unmixing transformations. Nonlinear blind source separation methods for spatial data are scarce. In this paper, we extend an identifiable variational autoencoder that can estimate nonlinear unmixing transformations to spatially dependent data, and demonstrate its performance for both stationary and nonstationary spatial data using simulations. In addition, we introduce scaled mean absolute Shapley additive explanations for interpreting the latent components through nonlinear mixing transformation. The spatial identifiable variational autoencoder is applied to a geochemical dataset to find the latent random fields, which are then interpreted by using the scaled mean absolute Shapley additive explanations. Finally, we illustrate how the proposed method can be used as a pre-processing method when making multivariate predictions.

关键词： Independent component analysis Multivariate spatial data Shapley values variational autoencoder

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Labyrinth morphological modeling and its application on unreferenced segmentation assessment

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年 85卷

作者： Li, Xiaoguang Zhu, Ziyao Yin, Hongxia Zhao, Pengfei Lv, Han Tang, Ruowei Qin, Yating Zhuo, Li Wang, Zhenchang Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Capital Med Univ Beijing Friendship Hosp Dept Radiol Beijing 100050 Peoples R China

The labyrinth of the inner ear is an important auditory and balanced sensory organ and is closely related to tinnitus, hearing loss, vertigo, and Meniere diseases. Quantitative description and measurement of the labyrinth is a challenging task in both clinical practice and medical research. A data-driven-based labyrinth morphological modeling method is proposed for extracting simple and low-dimensional representations or feature vectors to quantify the normal and abnormal labyrinths in morphology. Firstly, a two-stage pose alignment strategy is introduced to align the segmented inner ear labyrinths. Then, an energy-adaptive spatial and inter-slice dimensionality reduction strategy is adopted to extract compact morphological features via a variational autoencoder (VAE). Finally, a statistical model of the compact feature in the latent space is established to represent the morphology distribution of the labyrinths. As one of an application of our model, a reference-free quality evaluation for the segmentation of the labyrinth is explored. The experimental results show that the consistency between the proposed method and the Dice similarity coefficient (DSC) reaches 0.78. Further analysis showed that the model also has a high potential to apply to morphological analysis, such as anomaly detection, of the labyrinths.

关键词： Inner ear labyrinth modeling variational autoencoder Medical image analysis Temporal bone CT image

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GenCoder: A Generative AI-Based Adaptive Intra-Vehicle Intrusion Detection System

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IEEE ACCESS 2024年 12卷 150651-150663页

作者： Smolin, Mikhail Kyrgyz State Tech Univ Dept Appl Math & Comp Sci Bishkek 720044 Kyrgyzstan

With the rapid expansion of the vehicular cybersecurity (VCS) market and the increasing sophistication of cyberthreats, developing an adaptive intra-vehicular intrusion detection system (IDS) is crucial. This paper introduces GenCoder, a generative artificial intelligence (GenAI)-based IDS that uniquely addresses the dynamic and evolving nature of vehicular cyberthreats. GenCoder combines a five-layer deep neural network (DNN) with a variational autoencoder (VAE), overseen by a novel communication layer known as the GenCoder layer. This system dynamically adapts to new intrusion patterns by generating and utilizing new training data when deviations from known patterns are detected. The generated samples have a Shannon entropy (SE) value of 1.65 bits for four classes, indicating standard variety among the synthetic data. GenCoder demonstrates exceptional adaptability, pushing the accuracy, precision, recall, and F1-score from 84.79%, 83.58%, 83.70%, and 83.64% to 92.19%, 90.12%, 90.44%, and 90.28%, respectively, after introducing 50% feature deformation to testing data. The novel concept of an adaptive intra-vehicular IDS, the innovative GenCoder layer that establishes seamless communication among the DNN, the VAE, and the dataset, as well as the unique assessment strategies of adaptability make this research exceptional with the potential to create a new dimension in automotive IDS research.

关键词： Computer crime Automobiles Principal component analysis Intrusion detection Adaptive systems Protocols NSL-KDD Vehicle dynamics Support vector machines Artificial neural networks Artificial intelligence Generative AI Encoding Adaptive intrusion detection automotive cybersecurity deep neural network GenCoder layer generative artificial intelligence variational autoencoder vehicular intrusion detection system

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An Overview of Deep Generative Models in Functional and Evolutionary Genomics

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Biomedical Data Science 1000年第1期6卷 173-189页

作者： Burak Yelmen Flora Jay 2Institute of Genomics University of Tartu Tartu Estonia 1Laboratoire Interdisciplinaire des Sciences du Numérique CNRS UMR 9015 INRIA Université Paris-Saclay Orsay France email: flora.jay@lisn.fr

Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.

关键词： deep generative models variational autoencoder generative adversarial network functional genomics evolutionary genetics

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Wasserstein Adversarially Regularized Graph autoencoder

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NEUROCOMPUTING 2023年 541卷

作者： Liang, Huidong Gao, Junbin Univ Sydney Univ Sydney Business Sch Sydney NSW 2006 Australia

This paper introduces Wasserstein Adversarially Regularized Graph autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node embedding to a target distribution via the Wasserstein metric. To ensure the Lipschitz continuity, we propose two approaches: WARGA-WC that uses weight clipping method and WARGA-GP that uses gradient penalty method. The proposed models have been validated by link prediction and node clustering on real-world graphs with visualizations of node embeddings, in which WARGA generally outperforms other state-of-the-art models based on Kullback-Leibler (KL) divergence and typical adversarial framework. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Wasserstein distance Graph neural networks variational autoencoder Adversarial training

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VAPER: A deep learning model for explainable probabilistic regression

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年 63卷

作者： Jung, Seungwon Noh, Yoona Moon, Jaeuk Hwang, Eenjun Korea Univ Sch Elect Engn Seoul 02841 South Korea

A probabilistic regression model provides decision-makers with the regression output along with its quantitative uncertainty for given input variables. Even though this uncertainty may help to avoid serious consequences such as misdiagnosis or blackout due to overconfidence in the output, it only provides a measure of the uncertainty of the output, and has a limitation in that it cannot explain the reasons for the output and its uncertainty. If they can be presented along with their reasons, more suitable alternatives to the output can be found. However, despite the development of artificial intelligence methods to explain machine learning models and their outputs, few probabilistic regression models with this functionality have been proposed. Therefore, in this paper, we propose a variational autoencoder-based model for explainable probabilistic regression, called VAPER. VAPER provides a parametric probability distribution of an output variable over input variables and interprets it using layer-wise relevance propagation to investigate the effect of each input variable. To evaluate the effectiveness of the pro-posed model, we performed extensive experiments using several datasets. The experimental results demonstrated that VAPER has competitive regression performance compared to existing models, even with effective explainability.

关键词： Probabilistic regression Explainable artificial intelligence Layer -wise relevance propagation variational autoencoder

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Learning to balance the coherence and diversity of response generation in generation-based chatbots

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第4期17卷

作者： Wang, Shuliang Li, Dapeng Geng, Jing Yang, Longxing Leng, Hongyong Beijing Inst Technol Sch Comp Sci & Technol 5 South St Beijing 100081 Peoples R China Beijing Inst Technol Acad E Govt Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

Generating response with both coherence and diversity is a challenging task in generation-based chatbots. It is more difficult to improve the coherence and diversity of dialog generation at the same time in the response generation model. In this article, we propose an improved method that improves the coherence and diversity of dialog generation by changing the model to use gamma sampling and adding attention mechanism to the knowledge-guided conditional variational autoencoder. The experimental results demonstrate that our proposed method can significantly improve the coherence and diversity of knowledge-guided conditional variational autoencoder for response generation in generation-based chatbots at the same time.

关键词： variational autoencoder dialog system deep learning response generation chatbots

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Diabetes detection using deep learning techniques with oversampling and feature augmentation

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 202卷 105968-105968页

作者： Garcia-Ordas, Maria Teresa Benavides, Carmen Benitez-Andrades, Jose Alberto Alaiz-Moreton, Hector Garcia-Rodriguez, Isaias Univ Leon Escuela Ingenierias Ind & Informat SECOMUCI Res Grp Campus Vegazana S-N Leon 24071 Spain Univ Leon SALBIS Res Grp Dept Elect Syst & Automat Engn Campus Vegazana S-N Leon 24071 Spain

Background and objective : Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of their health status early enough. Late diagnosis brings about numerous health problems and a large number of deaths each year so the development of methods for the early diagnosis of this pathology is essential.& nbsp;& nbsp;Methods: In this paper, a pipeline based on deep learning techniques is proposed to predict diabetic peo-ple. It includes data augmentation using a variational autoencoder (VAE), feature augmentation using an sparse autoencoder (SAE) and a convolutional neural network for classification. Pima Indians Diabetes Database, which takes into account information on the patients such as the number of pregnancies, glu-cose or insulin level, blood pressure or age, has been evaluated.& nbsp;Results: A 92 . 31% of accuracy was obtained when CNN classifier is trained jointly the SAE for featuring augmentation over a well balanced dataset. This means an increment of 3.17% of accuracy with respect the state-of-the-art.& nbsp;Conclusions : Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

关键词： Diabetes Detection Deep learning Sparse autoencoder variational autoencoder Oversampling

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