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Data-driven fault detection for chemical processes using autoencoder with data augmentation

KOREAN JOURNAL OF CHEMICAL ENGINEERING 2021年第12期38卷 2406-2422页

作者： Lee, Hodong Kim, Changsoo Jeong, Dong Hwi Lee, Jong Min Seoul Natl Univ Sch Chem & Biol Engn Inst Chem Proc 1 Gwanak Ro Seoul 08826 South Korea Korea Inst Sci & Technol Clean Energy Res Ctr Seoul 02792 South Korea Univ Ulsan Sch Chem Engn 93 Daehak Ro Ulsan 44610 South Korea

Process monitoring plays an essential role in safe and profitable operations. Various data-driven fault detection models have been suggested, but they cannot perform properly when the training data are insufficient or the information to construct the manifold is confined to a specific region. In this study, a process monitoring framework integrated with data augmentation is proposed to supplement rare but informative samples for the boundary regions of the normal state. To generate data for augmentation, a variational autoencoder was employed to exploit its advantage of stable convergence. For the construction of the process monitoring system, an autoencoder that can extract useful features in an unsupervised manner was used. To illustrate the efficacy of the proposed method, a case study for the Tennessee Eastman process was applied. The results show that the proposed method can improve the monitoring performance compared to the autoencoder without data augmentation in terms of fault detection accuracy and delay, particularly within the feature space.

关键词： Process Monitoring Fault Detection and Isolation (FDI) autoencoder variational autoencoder Data Augmentation

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Wasserstein Adversarially Regularized Graph autoencoder

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NEUROCOMPUTING 2023年第1期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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Category-Stitch Learning for Union Domain Generalization

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第1期19卷 25-25页

作者： Liu, Yajing Xiong, Zhiwei Li, Ya Lu, Yuning Tian, Xinmei Zha, Zheng-Jun Univ Sci & Technol China 443 Huangshan Rd Hefei 230027 Peoples R China iFLYTEK Res 666 Wangjiang West Rd Hefei 230088 Peoples R China

Domain generalization aims at generalizing the network trained on multiple domains to unknown but related domains. Under the assumption that different domains share the same classes, previous works can build relationships across domains. However, in realistic scenarios, the change of domains is always followed by the change of categories, which raises a difficulty for collecting sufficient aligned categories across domains. Bearing this in mind, this article introduces union domain generalization (UDG) as a new domain generalization scenario, in which the label space varies across domains, and the categories in unknown domains belong to the union of all given domain categories. The absence of categories in given domains is the main obstacle to aligning different domain distributions and obtaining domain-invariant information. To address this problem, we propose category-stitch learning (CSL), which aims at jointly learning the domain-invariant information and completing missing categories in all domains through an improved variational autoencoder and generators. The domain-invariant information extraction and sample generation cross-promote each other to better generalizability. Additionally, we decouple category and domain information and propose explicitly regularizing the semantic information by the classification loss with transferred samples. Thus our method can breakthrough the category limit and generate samples of missing categories in each domain. Extensive experiments and visualizations are conducted on MNIST, VLCS, PACS, Office-Home, and DomainNet datasets to demonstrate the effectiveness of our proposed method.

关键词： Domain generalization variational autoencoder generator

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Traffic classification using distributions of latent space in software-defined networks: An experimental evaluation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 119卷

作者： Jang, Yehoon Kim, Namgi Lee, Byoung-Dai Kyonggi Univ Div AI & Comp Engn Suwon South Korea

With the emergence of new Internet services and the drastic increase in Internet traffic, traffic classification has become increasingly important to effectively satisfy the quality of service to users. The traffic classification system should be resilient and operate smoothly regardless of network conditions or performance and should be capable of handling various classes of Internet services. This paper proposes a traffic classification method in a software-defined network environment that employs a variational autoencoder (VAE) to accomplish this. The proposed method trains the VAE using six statistical features and extracts the distributions of latent features for the flows in each service class. Furthermore, it classifies the query traffic by comparing the distributions of latent features for the query traffic with the learned distributions of the service classes. For the experiment, the statistical features of network flows were collected from real-world domestic and overseas Internet services for training and testing. According to the experimental results, the proposed method has an average accuracy of 89%. This accuracy was 52%, 47%, 39%, 59%, and 26% higher than conventional statistics-based classification methods, MLP, AE+MLP, VAE+MLP, and SVM, respectively. This result clearly suggests that probability distributions of latent features, rather than specific values for latent features, can be used as more stable features.

关键词： Flow classification Jensen-Shannon divergence Latent features Software-defined network variational autoencoder

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Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

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REMOTE SENSING 2023年第15期15卷 3890-3890页

作者： Cheng, Ying Zhao, Liaoying Chen, Shuhan Li, Xiaorun Hangzhou Dianzi Univ Comp & Software Sch Hangzhou 310018 Peoples R China Zhejiang Univ Dept Elect Engn Hangzhou 310027 Peoples R China

Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by variations in environmental conditions. These and other factors interfere with the accurate discrimination of source type. Several spectral mixing models have been proposed for hyperspectral unmixing to address the spectral variability problem. The interpretation for the spectral variability of these models is usually insufficient, and the unmixing algorithms corresponding to these models are usually classic unmixing techniques. Hyperspectral unmixing algorithms based on deep learning have outperformed classic algorithms. In this paper, based on the typical extended linear mixing model and the perturbed linear mixing model, the scaled and perturbed linear mixing model is constructed, and a spectral unmixing network based on this model is constructed using fully connected neural networks and variational autoencoders to update the abundances, scales, and perturbations involved in the variable endmembers. Adding spatial smoothness constraints to the scale and adding regularization constraints to the perturbation improve the robustness of the model, and adding sparseness constraints to the abundance determination prevents overfitting. The proposed approach is evaluated on both synthetic and real data sets. Experimental results show the superior performance of the proposed method against other competitors.

关键词： hyperspectral unmixing endmember variability linear mixing model variational autoencoder

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Reconstruction and generation of 3D realistic soil particles with metaball descriptor

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COMPUTERS AND GEOTECHNICS 2023年第1期161卷

作者： Zhao, Yifeng Gao, Xiangbo Zhang, Pei Lei, Liang Li, Stan Z. Galindo-Torres, S. A. Zhejiang Univ Coll Environm & Resources Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Westlake Univ Sch Engn Key Lab Coastal Environm & Resources Zhejiang Prov 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Westlake Univ Sch Engn 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China

The morphology of soil particles is crucial in determining its granular characteristics and assembly responses. However, how to introduce accurate and various morphologies of realistic particles in modeling can be challenging, as it often requires time-consuming and costly X-ray Computed Tomography (XRCT). This has led to two prevalent problems in modeling: morphological reconstruction and generation. For reconstruction, we develop a geometric-based Metaball-Imaging algorithm. This algorithm is capable of accurately reconstructing the complex morphologies of realistic particles, including those with concave voids, which cannot be easily represented using other shape descriptors such as the spherical harmonic function. It employs a two-step approach, capturing the main contour of the particles using a series of non-overlapping spheres and then refining surface-texture details through gradient search. Four types of soil particles, hundreds of samples, are applied for evaluations. The result shows good matches on key morphological indicators (i.e., volume, surface area, sphericity, circularity, corey-shape factor, nominal diameter and surface-equivalent-sphere diameter), confirming its reconstruction precision. For generation, we propose the Metaball variational autoencoder. Assisted by deep neural networks, this method can generate new 3D particles in Metaball form, while retaining coessential morphological features with parental particles. Additionally, this method allows for control over the generated shapes through an arithmetic pattern, enabling the generation of particles with specific shapes. Two sets of XRCT images different in sample number and geometric features are chosen as parental data. On each training set, one thousand particles are generated for validations. The generation fidelity is demonstrated through comparisons of morphologies and shape-feature distributions between generated and parental particles. Examples are also provided to demonstrate contr

关键词： The metaball function Particle morphology Particle reconstruction Optimization method Particle generation 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年第1期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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FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae097页

作者： Yang, Chengyuan Kumar, Himansu Kim, Pora Univ Texas Hlth Sci Ctr Houston McWilliams Sch Biomed Informat Houston TX 77030 USA

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of similar to 5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

关键词： kinase fusion gene gene assessment network propagation variational autoencoder feature reduction

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Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

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SENSORS 2023年第20期23卷 8474-8474页

作者： Liu, Zhiguo Rong, Junlin Jiang, Yingru Zhang, Luxi Dalian Univ Commun & Network Lab Dalian 116622 Peoples R China

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D-S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module's training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.

关键词： trust management D-S evidence theory variational autoencoder secure routing

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Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization

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INTERNATIONAL JOURNAL OF PLASTICITY 2023年 171卷

作者： Kusampudi, Navyanth Diehl, Martin Max Planck Inst Eisenforsch GmbH Max Planck Str 1 D-40237 Dusseldorf Germany Katholieke Univ Leuven Dept Mat Sci Kasteelpark Arenberg 44 B-3001 Leuven Belgium Katholieke Univ Leuven Dept Comp Sci Celestijnenlaan 200 A B-3001 Louvain Belgium

The design of optimal microstructures requires first, the identification of microstructural features that influence the material's properties and, then, a search for a combination of these features that give rise to desired properties. For microstructures with complex morphologies, where the number of features is large, deriving these structure-property relationships is a challenging task. To address this challenge, we propose a generative machine learning model that can automatically identify low-dimensional descriptors of microstructural features that can be used to establish structure-property relationships. Based on this model, we present an integrated, data-driven framework for microstructure characterization, reconstruction, and design that is applicable to heterogeneous materials with polycrystalline microstructures. The proposed method is evaluated on a case study of designing dual-phase steel microstructures created with the multi-level Voronoi tessellation method. To this end, we train a variational autoencoder to identify the descriptors from these synthetic dual-phase steel microstructures. Subsequently, we employ Bayesian optimization to search for the optimal combination of the descriptors and generate microstructures with specific yield stress and low susceptibility for damage initiation. The presented results show how microstructure descriptors, determined by the variational autoencoder model, act as design variables for an optimization algorithm that identifies microstructures with desired properties.

关键词： Crystal plasticity variational autoencoder Voronoi tessellation Random forest

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