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Evolving Deep convolutional variational autoencoders for Image Classification

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第5期25卷 815-829页

作者： Chen, Xiangru Sun, Yanan Zhang, Mengjie Peng, Dezhong Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Eface Technol Co Ltd Chengdu 610093 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Peoples R China Shenzhen Peng Cheng Lab Shenzhen 518052 Peoples R China Chengdu Sobey Digital Technol Co Ltd Chengdu 610041 Peoples R China

variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures, which are often handcrafted by the human expertise in deep neural networks (DNNs). However, such expertise is not necessarily available to each of the end users interested. In this article, we propose a novel method to automatically design optimal architectures of VAEs for image classification, called evolving deep convolutional VAE (EvoVAE), based on a genetic algorithm (GA). In the proposed EvoVAE algorithm, the traditional VAEs are first generalized to a more generic and asymmetrical one with four different blocks, and then a variable-length gene encoding mechanism of the GA is presented to search for the optimal network depth. Furthermore, an effective genetic operator is designed to adapt to the proposed variable-length gene encoding strategy. To verify the performance of the proposed algorithm, nine variants of AEs and VAEs are chosen as the peer competitors to perform the comparisons on MNIST, street view house numbers, and CIFAR-10 benchmark datasets. The experiments reveal the superiority of the proposed EvoVAE algorithm, which wins 21 times out of the 24 comparisons and outperforms the best competitors by 1.39%, 14.21%, and 13.03% on the three benchmark datasets, respectively.

关键词： Computer architecture Encoding Task analysis Approximation algorithms Training Genetic algorithms Computer science convolutional variational autoencoder evolving deep learning genetic algorithm (GA) neural architecture search (NAS)

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CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 320卷 124569页

作者： Wang, Dongqiao Wang, Qiaohua Chen, Zhuoting Guo, Juncai Li, Shijun Huazhong Agr Univ Coll Engn Wuhan 430070 Peoples R China Minist Agr & Rural Agr Key Lab Agr Equipment Midlower Yangtze River Wuhan 430070 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Huazhong Agr Univ Coll Anim Sci & Vet Med Key Lab Agr Anim Genet Breeding & Reprod Minist Educ Wuhan 430070 Peoples R China

Unfertilized duck eggs not removed prior to incubation will deteriorate quickly, posing a risk of contaminating the normally fertilized duck eggs. Thus, detecting the fertilization status of breeding duck eggs as early as possible is a meaningful and challenging task. Most existing work usually focus on the characteristics of chicken eggs during mid-term hatching. However, little attention has been paid to the detection for duck eggs prior to incubation. In this paper, we present a novel hybrid deep learning detection framework for the fertilization status of pre -incubation duck eggs, termed CVAE-DF, based on visible/near-infrared (VIS/NIR) transmittance spectroscopy. The framework comprises the encoder of a convolutional variational autoencoder (CVAE) and an improved deep forest (DF) model. More specifically, we first collected transmittance spectral data (400 -1000 nm) of 255 duck eggs before hatching. The multiplicative scatter correction (MSC) method was then used to eliminate noise and extraneous information of the raw spectral data. Two efficient data augmentation methods were adopted to provide sufficient data. After that, CVAE was applied to extract representative features and reduce the feature dimension for the detection task. Finally, an improved DF model was employed to build the classification model on the enhanced feature set. The CVAE-DF model achieved an overall accuracy of 95.94 % on the test dataset. These experimental results in terms of four metrics demonstrate that our CVAE-DF method outperforms the traditional methods by a significant margin. Furthermore, the results also indicate that CVAE holds great promise as a novel feature extraction method for the VIS/NIR spectral analysis of other agricultural products. It is extremely beneficial to practical engineering.

关键词： Fertilization Pre-incubation duck eggs Visible/near-infrared spectroscopy convolutional variational autoencoder Improved deep forest

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A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年 1-21页

作者： Huang, Congfang Blondheim Jr, David Zhou, Shiyu Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Brunswick Corp Mercury Marine Div Fond Du Lac WI 54936 USA

Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling T2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T<^>2$$\end{document} statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.

关键词： Anomaly detection X-ray image convolutional variational autoencoder Generative adversarial networks Tensor decomposition Pattern extraction

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Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion

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JOURNAL OF HYDROLOGY 2024年 631卷

作者： Deng, Yaping Kang, Xueyuan Ma, Haichun Qian, Jiazhong Ma, Lei Luo, Qiankun Hefei Univ Technol Sch Resources & Environm Engn Hefei 230009 Peoples R China Nanjing Univ Sch Earth Sci & Engn Key Lab Surficial Geochem Minist Educ Nanjing 210023 Peoples R China

Characterization of fracture network is essential for understanding groundwater flow and solute transport, as well as waste storage. Deep -learning based ensemble smoother methods have proven to be effective in estimating hydraulic parameters in porous media. Compared to porous media, fracture fields are highly heterogeneous and typically non -Gaussian distributed, making the estimation of the fracture field from sparse borehole data extremely difficult. In this paper, we developed a joint hydrogeophysical inversion framework to improve the characterization of fracture networks. We first trained a convolutional variational autoencoder (CVAE) network to parameterize the fracture field, and then integrated with the ensemble smoother with multiple data assimilation (ESMDA) method to infer the fracture distribution by incorporating multiple datasets. Two numerical cases with different complexity were considered to assess the ability of the proposed joint inversion framework. The results show that the proposed hydrogeophyscial inversion framework can capture the main features of the fracture field. By integrating both the pressure and SP data, the fracture field can be reconstructed with an improved accuracy and reduced estimation uncertainty.

关键词： Fracture network Hydrogeophysical inversion Deep learning convolutional variational autoencoder Data assimilation

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Robust Unsupervised Anomaly Detection With variational autoencoder in Multivariate Time Series Data

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IEEE ACCESS 2022年 10卷 57835-57849页

作者： Yokkampon, Umaporn Mowshowitz, Abbe Chumkamon, Sakmongkon Hayashi, Eiji Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Fukuoka 8208502 Japan CUNY City Coll Dept Comp Sci New York NY 10031 USA

Accurate detection of anomalies in multivariate time series data has attracted much attention due to its importance in a wide range of applications. Since it is difficult to obtain accurately labeled data, many unsupervised anomaly detection algorithms for multivariate time series data have been developed. However, building such a system is challenging since it requires capturing temporal dependencies in each time series and must also encode the inter-correlations between different pairs of time series. To meet this challenge, we propose a Multi Scale convolutional variational autoencoder (MSCVAE) to detect anomalies in multivariate time series data. Firstly, multi scale attribute matrices are constructed from multivariate time series to characterize multiple levels of the system states at different time steps. Then, given the attribute matrices, a convolutional variational autoencoder is employed to generate reconstructed attribute matrices, and also an attention-based ConvLSTM network is used to capture the temporal patterns. In addition, a new ERR-based threshold setting strategy is developed to optimize anomaly detection performance instead of relying on the traditional ROC-based threshold setting strategy with an imbalanced dataset. Finally, the proposed framework is assessed by means of experiments on four datasets. The experimental results show that our proposed framework is superior to competing algorithms in terms of model performance and robustness, demonstrating that our model is effective in detecting anomalies in multivariate time series.

关键词： Time series analysis Anomaly detection Data models Stochastic processes Robustness Principal component analysis Generative adversarial networks Anomaly detection multivariate time series convolutional variational autoencoder threshold setting strategy

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Interpreting Disentangled Representations of Person-Specific convolutional variational autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

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INFORMATION 2023年第9期14卷 489-489页

作者： Ahmed, Taufique Longo, Luca Technol Univ Dublin Sch Comp Sci Appl Intelligence Res Ctr Artificial Intelligence & Cognit Load Lab Dublin D07 EWV4 Ireland

Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction. In most of the studies, VAE latent space interpretation is used to detect only the out-of-order distribution latent variable for anomaly detection. However, the interpretation and visualisation of all latent space components disclose information about how the model arrives at its conclusion. The main contribution of this study is interpreting the disentangled representation of VAE by activating only one latent component at a time, whereas the values for the remaining components are set to zero because it is the mean of the distribution. The results show that CNN-VAE works well, as indicated by matrices such as SSIM, MSE, MAE, and MAPE, along with SNR and correlation coefficient values throughout the architecture's input and output. Furthermore, visual plausibility and clustering demonstrate that each component contributes differently to capturing the generative factors in topographic maps. Our proposed pipeline adds to the body of knowledge by delivering a CNN-VAE-based latent space interpretation model. This helps us learn the model's decision and the importance of each component of latent space responsible for activating parts of the brain.

关键词： electroencephalography convolutional variational autoencoder latent space interpretation deep learning spectral topographic maps

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A study on improving temperature field prediction accuracy using a surrogate model assisted by airflow field

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RESULTS IN ENGINEERING 2025年 25卷

作者： Zhang, Yue Song, Jiashuo Cheng, Shirui Wang, Xinru North China Elect Power Univ Dept Automat Baoding 071000 Hebei Peoples R China North China Elect Power Univ Hebei Technol Innovat Ctr Simulat & Optimized Cont Beijing Peoples R China Baoding key Lab State Detect & Optimizat Regulat I Baoding Peoples R China

Due to the constraints of current measurement methodologies, there is a paucity of data regarding the temperature distribution within coal-fired boiler furnaces. While finite element analysis (FEA) offers high accuracy, its extensive computational requirements make it impractical for real-world applications. This paper presents a global surrogate model incorporating wind velocity field data to overcome this limitation and enable efficient temperature field prediction. Wind velocity, a key variable, is inherently linked to the dynamic behavior of the temperature field. By utilizing radial basis function interpolation, the wind data is transformed to match the dimensions of the temperature field, thereby improving the model's ability to predict complex temperature distributions. Additionally, convolutional variational autoencoders (CVAE) are employed to generate highquality data samples under varying operational conditions, enlarging the dataset and enhancing the model's generalization capacity. This study develops five deep-learning surrogate models-ResNet, AlexNet, VGG, SegNet, and U-Net-to predict temperature fields. Experimental results demonstrate that including wind velocity data significantly improves prediction accuracy, with the ResNet model achieving an average error of just 1.275 %. Compared to traditional computational fluid dynamics (CFD) simulations, the deep learning surrogate model reduces prediction time to milliseconds. The ResNet model surpasses other models, balancing accuracy and computational efficiency, delivering more precise temperature field predictions with lower computational costs. The proposed surrogate model exhibits strong generalization ability and real-time prediction potential, providing valuable support for intelligent boiler control and optimization.

关键词： Surrogate model Temperature field prediction Radial basis function interpolation convolutional variational autoencoder Deep learning surrogate models

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Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous autoencoders(E-HAE)

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Computer Systems Science & Engineering 2023年第9期46卷 3261-3284页

作者： Lelisa Adeba Jilcha Deuk-Hun Kim Julian Jang-Jaccard Jin Kwak ISAA Lab. Department of AI Convergence NetworkAjou UniversitySuwon16499Korea ISAA Lab. Institute for Information and CommunicationAjou UniversitySuwon16499Korea Department of Computer Science and Information Technology Massey UniversityAuckland0745New Zealand Department of Cyber Security Ajou UniversitySuwon16499Korea

Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or *** growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge *** such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection *** address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned *** collectively refer to the convergence of different technology sectors as the internet of blended *** proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational *** extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection ***,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational *** proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.

关键词： Network intrusion detection anomaly detection TON_IoT dataset smart grid smart city smart factory digital healthcare autoencoder variational autoencoder LSTM convolutional variational autoencoder ensemble learning

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A NOVEL UNSUPERVISED autoencoder-BASED HFOS DETECTOR IN INTRACRANIAL EEG SIGNALS 47

A NOVEL UNSUPERVISED AUTOENCODER-BASED HFOS DETECTOR IN INTR...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Weilai Zhong, Lanfeng Xiang, Weixi Kang, Tongzhou Lai, Dakun Univ Elect Sci & Technol China UESTC Chengdu 611731 Peoples R China

ISBN: (纸本)9781665405409

High frequency oscillations (HFOs) have demonstrated their potency acting as an effective biomarker in epilepsy. However, most of the existing HFOs detectors are based on manual feature extraction and supervised learning, which incur laborious feature selection and time-consuming labeling process. In order to tackle these issues, we propose an automatic unsupervised HFOs detector based on convolutional variational autoencoder (CVAE). First, each selected HFO candidate (via an initial detection method) is converted into a 2-D time-frequency map (TFM) using continuous wavelet transform (CWT). Then, CVAE is trained on the red channel of the TFM (R-TFM) dataset so as to achieve the goal of dimensionality reduction and reconstruction of input feature. The reconstructed R-TFM dataset is later classified by K-means algorithm. Experimental results show that the proposed method outperforms four existing detectors, and achieve 92.85% in accuracy, 93.91% in sensitivity, and 92.14% in specificity.

关键词： High frequency oscillation time-frequency analysis convolutional variational autoencoder unsupervised detector

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A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 210卷

作者： Hurtado, A. Calderon Alamdari, M. Makki Atroshchenko, E. Chang, K. C. Kim, C. W. Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia Kyoto Univ Grad Sch Engn Dept Civil & Earth Resources Engn Kyoto 6158540 Japan

In recent years, researchers have extensively explored the application of drive -by inspection technology for bridge damage assessment. This approach involves using the response of a sensing vehicle to identify damage. However, many existing methods rely on data collected from both healthy and damaged bridge conditions, which may not always be available. Therefore, this study introduces a fully unsupervised computer vision -based methodology for bridge structural health monitoring (BSHM) using drive -by inspection. It analyzes the time-frequency domain of a two -axle vehicle's response by deriving a novel formulation for the contact point response from vehicle axles. The axles signals are then processed through subtraction, filtering, and decomposition using empirical Fourier decomposition with an improved segmentation approach based on the Savitzky-Golay filter (SGEFD). Relevant Intrinsic Mode Functions (IMF) are extracted as features representing damage, and the Wavelet Synchro-squeezed transform (WSST) is obtained from these features and used as input for the damage assessment algorithm. The performance of two state-of-the-art unsupervised generative machine learning methods, namely convolutional variational autoencoders (CVAE) and convolutional adversarial autoencoders (CAAE), is compared for the damage assessment task. These methods are trained solely with the residual WSST obtained from the vehicle responses when traversing a bridge in its reference state. A damage index (DI) is defined based on the measured error between the original and reconstructed images, and a damage threshold is calculated from the DI distribution of samples from the benchmark bridge state. During testing, the error between the original and reconstructed WSST is compared to the damage threshold, enabling the classification of new samples as healthy or damaged. The methodology is evaluated using both numerical and experimental vehicle-bridge interaction (VBI) models, considering various

关键词： Indirect SHM Deep learning Empirical Fourier decomposition convolutional variational autoencoder convolutional adversarial autoencoder Bridge monitoring Damage assessment

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