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Anomaly Detection of Water Level Using deep autoencoder

SENSORS 2021年第19期21卷 6679页

作者： Nicholaus, Isack Thomas Park, Jun Ryeol Jung, Kyuil Lee, Jun Seoung Kang, Dae-Ki Dongseo Univ Dept Comp Engn Busan 47011 South Korea Buzzni AI Lab Seoul 08788 South Korea JCMEEK Seoul 07591 South Korea Infran R&D Ctr Seoul 07994 South Korea

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research's motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario.

关键词： anomaly detection deep autoencoder time-series

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Elastic deep autoencoder for text embedding clustering by an improved graph regularization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Daneshfar, Fatemeh Soleymanbaigi, Sayvan Nafisi, Ali Yamini, Pedram Univ Kurdistan Dept Comp Engn & Informat Technol Sanandaj Iran Univ Kurdistan Dept Elect & Comp Eng Allamah Hamdi BlvdPOB 416 Sanandaj *** Kurdistan Iran

Text clustering is a task for grouping extracted information of the text in different clusters, which has many applications in recommender systems, sentiment analysis, and more. deep learning-based methods have become increasingly popular due to their high accuracy in identifying nonlinear structures. They usually consist of two major parts: dimensionality reduction and clustering. autoencoders are simple unsupervised neural networks used for better representation of low-dimensional data and have shown good performance in dealing with nonlinear features. However, while they utilize the Frobenius norm to deal well with Gaussian noise, they are sensitive to outlier data and Laplacian noise. In this paper, a deep autoencoder with an adapted elastic loss for text embedding clustering (EDA-TEC) is proposed. The elastic loss is a combination of the Frobenius norm and L2,1-norm to consider both types of noises. Additionally, to maintain the high-dimensional data geometric structure, a modified graph regularization term based on the weighted cosine similarity measure is used. EDATEC also improves clustering results by considering the sparsity regularization of the manifold representation data. In this jointly end-to-end deep learning model, better representation and text clustering results are achieved with high accuracy on common datasets compared to existing methods.1

关键词： deep autoencoder Text clustering Graph regularization Text embedding

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DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 146卷 105578-105578页

作者： Zhao, Shuchang Zhang, Li Liu, Xuejun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing 211106 Peoples R China Collaborat Innovat Ctr Novel Software Technol & In Nanjing 210023 Peoples R China Nanjing Forestry Univ Coll Comp Sci & Technol Nanjing 210037 Peoples R China

Single-cell RNA sequencing (scRNA-seq) can reveal differences in genetic material at the single-cell level and is widely used in biomedical studies. However, the minute RNA content within individual cells often results in a high number of dropouts and introduces random noise of scRNA-seq data, concealing the original gene expression pattern. Therefore, data normalization is critical in the analysis pipeline to adjust for unexpected biological and technical effects, leading to a particular bimodal expression pattern exhibited in the semi continuous normalized data. We further find the positive continuous expression presents a right-skewed distribution, which is still under-explored by mainstream dimensionality reduction and imputation methods. We introduced a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality reduction and imputation of scRNA-seq data. DAE-TPGM uses a two-part-gamma model to capture the statistical characteristics of semi-continuous normalized data and adaptively explores the potential relationships between genes for promoting data imputation by deep autoencoder. Just as the classic application scenarios that use an autoencoder in dimensionality reduction, our personalized autoendoer can capture phenotypic information on the peripheral blood mononuclear cells (PBMC) better and clearly infer continuous phenotype information for hematopoiesis in mice. Compared with that of mainstream imputation methods such as MAGIC, SAVER, scImpute and DCA, the new model achieved substantial improvement on the recognition of cellular phenotypes in two real datasets, and the comprehensive analyses on synthetic "ground truth" data demonstrated that our method obtains competitive advantages over other imputation methods in discovering underlying gene expression patterns in time-course data.

关键词： Single-cell RNA sequencing deep autoencoder Two-part model Gamma distribution Dimensionality reduction Imputation

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A Data Compression Method for Wellbore Stability Monitoring Based on deep autoencoder

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SENSORS 2024年第12期24卷 4006页

作者： Song, Shan Zhao, Xiaoyong Zhang, Zhengbing Luo, Mingzhang Yangtze Univ Sch Elect Informat & Elect Engn Jingzhou 434023 Peoples R China China Natl Petr Corp Bohai Drilling Engn Co Ltd Direct Drilling Branch Tianjin 300280 Peoples R China

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data;compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

关键词： wellbore safety monitoring deep autoencoder well trajectory data compression logging while drilling (LWD)

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Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder

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MEASUREMENT 2021年 179卷 109452-109452页

作者： Pei, Xinglong Zheng, Xiaoyang Wu, Jinliang Chongqing Univ Technol Sch Artificial Intelligence Chongqing 400054 Peoples R China

Accurate fault diagnosis is essential to ensure the stability and reliability of machine systems. deep learning (DL) techniques have demonstrated potential in realizing intelligent fault diagnosis. However, the obtained bearing signals in the actual environment involve various interferences and are consistently nonstationary. Moreover, it is difficult to simultaneously acquire a large amount of balanced data to ensure suitable training. Consequently, traditional DL-based methods cannot achieve a satisfactory diagnostic accuracy. To overcome this problem and enhance the diagnostic performance in complex situations, this paper proposes a method to realize effective intelligent bearing fault diagnosis. A novel index is established to evaluate the quality of the demodulation spectra, along with a novel compressed sensing deep autoencoder based on compressed sensing. Additionally, a multiscale neural network architecture incorporating a multiscale coarse-grained procedure is adopted. Experimental results and comparative analysis with existing intelligent diagnosis methods demonstrate the effectiveness and robustness of the proposed method.

关键词： Bearing fault diagnosis Teager energy operator deep autoencoder Compressed sensing Multiscale feature extraction

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Fast deep autoencoder for fe derate d learning

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PATTERN RECOGNITION 2023年第1期143卷

作者： Novoa-Paradela, David Fontenla-Romero, Oscar Guijarro-Berdinas, Bertha Univ A Coruna CITIC Campus Elvina s-n La Coruna 15008 Spain

This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (deep autoencoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces training time. Training can be performed incrementally, in parallel and distributed and, thanks to its mathematical formulation, the information to be exchanged does not endanger the privacy of the training data. The method has been evaluated and compared with other state-of-the-art autoencoders, showing interesting results in terms of accuracy, speed and use of available resources. This makes DAEF a valid method for edge computing and federated learning, in addition to other classic machine learning scenarios.& COPY;2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： deep autoencoder Anomaly detection Federated learning Edge computing Machine learning

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A deep autoencoder approach for detection of brain tumor images

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 102卷

作者： Nayak, Dillip Ranjan Padhy, Neelamadhab Mallick, Pradeep Kumar Singh, Ashish GIET Univ Sch Engn & Technol CSE Gunupur 765022 Odisha India Kalinga Inst Ind Technol Deemed be Univ Sch Comp Engn Bhubaneswar 751024 India

Brain tumor detection received much attention due to its clinical significance for early treatment. Accurate diagnosis and classification of brain tumors are still challenging despite many major contributions are available. Existing methods mainly focus on accuracy in which classification problems like overfitting and underfitting have remained a major concern. This paper presents a technique for brain tumor identification using a deep autoencoder based on spectral data augmentation. In the first step, the morphological cropping process is applied to the original brain images to reduce noise and resize the images. Then Discrete Wavelet Transform (DWT) is used to solve the data-space problem with feature reduction. Finally, a dense layer is proposed for appropriate feature extraction and classification of the brain tumor images. The comparative analysis shows that the proposed algorithm outperforms with an accuracy of 97% and an AUC ROC score of 99.46%.(c) 2017 Elsevier Inc. All rights reserved.

关键词： Brain tumor detection MRI images Spectral data augmentation deep autoencoder Discrete wavelet transform

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Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 163卷 108148-108148页

作者： Lee, Hyunseong Lim, Hyung Jin Skinner, Travis Chattopadhyay, Aditi Hall, Asha Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA Kyonggi Univ Dept Smart City Engn Civil Engn Suwon 16227 South Korea US Combat Capabil Dev Command CCDC Mech Div Vehicle Technol Directorate Army Res Lab Aberdeen Proving Ground MD 21005 USA

This paper presents the development of a robust automatic damage diagnosis technique that uses ultrasonic Lamb waves and a deep autoencoder (DAE) to detect and classify fatigue damage in composite structures. Piezoelectric (PZT) transducers are installed on carbon fiber reinforced polymer (CFRP) composite plate specimens to interrogate structural integrity under uniaxial fatigue loading. Fatigue damage evolution from matrix cracking to delamination is monitored by periodically acquiring the ultrasonic wave response. A deep autoencoder (DAE) model is adopted for effective tracking of ultrasonic response variations and for diagnosing fatigue damage in the composite specimens. The ultrasonic signals collected from pristine specimens are processed and used for training the DAE model. To improve the accuracy and sensitivity of the damage diagnosis, the architecture and hyperparameters of the DAE model are optimized, and a statistical detection baseline is defined to capture damage indicators. The ultrasonic signals obtained after applying additional fatigue cycles are introduced into the trained DAE model to validate the damage detection and classification capabilities. The damage sensitive features automatically extracted from the bottleneck layer of the DAE model are used to classify the fatigue damage mode. Singular value decomposition (SVD) is used to further reduce feature dimensionality. The patterns in the reduced features are then analyzed using a density-based spatial clustering of applications with noise (DBSCAN) algorithm. The results show that the proposed technique can accurately detect and classify the fatigue damage in composite structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from ultrasonic signals for damage diagnosis.

关键词： PZT transducers Composite fatigue Damage detection and classification deep autoencoder Feature learning (max 6) Lamb waves

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Health indicator for machine condition monitoring built in the latent space of a deep autoencoder

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年 224卷

作者： Gonzalez-Muniz, Ana Diaz, Ignacio Cuadrado, Abel A. Garcia-Perez, Diego Univ Oviedo Elect Engn Dept Edif Dept Oeste 2Campus Viesques S-N Gijon 33204 Spain

The construction of effective health indicators plays a key role in the engineering systems field: they reflect the degradation degree of the system under study, thus providing vital information for critical tasks ranging from anomaly detection to remaining useful life estimation, with benefits such as reduced maintenance costs, improved productivity or increased machine availability. The reconstruction error of deep autoencoders has been widely used in the literature for this purpose, but this approach does not fully exploit the hierarchical nature of deep models. Instead, we propose to take advantage of the disentangled representations of data that are available in the latent space of autoencoders, by using the latent reconstruction error as machine health indicator. We have tested our proposal on three different datasets, considering two types of autoencoders (deep autoencoder and variational autoencoder), and comparing its performance with that of state-of-the-art approaches in terms of well-known quality metrics. The results of the research demonstrate the capability of our health indicator to outperform conventional approaches, in the three datasets, and regardless of the type of autoencoder used to generate the residuals. In addition, we provide some intuition on the suitability of latent spaces for the monitoring of machinery condition.

关键词： Health indicator deep autoencoder Latent space Anomaly detection Engineering systems

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Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs

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BRIEFINGS IN BIOINFORMATICS 2024年第1期25卷 bbad483页

作者： Zhou, Zhecheng Zhuo, Linlin Fu, Xiangzheng Zou, Quan Wenzhou Univ Technol Coll Data Sci & Artificial Intelligence Wenzhou Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://***/ZZCrazy00/JDASA-MRD.

关键词： microbial responses to drugs deep autoencoder microbe and drug subgraphs multi-hop neighborhood information

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