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A multi-information fusion anomaly detection model based on convolutional neural networks and autoencoder

SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Zhao, Zhongnan Guo, Hongwei Wang, Yue Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Heilongjiang Inst Technol Coll Sci Harbin 150010 Peoples R China

Network traffic anomaly detection, as an effective analysis method for network security, can identify differentiated traffic information and provide secure operation in complex and changing network environments. To avoid information loss caused when handling traffic data while improving the detection performance of traffic feature information, this paper proposes a multi-information fusion model based on a convolutional neural network and autoencoder. The model uses a convolutional neural network to extract features directly from the raw traffic data, and a autoencoder to encode the statistical features extracted from the raw traffic data, which are used to supplement the information loss due to cropping. These two features are combined to form a new integrated feature for network traffic, which has the load information from the original traffic data and the global information of the original traffic data obtained from the statistical features, thus providing a complete representation of the information contained in the network traffic and improving the detection performance of the model. The experiments show that the classification accuracy of network traffic anomaly detection using this model outperforms that of classical machine learning methods.

关键词： Anomaly detection Multi-information fusion CNN autoencoder Traffic cropping

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Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期199卷

作者： Yang, Kang Kim, Sungwon Harley, Joel B. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Utah Dept Mech Engn Salt Lake City UT 84112 USA

Unsupervised damage detection in the presence of both regular environmental variations, such as a daily change in temperature and humidity, and irregular environmental variations, such as rain and snow, remains a critical challenge in guided wave structural health monitoring. This paper proposes an optimal autoencoder-based damage detection strategy to solve this problem. Although the autoencoder and other neural networks have been used to detect anomalies in structural health monitoring, the training data has been also required to be collected from a known intact structure, which increases the time and labor cost for inspection and limits the application of such methods. Instead, our autoencoder is trained with guided wave data that contain uncontrolled regular, irregular, and damage variations. We investigate hyperparameters that will negatively influence autoencoder-based damage detection, including the training time and damage duration. We also propose sparsity, dropout, and weight decay regularization strategies to enhance the robustness of the autoencoder to these hyperparameters. Results show that our optimal autoencoder method can achieve an area under the receiver operating curve score near 0.92 for detecting damage present in the last 16 days, which improves previous local principal components analysis reconstruction methods with a score of 0.88.

关键词： Structural health monitoring Guided waves Big data sets autoencoder Unsupervised damage detection

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Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting

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APPLIED GEOCHEMISTRY 2023年 158卷

作者： Mirzabozorg, Seyyed Ataollah Agha Seyyed Abedi, Maysam Univ Tehran Coll Engn Sch Min Engn Tehran Iran

In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly identifying prospective areas. Data sets with strongly imbalanced distributions of the target variable (deposits) and insufficient training data sets impose obstacles to these kinds of models which can significantly impact adversely on the performance of the models. Moreover, in some cases, negative training data sets as the non-deposit locations aren't really true negative data, which cause higher uncertainty in a mineral potential map. In this study, for handling these challenges the deep autoencoder neural network is adopted. The autoencoder can be trained to reconstruct geospatial data set in totally unsupervised manner and identify prospective areas based on the reconstruction error, where higher error corresponds with areas of higher mineral potential. In order to confirm the efficiency of the autoencoder algorithm in mineral potential modeling, the model was compared with a popular data-driven approach that assigned a weight to the evidence layer by using a concentration-area (C-A) fractal model and a prediction-area (P-A) plot, and combined them using a multi-class index overlay method. Receiver operating characteristic (ROC) curve, success-rate curve, and P-A plot were adopted to evaluate the predictive ability of Fe prospectivity models pertaining to the Esfordi district of Iran. Also, we use an area under the ROC curve (AUC) and partial AUC (pAUC) to quantitatively evaluate the overall and sensitivity performance of models, respectively.

关键词： autoencoder Unsupervised learning Mineral potential mapping Esfordi Iron deposits

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SIGMA: Spectral Interpretation Using Gaussian Mixtures and autoencoder

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GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS 2023年第1期24卷 e2022GC010530-e2022GC010530页

作者： Tung, Po-Yen Sheikh, Hassan A. Ball, Matthew Nabiei, Farhang Harrison, Richard J. Univ Cambridge Dept Earth Sci Cambridge England Univ Cambridge Dept Mat Sci & Met Cambridge England MediaTek Res Camborne England

Identification of unknown micro- and nano-sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope-energy dispersive X-ray spectroscopy (SEM-EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non-ideal sample preparation, and the presence of mixed chemical signals generated within the electron-beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine-learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel-wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non-negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM-EDS data sets: a synthetic mixture sample and a real-world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background-subtracted single-phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe-bearing particles and their background-subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user-friendly Python code with a graphical user interface for ease of use by general users.

关键词： machine learning hyperspectral imaging data energy dispersive X-ray spectroscopy dimensionality reduction autoencoder Gaussian mixture modeling

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Integrating transformer and autoencoder techniques with spectral graph algorithms for the prediction of scarcely labeled molecular data

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期153卷 106479-106479页

作者： Hayes, Nicole Merkurjev, Ekaterina Wei, Guo-Wei Michigan State Univ Dept Math E Lansing MI 48824 USA Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Biochem & Mol Biol E Lansing MI 48824 USA

In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled data sets. Although transfer learning can be advantageous, it requires the existence of a related large data set. This work introduces three graph-based models incorporating Merriman-Bence- Osher (MBO) techniques to tackle this challenge. Specifically, graph-based modifications of the MBO scheme are integrated with state-of-the-art techniques, including a home-made transformer and an autoencoder, in order to deal with scarcely-labeled data sets. In addition, a consensus technique is detailed. The proposed models are validated using five benchmark data sets. We also provide a thorough comparison to other competing methods, such as support vector machines, random forests, and gradient boosting decision trees, which are known for their good performance on small data sets. The performances of various methods are analyzed using residue-similarity (R-S) scores and R-S indices. Extensive computational experiments and theoretical analysis show that the new models perform very well even when as little as 1% of the data set is used as labeled data.

关键词： Spectral graph Transformer autoencoder Small data Scarcely labeled data R-S scores

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A convolutional autoencoder-based approach with batch normalization for energy disaggregation

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JOURNAL OF SUPERCOMPUTING 2021年第3期77卷 2961-2978页

作者： Chen, Huan Wang, Yue-Hsien Fan, Chun-Hung Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 402 Taiwan

Non-intrusive loading monitoring (NILM) is a load analyzing algorithm that performs the energy dis-aggregation of power load for the smart meter technology. NILM is a highly valuable application due to its cost effectiveness, but it is a very challenging research because the noisy low-level features are not easily distinguishable when multiple appliances are used together. This paper proposes a deep learning-based scheme, named the CAEBN-HC, to address this issue. The proposed CAEBN-HC is designed based on the one-dimensional convolutional neural networks (1D-CNN) autoencoder and uses advanced training techniques, particularly the batch normalization (BN) and hill climbing (HC) algorithm to solve the NILM problem. The 1D-CNN autoencoder is used to extract the temporal features, and the BN is used to re-adjust the output distribution of each layer to prevent the gradient vanishing or explosion problem in the training process. In addition, the HC is used to perform the hyperparameter tuning. The NILM problem is first modeled as a regression problem, and the proposed method can predict the target signal correctly. To validate the effectiveness of the proposed scheme, the REDD appliance and power usage dataset is applied as a benchmark for performance comparison. Results showed that the proposed CAEBN-HC performed the best when compared with the LSTM and the conventional convolutional autoencoder (CAE) scheme without batch normalization and hyperparameter optimization.

关键词： NILM autoencoder Deep learning CNN Energy disaggregation Batch normalization Hill climbing algorithm

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LSTM-autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

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SENSORS 2023年第2期23卷 1009-1009页

作者： Do, Jae Seok Kareem, Akeem Bayo Hur, Jang-Wook Kumoh Natl Inst Technol Dept Mech Engn Dept Aeronaut Mech & Elect Convergence Engn 61 Daehak RoGyeonsang Buk Do Gumi Si 39177 South Korea

Industry 5.0, also known as the "smart factory", is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system's operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

关键词： anomaly detection autoencoder automatic storage and retrieval system deep learning long short-term memory signal processing vibration sensors

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An Attention-Based 3D Convolutional autoencoder for Few-Shot Hyperspectral Unmixing and Classification

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REMOTE SENSING 2023年第2期15卷 451-451页

作者： Li, Chunyu Cai, Rong Yu, Junchuan Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Aeronaut & Astronaut Beijing 100049 Peoples R China Peoples Publ Secur Univ China Invest Coll Beijing 100038 Peoples R China China Aero Geophys Survey & Remote Sensing Ctr Lan Beijing 100083 Peoples R China

Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial-spectral features in an unsupervised or semi-supervised manner. In recent years, as a result of the development of computer vision, deep learning techniques have demonstrated their superiority in tackling the problems of hyperspectral unmixing (HU) and classification. In this paper, we present a new semi-supervised pipeline for few-shot hyperspectral classification, where endmember abundance maps obtained by HU are treated as latent features for classification. A cube-based attention 3D convolutional autoencoder network (CACAE), is applied to extract spectral-spatial features. In addition, an attention approach is used to improve the accuracy of abundance estimation by extracting the diagnostic spectral features associated with the given endmember more effectively. The endmember abundance estimated by the proposed model outperforms other convolutional neural networks (CNNs) with respect to the root mean square error (RMSE) and abundance spectral angle distance (ASAD). Classification experiments are performed on real hyperspectral datasets and compared to several supervised and semi-supervised models. The experimental findings demonstrate that the proposed approach has promising potential for hyperspectral feature extraction and has better performance relative to CNN-based supervised classification under small-sample conditions.

关键词： hyperspectral unmixing autoencoder deep learning few-shot classification

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Anomaly detection for high-dimensional dynamic data stream using stacked habituation autoencoder and union kernel density estimator

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第22期35卷 e7718-e7718页

作者： Zeng, Zhixia Huang, Ruohe Xiao, Ruliang Lin, Xinhong Zhang, Shi Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou Peoples R China

With the rapid development of communication technology, various complex heterogeneous sensor network applications produce a large number of high-dimensional dynamic data streams, which results in more difficult to anomaly detection than ever before. So, anomaly detection for high-dimensional dynamic data streams is of a more and more challenging problem. This paper proposes a novel method for detecting anomalies in high-dimensional dynamic data streams by utilizing several components. Firstly, it uses a stacked habituation autoencoder with habituation physiological mechanism to detect similarity anomalies more easily and capture feature relationships. Secondly, a union kernel density estimator with micro-cluster is designed to improve online anomaly detection accuracy by estimating the data density. Lastly, candidate anomaly sets and a delayed processing approach are utilized to cope with conceptual drift and evolution in the data stream, allowing the system to adapt to changes in the data over time. Extensive experiments on four high-dimensional dynamic data streams of the Internet of Things show that the proposed method is very effective.

关键词： anomaly detection autoencoder habituation high-dimensional dynamic data streams Kernel density estimation

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Clustering and Selection of Hurricane Wind Records Using autoencoder and k-Means Algorithm

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JOURNAL OF STRUCTURAL ENGINEERING 2023年第8期149卷 04023096-04023096页

作者： Du, Xinlong Hajjar, Jerome F. Bond, Robert Bailey Ren, Pu Sun, Hao Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China

In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is of great interest. Such a suite generally consists of a large number of hurricane wind records, which may lead to highly computational cost for structural analysis. To reduce the computational demand while still preserving the accuracy of the uncertainty quantification process, this paper proposes a machine learning approach to select a representative subset of all collected hurricane wind records for a location. First, hurricane wind records, which are expressed as time series with information that includes both wind speed and direction, are collected from a synthetic hurricane catalog. The high dimensional hurricane wind records are then compressed into a set of low dimensional latent feature vectors using an artificial neural network, designated as an autoencoder. The latent feature vectors represent the important patterns of wind records such as duration, magnitude, and the changing of wind speeds and directions over time. The wind records are then clustered by applying the k-means algorithm on the latent features, and a subset of records is selected from each cluster. The wind records selected from each cluster are those whose latent feature points are closest to the centroid of all latent feature points in that cluster. In order to do regional analysis while taking into account that the hurricane wind records are site-specific, this paper suggests that a region can be discretized into a set of grids, with the proposed hurricane selection approach applied to each grid. This procedure is demonstrated using Massachusetts as a testbed.

关键词： Hurricane selection Time series clustering autoencoder k-means Uncertainty quantification Regional analysis Wind direction

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