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Multi-Layer Reconstruction Errors Autoencoding and Density Estimate for Network Anomaly Detection

Computer Modeling in Engineering & Sciences 2021年第7期128卷 381-397页

作者： Ruikun Li Yun Li Wen He Lirong Chen Jianchao Luo University of Electronic Science and Technology of China Chengdu611730China Guangdong Weichen Information Technology Co. Ltd.Guangzhou510000China Chongqing Changan Automobile Corporation Chongqing400000China

Anomaly detection is an important method for intrusion *** recent years,unsupervised methods have been widely researched because they do not require *** example,a nonlinear autoencoder can use reconstruction errors to attain the discrimination *** method is not effective when the model complexity is high or the data contains *** method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal ***,compressed features may lose some of the high-dimensional distribution information of the original *** this paper,we present an efficient anomaly detection framework for unsupervised anomaly detection,which includes network data capturing,processing,feature extraction,and anomaly *** employ a deep autoencoder to obtain compressed features and multi-layer reconstruction errors,and feeds them the same to the Gaussian mixture model to estimate the *** proposed approach is trained and tested on multiple current intrusion detection datasets and real network scenes,and performance indicators,namely accuracy,recall,and F1-score,are better than other autoencoder models.

关键词： Anomaly detection deep autoencoder density estimate

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Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules

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SOLAR ENERGY 2021年 220卷 914-926页

作者： Otamendi, Urtzi Martinez, Inigo Quartulli, Marco Olaizola, Igor G. Viles, Elisabeth Cambarau, Werther Vicomtech Fdn Basque Res & Technol Alliance BRTA Donostia San Sebastian 20009 Spain Univ Navarra TECNUN Sch Engn Donostia San Sebastian 20018 Spain Univ Navarra Inst Data Sci & Artificial Intelligence Pamplona 31009 Spain Tecnalia Res & Innovat Basque Res & Technol Alliance BRTA Donostia San Sebastian 20009 Spain

In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life. Of all defects, cell-level anomalies can lead to serious failures and may affect surrounding PV modules in the long run. These fine defects are usually captured with high spatial resolution electroluminescence (EL) imaging. The difficulty of acquiring such images has limited the availability of data. For this work, multiple data resources and augmentation techniques have been used to surpass this limitation. Current state-of-the-art detection methods extract barely low-level information from individual PV cell images, and their performance is conditioned by the available training data. In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. The proposed modular pipeline combines three deep learning techniques: 1. object detection (modified Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised segmentation (autoencoder). The modular nature of the pipeline allows to upgrade the deep learning models to the further improvements in the state-of-the-art and also extend the pipeline towards new functionalities.

关键词： Electroluminescence images Photovoltaic modules deep learning Anomaly detection Weakly supervised segmentation deep autoencoder

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Development of a tool wear online monitoring system for dry gear hobbing machine based on new experimental approach and DAE-BPNN-integrated mathematic structure

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第1-2期116卷 685-698页

作者： Zou, Zheng Cao, Rupeng Chen, Wei Lei, Sicong Gao, Xu Yang, Yong Chongqing Univ Technol Chongqing 40054 Peoples R China Chongqing Machine Tool Grp Co Ltd Chongqing Peoples R China

The hob wear and tear of hob adversely affect the stability of dry gear hobbing accuracy. Quick and accurate online recognition of current hob wear state is crucial to curb this type of machining error during the continuous gear hobbing process. For most of CNC machine tools, an in-depth analysis of the spindle power consumption signal is the common way to monitor the tool wear condition. However, the variation of the spindle current signal is not only related to tool wear state but also the thermal-induced error and the different machining allowances of workpieces to be cut. Therefore, it is barely possible to find one or two individual signal features to precisely characterize the change of hob wear state. In this study, to find the features with strong characterization abilities, we proposed a new tool wear experiment strategy to collect the power consumption data and the thermal-induced error in the entire thermal deformation developing period as well as the representative value of different machining allowances of workpieces. Based on collected spindle power consumption data, we indiscriminately extracted both the time-domain and the frequency-domain features and formed a high-dimensional 47*100 feature dataset. Subsequently, some extracted features were excluded by conducting a correlation analysis with the help of collected thermal error data and machining allowance data, and the rest was further compressed to nine new synthetic features with a dimension-reduced treatment by using the deep autoencoder algorithm. Finally, a mapping model between the synthetic features and different hob wear states was developed by adopting the back propagation neural network classification algorithm. To prove the practicability of this mapping model, another gear cutting experiment was conducted and the results showed the accuracy of this hob wear state online monitoring approach can reached to over 90%.

关键词： Tool wear condition monitoring Dry gear hobbing machine deep autoencoder Spindle power consumption

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Non-intrusive quality assessment of noise-suppressed speech using unsupervised deep features

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SPEECH COMMUNICATION 2021年 130卷 27-44页

作者： Soni, Meet H. Patil, Hemant A. Tata Consultancy Serv Mumbai Maharashtra India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

Objective quality assessment aims towards evaluating the perceptual quality of a signal using a machine-based algorithm. Due to different challenges involved in the subjective evaluation of speech quality, it is necessary to develop objective measures. The goal of any non-intrusive quality assessment metric for noise-suppressed speech is to assess the quality of a noise-suppressed signal in the absence of any clean reference signal. As per the ITU-T P.835 recommendations, the quality assessment of noise-suppressed speech involves predicting three quality scores, namely, signal quality, background quality, and overall quality score, and hence, considered in this study. In recent literature, the non-intrusive quality assessment problem is presented as a regression problem, in which the mapping between a set of acoustic features and corresponding quality scores is found using a perceptual model. Recently, we proposed the use of deep autoencoder (DAE) features and Subband autoencoder (SBAE) features for acoustic representation and an Artificial Neural Network (ANN) as a regression model. DAE and SBAE are variants of autoencoder architecture that have bottleneck structure in the hidden layers. Such architecture represents the class of generalized nonlinear Principal Component Analysis (PCA) that guarantees reconstruction of the input features with arbitrary accuracy. Both the features (DAE and SBAE) are extracted using unsupervised deep learning architectures, and they demonstrated better performance than the state-of-the-art spectral feature set, namely, Mel Filterbank Energies (FBEs). In this paper, we present more detailed analysis of previously proposed features, i.e., DAE and SBAE features, and analyze the usefulness of these features in predicting signal as well as background quality scores in addition to the overall quality score. We compare the performance of all the three features with each other as well as with current ITU-T P.563 metric for non-intrusive speec

关键词： Non-intrusive quality assessment deep autoencoder Subband autoencoder Regression Artificial neural network

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A deep learning approach for collaborative prediction of Web service QoS

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2021年第1期15卷 5-20页

作者： Smahi, Mohammed Ismail Hadjila, Fethallah Tibermacine, Chouki Benamar, Abdelkrim Univ Tlemcen Comp Sci Dept LRIT Tilimsen Algeria Univ Montpellier LIRMM Montpellier France CNRS Montpellier France

Web services are a cornerstone of many crucial domains, including cloud computing and the Internet of Things. In this context, QoS prediction for Web services is a highly important and challenging issue that facilitates the building of value-added processes such as compositions and workflows of services. Current QoS prediction approaches, like collaborative filtering methods, mainly suffer from obstacles related to data sparsity and the cold-start problem. Moreover, previous studies have not conducted in-depth explorations of the impact of the geographical characteristics of services/users and QoS ratings on the prediction problem. To address these difficulties, we propose a deep-learning-based approach for QoS prediction. The main idea consists of combining a matrix factorization model based on a deep autoencoder (DAE) with a clustering technique based on geographical characteristics to improve the effectiveness of prediction. The overall method proceeds as follows: first, we cluster the input QoS data using a self-organizing map that incorporates knowledge of geographical neighborhoods;by doing so, we can reduce the data sparsity while preserving the topology of the input data. In addition, the clustering step effectively overcomes the cold-start problem. Next, we train a DAE that minimizes the squared loss between the ground-truth QoS and the predicted one, for each cluster. Finally, the unknown QoS of a new service is predicted using the trained DAE related to the closest cluster. To evaluate the effectiveness and robustness of our approach, we conducted a comprehensive set of experiments based on a real-world Web service QoS dataset. The experimental results show that our method achieves better prediction performance than several state-of-the-art methods.

关键词： Web service QoS prediction deep autoencoder Self-organizing map

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A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Li, Wanxiang Shang, Zhiwu Zhang, Jie Gao, Maosheng Qian, Shiqi Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China Tianjin Modern Electromech Equipment Technol Key L Tianjin 300387 Peoples R China

During the operation of rotating machinery, the occurrence of unknown fault types makes it impossible for the artificial intelligence-based fault diagnosis model to distinguish. Furthermore, due to the excessive generalization capability of the autoencoder, the unsupervised anomaly detection method based on the autoencoder is difficult to effectively distinguish normal and abnormal samples. To address the above problem, this paper proposed an unsupervised anomaly detection method based on memory augmented temporal convolutional autoencoder (MATCAE). Firstly, a novel temporal convolutional autoencoder model is constructed based on dilated causal convolution, skip connection and autoencoder to facilitate the model to learn the temporal features of the input data, thereby enhancing the model's ability to capture the complex structure of the data. Then, a memory augmented module is designed using a memory matrix and an attention mechanism to expand the distribution interval between the reconstructed samples of normal and abnormal samples and reduce the sample capacity in the overlapping area. Finally, an anomaly detection module based on Euclidean distance, cosine distance and absolute mean square error is designed to improve the reliability of the metric between the input and reconstructed samples. To verify the effectiveness of the proposed method, experimental validation is carried out on a gearbox anomaly detection dataset. The experimental results show that the proposed method has higher anomaly detection accuracy and better noise robustness than other advanced anomaly detection methods, where the average performance metric is improved by 26.86% at the highest and 2.80% at the lowest.

关键词： Rotating machinery Anomaly detection Dilated convolutional deep autoencoder Unsupervised learning Memory augmented

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A hybrid approach for artwork recommendation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Gatti, Ignacio Diaz-Pace, J. Andres Schiaffino, Silvia UNCPBA CONICET ISISTAN Tandil Argentina

Museums usually exhibit thousands of artworks, and nowadays, they often have their collections online for visitors. In these collections, the curators are responsible for organizing the artworks seeking a delicate balance between emotion and reason. Given an initial artwork, however, a visitor is likely to select and admire a set of related artworks that match her interests. This setting can be seen as a recommendation problem in the art domain. Although image recommendation systems have been previously developed, considering the artwork nature is a fundamental aspect when designing a recommender system in this domain. Thus, we propose a hybrid recommendation approach that combines deep autoencoders with a social influence graph in order to capture the visual aspects and context of artworks (represented by images). These mechanisms inform the generation of rankings of related artworks. In this context, we report on a case study with a group of art experts who assessed the rankings of artworks recommended by our approach. Although preliminary, the results showed a better precision than traditional strategies based solely on image features or metadata. Furthermore, the recommendations exhibited diversity properties, avoiding typical over-specialization problems of content-based techniques.

关键词： Recommender systems Artwork deep autoencoder Ontology

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A White Shark Equilibrium Optimizer with a Hybrid deep-Learning-Based Cybersecurity Solution for a Smart City Environment

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SENSORS 2023年第17期23卷 7370-7370页

作者： Almuqren, Latifah Aljameel, Sumayh S. Alqahtani, Hamed Alotaibi, Saud S. Hamza, Manar Ahmed Salama, Ahmed S. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Comp Sci Dept POB 1982 Dammam 31441 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Unit Cybersecur Abha 61421 Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Informat Syst Mecca 24382 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

关键词： smart grids DDoS attacks cybersecurity feature selection deep autoencoder smart cities

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Prediction of multiple fatigue crack growth based on modified Paris model with particle filtering framework

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期190卷

作者： Wang, Li Zhang, Chao Tao, Chongcong Ji, Hongli Yang, Yu Qiu, Jinhao Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China Aircraft Strength Res Inst China Struct Damage Monitoring Lab Xian 710065 Peoples R China

This paper presents a novel method based on modified Paris model with particle filtering (PF) framework to prognosis fatigue multi-cracks in metal structures. Since conventional Paris model is only applicable for the single crack, modified Paris model is proposed by introducing the mutual interaction between multiple cracks into stress intensity factor (SIF) expression and utilized to portray multiple fatigue crack growth process. To accurately monitor multi-cracks lengths, Lamb waves are periodically acquired during the fatigue growth of multiple cracks and fed into a deep autoencoder (DAE) network to automatically track response signals variations. Online monitoring model of multi-cracks lengths is then constructed by fitting the mapping relationship between the deep damage feature extracted by the bottleneck layer of DAE network and crack length. PF framework is further adopted to strategically fusing the prediction results of modified Paris model and real-time measurements of online monitoring model to reduce the uncertainties of material parameters and obtain more reliable prognosis results of multi-cracks lengths. The proposed method is demonstrated on center-hole metal specimens with two fatigue cracks. Experimental results show that the proposed method can accurately prognosis two fatigue cracks lengths, in which modified Paris model could more accurately describe multiple fatigue crack growth than conventional Paris model, and online monitoring model based on deep damage feature could more accurately track the fatigue crack length than artificial damage feature.

关键词： Fatigue crack growth Paris model deep autoencoder Lamb waves Particle filtering Metal structures

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A Hybrid deep Ensemble for Speech Disfluency Classification

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第8期40卷 3968-3995页

作者： Pravin, Sheena Christabel Palanivelan, M. Rajalakshmi Engn Coll Dept ECE Chennai Tamil Nadu India

In this paper, a novel Hybrid deep Ensemble (HDE) is proposed for automatic speech disfluency classification on a sparse speech dataset. Categorizations of speech disfluencies for diagnosis of speech disorders have so long relied on sophisticated deep learning models. Such a task can be accomplished by a straightforward approach with high accuracy by the proposed model which is an optimal combination of diverse machine learning and deep learning algorithms in a hierarchical arrangement which includes a deep autoencoder that yields the compressed latent features. The proposed model has shown considerable improvement in downgrading processing time overcoming the issues of cumbersome hyper-parameter tuning and huge data demand of the deep learning algorithms with high classification accuracy. Experimental results show that the proposed Hybrid deep Ensemble has superior performance compared to the individual base learners, and the deep neural network as well. The proposed model and the baseline models were evaluated in terms of Cohen's kappa coefficient, Hamming loss, Jaccard score, F-score and classification accuracy.

关键词： Hybrid deep Ensemble Speech disfluency classification Sparse speech dataset deep autoencoder Latent features

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