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Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder

ENERGY REPORTS 2022年 8卷 998-1003页

作者： Qu, Chong Zhou, Zhiguo Liu, Zhiwen Jia, Shuli Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Shanghai Marine Diesel Engine Res Inst Automat Engn Dept Shanghai 201108 Peoples R China

Marine diesel engine with high thermal efficiency and good economy has become the main power of ships. Anomaly detection is an important method to improve the operation reliability of marine diesel engine. Most anomaly detection research focuses on failures that have occurred, and few studies consider anomaly prediction. A predictive anomaly detection method based on echo state network (ESN) and deep autoencoder is proposed. Historical sample data is collected and used to train the prediction network ESN and the anomaly detection network deep auto-encoder. After training, the prediction network ESN is used to predict the sensor data sequence in the future. And the predicted sequence is input into the anomaly detection network deep auto-encoder to obtain the predictive anomaly detection result. The relative error and root mean square error of the proposed method are at least 0.089 and 1.002 lower than other methods, respectively. Compared with other anomaly detection methods, the proposed autoencoder method obtains the best precision, accurate, recall indicators. Experiments show that it is feasible to establish a predictive anomaly detection method. More experiments under different conditions need to be studied, and higher performance algorithms need to be developed in the future. (C) 2022 Published by Elsevier Ltd.

关键词： Anomaly detection Prediction Marine diesel engine Echo state network deep autoencoder

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Research on unsupervised anomaly data detection method based on improved automatic encoder and Gaussian mixture model

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-16页

作者： Liu, Xiangyu Zhu, Shibing Yang, Fan Liang, Shengjun Univ Space Engn Beijing 101416 Peoples R China Beijing Informat Sci & Technol Univ Beijing 100026 Peoples R China

With the development of cloud computing, more and more security problems like "fuzzy boundary" are exposed. To solve such problems, unsupervised anomaly detection is increasingly used in cloud security, where density estimation is commonly used in anomaly detection clustering tasks. However, in practical use, the excessive amount of data and high dimensionality of data features can lead to difficulties in data calibration, data redundancy, and reduced effectiveness of density estimation algorithms. Although auto-encoders have made fruitful progress in data dimensionality reduction, using auto-encoders alone may still cause the model to be too generalized and unable to detect specific anomalies. In this paper, a new unsupervised anomaly detection method, MemAe-gmm-ma, is proposed. MemAe-gmm-ma generates a low-dimensional representation and reconstruction error for each input sample by a deep auto-encoder. It adds a memory module inside the auto-encoder to better learn the inner meaning of the training samples, and finally puts the low-dimensional information of the samples into a Gaussian mixture model (GMM) for density estimation. MemAe-gmm-ma demonstrates better performance on the public benchmark dataset, with a 4.47% improvement over the MemAe model standard F1 score on the NSL-KDD dataset, and a 9.77% improvement over the CAE-GMM model standard F1 score on the CIC-IDS-2017 dataset.

关键词： Cloud security Unsupervised machine learning Anomalous data detection Memory module deep autoencoder Gaussian mixture model

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deep Rating and Review Neural Network for Item Recommendation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第11期33卷 6726-6736页

作者： Xi, Wu-Dong Huang, Ling Wang, Chang-Dong Zheng, Yin-Yu Lai, Jian-Huang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Computat Sci Guangzhou 518055 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Sun Yat Sen Univ Lingnan Univ Coll Guangzhou 510275 Peoples R China

To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users' overall preference for the items, while the review text contains rich information about the users' preferences and the attributes of the items. In real life, reviews with the same rating may have completely opposite semantic information. If only the ratings are used for error backpropagation, the latent factors of these reviews will tend to be consistent, resulting in the loss of a large amount of review information. In this article, we propose a novel deep model termed deep rating and review neural network (DRRNN) for recommendation. Specifically, compared with the existing models that adopt the review text as the auxiliary information, DRRNN additionally considers both the target rating and target review of the given user-item pair as ground truth for error backpropagation in the training stage. Therefore, we can keep more semantic information of the reviews while making rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.

关键词： Predictive models Semantics Recommender systems Collaboration Backpropagation Neural networks Electronic mail deep autoencoder deep neural networks (DNNs) ratings and reviews recommender systems

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Index tracking based on deep neural network

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COGNITIVE SYSTEMS RESEARCH 2019年 57卷 107-114页

作者： Ouyang, Hongbing Zhang, Xiaowei Yan, Hongju Huazhong Univ Sci & Technol Sch Econ Wuhan Hubei Peoples R China

deep learning has a strong ability to extract feature representations from data, since it has a great advantage in processing nonlinear and non-stationary data and reflecting nonlinear interactive relationship. This paper proposes to apply deep learning algorithms including deep neural network and deep autoencoder to track index performance and introduces a dynamic weight calculation method to measure the direct effects of the stocks on index. The empirical study takes historical data of Hang Seng Index (HSI) and its constituents to analyze the effectiveness and practicability of the index tracking method. The results show that the index tracking method based on deep neural network has a smaller tracking error, and thus can effectively track the index. (C) 2018 Elsevier B.V. All rights reserved.

关键词： deep neural network deep autoencoder Index tracking

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

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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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Inverse optimization strategy for improved differential privacy in deep auto encoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第25期83卷 65857-65887页

作者： Vasa, Jalpesh Thakkar, Amit Charotar Univ Sci & Technol CHARUSAT Chandubhai S Patel Inst Technol CSPIT Fac Technol & Engn FTE Smt K D Patel Dept Informat Technol CHARUSAT Campus Changa 388421 Gujarat India Charotar Univ Sci & Technol CHARUSAT Chandubhai S Patel Inst Technol CSPIT Fac Technol & Engn FTE Dept Comp Sci & Engn Changa 388421 Gujarat India

deep learning (DL) models are used in a variety of real-world applications but are often vulnerable to privacy attacks. Nevertheless, this DL model is attacked by membership inference attacks, model inversion attacks, reconstruction attacks, model extraction attacks, gradient leakage attacks, correlation attacks, and white box attacks (inference attacks). In order to mitigate this issue, various existing research has attempted to design an effective privacy mechanism. However, the existing schemes failed to obtain higher security in DL because of several limitations like computational complexity, lower efficiency, difficult-to-select parameters, cumulative privacy loss, etc. Recently, the auto-encoder-based deep learning model has become more popular due to its great ability, and its variants have achieved notable success in various fields such as medicine, healthcare and NLP (Natural Language Programming). However, the privacy of the auto-encoder model is affected because of the vulnerable attacks. Thus, to avoid this issue, the proposed study prefers the differential privacy (DP) method for securing the deep auto-encoder model. DP is a privacy-preserving technique that can be used to protect deep learning models from the aforementioned attacks. In this paper, a Differential Privacy-Improved Stochastic Gradient Descent (DP-ISGD) algorithm is proposed to improve the privacy and utility of the deep autoencoder method by adding Gaussian noise to the gradients before the clipping process. Thus, the convergence speed and accuracy of the proposed algorithm are enhanced. The experimentation is conducted in the Python platform, and metrics like convergence, accuracy and TPVD (Total Parameters Value Difference) are evaluated to measure the performance of the proposed study. The comparative analysis is performed for the no privacy, privacy with SGD, privacy with batch gradient descent (BGD) and mini-batch gradient descent (MBGD) models. The proposed approach is evaluated aga

关键词： Differential privacy Batch gradient descent Improved stochastic gradient descent deep autoencoder Clipping

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FedEVCP: Federated Learning-Based Anomalies Detection for Electric Vehicle Charging Pile

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COMPUTER JOURNAL 2024年第4期67卷 1521-1530页

作者： Lin, Zhaoliang Li, Jinguo Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai 201306 Peoples R China

Vehicle-to-Grid (V2G) is a technology that enables electric vehicles to use smart charging methods to harness low-cost and renewable energy when it is available, and obtain income by feeding energy back into the grid. With the rise of V2G technology, the use of electric vehicles has begun to increase dramatically, which relies on the reliable Electric Vehicle Charging Pile (EVCP). However, most EVCPs are online and networked, introducing many potential network threats, such as Electricity Theft, Identity Theft and False Data Injection etc. Prior work has mostly focused on machine learning, which is not able to effectively capture the relationships and structures in network traffic, making it difficult to deal with the propagation and infection of the novel network attacks. Moreover, most neural network models collect and transfer data from EVCPs to the central server for training, which makes the central server attractive to attackers. It poses a serious threat to user privacy. To address these issues, propose an anomaly detection model that incorporates Federated Learning and deep autoencoder, which can increase the amount and diversity of data used to train deep learning models without compromising privacy. The proposed model forms a layer-by-layer unsupervised representation learning algorithm by autoencoder stacking, while batch normalization of hidden layers accelerates the convergence of the model to avoid overfitting and local optima, and introduces an attention mechanism to enhance key features of sequences composed of data vectors to improve the accuracy rate. To prevent the risk of user privacy leakage on the central server, EVCP is allowed to retain local data for model training and send model parameters to the central server for constructing new global models. Experimental results show that the proposed scheme achieves improved detection accuracy with superior performance than other similar models.

关键词： vehicle-to-grid charging pile federated learning deep autoencoder attention mechanism privacy leakage

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Improved image clustering with deep semantic embedding

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PATTERN RECOGNITION LETTERS 2020年 130卷 225-233页

作者： Guo, Jun Yuan, Xuan Xu, Pengfei Bai, Hao Liu, Baoying Northwest Univ Xuefudajie 1 Xian 710127 Shaanxi Peoples R China

Dimensionality reduction has found extensive use in the area of high dimensional data clustering. But unfortunately some valuable discriminative information might be lost in the process of dimensionality reduction, including feature extraction and feature selection. This issue inevitably degrades the performance of clustering algorithms. Recently, the semantic space embedding is emerging as a promising technique for state-of-the-art clustering methods, which can provide extra discriminative information and reasonably improve the clustering performance. In this paper, we plan to improve the performance of high dimensional image clustering by embedding semantic information into the original visual space. Inspired by the great success of deep learning, we employed a multi-layer autoencoder based on deep neural networks (DNNs) to undertake the semantic feature embedding and dimensionality reduction. By this way, the final image clustering task is carried out in the lower-dimensional feature space with deep semantic embedding. A series of experiments on acknowledged benchmark image datasets demonstrate that the proposed approaches can achieve superior performance over several existing clustering methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Semantic embedding Image clustering deep neural networks deep autoencoder

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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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Rolling bearing performance degradation assessment based on improved convolutional neural network with anti-interference

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MEASUREMENT 2020年 151卷 107219-000页

作者： Dong, Shaojiang Wu, Wenliang He, Kun Mou, Xiaoyan Chongqing Jiaotong Univ Sch Mechatron & Automot Engn Chongqing 400074 Peoples R China

Aiming at the difficulty in evaluating and identifying the degradation performance of rolling bearing, an intelligent approach based on deep autoencoder (DAE), t-distribution stochastic neighbor embedding (tSNE) and the improved convolutional neural network (CNN) is proposed in this research. Firstly, the characteristics about the performance degradation of bearing signal are extracted, expressed and reduced by the DAE and the t-SNE model. Subsequently, the Mahalanobis distance (MD) in the low-dimensional feature space is constructed as an indicator for reflecting the bearing performance degradation. After that, the CNN is trained based on the tagged bearing data. In order to suppress the over-fitting of the model, training samples are added with noise. Moreover, Leaky ReLU (LReLU) function and dropout are used as the activation function to improve the anti-interference ability. Finally, the results of performance degradation assessment show that the proposed method has more accurate performance than some existing methods. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Rolling bearing Performance degradation indicator deep autoencoder T-distribution stochastic neighbor embedding Improved convolutional neural network

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