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

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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deep learning shear capacity prediction of fibrous recycled aggregate concrete beams strengthened by side carbon fiber-reinforced polymer sheets

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COMPOSITE STRUCTURES 2022年 300卷

作者： Ghoniem, Amr Zagazig Univ Fac Engn Struct Engn Dept Zagazig 44519 Egypt

The goal of this paper is to evaluate the shear capacity of fibrous recycled aggregate concrete beams side bonded with carbon fiber-reinforced polymer (CFRP) based on numerical modeling and deep learning. Forty-five models were numerically investigated in ANSYS with different lengths and layers of vertical CFRP sheets. Concrete properties built for coarse recycled aggregate and various fibers as 3D and 5D steel, synthetic, and hybrid fibers added at a 0.75% volume fraction. Finally, feedforward, cascade-forward backpropagation, and NARX network algorithms were adopted to predict the shear capacity. Shear capacity improvement was significantly affected by CFRP rehabilitation. Double-layered rehabilitation applied on the zone between support and load was the most adequate. The performance of cascade-forward backpropagation in the estimation of shear capacity was superior to the other deep autoencoder models. The present paper offers good guidance on the practice field as a valuable supplement to the laboratory investigation and theoretical calculations.

关键词： CFRP Concrete properties deep autoencoder Hybrid fiber Numerical RCA

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A hybrid approach for artwork recommendation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 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 novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region

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COASTAL ENGINEERING 2024年 190卷

作者： Naeini, Saeed Saviz Snaiki, Reda Univ Quebec Ecole Technol Super Dept Construct Engn Montreal PQ H3C 1K3 Canada

Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a lowdimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge.

关键词： Storm surge Significant wave height deep learning deep autoencoder

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Data-driven learning to identify biomarkers in bipolar disorder

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 226卷 107112-107112页

作者： Li, Zhuangzhuang Li, Wenmei Yan, Wei Zhang, Rongrong Xie, Shiping Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210023 Peoples R China Nanjing Med Univ Dept Psychiat Affiliated Nanjing Brain Hosp Nanjing 210029 Peoples R China

Background and Objective: Bipolar disorder (BD) is one of the primary causes of disability globally and can be easily misdiagnosed as schizophrenia or major depression due to their similar symptoms. Hence, it is of great significance to explore the pathogenesis of BD. Statistical analysis is currently the most common method for exploring the neuropathological mechanisms of psychiatric disorders. However, this method only considers the relationship between groups and does not reflect the individual-level diagnosis. There-fore, we developed machine learning algorithms to measure pathological brain changes in psychiatric dis-orders. Methods: An autoencoder and a feature selection method are proposed to identify the abnormal structural patterns of BD in this study. The autoencoder was constructed using structural imaging data from 1113 healthy controls, which aims to define the normal range of anatomical deviations to distin-guish healthy individuals from BD patients. The biomarkers of BD were identified by the reconstruction errors in each brain region. The proposed feature selection (FS)-select framework aimed to determine the optimal FS method and identify the most reproducible feature associated with BD. Results: We found that the left orbital region of the middle frontal gyrus had the greatest difference between healthy controls and BD patients using a trained autoencoder. The most reproducible feature was the left orbital region of the middle frontal gyrus by FS-select framework when using the different cross-validation strategies. Conclusions: A consistent result was obtained from the above two proposed methods wherein a signifi-cant difference between healthy controls and BD patients was identified in the left orbital region of the middle frontal gyrus.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Bipolar disorder Structural magnetic functional imaging deep autoencoder Feature selection

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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 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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Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection

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INFORMATION PROCESSING & MANAGEMENT 2022年第2期59卷

作者： An, Peng Wang, Zhiyuan Zhang, Chunjiong Ningbo Univ Technol Coll Elect & Informat Engn Ningbo 315211 Zhejiang Peoples R China Tongji Univ Int Coll Football Shanghai 200092 Peoples R China Tongji Univ Dept Comp Sci Shanghai 201804 Peoples R China

Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.

关键词： Multidomain data Cyberattacks deep autoencoder GMM

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Modal decomposition-based hybrid model for stock index prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷

作者： Lv, Pin Shu, Yating Xu, Jia Wu, Qinjuan Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China

Stock index prediction is considered one of the most challenging issues in the financial sector owing to its noise, volatility, and instability. Traditional stock index prediction methods, such as statistical and machine learning methods, cannot achieve a high denoising effect, and also cannot mine enough data features from the stock data, resulting in a poor prediction performance. deep learning has become an effective tool to predict non-stationary and nonlinear stock indices with strong learning ability. However, there is still room for prediction accuracy improvement if a single deep learning prediction model is replaced with a hybrid model. Therefore, this study proposes a novel deep learning hybrid model for stock index prediction named CEEMDAN-DAE-LSTM. In this hybrid model, the stock index is first decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) into a series of intrinsic mode functions (IMFs) arranged from high to low frequency. Next, the deep autoencoder (DAE) is applied to remove redundant data and extract deep-level features. Then, high-level abstract features are separately fed into long short-term memory (LSTM) networks to predict the stock returns of the next trading day. Finally, the final predicted value is obtained by synthesizing the value of each component. Empirical research results on six stock indices representing both developed and emerging markets showed that our model is superior to other reference models in terms of prediction accuracy and stock index trends;furthermore, it has higher prediction performance for stock indices with greater volatility. In general, this model could be applicable to various stock markets with different degrees of development.

关键词： Stock index prediction deep learning hybrid prediction model Adaptive noise complete ensemble empirical mode decomposition deep autoencoder Long short-term memory

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Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using deep Learning

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ENERGIES 2022年第24期15卷

作者： Korkos, Panagiotis Kleemola, Jaakko Linjama, Matti Lehtovaara, Arto Tampere Univ Fac Engn & Nat Sci Tribol & Machine Elements Mat Sci & Environm Engn POB 589 Tampere 33014 Finland Suomen Hyotytuuli Oy POB 305 Pori 28601 Finland Tampere Univ Fac Engn & Nat Sci Automat Technol & Mech Engn Unit POB 589 Tampere 33014 Finland

Wind turbine operators usually use data from a Supervisory Control and Data Acquisition system to monitor their conditions, but it is challenging to make decisions about maintenance based on hundreds of different parameters. Information is often hidden within measurements that operators are unaware of. Therefore, different feature extraction techniques are recommended. The pitch system is of particular importance, and operators are highly motivated to search for effective monitoring solutions. This study investigated different dimensionality reduction techniques for monitoring a hydraulic pitch system in wind turbines. These techniques include principal component analysis (PCA), kernel PCA and a deep autoencoder. Their effectiveness was evaluated based on the performance of a support vector machine classifier whose input space is the new extracted feature set. The developed methodology has been applied to data from a wind farm consisting of five 2.3 MW fixed-speed onshore wind turbines. The available dataset is composed of nine pitch events representing normal and faulty classes. The results indicate that the features extracted by the deep autoencoder are more informative than those extracted by PCA and kernel PCA. These features led to the achievement of a 95.5% F1-score, proving its superiority over the traditional usage of original features.

关键词： pitch system wind turbine SCADA fault detection feature extraction deep autoencoder

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An Overview of deep Generative Models

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IETE TECHNICAL REVIEW 2015年第2期32卷 131-139页

作者： Xu, Jungang Li, Hui Zhou, Shilong Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 101408 Peoples R China

As an important category of deep models, deep generative model has attracted more and more attention with the proposal of deep Belief Networks (DBNs) and the fast greedy training algorithm based on restricted Boltzmann machines (RBMs). In the past few years, many different deep generative models are proposed and used in the area of Artificial Intelligence. In this paper, three important deep generative models including DBNs, deep autoencoder, and deep Boltzmann machine are reviewed. In addition, some successful applications of deep generative models in image processing, speech recognition and information retrieval are also introduced and analysed.

关键词： deep belief networks Restricted boltzmann machine deep autoencoder deep generative model deep boltzmann machine

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