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Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing

JOURNAL OF SUPERCOMPUTING 2015年第8期71卷 3037-3053页

作者： Yang, Qiangpeng Zhou, Yu Yu, Yao Yuan, Jie Xing, Xianglei Du, Sidan Nanjing Univ Sch Elect Sci & Engn Nanjing 210008 Jiangsu Peoples R China Harbin Engn Univ Harbin Peoples R China

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. There are many proposals for resource management approaches for cloud infrastructures, but effective resource management is still a major challenge for the leading cloud infrastructure operators (e.g., Amazon, Microsoft, Google), because the details of the underlying workloads and the real-world operational demands are too complex. Among those proposals, accurate host load prediction is one of the most effective measures to address this challenge. In this paper, we proposed a new method for host load prediction, which uses an autoencoder as the pre-recurrent feature layer of the echo state networks. The aim of our proposed method is to predict the host load in the future interval based on Google cluster usage dataset. Experiments performed on Google load traces show that our proposed method achieves higher accuracy than the state-of-the-art methods.

关键词： Host load prediction autoencoder Echo state networks

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Using an autoencoder in the design of an anomaly detector for smart manufacturing

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PATTERN RECOGNITION LETTERS 2020年 136卷 272-278页

作者： Alfeo, Antonio L. Cimino, Mario G. C. A. Manco, Giuseppe Ritacco, Ettore Vaglini, Gigliola Univ Pisa Largo Lucio Lazzarino 1 I-56122 Pisa Italy CNR ICAR Via Pietro Bucci 8-9C I-87036 Arcavacata Di Rende Italy

According to the smart manufacturing paradigm, the analysis of assets' time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets' anomalous operational conditions. To support smart manufacturing operators with no data science background, we propose an anomaly detection approach based on deep learning and aimed at providing a manageable machine learning pipeline and easy to interpret outcome. To do so we combine (i) an autoencoder, a deep neural network able to produce an anomaly score for each provided time series, and (ii) a discriminator based on a general heuristics, to automatically discern anomalies from regular instances. We prove the convenience of the proposed approach by comparing its performances against isolation forest with different case studies addressing industrial laundry assets' power consumption and bearing vibrations. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Fault detection Anomaly detection Smart manufacturing Smart industry Interpretable machine learning autoencoder Anomaly discriminator

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Atypical Sample Regularizer autoencoder for Cross-Domain Human Activity Recognition

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INFORMATION SYSTEMS FRONTIERS 2021年第1期23卷 71-80页

作者： Prabono, Aria Ghora Yahya, Bernardo Nugroho Lee, Seok-Lyong Hankuk Univ Foreign Studies 81 Oedae Ro Yongin 17035 Gyeonggi Do South Korea

The sensor-based human activity recognition (HAR) using machine learning requires a sufficiently large amount of annotated data to realize an accurate classification model. This requirement stimulates the advancement of the transfer learning research area that minimizes the use of labeled data by transferring knowledge from the existing activity recognition domain. Existing approaches transform the data into a common subspace between domains which theoretically loses information, to begin with. Besides, they are based on the linear projection which is bound to linearity assumption and its limitations. Some recent works have already incorporated nonlinearity to find a latent representation that minimizes domain discrepancy based on an autoencoder that includes statistical distance minimization. However, such approach discovers latent representation for both domains at once, which causes sub-optimal representation because both domains compensate each other's reconstruction error during the training. We propose an autoencoder-based approach on domain adaptation for sensor-based HAR. The proposed approach learns a latent representation which minimizes the discrepancy between domains by reducing statistical distance. Instead of learning representation of both domains simultaneously, our method is a two-phase approach which first learns the representation for the domain of interest independently to ensure its optimality. Subsequently, the useful information from the existing domain is transferred. We test our approach on the publicly available sensor-based HAR datasets, using cross-domain setup. The experimental result shows that our approach significantly outperforms the existing ones.

关键词： Transfer learning autoencoder Human activity recognition Machine learning

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Enhanced Denoising autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 640-651页

作者： Guanyu Tian Yingzhong Gu Zhe Yu Qibing Zhang Di Shi Qun Zhou Zhiwei Wang GEIRI North America San JoseCA 95134 USA GEIRI North America San JoseCA 95134 USA on leave from Department of Electrical and Computer EngineeringUniversity of Central FloridaOrlandoFL 32816 USA Grid Dispatch Center State Grid Jiangsu Electric Power Company Ltd.Nanjing 210024China Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816 USA

Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)***,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it *** data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and *** pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this *** data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder *** characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical *** use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.

关键词： autoencoder bad data processing linear state estimation PMU

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Classification of Imbalanced Data Using SMOTE and autoencoder Based Deep Convolutional Neural Network

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第3期31卷 437-469页

作者： Alex, Suja A. Nayahi, J. Jesu Vedha St Xaviers Catholic Coll Engn Informat Technol Nagercoil India Anna Univ Reg Campus Comp Sci & Engn Tirunelveli India

The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches are also suitable for handling class imbalance problem. Data augmentation is one of the preprocessing techniques used to handle skewed class distribution. Synthetic Minority Oversampling Technique (SMOTE) is a promising class balancing approach and it generates noise during the process of creation of synthetic samples. In this paper, autoencoder is used as a noise reduction technique and it reduces the noise generated by SMOTE. Further, Deep one-dimensional Convolutional Neural Network is used for classification. The performance of the proposed method is evaluated and compared with existing approaches using different metrics such as Precision, Recall, Accuracy, Area Under the Curve and Geometric Mean. Ten data sets with imbalance ratio ranging from 1.17 to 577.87 and data set size ranging from 303 to 284807 instances are used in the experiments. The different imbalanced data sets used are Heart-Disease, Mammography, Pima Indian diabetes, Adult, Oil-Spill, Phoneme, Creditcard, BankNoteAuthentication, Balance scale weight & distance database and Yeast data sets. The proposed method shows an accuracy of 96.1%, 96.5%, 87.7%, 87.3%, 95%, 92.4%, 98.4%, 86.1%, 94% and 95.9% respectively. The results suggest that this method outperforms other deep learning methods and machine learning methods with respect to G-mean and other performance metrics.

关键词： Unbalanced data SMOTE deep learning autoencoder convolutional neural network

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Improved Denoising autoencoder for Maritime Image Denoising and Semantic Segmentation of USV

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China Communications 2020年第3期17卷 46-57页

作者： Yuhang Qiu Yongcheng Yang Zhijian Lin Pingping Chen Yang Luo Wenqi Huang Electronic Information Engineering Fuzhou UniversityFuzhou 350116China Nautical College Jimei UniversityXiamen 350116China

Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely *** former has recently researched on autoencoder model used for image denoising,but the existed models are too complicated to be suitable for real-time detection of *** this paper,we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising ***,we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning,and compared them with original U-Net model based on different ***,we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation *** studies are provided to prove the feasibility of our proposed denoising and segmentation ***,a simple integrated communication system combining image denoising and segmentation for USV is shown.

关键词： USV denoising autoencoder semantic segmentation U-Net

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Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid autoencoder

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NEURAL PROCESSING LETTERS 2020年第2期52卷 961-975页

作者： Zhou, Fuqiang Wang, Lin Li, Zuoxin Zuo, Wangxia Tan, Haishu Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Foshan Univ Dept Elect Informat Engn Foshan 528000 Peoples R China

Abnormal detection plays an important role in video surveillance. LSTM encoder-decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder-decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder-decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.

关键词： autoencoder LSTM Abnormality detection

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Domain Adaptation Network Based on autoencoder

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Chinese Journal of Electronics 2018年第6期27卷 1258-1264页

作者： WANG Xuesong MA Yuting CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology

The domain adaptation uses labeled source domain data to train a classifier to be used in the target domain with no or small amount of labeled data. Usually there exists discrepancy in terms of marginal and conditional distributions for both source and target domains,which is of critical importance to minimize the distribution discrepancy between domains. As a classical model in deep learning, the autoencoder is capable of realizing distribution matching and enhancing classification accuracy by extracting more abstract and effective features from data. A Domain adaptation network based on autoencoder(DANA) is proposed. The DANA structure consists of a couple of encoding layers: a feature extraction layer and a classification layer. For the feature extraction layer,the marginal distributions of source and target domains are matched by using the nonparametric maximum mean discrepancy measurement. For the classification layer, the softmax regression model is applied to encode the label information of source domains meanwhile to match the conditional distribution. Experimental results on ImageNet,Corel and Leaves datasets have shown the enhanced classification accuracy by our proposed algorithm compared with the classical methods.

关键词： autoencoder Domain adaptation Distribution matching Feature extraction layer Classification layer

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Non-Technical Losses Detection Using autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

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IEEE ACCESS 2022年 10卷 56863-56875页

作者： Pamir Javaid, Nadeem Qasim, Umar Yahaya, Adamu Sani Alkhammash, Eman H. Hadjouni, Myriam COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia Univ Engn & Technol & Lahore Dept Comp Sci New Campus Lahore 54000 Pakistan Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

关键词： Data models Support vector machines Logic gates Hardware Feature extraction Convolutional neural networks Meters autoencoder bidirectional gated recurrent unit deep learning algorithms smart grid electricity theft detection machine learning algorithms synthetic theft attacks

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Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第5期38卷 1701-1720页

作者： Wang, Shichao Zhai, Peihe Yu, Xiaoge Han, Jin Shi, Longqing Shandong Univ Sci & Technol Coll Earth Sci & Engn Qingdao Peoples R China Shandong Univ Sci & Technol Coll Resource Tai An Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China

To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel water source identification model based on an improved autoencoder-the "Masked autoencoder-based Classifier" model. This model, through a unique autoencoder framework and a custom 'masked_loss' loss function, achieves semi-supervised learning and dimensionality reduction of groundwater sample ionic data. By configuring the hidden layers, the classifier component of the model directly receives data processed by the encoder component. This not only improves the model's performance but also optimizes its complexity. Through an evaluation of the model's fitting effectiveness, our model achieved an average accuracy of 88.8% across 20 runs, with precision, recall, F1-score, and MCC reaching 88.1%, 80.6%, 0.827, and 0.833, respectively, significantly outperforming other classic models. The model successfully identified the sources of three sets of inrush water samples, with a high number of successful runs and clear average probabilities. This work contributes not only to the field of mine water inrush source identification but also offers a new perspective for the broader field of machine learning.

关键词： Mine water Water source identification Manifold reduction Semi-supervised learning autoencoder

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