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A novel smart meter data compression method via stacked convolutional sparse auto-encoder

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年第0期118卷 105761-000页

作者： Wang, Shouxiang Chen, Haiwen Wu, Lei Wang, Jianfeng Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Xianghe Elect Co Ltd Tianjin 30072 Peoples R China Stevens Inst Technol ECE Dept Hoboken NJ 07030 USA State Grid Tianjin Elect Power Co Tianjin 300000 Peoples R China

With the wide deployment of smart meters in distribution systems, a new challenge emerges for the storage and transmission of huge volume of power consumption data collected by smart meters. In this paper, a deep-learning-based compression method for smart meter data is proposed via stacked convolutional sparse auto-encoder (SCSAE). An efficient and lightweight auto-encoder structure is first designed by leveraging the unique characteristics of smart meter readings. Specifically, the encoder is designed based on 2D separable convolution layers and the decoder is based on transposed convolution layers. Compared with the existing auto-encoder method and traditional methods, the proposed structure is redesigned, and the parameters and reconstruction errors are efficiently reduced. In addition, cluster-based indexes are used to represent the regularity of power consumption behavior and the relationship between electricity consumption behavior and compression effect is studied. Case studies illustrate that the proposed method can attain significant enhancement in model size, computational efficiency, and reconstruction error reduction while maintaining the most abundant details. And grouping compression considering users' electricity consumption rules can further improve the compression effect.

关键词： Smart meter Lossy compression Separable convolution auto-encoder

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Adversarial auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative Hypergraphs

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2023年第2期41卷 33-33页

作者： Wu, Hanrui Long, Jinyi Li, Nuosi Yu, Dahai Ng, Michael K. Jinan Univ Coll Informat Sci & Technol Guangzhou 510000 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangdong Key Lab Tradit Chinese Med Informat Tec Pazhou Lab Guangzhou Peoples R China TCL Corp Res Hong Kong Hong Kong Peoples R China Univ Hong Kong Inst Data Sci Pofulam Rd Hong Kong 999077 Peoples R China Univ Hong Kong Dept Math Pofulam Rd Hong Kong 999077 Peoples R China

This article presents a novelmodel named Adversarial auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users' positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the cold-start item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, normalized discounted cumulative gain, and hit rate verify the effectiveness of the proposed method.

关键词： Recommendation systems cold-start recommendation auto-encoder hypergraph adversarial learning domain adaptation

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Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115729-115729页

作者： Cai, Jinyu Wang, Shiping Guo, Wenzhong Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Fuzhou Univ Network Comp & Intelligent Informat Proc Lab Fuzhou 350116 Peoples R China

Deep clustering attempts to capture the feature representation that benefits the clustering issue. Although the existing deep clustering methods have achieved encouraging performance in many research fields, there still present some shortcomings, such as the lack of consideration of local structure retention and sparse characteristics of input data. To this end, we propose a deep stacked sparse embedded clustering method in this paper, which considers both the local structure preservation and sparse property of inputs. The proposed method is trained to capture the feature representation for an input data by the guidance of the clustering and reconstruction loss, where the reconstruction loss prevents the corruption of feature space and preserve the local structure. Besides, sparse constraint is added to the encoder to avoid learning of unimportant features. Through simultaneously minimizing the reconstruction and clustering loss, the proposed method is able to jointly learn the clustering oriented features and optimize the assignment of cluster labels. Then we conduct amounts of comparative experiments, which consists of seven clustering methods and six publicly available image data sets. Eventually, comprehensive experiments validate the effectiveness of introducing sparse property and preserving local structure in the proposed method.

关键词： Machine learning Deep clustering Feature representation auto-encoder Neural networks

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Application of Improved Deep auto-encoder Network in Rolling Bearing Fault Diagnosis

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Journal of Computer and Communications 2018年第7期6卷 41-53页

作者： Jian Di Leilei Wang School of Control and Computer Engineering North China Electric Power University Baoding China

Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.

关键词： Fault Diagnosis Rolling Bearing Deep auto-encoder Network CAPSO Algorithm Feature Extraction

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A comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder and XGBoost algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第12期35卷 126144-126144页

作者： Chi, Huashan Wei, Yuanyu Yuan, Bo Sun, Qingchao Shu, Liming Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab High Performance Precis Mfg Dalian 116024 Peoples R China Liaoning Huanghai Lab Dalian 116024 Peoples R China

Accurately forecasting the remaining useful life (RUL) stands as a pivotal and formidable task within the realm of prognostics and health management. However, there is limited research that considers integrating early fault diagnosis during the bearing's lifecycle with the prediction of its RUL. In this article, a comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder (AE) and XGBoost algorithm is proposed. To achieve this, an unsupervised piecewise function-based deep stacked convolutional AE was designed to construct the health indicator (HI) of the bearing for reducing the dependency on prior knowledge and furnishing a dynamic foundation for predicting RUL. The 4 sigma criterion based on HI's increment was proposed for determining the fault occurrence time (FOT) of the bearing's operational process. Subsequently, an XGBoost algorithm model was utilized to predict the RUL of faulty bearings. The efficacy of the bearing prognosis framework was validated by two real bearing test datasets. Results indicate the employed criterion of construct HI can flexibly adjust to various operational conditions and accurately pinpoint the bearing's FOT. Furthermore, the findings demonstrate the proposed bearing prognosis framework achieves superior performance compared with several conventional anomaly detection methods.

关键词： bearing degradation prognosis remaining useful life (RUL) prediction health indicator (HI) fault occurrence time (FOT) recognition auto-encoder

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Prediction of Array Antenna Assembly Accuracy Based on auto-encoder and Boosting-OSKELM

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PROCESSES 2023年第5期11卷 1460页

作者： Tong, Yifei Wang, Miao Zhou, Tong Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

As a critical component for space exploration, navigation, and national defense, array antenna secures an indispensable position in national strategic significance. However, various parts and complex assembly processes make the array antenna hard to meet the assembly standard, which causes repeated rework and delay. To realize the accurate and efficient prediction of the assembly accuracy of array antenna, a prediction method based on an auto-encoder and online sequential kernel extreme learning machine with boosting (Boosting-OSKELM) is proposed in this paper. The method is mainly divided into two steps: Firstly, the auto-encoder with the fine-tuning trick is used for training and representation reduction of the data. Then, the data are taken as the input of Boosting-OSKELM to complete the initial training of the model. When new sample data is generated, Boosting-OSKELM can realize the online correction of the model through rapid iteration. Finally, the test shows that the average MSE of Boosting-OSKELM and ANN is 0.061 and 0.12, and the time consumption is 0.85 s and 15 s, respectively. It means that this method has strong robustness in prediction accuracy and online learning ability, which is conducive to the development of array antenna assembly.

关键词： array antenna assembly accuracy auto-encoder boosting-OSKELM

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Rare Failure Prediction Using an Integrated auto-encoder and Bidirectional Gated Recurrent Unit Network

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IFAC-PapersOnLine 2020年第3期53卷 276-282页

作者： Maren David Dangut Zakwan Skaf Ian K. Jennions Integrated Vehicle Health Management (IVHM) Center Cranfield University Bedford MK430AL United Kingdom Mechanical Engineering Department Higher Colleges of Technology United Arab Emirates

Aircraft fault detection and prediction is a critical element of preventing failures, reducing maintenance costs, and increasing fleet availability. This paper considers a problem of rare failure prediction in the context of aircraft predictive maintenance. It presents a novel approach of predicting extremely rare failures, based on combining two deep learning techniques, auto-encoder (AE) and Bidirectional Gated Recurrent Unit (BGRU) network. AE is modified and trained to detect rare failure, and the result from AE is fed into the BGRU to predict the next occurrence of failure. The applicability of the proposed approach is evaluated using real-world test cases of log-based warning and failure messages obtained from the aircraft central maintenance system fleet database and the records of maintenance history. The proposed AE-BGRU model is compared with other similar deep learning methods, the proposed approach is 25% better in precision, 14% in the recall, and 3% in G-mean. The result also shows robustness in predicting failure within a defined useful period.

关键词： predictive maintenance machine learning extreme rare failure auto-encoder GRU network aircraft

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An integration method using distributed optical fiber sensor and auto-encoder based deep learning for detecting sulfurized rust self-heating of crude oil tanks

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2022年 74卷

作者： Zhu, Zhi-Chao Chu, Cheng-Wei Bian, Hai-Tao Jiang, Jun-Cheng Nanjing Tech Univ Coll Safety Sci & Engn Nanjing 210009 Jiangsu Peoples R China Changzhou Univ Sch Environm & Safety Engn Changzhou 213164 Jiangsu Peoples R China

Sulfurized rust is the production of corrosion in crude oil tanks. It will be oxidized and self-heating when contacting with air, and the rise of temperature can cause severe accidents. This paper focuses on the temperature measurement of distributed optical fiber sensor (DOFS) and the research on anomaly detection methods aided by deep learning. An experimental apparatus was set up to simulate the temperature change during sulfurized rust self-heating, then some artificial ambient temperature was added to interference anomaly detection. The DOFS returned normal temperature, artificial ambient temperature and self-heating temperature data for analysis. Furthermore, four auto-encoder (AE) based algorithms and several traditional machine learning methods were tested on the collected temperature data for anomaly detection. Test revealed that Convolutional Neural Networks auto-encoder (CNN-AE) was successful in detecting the anomaly situations at an accuracy level of 0.98. The study demonstrates that DOFS and deep learning would be a potential solution for detecting anomaly temperature change to prevent self-heating accident caused by sulfurized rust.

关键词： Oil tank Sulfurized rust Distributed optical fiber sensor auto-encoder Anomaly temperature change detection

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Stacked auto-encoder based tagging with deep features for content-based medical image retrieval

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 161卷 113693-113693页

作者： Ozturk, Saban Amasya Univ Dept Elect & Elect Engn TR-05001 Amasya Turkey

Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images is very convenient in large and balanced datasets. Still, it is generally not possible to find such datasets in the medical domain. In this study, a four-step and effective hash code generation technique is presented to reduce the semantic gap between low-level features and high-level semantics for unbalanced medical image datasets. In the first stage, the convolutional neural network (CNN) architecture, the most effective feature representation method available today, is employed to extract discriminative features from images automatically. The features obtained in the last fully connected layer (FCL) at the output of the CNN architecture are used for hash code generation. In the second stage, using the Synthetic Minority Over-sampling Technique (SMOTE), the imbalance between the classes in the dataset is reduced. The solution to the unbalanced problem increases performance by almost 3%. In the third stage, balanced features are converted to a code of 13 symbols by using deep stacked auto-encoder. Finally, this code is translated to the standard 13-character labeling and retrieval code used by the 'Image retrieval in the medical application' (IRMA) dataset, since this is the database with which experiments have been done. IRMA error parameter, classification performance, and retrieval performance of the proposed method are more successful than other state-of-the-art methods. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： CBMIR CNN Retrieval SMOTE IRMA auto-encoder

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A deep neural network based on kernel function and auto-encoder for bearing fault diagnosis

A deep neural network based on kernel function and auto-enco...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Discovering New Horizons in Instrumentation and Measurement

作者： Wang, Fengtao Dun, Bosen Deng, Gang Li, Hongkun Han, Qingkai Dalian Univ Technol Inst Vibrat Engn Sch Mech Engn Dalian Peoples R China

ISBN: (纸本)9781538622223

Intelligent diagnosis technology has been widely applied in the classification of rotating machinery condition. The determination of the effective features from raw vibration data and the provision of accurate fault diagnosis results are important tasks for a bearing fault diagnosis. In this paper, a deep neural network based on Gaussian radial basis kernel function and auto-encoder (AE) is proposed. Firstly, a new AE is constructed with a kernel function to enhance the feature learning capability. Subsequently, a stacked AE network is developed with one new AE and T AEs. Finally, an error back-propagation algorithm is used to fine-tune the model parameters and then the deep characteristics are used to train the intelligent diagnosis model. Experimental data for an aircraft-engine inter-shaft bearing are used to verify the method. The results show that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE.

关键词： Rolling bearing Gaussian kernel function Deep neural network auto-encoder Fault diagnosis

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