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Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of autoencoder and Deep Belief Network

IEEE ACCESS 2022年 10卷 82156-82163页

作者： Al-Khazraji, Huthaifa Nasser, Ahmed R. Hasan, Ahmed M. Al Mhdawi, Ammar K. Al-Raweshidy, Hamed Humaidi, Amjad J. Univ Technol Baghdad Control & Syst Engn Dept Baghdad 10066 Iraq Edge Hill Univ Dept Comp Sci Ormskirk L39 4QP England Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England

Remaining Useful Life (RUL) is used to provide an early indication of failures that required performing maintenance and/or replacement of the system in advance. Accurate RUL prediction offers cost-effective operation for decision-makers in the industry. The availability of data using intelligence sensors leverages the power of data-driven methods for RUL estimation. Deep Learning is one example of a data-driven method that has a lot of applications in the industry. One of these applications is the RUL prediction where DL algorithms achieved good results. This paper presents an autoencoder-based Deep Belief Network (AE-DBN) model for Aircraft engines' RUL estimation. The AE-DBN DL model is utilized the feature extraction characteristic of AE and superiority in learning long-range dependencies of DBN. The efficiency of the proposed DL algorithm is evaluated by comparison between the proposed AE-DBRN and the state-of-the-art related method for RUL perdition for four datasets. Based on the Root Mean Square Error (RMSE) and Score indices, the outcomes reveal that the AE-DBN RUL prediction model is superior to other DL approaches.

关键词： Atmospheric modeling Aircraft Feature extraction Predictive models Aircraft propulsion Estimation Data models Artificial intelligence deep learning remaining useful life autoencoder deep belief network aircraft engine

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LS-LSTM-AE: Power load forecasting via Long-Short series features and LSTM-autoencoder

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ENERGY REPORTS 2022年 8卷 596-603页

作者： Tong, Xin Wang, Jingya Zhang, Changlin Wu, Teng Wang, Haitao Wang, Yu Peoples Publ Secur Univ China Beijing 100038 Peoples R China Henan Police Coll Zhengzhou 450046 Peoples R China Kanazawa Univ Kakuma Machi Kanazawa Ishikawa 9201164 Japan China Peoples Police Univ Langfang 065000 Peoples R China

Aiming at weak representation ability and severe loss of time series features in the traditional methods when facing large-scale and complex power load forecasting tasks, an LSTM-autoencoder model that integrates long-term and short-term features of the samples is proposed for load forecasting. The encoder part simultaneously receives long time series and short time series as input to extract time series features of different levels and generate related latent vectors. The decoder tries to reconstruct the input sequence while outputting the prediction results to ensure that the latent vectors are meaningful. In addition, the model also uses a mixture of supervised and unsupervised training methods. Experiments based on a publicly available dataset from Alberta Electric System Operator showed that the method presented in this research is superior to many existing mainstream methods, with a mean absolute error of less than 52MW between the prediction results and the actual load values. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Load forecasting Deep learning LSTM autoencoder Unsupervised learning Supervised learning

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A Semi-Supervised autoencoder-Based Approach for Protein Function Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 4957-4965页

作者： Dhanuka, Richa Tripathi, Anushree Singh, Jyoti P. Natl Inst Technol Patna Patna 800005 Bihar India

After the development of next-generation sequencing techniques, protein sequences are abundantly available. Determining the functional characteristics of these proteins is costly and time-consuming. The gap between the number of protein sequences and their corresponding functions is continuously increasing. Advanced machine-learning methods have stepped up to fill this gap. In this work, an advanced deep-learning-based approach is proposed for protein function prediction using protein sequences. A set of autoencoders is trained in a semi-supervised manner with protein sequences. Each autoencoder corresponds to a single protein function only. In particular, 932 autoencoders corresponding to 932 biological processes and 585 autoencoders corresponding to 585 molecular functions are trained separately. Reconstruction losses of each protein sample for every autoencoder are used as a feature to classify these sequences into their corresponding functions. The proposed model is tested on test protein samples and achieves promising results. This method can be easily extended to predict any number of functions having an ample amount of supporting protein sequences. All relevant codes, data and trained models are available at https://***/richadhanuka/PFP-autoencoders.

关键词： Proteins Protein engineering Predictive models Data models Protein sequence Feature extraction Biological system modeling autoencoder bioinformatics machine learning multi-label classification protein function

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Fermentation process quality prediction using teacher student stacked sparse recurrent autoencoder

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2022年第10期100卷 2907-2917页

作者： Gao, Xuejin Meng, Lingjun Gao, Huihui Han, Huayun Qi, Yongsheng Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Inner Mongolia Univ Technol Sch Elect Power Hohhot Peoples R China

For predicting the value of the quality variable in fermentation processes, traditional data-driven methods do not use information in large amounts of unlabelled data. To solve this data-rich but information-poor (DRIP) problem, a teacher student stacked sparse recurrent autoencoder (TS-SSRAE) model is proposed. Compared with traditional data-driven methods, the proposed method has three main advantages. First, an autoencoder is an unsupervised method which can effectively extract rich information in unlabelled data. The proposed stacked recurrent autoencoder (SRAE) with long short-term memory (LSTM) recurrent neural unit is superior to traditional autoencoders when extracting the dynamic correlation information in the fermentation process. Second, sparse constraints can make it much easier for hidden neurons to obtain useful information in a single moment. Finally, the LSTM recurrent neural unit is complex and the inputs of a SRAE must be a sequence, which increases the complexity of the model to a certain extent. So, the knowledge distillation is employed to simplify the model and reduce the computing time. In order to demonstrate its effectiveness, the proposed method is applied to the penicillin fermentation process for a simulation experiment and Escherichia coli production of interleukin-2. The results show that the proposed method based on TS-SSRAE can have better performance than conventional methods.

关键词： autoencoder fermentation process knowledge distillation LSTM sparse

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Scalable Discrete Matrix Factorization and Semantic autoencoder for Cross-Media Retrieval

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 5947-5960页

作者： Zhang, Donglin Wu, Xiao-Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

Hashing methods have sparked great attention on multimedia tasks due to their effectiveness and efficiency. However, most existing methods generate binary codes by relaxing the binary constraints, which may cause large quantization error. In addition, most supervised cross-modal approaches preserve the similarity relationship by constructing an n x n large-size similarity matrix, which requires huge computation, making these methods unscalable. To address the above challenges, this article presents a novel algorithm, called scalable discrete matrix factorization and semantic autoencoder method (SDMSA). SDMSA is a two-stage method. In the first stage, the matrix factorization scheme is utilized to learn the latent semantic information, the label matrix is incorporated into the loss function instead of the similarity matrix. Thereafter, the binary codes can be generated by the latent representations. During optimization, we can avoid manipulating a large nxn similarity matrix, and the hash codes can be generated directly. In the second stage, a novel hash function learning scheme based on the autoencoder is proposed. The encoder-decoder paradigm aims to learn projections, the feature vectors are projected to code vectors by encoder, and the code vectors are projected back to the original feature vectors by the decoder. The encoder-decoder scheme ensures the embedding can well preserve both the semantic and feature information. Specifically, two algorithms SDMSA-lin and SDMSA-ker are developed under the SDMSA framework. Owing to the merit of SDMSA, we can get more semantically meaningful binary hash codes. Extensive experiments on several databases show that SDMSA-lin and SDMSA-ker achieve promising performance.

关键词： autoencoder cross-modal retrieval hashing

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Learning Stretch-Shrink Latent Representations With autoencoder and K-Means for Software Defect Prediction

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IEEE ACCESS 2022年 10卷 117827-117835页

作者： Phan, Viet Anh Le Quy Don Tech Univ Fac Informat Technol Dept Informat Secur Hanoi 100000 Vietnam

Detecting defective source code to localize and fix bugs is important to reduce software development efforts. Although deep learning models have made a breakthrough in this field, many issues have not been resolved, such as labeled data shortage and the small size of defective elements. Given two similar programs that differ from each other by an operator or statement, one may be clean while the other may be defective. To address these issues, this study proposes a new deep learning model to facilitate the learning of distinguishing features. The model comprises of three main components: 1) a convolutional neural network-based classifier, 2) an autoencoder, and 3) a k-means cluster. In our model, the autoencoder assists the classifier in generating program latent representations. The k-means cluster provides penalty functions to increase the distinguishability among latent representations. We evaluated the effectiveness of the model according to performance metrics and latent representation quality. The experimental results on the four defect prediction datasets show that the proposed model outperforms the baselines thanks to the generation of sophisticated features.

关键词： autoencoder k-means convolutional neural networks latent representations

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User-centric hybrid semi-autoencoder recommendation system

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第16期81卷 23091-23104页

作者： Tewari, Anand Shanker Parhi, Ityendu Al-Turjman, Fadi Abhishek, Kumar Ghalib, Muhummad Rukunuddin Shankar, Achyut NIT Patna CSE Dept Patna Bihar India Near East Univ Dept Artificial Intelligence Engn Res Ctr AI & IoT Mersin 10 Nicosia Turkey Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Amity Univ Amity Sch Engn & Technol Dept CSE Noida India

Recommendation System is one of such solutions to overcome information overload issues and to identify products most relevant to users and provide suggestions to users for items they might be interested in consuming or elements matching their needs. The significant challenge of several recommendation approaches is that they suggested a huge number of things to the target user. But the exciting items, according to the target user, are seen at the bottom of the recommended list. The proposed approach has improved the quality of recommendations by implementing some of the unique features in the new framework of auto encoder called semi-autoencoder, which contains the rating information as well as some additional information of users. autoencoder is widely used in the recommender system because it gives the best result for feature extraction, dimensionality reduction, regeneration of data, and a better understanding of the user's characteristics. The experimental results are compared with some established popular methods using precision, recall, and F-measure evaluation measures. Users generally don't want to see lots of suggestions. With its six building blocks, the proposed approach gives better performance for the top 10 recommendations compared to other well-known methods.

关键词： Recommendation system autoencoder E-commerce Dimensionality reduction Collaborative filtering

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Short-Term Load Forecasting Method Based on autoencoder and LSTNet Models 4

Short-Term Load Forecasting Method Based on Autoencoder and ...

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4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Ding, Yi Pang, Chao Wei, Liyong Wang, En Zhao, Chenyang Gao, Qi Bian, Wenyu Li, Boyang State Grid Tianjin Electric Power Research Institute North China Electric Power University Tianjin China State Grid Tianjin Electric Power Research Institute Tianjin China State Grid Tianjin Integrated Energy Services Limited Company Tianjin China State Grid Tianjin Electric Power Company Tianjin China North China Electric Power University Beijing China

ISBN: (纸本)9798350359589

The accuracy of load forecasting is very important to the safe and economical operation of power system. This paper presents a LSTNet model based on autoencoder feature extraction for load time series prediction. In this model, the autoencoder extracts the key features of the electrical load time series data by minimizing the reconstruction error, encodes these features into a lower-dimensional vector, and inputs the features into the LSTNet model after dimensionality reduction for network training and prediction. The experimental results show that the autoencoder-based LSTNet model has more advantages than the traditional LSTNet model and the long short-term memory (LSTM) neural network model in the Tianjin electric load forecasting task, and the prediction accuracy is increased by about 1.5% in the experimental case. This study provides a new method for power system load forecasting and demonstrates the potential of autoencoders and LSTNet models. © 2024 IEEE.

关键词： autoencoder Deep learning electric load forecasting LSTNet

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autoencoder Feature Residuals for Network Intrusion Detection: Unsupervised Pre-training for Improved Performance 21

Autoencoder Feature Residuals for Network Intrusion Detectio...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Lewandowski, Brian Paffenroth, Randy Worcester Polytech Inst Comp Sci Worcester MA 01609 USA Worcester Polytech Inst Data Sci Math Sci & Comp Sci Worcester MA 01609 USA

ISBN: (纸本)9781665462839

Network intrusion detection is a constantly evolving field as researchers and practitioners work towards keeping up with novel attacks and growing amounts of network data. To aid in this challenge researchers have been exploring the use of deep learning techniques such as neural networks in order to detect zero-day attacks and reduce the amount of manual analysis required when a network intrusion detection system alert is generated. Herein we use an unsupervised pre-training step in order to take advantage of autoencoder feature residuals. We show that autoencoder feature residuals can be used in place of or in addition to an original feature set as input to a neural network classifier to improve classification performance. Often in such problems, experts perform feature engineering to optimize classification performance. However, such data manipulation is expensive and time consuming. Our novel approach provides a path that can alleviate the need for manual feature extraction while "doing no harm". That is, if the provided features are in some sense optimal, then our methodology will not degrade the classification performance. However, if the provided features are inefficient, then we demonstrate that our methodology can substantially improve classification performance on a broad range of benchmark cybersecurity datasets. Another practical side effect of using autoencoder feature residuals comes to light by analyzing the potential data compression benefits they provide.

关键词： autoencoder network intrusion detection deep learning

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autoencoder ensembles for network intrusion detection 24

Autoencoder ensembles for network intrusion detection

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24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Long, Chun Xiao, JianPing Wei, Jinxia Zhao, Jing Wan, Wei Du, Guanyao CAS Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9791188428090

Machine learning methods have been widely used in the field of intrusion detection. However, most methods require labeled data sets, and the overhead is very high. Network data is often high-dimensional and has the problem of data imbalance, which makes many techniques unable to adapt to the real network environment. In this paper, we propose a network intrusion detection model based on autoencoder ensembles. This model uses a recursive feature addition algorithm to select the optimal subset of features, which can significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. After feature selection, the feature subset is grouped, and then each group is mapped to an autoencoder. Multiple such autoencoders ensembles form the detection model. Only normal samples are used for training. The detection model is unsupervised, which improves the efficiency of detecting known and unknown attacks. The experimental results show that feature selection can effectively reduce training and detection time. Our model has high detection accuracy and strong adaptability.

关键词： autoencoder Feature Selection Machine Learning Network Intrusion Detection System Recursive Feature Addition

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