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3rd Asian Conference on Frontiers of Power and Energy

作者： Liang, Hongfu Luo, Tian Zhang, Xuan Chen, Jun Ding, Bo State Grid Chongqing Elect Power Co Shinan Power Supply Branch Chongqing Peoples R China Chongqing Guanghui Power Supply Serv Co Ltd Shinan Branch Chongqing Peoples R China Beijing Zhongdian Puhua Informat Technol Co Ltd Chongqing Business Dept Xian Branch Chongqing Peoples R China

ISBN: (纸本)9798350378467;9798350367676

A fault diagnosis method for mobile energy storage cabin based on digital twin technology and deep autoencoder is proposed to address the problems of time-consuming, labor-intensive, and low accuracy in traditional fault diagnosis methods. Firstly, a fault diagnosis model based on digital twin technology is constructed based on the mechanism and data model of mobile energy storage cabin, and the reliability of diagnosis is improved through the combination of virtual and real. Then, a deep autoencoder model is designed, which adopts an adaptive data filtering mechanism based on an improved spectral clustering algorithm, and a fusion loss function is designed to optimize the model parameters. Finally, the deep autoencoder model is deployed in the digital twin model to achieve fault diagnosis by analyzing energy storage cabin data. Based on the selected mobile energy storage cabin data, experimental analysis is conducted on the proposed method, and the results shows that the average accuracy and time consumption of fault diagnosis are 96.38% and 3.73s, respectively, demonstrating significant advantages in state diagnosis.

关键词： mobile energy storage cabin digital twin technology deep autoencoder fault diagnosis improved spectral clustering algorithm fusion loss function

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Predicting Earthquakes with Ionospheric Data: A Hybrid Approach Utilizing deep autoencoder and LSTM Networks 3

Predicting Earthquakes with Ionospheric Data: A Hybrid Appro...

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3rd International Conference on Computing and Machine Intelligence (ICMI)

作者： Abri, Rayan Abri, Sara Artuner, Harun Cetin, Salih Dept Artificial Intelligence Mavinci Ankara Turkiye Hacettepe Univ Dept Comp Engn Ankara Turkiye Mavinci Cambridge England

ISBN: (纸本)9798350372977;9798350372984

An integral part of the Earth's atmosphere is driven by the ionosphere. Solar flares induce ionosphere anomalies as a result of coronal mass ejection, seismic activity, and geomagnetic activity. Total Electron Content is the primary metric used to study the ionosphere's structure (TEC). GPS-derived TEC values are useful for examining how the ionospheric response to earthquakes is affected. In order to identify earthquakes, this article examines the relationships between TEC data and earthquakes. Our aim is to suggest a classification strategy for identifying earthquakes that occurred in earlier days. This research discusses the ionospheric variability during moderate and severe earthquake events of varied intensity for the years 2012-2019. deep autoencoders are used by the suggested model to extract features from TEC data. A Stacked LSTM model was constructed using the features gathered to forecast the earthquakes that occurred in the preceding days. For evaluation, the suggested hybrid model is compared with the Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifier models. According to the findings, the suggested hybrid model increases earthquake detection with an accuracy rate of roughly 0.84 and is a useful tool for identifying earthquakes based on prior days.

关键词： Total electron content Long short term memory deep autoencoder Support vector machine Linear Discriminant Analysis

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Classification of Motor Imagery EEG Signals Based on deep autoencoder and Convolutional Neural Network Approach

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IEEE ACCESS 2022年 10卷 48071-48081页

作者： Hwaidi, Jamal F. Chen, Thomas M. Univ London Dept Elect & Elect Engn London EC1V 0HB England

The technology of the brain-computer interface (BCI) employs electroencephalogram (EEG) signals to establish direct interaction between the human body and its surroundings with promising applications in medical rehabilitative services and cognitive science. deep learning approaches, particularly the detection and analysis of motor imagery signals using convolutional neural network (CNN) frameworks have produced outstanding results in the BCI system in recent years. The complex process of data representation, on the other hand, limits practical applications, and the end-to-end approach reduces the accuracy of recognition. Moreover, since noise and other signal sources can interfere with brain electrical capacitance, EEG classifiers are difficult to improve and have limited generalisation ability. To address these issues, this paper proposes a new approach for EEG motor imagery signal classification by using a variational autoencoder to remove noise from the signals, followed by a combination of deep autoencoder (DAE) and a CNN architecture to classify EEG motor imagery signals which is capable of training a deep neural network to replicate its input to output using encoding and decoding operations. Experimental results show that the proposed approach for motor imagery EEG signal classification is feasible and that it outperforms current CNN-based approaches and several traditional machine learning approaches.

关键词： Electroencephalography Feature extraction Convolutional neural networks deep learning Support vector machines Training Brain modeling Electroencephalography deep autoencoder convolutional neural network variational autoencoder motor imagery

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Image encryption based on logistic chaotic systems and deep autoencoder

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PATTERN RECOGNITION LETTERS 2022年 153卷 59-66页

作者： Sang, Yongpeng Sang, Jun Alam, Mohammad S. Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Texas A&M Univ Kingsville Frank H Dotterweich Coll Engn Kingsville TX USA

In this paper, we propose a novel image encryption method based on logistic chaotic systems and deep autoencoder. In the encryption phase, first, the plaintext image is randomly scrambled by a logistic chaotic system. Then, the random scrambled image is encoded by a deep autoencoder to generate the ciphertext image. In order to obtain the ciphertext image with uniform distribution, we incorporated the uniform distribution constraint into the training of the deep autoencoder. The resulting ciphertext image contains high randomness, which is critical for an excellent image encryption algorithm. Histogram analysis, information entropy analysis, key space analysis, key sensitivity analysis, correlation analysis, and ablation experiments show that the proposed encryption algorithm can effectively resist attacks and has excellent encryption performance while providing high security. (c) 2021 Published by Elsevier B.V.

关键词： Image encryption Logistic chaotic systems deep autoencoder Uniform distribution

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Joint network embedding of network structure and node attributes via deep autoencoder

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NEUROCOMPUTING 2022年 468卷 198-210页

作者： Pan, Yu Zou, Junhua Qiu, Junyang Wang, Shuaihui Hu, Guyu Pan, Zhisong Army Engn Univ Inst Command & Control Engn Nanjing Peoples R China Jiangnan Inst Comp Technol Math Engn & Adv Comp Wuxi Jiangsu Peoples R China

Network embedding aims to learn a low-dimensional vector for each node in networks, which is effective in a variety of applications such as network reconstruction and community detection. However, the majority of the existing network embedding methods merely exploit the network structure and ignore the rich node attributes, which tend to generate sub-optimal network representation. To learn more desired network representation, diverse information of networks should be exploited. In this paper, we develop a novel deep autoencoder framework to fuse topological structure and node attributes named FSADA. We firstly design a multi-layer autoencoder which consists of multiple non-linear functions to capture and preserve the highly non-linear network structure and node attribute information. Particularly, we adopt a pre-processing procedure to pre-process the original information, which can bet -ter facilitate to extract the intrinsic correlations between topological structure and node attributes. In addition, we design an enhancement module that combines topology and node attribute similarity to construct pairwise constraints on nodes, and then a graph regularization is introduced into the frame-work to enhance the representation in the latent space. Our extensive experimental evaluations demon-strate the superior performance of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep autoencoder deep learning Network analysis Network embedding Data mining Pattern recognition

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Matrix Factorization and deep autoencoder based Clustering Scheme for Large-scale UAV Networks 97

Matrix Factorization and Deep Autoencoder based Clustering S...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Fang, Jiaolan Wang, Chan Li, Rongpeng Wei, Hanyu Zhao, Minjian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350311143

In recent years, unmanned aerial vehicle (UAV) ad-hoc networks have achieved rapid development due to their autonomy and high reliability. Typically, clustering is widely adopted to reduce the degradation of network performance in large-scale UAV networks. However, due to the limited channel resources and complex communication environment, it becomes challenging to obtain complete information needed for clustering from remote nodes. In addition, the high dimension and nonlinear relationships of data also make large-scale clustering diffcult. Therefore, in this paper, we propose a noval distributed clustering scheme. First, a matrix factorization (MF) based time series algorithm is proposed to predict and complement the incomplete information. Second, we adopt deep autoencoder to wisely incorporate the non-linear relationship of information needed for clustering. Finally we extract features as input to k-means to obtain clustering results. Simulation results demonstrate the effectiveness of the proposed clustering scheme.

关键词： UAV ad-hoc networks clustering scheme matrix factorization deep autoencoder

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A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder

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RENEWABLE ENERGY 2022年第0期194卷 659-673页

作者： Yang, Mao Zhao, Meng Huang, Dawei Su, Xin Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China

The improvement of photovoltaic (PV) power prediction precision plays a crucial role in the new energy consumption. This paper proposes a composite prediction framework (DC (DWT-DAE)-CNN) consisting of dual clustering and convolutional neural network to achieve day-ahead prediction of PV power. To avoid the temporal uncertainty of PV power and the high-dimensional complexity of numerical weather prediction, the raw data are processed by Discrete Wavelet Transform (DWT) and deep autoencoder respectively (DAE) to reduce the data redundancy. Secondly, a dual clustering pattern based on dynamic time warping distance clustering and Fuzzy C-Mean (FCM) clustering is proposed to progressively realize the dynamic characteristics of the power curve and numerical clustering of the weather information data. Finally, the data from PV plants in northeast China are used for validation. The results show that the annual average day-ahead prediction AR of the DC (DWT-DAE)-CNN model can reach 90.17%, which is better than other competing models. In addition, the dual clustering pattern performs better than other traditional clustering patterns with the same predictor. Using this method to predict the PV output power can provide better theoretical guidance for the stable and safe operation of grid-connected PV. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： PV power Day-ahead prediction Dual clustering Discrete wavelet transform deep autoencoder Convolutional neural network

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DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder

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BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac478-bbac478页

作者： Peng, Li Tu, Yuan Huang, Li Li, Yang Fu, Xiangzheng Chen, Xiang Hunan Univ Sci & Technol Xiangtan Hunan Peoples R China Hunan Key Lab Serv Comp & Novel Software Xiangtan Hunan Peoples R China Tsinghua Univ Beijing Peoples R China Xiangtan Univ Xiangtan Peoples R China Hunan Univ Changsha Hunan Peoples R China

MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.

关键词： microRNA small molecule association prediction deep autoencoder scalable tree boosting

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A deep autoencoder-Based Hybrid Recommender System

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2022年第1期13卷 1-19页

作者： Bougteb, Yahya Ouhbi, Brahim Frikh, Bouchra Zemmouri, Elmoukhtar Moulay Ismail Univ ENSAM LM2I Lab Meknes Morocco Ecole Natl Sci Appl Fes Fes Morocco Moulay Ismail Univ ENSAM Ensam Morocco

Recommender systems build their suggestions on rating data, given explicitly or implicitly by users on items. These ratings create a huge sparse user-item rating matrix, which opens two challenges for researchers on the field. The first challenge is the sparsity of the rating matrix, and the second one is the scalability of the data. This article proposes a hybrid recommender system based on deep autoencoder to learn the user interests and reconstruct the missing ratings. Then, SVD++ decomposition is employed, in parallel, to hold information of correlation between different features factors. Additionally, the authors gave a deep analysis of the top-N recommender list from the user's perspective to ensure that the proposed model can be used for practical application. Experiments showed that the proposed model performed better with high-dimensional datasets and outperformed various hybrid algorithms in terms of RMSE, MAE, and in terms of the final top-N recommendation list.

关键词： deep autoencoder deep Learning Factorization Machines Learning User's Profile Recommender Systems Sparsity Problem SVD++ Top-N Recommendation List

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A Secure deep autoencoder-based 6G Channel Estimation to Detect/Mitigate Adversarial Attacks 5

A Secure Deep Autoencoder-based 6G Channel Estimation to Det...

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5th IEEE Global Power, Energy and Communication Conference (GPECOM)

作者： Oleiwi, Haider W. Mhawi, Doaa N. Al-Raweshidy, H. S. Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England Middle Tech Univ Tech Inst Management Baghdad 10010 Iraq

ISBN: (纸本)9798350301984

Channel estimation (CE) is critical in wireless communications. However, it is vulnerable to adversarial attacks (AA) that are associated with the incorporated artificial intelligence (AI) functionality in 6G wireless communication systems/networks. The hazardous threat can compromise communications' confidentiality and integrity due to the expected infrastructure, features, and AI models of the 6G paradigm. This paper proposed a deep autoencoder (DAE)-based 6G CE model to detect and prevent AA. It was trained using a dataset generated from the MATLAB toolbox for AA and incorporated a secure transmission protocol. Simulations were conducted to evaluate the model's performance under different parameters (i.e., CE and DAE) with maximal epsilon values range (0.5-3.0). The results proved the model's sufficiency of accuracy and security to detect AA compared to existing CE techniques. The proposal provided a promising solution for a secure 6G DAE-based CE and showed robustness against AA. Additionally, it offered a feasible solution for the deep learning training data required and avoids overfitting. Overall, the proposed model provides a valuable contribution towards enhancing the security of 6G networks, and its performance should be further validated in real-world scenarios.

关键词： 6G Wireless Communication Networks Adversarial Attacks Artificial Intelligence Channel Estimation Cybersecurity deep autoencoder

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