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A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecasting

ELECTRICAL ENGINEERING 2024年第4期106卷 4239-4255页

作者： Ibrahim, Mohamed Sayed Gharghory, Sawsan Morkos Kamal, Hanan Ahmed Cairo Univ Fac Engn Elect & Commun Engn Dept Giza Egypt Elect Res Inst Comp & Syst Dept Cairo Egypt

Solar energy is one of the main renewable energies available to fulfill global clean energy targets. The main issue of solar energy like other renewable energies is its randomness and intermittency which affects power grids stability. As a solution for this issue, energy storage units could be used to store surplus energy and reuse it during low solar generation intervals. Also, in order to sustain stable power grid and better grid operation and energy storage management, photovoltaic (PV) power forecasting is inevitable. In this paper, new hybrid model based on deep learning techniques is proposed to predict short-term PV power generation. The proposed model incorporates convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder network. The new model differentiates itself in accomplishing high prediction accuracy by extracting spatial features in time series via CNN layers and temporal features between the time series data through LSTM. The introduced model is tested on dataset of power generation from southern UK solar farm and the weather data corresponding to same location and time intervals;the forecasting performance of the suggested model is evaluated in metrics of root-mean-square error (RMSE) and mean absolute error (MAE). The used model is compared with different models from the literature either of pure type of network such as LSTM and gated recurrent unit (GRU) or hybrid combination of different networks like CNN-LSTM and CNN-GRU. The results show that proposed model provides enhanced results and reduces training time significantly compared to other competitive models, where the performance of the proposed model improved averagely by 5% to 25% in terms of RMSE and MAE performance metrics, and the execution time of training significantly reduced with almost 70% less compared to other models.

关键词： CNN LSTM GRU autoencoder PV

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autoencoder Image Denoising to Increase Optical Character Recognition Performance in Text Conversion

Autoencoder Image Denoising to Increase Optical Character Re...

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International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) - Blockchain Technology, Intelligent Systems, and the Applications for Human Life

作者： Alamsyah, Nur Fauzan, Mohamad Nurkamal Putrada, Aji Gautama Pane, Syafrial Fachri Telkom Univ Adv & Creat Networks Res Ctr Bandung Indonesia

ISBN: (纸本)9798350334449

Document digitization has an important role in helping the company's activities be more efficient, such as detecting text in invoice document images using optical character recognition (OCR). However, writing in images has many problems, especially tediously saved documents that can cause noise or interference in the picture, resulting in difficultly recognized writing. Our research aims to build an autoencoder for denoising text images and evaluate the OCR's performance in converting the denoised image into text. The first step in the research is to test the OCR characteristics on the original text image and the text image given Gaussian noise. The next step is to build the optimal autoencoder model for denoising by studying the effect of dataset size and optimizer type. The last step is to test the OCR performance on the denoised text image produced by the optimum autoencoder model. The test results show that datasetsize affects denoising performance and OCR performance. From several autoencoder models compared, the autoencoder with dataset size = 40 has the optimum performance, where the MSE values of the model for train and validation are 1277 and 1385, respectively. With images denoised from the optimum model, the OCR performance in converting images into text is 100%.

关键词： optical character recognition Gaussian noise denoising autoencoder text image

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ContrastNet: Unsupervised feature learning by autoencoder and prototypical contrastive learning for hyperspectral imagery classification

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NEUROCOMPUTING 2021年 460卷 71-83页

作者： Cao, Zeyu Li, Xiaorun Feng, Yueming Chen, Shuhan Xia, Chaoqun Zhao, Liaoying Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou 310027 Peoples R China State Grid Jiaxing Power Supply Co Jiaxing 314100 Zhejiang Peoples R China HangZhou Dianzi Univ China Inst Comp Applicat Technol Hangzhou 310018 Peoples R China

Hyperspectral classification is a fundamental problem for applying hyperspectral technology, and unsupervised learning is a promising direction to address the issue. Unsupervised feature learning algorithm extracts representative features, which help efficiently classify the hyperspectral pixels. This paper combines the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. First, two different encoders are used to extract different features as augmentation function in a contrastive network (ContrastNet). Second, the prototypical contrastive learning method is adopted to train a contrastive network. Third, features extracted by the contrastive network are used for classification. Experiments have proved that our two proposed autoencoder networks show good feature learning capabilities by themselves and the designed contrastive learning network can further learn better representative features from the two modules. The three modules compose an efficient framework for unsupervised feature learning. Our method also performs reasonably fast in the testing phase, which implies that it is applicable in practice under specific conditions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Unsupervised learning autoencoder Contrastive learning Hyperspectral imagery classification

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An autoencoder with adaptive transfer learning for intelligent fault diagnosis of rotating machinery

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第5期32卷

作者： Tang, Zhi Bo, Lin Liu, Xiaofeng Wei, Daiping Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data;to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.

关键词： rotating machinery intelligent fault diagnosis autoencoder transfer learning adaptive optimization

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Representation learning of 3D meshes using an autoencoder in the spectral domain

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COMPUTERS & GRAPHICS-UK 2022年 107卷 131-143页

作者： Lemeunier, Clement Denis, Florence Lavoue, Guillaume Dupont, Florent Univ Lyon CNRS INSA Lyon UCBLLIRISUMR5205 F-69622 Villeurbanne France Univ Lyon UCBL CNRS INSA LyonLIRISUMR5205 F-69622 Villeurbanne France Univ Lyon Cent Lyon CNRS INSA LyonUCBLLIRISUMR5205ENISE F-42023 Saint Etienne France

Learning on surfaces is a difficult task: the data being non-Euclidean makes the transfer of known techniques such as convolutions and pooling non trivial. Common methods deploy processes to apply deep learning operations to triangular meshes either in the spatial domain by defining weights between nodes, or in the spectral domain using first order Chebyshev polynomials followed by a return in the spatial domain. In this study, we present a Spectral autoencoder (SAE) enabling the application of deep learning techniques to 3D meshes by directly giving spectral coefficients obtained with a spectral transform as inputs. With a dataset composed of surfaces having the same connectivity, it is possible with the Graph Laplacian to express the geometry of all samples in the frequency domain. Then, by using an autoencoder architecture, we are able to extract important features from spectral coefficients without going back to the spatial domain. Finally, a latent space is built from which reconstruction and interpolation is possible. This method allows the treatment of meshes with more vertices by keeping the same architecture, and allows to learn on big datasets with short computation times. Through experiments, we demonstrate that this architecture is able to give better results than state of the art methods in a faster way. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Geometric Deep Learning Spectral analysis autoencoder Human body triangular meshes

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Spectral-Spatial Feature Extraction With Dual Graph autoencoder for Hyperspectral Image Clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8500-8511页

作者： Zhang, Yongshan Wang, Yang Chen, Xiaohong Jiang, Xinwei Zhou, Yicong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China

autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative representations with the assumption that connected samples share the same label and they cannot directly embed the geometric structure into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE) to learn discriminative representations for HSIs. Utilizing the relationships of pair-wise pixels within homogenous regions and pair-wise spectral bands, DGAE first constructs the superpixel-based similarity graph with spatial information and band-based similarity graph to characterize the geometric structures of HSIs. With the developed dual graph convolution, more discriminative feature representations are learnt from the hidden layer via the encoder-decoder structure of DGAE. The main advantage of DGAE is that it fully exploits both the geometric structures of pixels with spatial information and spectral bands to promote nonlinear feature extraction of HSIs. Experiments on HSI datasets show the superiority of the proposed DGAE over the state-of-the-art methods.

关键词： Feature extraction Data mining Principal component analysis Hyperspectral imaging Convolution Task analysis Decoding Hyperspectral imagery dimensionality reduction feature extraction autoencoder graph convolution

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Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第6期20卷 1827-1840页

作者： Xu, Xinyao Qin, Fangbo Zhao, Wenjun Xu, De Wang, Xingang Yang, Xihao Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China State Key Lab Smart Mfg Special Vehicles & Transm Baotou City 014000 Inner Mongolia Peoples R China

The anomaly detection for multimode industrial process is a challenging problem, because the multiple operation modes present various main distributions of monitored variables, and the dynamic sequential characteristics exist within each operation mode. This paper proposes an anomaly detection method based on sequence-to-sequence gated recurrent units (SGRU). First, to better model both the cross-mode trends and mode-specific sequential characteristics, a main reconstruction module and residual reconstruction module are integrated to improve the ability to represent complex process. Both modules are implemented by SGRUs. Second, a reconstruction error prediction module is designed to estimate the mean values of mode-specific reconstruction errors, which helps to determine the more reliable alarm thresholds. Third, the two anomaly indicators are utilized to represent the deviation degree of monitored variables against the normal conditions, according to the statistical errors and biases of reconstructions, respectively. The effectiveness of the proposed method is validated on simulations with multimode process, and on the practical data set collected from the Cleaning-in-Place multimode process of an aseptic beverage filling line in a real factory.

关键词： Anomaly detection autoencoder gated recurrent unit multimode process

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GenCoder: A Novel Convolutional Neural Network Based autoencoder for Genomic Sequence Data Compression

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第3期21卷 405-415页

作者： Sheena, K. S. Nair, Madhu S. Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 India

Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term storage and analysis of genomic sequence data are required. Data-specific compression algorithms can effectively manage a large volume of data. In recent times, deep learning has achieved great success in many compression tools and is gradually being used in genomic sequence compression. Significantly, autoencoder has been applied in dimensionality reduction, compact representations of data, and generative model learning. It can use convolutional layers to learn essential features from input data, which is better for image and series data. autoencoder reconstructs the input data with some loss of information. Since accuracy is critical in genomic data, compressed genomic data must be decompressed without any information loss. We introduce a new scheme to address the loss incurred in the decompressed data of the autoencoder. This paper proposes a novel algorithm called GenCoder for reference-free compression of genomic sequences using a convolutional autoencoder and regenerating the genomic sequences from a latent code produced by the autoencoder, and retrieving original data losslessly. Performance evaluation is conducted on various genomes and benchmarked datasets. The experimental results on the tested data demonstrate that the deep learning model used in the proposed compression algorithm generalizes well for genomic sequence data and achieves a compression gain of 27% over the best state-of-the-art method.

关键词： Genomics Bioinformatics Sequential analysis DNA Costs Compressors Encoding Genomic sequence deep learning autoencoder FASTA lossless compression

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Enhancing image classification using adaptive convolutional autoencoder-based snow avalanches algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第10期18卷 6867-6879页

作者： Dhiravidachelvi, E. Devadas, T. Joshva Kumar, P. J. Sathish Pandi, S. Senthil Mohamed Sathak Engn Coll Dept Elect & Commun Engn Kilakarai Tamil Nadu India VIT Univ Sch Comp Sci & Engn Vellore India Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

The disease that causes a large number of deaths annually across the world is brain cancer and it has become an important research topic in the field of medical image processing in recent times. There are various techniques for the detection of brain tumors (BT) but magnetic resonance imaging (MRI) diagnosing techniques show superior performance in the prognosis and examination of brain tumors in the early stages. The manual detection of brain tumors by radiologists leads to many limitations like errors and lack of detection accuracy. Hence, there is a need for computer-aided diagnostic techniques to help radiologists in detecting brain tumors accurately from the MRI images. To make this process more effective, the implementation of an automated technique is a preferred choice. In this paper, an effective detection and classification technique Adaptive convolutional autoencoder-based Snow Avalanches (ACAE-SA) Algorithm is proposed. This algorithm comprises an Adaptive CNN component and an autoencoder to detect and categorize BT from the MRI images. To mitigate the computational complexities in these components a Snow Avalanches algorithm is integrated into this work as an optimization technique. For the validation of the proposed architecture two MRI image datasets namely figshare and BraTS 2018 are used. The proposed technique proved its effectiveness in the detection and classification of brain tumors from the MRI images and outperformed the state-of-the-art techniques.

关键词： Adaptive CNN autoencoder Brain tumor Medical image processing MRI images Snow avalanches algorithm

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Evolving Domain Generalization via Latent Structure-Aware Sequential autoencoder

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 14514-14527页

作者： Qin, Tiexin Wang, Shiqi Li, Haoliang City Univ Hong Kong Dept Elect & Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Comp Sci Kowloon Hong Kong Peoples R China

Domain generalization (DG) refers to the problem of generalizing machine learning systems to out-of-distribution (OOD) data with knowledge learned from several provided source domains. Most prior works confine themselves to stationary and discrete environments to tackle such generalization issue arising fromOODdata. However, in practice, manytasks in non-stationary environments (e.g., autonomous-driving car system, sensor measurement) involve more complex and continuously evolving domain drift, emerging new challenges for model deployment. In this paper, we first formulate this setting as the problem of evolving domain generalization. To deal with the continuously changing domains, we propose MMD-LSAE, a novel framework that learns to capture the evolving patterns among domains for better generalization. Specifically, MMD-LSAE characterizes OOD data in non-stationary environments with two types of distribution shifts: covariate shift and concept shift, and employs deep autoencoder modules to infer their dynamics in latent space separately. In these modules, the inferred posterior distributions of latent codes are optimized to align with their corresponding prior distributions via minimizing maximum mean discrepancy (MMD). We theoretically verify that MMD-LSAE has the inherent capability to implicitly facilitate mutual information maximization, which can promote superior representation learning and improved generalization of themodel. Furthermore, the experimental results on both synthetic and real-world datasets show that our proposed approach can consistently achieve favorable performance based on the evolving domain generalization setting.

关键词： Domain generalization non-stationary environments autoencoder kernel methods

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