检索结果-内蒙古大学图书馆

Multi-subband fusion algorithm based on autoencoder

IET SIGNAL PROCESSING 2022年第9期16卷 1071-1084页

作者： Jiang, Yilin Zhang, Liting Li, Li Li, Jinxin Yang, Yaozu Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Dalian Naval Acad Dept Informat Syst Dalian Peoples R China

A novel algorithm for multi-subband signal fusion achieves performance superior to traditional all-pole model, matrix pencil algorithm and deep-neural-network (Deep neural network (DNN)). The method uses a deep-learning autoencoder more fully described as a multi-subband fusion autoencoder (MSFAE). This autoencoder comprises two parts: a multi-subband encoder and a full-band decoder. Full-band echo distance envelopes are used as training data for the full-band autoencoder, to obtain the full-band coding and the full-band decoder. Then, the multi-subband echo distance envelopes are used as training data, and the full-band coding is used as labels, to train the multi-subband encoder. Finally, the multi-subband encoder and the full-band decoder are combined to obtain the MSFAE. The multi-subband distance envelopes are input to the MSFAE to obtain the full-band distance envelopes, improving the radar distance resolution and obtaining high-resolution range profiles. In contrast with the traditional all-pole model and matrix pencil algorithm, the authors' MSFAE directly processes the information in the frequency domain, avoiding the error of pole estimation in the echo domain. In contrast with DNN, the authors' MSFAE needs only multi-subband distance envelopes as input, avoiding noise subband redundancy. The experimental results show that the fusion accuracy of MSFAE is higher than the traditional all-pole model, matrix pencil algorithm and DNN. The MSFAE has superior performance using the fusion method, even at low signal-to-noise ratio.

关键词： autoencoder deep learning HRRP neural network subband fusion

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Bottom-Up Visual Saliency Estimation With Deep autoencoder-Based Sparse Reconstruction

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2016年第6期27卷 1227-1240页

作者： Xia, Chen Qi, Fei Shi, Guangming Xidian Univ Minist Educ Sch Elect Engn Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Research on visual perception indicates that the human visual system is sensitive to center-surround (C-S) contrast in the bottom-up saliency-driven attention process. Different from the traditional contrast computation of feature difference, models based on reconstruction have emerged to estimate saliency by starting from original images themselves instead of seeking for certain ad hoc features. However, in the existing reconstruction-based methods, the reconstruction parameters of each area are calculated independently without taking their global correlation into account. In this paper, inspired by the powerful feature learning and data reconstruction ability of deep autoencoders, we construct a deep C-S inference network and train it with the data sampled randomly from the entire image to obtain a unified reconstruction pattern for the current image. In this way, global competition in sampling and learning processes can be integrated into the nonlocal reconstruction and saliency estimation of each pixel, which can achieve better detection results than the models with separate consideration on local and global rarity. Moreover, by learning from the current scene, the proposed model can achieve the feature extraction and interaction simultaneously in an adaptive way, which can form a better generalization ability to handle more types of stimuli. Experimental results show that in accordance with different inputs, the network can learn distinct basic features for saliency modeling in its code layer. Furthermore, in a comprehensive evaluation on several benchmark data sets, the proposed method can outperform the existing state-of-the-art algorithms.

关键词： autoencoder center-surround (C-S) difference deep learning reconstruction saliency unsupervised feature learning

来源：评论

学校读者我要写书评

暂无评论

Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 247卷 108114-108114页

作者： Dai, Peishan Shi, Yun Lu, Da Zhou, Ying Luo, Jialin He, Zhuang Chen, Zailiang Zou, Beiji Tang, Hui Huang, Zhongchao Liao, Shenghui Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Dept Psychiat Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Natl Clin Res Ctr Mental Disorders Changsha 410083 Hunan Peoples R China Cent South Univ Sch Basic Med Sci Dept Biomed Engn Changsha 410083 Hunan Peoples R China

Background and objective: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. Methods: We developed a Residual Denoising autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over -sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. Results: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. Conclusions: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

关键词： Recurrent major depressive disorder Functional magnetic resonance imaging Functional connectivity autoencoder Residual network Deep learning

来源：评论

学校读者我要写书评

暂无评论

Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第24期28卷 31084-31096页

作者： Tercan, Emre Atasever, Umit Haluk Gen Directorate Highways Dept Survey Project & Environm 13th Reg TR-07090 Antalya Turkey Erciyes Univ Fac Engn Dept Geomat Engn TR-38039 Kayseri Turkey

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen's KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.

关键词： Machine learning autoencoder Remote sensing Image classification Water body extraction RASAT Environmental monitoring Lake management

来源：评论

学校读者我要写书评

暂无评论

Perturbation of deep autoencoder weights for model compression and classification of tabular data

引用

NEURAL NETWORKS 2022年 156卷 160-169页

作者： Abrar, Sakib Samad, Manar D. Tennessee State Univ Dept Comp Sci Nashville TN 37209 USA Tennessee State Univ Dept Comp Sci 3500 John A Merritt BlvdPOB 9604 Nashville TN 37209 USA

Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, in tabular data classification, DNNs are challenged by the often superior performance of traditional machine learning models. This paper proposes periodic perturbations (prune and regrow) of DNN weights, especially at the self-supervised pretraining stage of deep autoencoders. The proposed weight perturbation strategy outperforms dropout learning or weight regularization (L1 or L2) for four out of six tabular data sets in downstream classification tasks. Unlike dropout learning, the proposed weight perturbation routine additionally achieves 15% to 40% sparsity across six tabular data sets, resulting in compressed pretrained models. The proposed pretrained model compression improves the accuracy of downstream classification, unlike traditional weight pruning methods that trade off performance for model compression. Our experiments reveal that a pretrained deep autoencoder with weight perturbation can outperform traditional machine learning in tabular data classification, whereas baseline fully-connected DNNs yield the worst classification accuracy. However, traditional machine learning models are superior to any deep model when a tabular data set contains uncorrelated variables. Therefore, the performance of deep models with tabular data is contingent on the types and statistics of constituent variables.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Weight pruning autoencoder Tabular data Model compression Sparse learning Deep neural network

来源：评论

学校读者我要写书评

暂无评论

Application of Extreme Learning Machine- autoencoder to Medium Term Electricity Price Forecasting

引用

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第6期59卷 7214-7223页

作者： Najafi, Arsalan Homaee, Omid Golshan, Mehdi Jasinski, Michal Leonowicz, Zbigniew Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland Islamic Azad Univ Dept Comp Engn Sepidan Branch Sepidan *** Iran VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Elect Power Engn Ostrava 70800 Czech Republic

Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary which makes forecasting very complicated. Although accurate forecasting plays a crucial role in the electricity market for traders, retailers, large consumers as well as generation companies in terms of economic efficiency and power systems safety. Hence, this article proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind this is to use trained weights for hidden layers instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. Then, the obtained output weights are used again as the input weights for a new ELM network. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on the previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.

关键词： Forecasting electricity price extreme learning machine autoencoder electricity market

来源：评论

学校读者我要写书评

暂无评论

Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with autoencoder Classifier

引用

NEURAL PROCESSING LETTERS 2022年第4期54卷 3489-3505页

作者： Sheela, S., V Radhika, K. R. BMS Coll Engn Dept Informat Sci & Engn Bangalore Karnataka India

In recent decades, iris recognition is a trustworthy and important biometric model for human recognition. Criminal to commercial products, citizen confirmation and border control are few application areas. The research work is a deep learning based integrated model for accurate iris detection and recognition. Initially, eye images are considered from two datasets, the Chinese Academy of Sciences Institute of Automation (CASIA) and the Indian Institute of Technology (IIT) Delhi v1.0. Iris region is accurately segmented using Daugman's algorithm and Circular Hough Transform (CHT). Feature extraction is hybrid that is performed using Dual Tree Complex Wavelet Transform (DTCWT), Gabor filter, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) from the segmented iris regions. A Multiobjective Artificial Bee Colony (MABC) algorithm is proposed to eliminate noisy and redundant feature vectors by estimating consistent information. In MABC algorithm, two multi-objective functions are formulated as reduction in number of features and classification error rate. The selected active feature vectors are given as input to autoencoder classification for iris recognition. The experimental outcome shows that MABC-autoencoder model obtained 99.67% and 98.73% accuracy on CASIA-Iris, and IIT Delhi v1.0 iris datasets. Performance evaluation is based on accuracy, specificity, Critical Success Index (CSI), sensitivity, Fowlkes Mallows (FM) index, and Mathews Correlation Coefficient (MCC).

关键词： autoencoder Circular Hough transform Daugman's algorithm Iris recognition Local binary pattern Multi objective artificial bee colony

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：