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8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (IEEE CSDE)

作者： Raj, Mohammad Mainuddin Tasdid, Samaul Haque Nidra, Maliha Ahmed Noor, Jobaer Ria, Sanjana Amin Alam, Md Ashraful Brac Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665495523

Color machine vision is a riveting technology crucial in pioneering innovations like autonomous vehicles, autonomous drone deliveries, automated stores, robots, infrastructure and surveillance monitoring programs for security, manufacturing defect monitoring and more. When it comes to real life applications of automated machines, safety is a major concern and to ensure utmost safety the unpredictable has to be taken into consideration. We propose and demonstrate a color vision approach that allows image normalization hinged on autoencoder techniques employing deep neural networks. The model is composed of image preprocessing, encoding and decoding. The images are resized in preprocessing portion the images go through a cognitive operation where the input image becomes suitable to enter the autoencoding technique section. The autoencoder is comprised of two core components - encoder and decoder. To employ this system deep neural network is applied which generates a code of an image in the encoding process. Sequentially, the code changes over to decoding. Decoder portion decodes it and regenerates the initial image extracting it from the code of the encoder portion. It allows normalizing color images under different weather conditions such as images captured during rainy or foggy weather conditions. We devise it such that rainy and foggy images are normalized concurrently and in real time. The autoencoder is trained with numerous rainy and foggy datasets utilizing CNN. In this research we investigate the model normalizing images in two different weather conditions - rainy and foggy conditions in real time. We used SSIM and PSNR to verify the accuracy of the model and confirm its capability reconstructing images in real time for advanced real life color vision implementations.

关键词： Color Vision Deep Neural Network CNN autoencoder

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Binarization Strategy Using Multiple Convolutional autoencoder Network for Old Sundanese Manuscript Images 16th

Binarization Strategy Using Multiple Convolutional Autoencod...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Paulus, Erick Burie, Jean-Christophe Verbeek, Fons J. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Univ La Rochelle Lab Informat Image Interact L3i Ave Michel Crepeau F-17042 La Rochelle 1 France Univ Padjadajaran Comp Sci Dept Bandung Indonesia

ISBN: (纸本)9783030861599;9783030861582

The binarization step for old documents is still a challenging task even though many hand-engineered and deep learning algorithms have been offered. In this research work, we address foreground and background segmentation using a convolutional autoencoder network with 3 supporting components. The assessment of several hyper-parameters including the window size, the number of convolution layers, the kernel size, the number of filters as well as the number of encoder-decoder layers on the network is conducted. In addition, the skip connections approach is considered in the decoding procedure. Moreover, we evaluated the summation and concatenation function before the up-sampling process to reuse the previous low-level feature maps and to enrich the decoded representation. Based on several experiments, we determined that kernel size, the number of filters, and the number of encoder-decoder blocks have a little impact in term of binarization performance. While the window size and multiple convolutional layers are more impactful than other hyper-parameters. However, they require more storage and may increase computation costs. Moreover, a careful embedding of batch normalization and dropout layers also provides a contribution to handle overfitting in the deep learning model. Overall, the multiple convolutional autoencoder network with skip connection successfully enhances the binarization accuracy on old Sundanese palm leaf manuscripts compared to preceding state of the art methods.

关键词： Binarization autoencoder Palm leaf manuscript Deep learning

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Power Quality Disturbances Classification Using Sparse autoencoder (SAE) Based on Deep Neural Network 11

Power Quality Disturbances Classification Using Sparse Autoe...

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11th IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Manan, Nurul Asiah Shahbudin, Shahrani Kassim, Murizah Mohamad, Roslina Rahman, Farah Yasmin Abdul Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9781665403382

Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing to the accuracy of the classification analysis. In this paper, an approach to classify the power quality disturbances is presented using the deep neural network algorithm. A raw data containing various types of the power quality disturbances, like swell, interruption, harmonics, and normal signal is evaluated. This several types of power quality disturbance will be extracted using the Sparse autoencoder (SAE). The various values of weight decay parameter, A and sparsity parameter, p are applied to determine which features give optimal values. Optimal features learned from the SAE are then used to train a neural network classifier for identifying power quality disturbances.

关键词： deep learning power quality disturbance autoencoder sparse autoencoder neural network

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Stochastic gate-based autoencoder for unsupervised hyperspectral band selection

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PATTERN RECOGNITION 2022年 132卷

作者： Sun, He Zhang, Lei Wang, Lizhi Huang, Hua Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China

Due to its strong feature representation ability, the deep learning (DL)-based method is preferable for the unsupervised band selection task of hyperspectral image (HSI). However, the current DL-based UBS methods have not further investigated the nonlinear relationship between spectral bands, a more robust DL model with effective loss function is desired. To solve the above problem, a novel stochastic gate -based autoencoder (SGAE) has been proposed for the UBS task. With the proposed stochastic gate layer, the desired band subset with learnable parameters can be directly obtained. For obtaining better UBS results, a nonlinear regularization term is added with the loss function to supervise the training process of SGAE. Furthermore, an early stopping criteria with a regularization term-based threshold is developed. Experimental results on four publicly available remote sensing datasets prove the effectiveness of our SGAE.(c) 2022 Published by Elsevier Ltd.

关键词： Hyperspectral data Unsupervised band selection autoencoder Stochastic gate

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Arrhythmia classification of LSTM autoencoder based on time series anomaly detection

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartB期71卷 103228-103228页

作者： Liu, Pengfei Sun, Xiaoming Han, Yang He, Zhishuai Zhang, Weifeng Wu, Chenxu Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Laser Spect Technol & A Harbin 150080 Peoples R China

Electrocardiogram (ECG) is widely used in the diagnosis of heart disease because of its noninvasiveness and simplicity. The time series signals contained in the signal are usually obtained by the professional medical staff and used for the classification of heartbeat diagnosis. Professional physicians can use the electrocardiogram to know whether the patient has serious congenital heart disease and whether there is an abnormal heart structure. A lot of work has been done to achieve automatic classification of arrhythmia types. For example, autoencoder can obtain the time series characteristics of ECG signals and be used for ECG signal classification. However, some traditional methods are abstruse and difficult to understand in principle. In the classification of arrhythmias carried out in recent years, some researchers only use autoencoder to provide structural characteristics, without giving too much explanation to the design reasons. Therefore, we optimized a new network layer design based on LSTM to obtain the autoencoder structure. This structure can cooperate with the ECG preprocessing process designed by us to obtain better arrhythmia classification effect. This method enables direct input of ECG signals into the model without complicated preprocessing such as manual parameter input. Also, it eliminates the gradient vanishing problem existing in traditional convolutional neural network. We used five different types of ECG data in MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database: atrial premature beats (APB), left bundle branch block (LBBB), normal heartbeat (NSR), right bundle branch block (RBBB) and ventricular premature beats (PVC). High accuracy, precision and recall were obtained. Compared with traditional methods, this method has better performance in arrhythmia classification.

关键词： Heartbeat classification Arrhythmia Deep learning LSTM autoencoder

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Feature variance regularization method for autoencoder-based one-class classification

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COMPUTERS & CHEMICAL ENGINEERING 2022年 161卷

作者： Kim, Boeun Ryu, Kyung Hwan Kim, Ji Hee Heo, Seongmin [a]Andlinger Center for Energy and the Environment Princeton University Princeton NJ 08544 USA [b]Department of Chemical Engineering Sunchon National University 225 Jungang-ro Suncheon Jeollanam-do 57922 Republic of Korea [c]Department of Chemical Engineering Dankook University Yongin 16890 Republic of Korea

One-class classification (OCC) has been being used in various research fields, since it is able to design classifiers using the data from a single class. Among various methods for OCC, principal component analysis (PCA) is one of the most widely used ones, whose nonlinear extension can be performed by autoencoders. Although the existing regularization methods, such as L1 and L2 regularizations, can prevent the total variance of autoencoder's bottleneck layer from exploding, they cannot effectively reduce it below certain levels to alleviate the problem of variance inflation. To this end, in this work, a novel variance regularization method is proposed, which directly controls the total variance of the bottleneck layer. Case studies are carried out using two datasets (MNIST dataset and Tennessee Eastman dataset) to illustrate its ability as a regularizer, and as an enhancer for the design of one-class classifiers (in terms of performance and training time).(c) 2022 Elsevier Ltd. All rights reserved.

关键词： One-class classification autoencoder Neural network training Regularization Feature variance

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End-to-End Convolutional autoencoder for Nonlinear Hyperspectral Unmixing

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REMOTE SENSING 2022年第14期14卷 3341-3341页

作者： Dhaini, Mohamad Berar, Maxime Honeine, Paul Van Exem, Antonin Univ Rouen Normandie LITIS Lab F-76000 Rouen France Tellux Co F-76000 Rouen France

Hyperspectral Unmixing is the process of decomposing a mixed pixel into its pure materials (endmembers) and estimating their corresponding proportions (abundances). Although linear unmixing models are more common due to their simplicity and flexibility, they suffer from many limitations in real world scenes where interactions between pure materials exist, which paved the way for nonlinear methods to emerge. However, existing methods for nonlinear unmixing require prior knowledge or an assumption about the type of nonlinearity, which can affect the results. This paper introduces a nonlinear method with a novel deep convolutional autoencoder for blind unmixing. The proposed framework consists of a deep encoder of successive small size convolutional filters along with max pooling layers, and a decoder composed of successive 2D and 1D convolutional filters. The output of the decoder is formed of a linear part and an additive non-linear one. The network is trained using the mean squared error loss function. Several experiments were conducted to evaluate the performance of the proposed method using synthetic and real airborne data. Results show a better performance in terms of abundance and endmembers estimation compared to several existing methods.

关键词： convolutional neural network autoencoder hyperspectral imaging nonlinear spectral unmixing

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autoencoder-based multi-task learning for imputation and classification of incomplete data

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APPLIED SOFT COMPUTING 2021年 98卷 106838-106838页

作者： Lai, Xiaochen Wu, Xia Zhang, Liyong Dalian Univ Technol Sch Software Dalian 116600 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China

The existence of missing values in real-world datasets increases the difficulty of data analysis. In this paper, we propose an autoencoder (AE)-based multi-task learning (MTL) model and optimize missing values dynamically to classify incomplete datasets having interdependencies among attributes. Specifically, we first design the input structure of hidden neurons in a dynamic way to enhance the imputation performance of AE, and then reorganize the output layer and construct an MTL model to achieve imputation and classification simultaneously. During network training and prediction, missing values are treated as variables and optimized dynamically accompanying with network parameters under the consideration of the incomplete model input. The optimization of missing values promotes the MTL model to match the regression and classification structures implied in incomplete data, thus reducing the impact of the perturbation caused by missing values effectively. The experiments on several datasets validate the effectiveness of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Incomplete data Imputation of missing values Classification regression autoencoder

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Data-Driven Approach for Credit Card Fraud Detection with autoencoder and One-Class Classification Techniques

Data-Driven Approach for Credit Card Fraud Detection with Au...

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International-Federation-of-Information-Processing-Working-Group-5.7 (IFIP WG 5.7) International Conference on Advances in Production Management Systems (APMS)

作者： Ouedraogo, Abdoul-Fatao Heuchenne, Cedric Nguyen, Quoc-Thong Tran, Hien Dong A Univ Inst Artificial Intelligence & Data Sci Da Nang Vietnam Univ Liege HEC Management Sch B-4000 Liege Belgium Univ Lille GEMTEX ENSAIT F-59000 Lille France

ISBN: (纸本)9783030858742;9783030858735

With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. Its high turnover is the reason making this sector is lucrative with fraud. This gave rise to fraudulent activity on the accounts of private users, banks, and other services. For this reason, in recent years, many studies have been carried out using machine learning techniques to detect and block fraudulent transactions. This article aims to present a new approach based on real-time data combining two methods for the detection of credit card fraud. We first use the variational autoencoder(VAE) to obtain representations of normal transactions, and then we train a support vector data description (SVDD) model with these representations. The advantage of the representation learned automatically by the variational autoencoder is that it makes the data smoother, which makes it possible to increase the detection performance of one-class classification methods. The performance evaluation of the proposed model is done on real data from European credit cardholders. Our experiments show that our approach has obtained good results with a very high fraud detection rate.

关键词： Anomalies detection Outliers autoencoder Variational autoencoder One class classification Credit card fraud

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Deep autoencoder-based Massive MIMO CSI Feedback with Quantization and Entropy Coding

Deep Autoencoder-based Massive MIMO CSI Feedback with Quanti...

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IEEE Global Communications Conference (GLOBECOM)

作者： Ravula, Sriram Jain, Swayambhoo Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Microsoft Sunnyvale CA USA

ISBN: (纸本)9781728181042

Techniques which leverage channel state information (CSI) at a transmitter to adapt wireless signals to changing propagation conditions have been shown to improve the reliability of modern multiple input multiple output (MIMO) communication systems. To reduce overhead, previous works have proposed to compress CSI matrices using a trained deep autoencoder (AE) at the receiver before feeding it back to the transmitter, and recent work has proposed to quantize and perform entropy coding on the compressed CSI to further reduce communication complexity. While these methods are effective, they either do not incorporate quantization and lossless coding into their end-to-end optimization, or do not achieve performance comparable to methods that do not use quantization and entropy coding. In this work, we propose a new AE-based feedback method which uses an entropy bottleneck layer to both quantize and losslessly code the compressed CSI. This bottleneck layer allows us to jointly optimize bit-rate and distortion to achieve a highly-compressed CSI representation which preserves important channel information. Our method achieves better reconstruction quality than existing autoencoder-based CSI feedback methods for a wide range of bit-rates on simulated data, in both indoor and outdoor wireless settings.

关键词： Channel Feedback CSI autoencoder Compression MIMO

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