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Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Singh, Deepali Gupta, Rinki Kumar, Arun Bahl, Rajendar Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi India Amity Univ Uttar Pradesh Elect & Commun Engn Dept Noida India Amity Univ Dept Mech & Automat Engn Noida Uttar Pradesh India

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered -x least mean square (FxLMS) technique.

关键词： Active noise control stacked autoencoder Nonlinear distortion Deep learning Anechoic room

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An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

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GEOENVIRONMENTAL DISASTERS 2020年第1期7卷 1-16页

作者： Nam, Kounghoon Wang, Fawu Shimane Univ Dept Earth Sci 1060 Nishikawatsu Cho Matsue Shimane 6908504 Japan

BackgroundLandslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme *** prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%).ConclusionsThis study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment.

关键词： stacked autoencoder Sparse autoencoder Support vector machine Random forest Landslide susceptibility

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A novel lossy image compression algorithm using multi-models stacked autoencoders

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ARRAY 2023年 19卷

作者： Fraihat, Salam Al-Betar, Mohammed Azmi Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates

The extensive use of images in many fields increased the demand for image compression algorithms to over-come the transfer bandwidth and storage limitations. With image compression, disk space, and transmission speed can be efficiently reduced. Some of the traditional techniques used for image compression are the JPEG and ZIP formats. The compression rate (CR) in JPEG can be high but to the detriment of the quality factor of the image. ZIP has a low compression rate, where the quality remains almost unaffected. Machine learning (ML) is considered an essential technique for image compression using different algorithms. The most widely used algorithm is Deep Learning (DL), which represents the features of the image at different scales by using different types of layers. In this research, an autoencoder (AE) deep learning-based compression algorithm is proposed for lossy image compression and experimented with using three standard dataset types: MNIST, Grayscale, and Color images datasets. A stacked AE (SAE) for image compression and a binarized content-based image filter are used with a high compression rate while keeping the quality above 85% using structural similarity index metric (SSIM) compared to traditional techniques. In addition, a convolutional neural network (CNN) classification model has been utilized as SAEs compression model selector for each image class. Experimental results demonstrate that the proposed SAE image compression algorithm outperforms the JPEG-encoded algorithm in terms of compression rate (CR) and image quality. The CR that the proposed model achieved with an acceptable reconstruction accuracy was about 85%, which is almost 20% higher than the standard JPEG's compression rate, with an accuracy of 94.63% SSIM score.

关键词： Deep learning stacked autoencoder Lossy image compression Image classification Image processing

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Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

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Chinese Journal of Chemical Engineering 2020年第9期28卷 2358-2367页

作者： Cen Guo Wenkai Hu Fan Yang Dexian Huang Department of Automation Tsinghua UniversityBeijing 10084China Cornell University NY 14850United States of America University of Alberta EdmontonAB T6G 1H9Canada

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

关键词： Alarm configuration Deep learning Fault detection and diagnosis Incomplete data stacked autoencoder

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High-power disk laser welding statuses monitoring based on analyses of multiple-sensor signals

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JOURNAL OF MANUFACTURING PROCESSES 2019年第0期41卷 221-230页

作者： Zhang, Yanxi Zhang, Nanfeng You, Deyong Gao, Xiangdong Katayama, Seiji Guangdong Univ Technol Guangdong Prov Welding Engn Technol Res Ctr Guangzhou 510006 Guangdong Peoples R China Osaka Univ Joining & Welding Res Inst 11-1 Mihogaoka Ibaraki Osaka 5670047 Japan

Welding statuses monitoring is crucial to quality control during high power disk laser welding of thick plates. A multiple-sensor-system is designed to capture the features of the keyhole, plume, spatter, optical and spectrum information of the welding area. These signals comprehensively depict the welding statuses, and are used to online monitor the welding statuses. The correlations between the captured multiple signals are analyzed by the correlation analysis method and Linear discriminative analysis (LDA) analysis and stacked Auto-Encoder (SAE) are implemented, and the dimension-reduced non-linear transformation of the original features acquired by SAE shows better discriminative and representative capacity than the linear combination of the original features acquired by the LDA. This research not only investigates the correlations between the signals of the keyhole, plume, photodiode and spectrum information during high power disc laser welding but also provides a novel method to conduct online welding statuses monitoring. Three different welding experiments under different parameters were conducted, and these experiments include blowout, humping and undercut defects, respectively.

关键词： Welding statuses monitoring Correlation analysis Linear discriminative analysis stacked autoencoder Multiple-sensor

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Framework for imbalanced fault diagnosis of rolling bearing using autoencoding generative adversarial learning

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第1期45卷 1-11页

作者： Rathore, Maan Singh Harsha, S. P. Indian Inst Technol Roorkee Dept Mech & Ind Engn Adv Mech Vibrat Lab Roorkee 247667 Uttarakhand India

This paper proposes a data augmentation model SAE-WGAN (stacked autoencoder with Wasserstein generative adversarial network), to reduce data imbalance condition of bearing fault diagnosis. To stabilize Wasserstein generative adversarial network training, both Wasserstein distance and informative noise vectors from stacked autoencoder are utilized to improve the quality of generated samples. For the quantitative evaluation of generated samples, both normalized cross-correlation and Kullback-Leibler divergence metrics are employed. Experimental validation and comparisons with state-of-art methods are presented to verify the effectiveness of the generation model. The results show an improvement of 6.58%, and 10.23% compared to Generative adversarial network, and Variational autoencoder, respectively. Furthermore, one-dimensional convolutional neural network is utilized for fault classification, and its performance is assessed using the receiver operating characteristic curve and area under curve values. The comparison results revealed the superior performance of the proposed generation model for bearing intelligent fault diagnosis under paucity of faulty data.

关键词： Generative adversarial network stacked autoencoder Deep convolutional neural network Normalized cross-correlation Receiver operating characteristic

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Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

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INFORMATION SYSTEMS FRONTIERS 2020年第5期22卷 1067-1083页

作者： Smiti, Salima Soui, Makram Univ Manouba Natl Sch Comp Sci Manouba Tunisia Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and stacked autoencoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k- nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm.

关键词： Bankruptcy prediction Deep learning stacked autoencoder Borderline SMOTE Imbalanced dataset

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SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第17期77卷 22809-22820页

作者： Xiao, Zhiyong Wang, Canhua Jia, Nan Wu, Jianhua Nanchang Univ Sch Mechatron Engn Nanchang 330031 Jiangxi Peoples R China Jiangxi Agr Univ Sch Software Nanchang 330045 Jiangxi Peoples R China Jiangxi Univ Tradit Chinese Med Sch Comp Nanchang 330004 Jiangxi Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China

This paper employs a novel-deep learning method and brain frequencies to discriminate school-aged children with autism spectrum disorders (ASD) from typically developing (TD) school-aged children with functional magnetic resonance imaging (fMRI) data of 84 subjects from the ABIDE (Autism Brain Imaging Data Exchange) database. Firstly, the fMRI data were preprocessed, and then each subject's dataset was decomposed into 30 independent components (IC). Secondly, some key ICs were selected and inputted into a stacked autoencoder (SAE). The SAE was adopted for features subtraction and dimensionality reduction. Finally, a softmax classifier was used to discriminate the school-aged children with ASD from TD school-aged children. The average accuracy of the work was as high as 87.21% (average sensitivity = 92.86%, average specificity = 84.32%). The results of classification demonstrated that the proposed method may have the potential to automatically discriminate school-aged children with ASD from TD school-aged children. Attempts to use deep learning-based algorithms and brain frequencies to discriminate school-aged children with ASD from TD school-aged children should likely be a key step forward in auxiliary clinical utility.

关键词： stacked autoencoder Classification School-aged children Autism spectrum disorder Brain frequency

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A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data

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APPLIED INTELLIGENCE 2023年第1期53卷 272-288页

作者： Cui, Jiyuan Zong, Liansong Xie, Jianhua Tang, Mingwei Xihua Univ Sch Comp & Software Engn Chengdu 610039 Sichuan Peoples R China

The high dimension, complexity, and imbalance of network data are hot issues in the field of intrusion detection. Nowadays, intrusion detection systems face some challenges in improving the accuracy of minority classes detection, detecting unknown attacks, and reducing false alarm rates. To address the above problems, we propose a novel multi-module integrated intrusion detection system, namely GMM-WGAN-IDS. The system consists of three parts, such as feature extraction, imbalance processing, and classification. Firstly, the stacked autoencoder-based feature extraction module (SAE module) is proposed to obtain a deeper representation of the data. Secondly, on the basis of combining the clustering algorithm based on gaussian mixture model and the wasserstein generative adversarial network based on gaussian mixture model, the imbalance processing module (GMM-WGAN) is proposed. Thirdly, the classification module (CNN-LSTM) is designed based on convolutional neural network (CNN) and long short-term memory (LSTM). We evaluate the performance of GMM-WGAN-IDS on the NSL-KDD and UNSW-NB15 datasets, comparing it with other intrusion detection methods. Finally, the experimental results show that our proposed GMM-WGAN-IDS outperforms the state-of-the-art methods and achieves better performance.

关键词： Intrusion detection system stacked autoencoder Gaussian mixture model Wasserstein generative adversarial network Imbalance processing Feature extraction Deep learning

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SMOTE-based adaptive coati kepler optimized hybrid deep network for predicting the survival of heart failure patients

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第24期83卷 65497页

作者： Barfungpa, Sonam Palden Samantaray, Leena Sarma, Hiren Kumar Deva Biju Patnaik Univ Technol Rourkela Odisha India Adv Tech Training Ctr Bardang Sikkim India Ajay Binay Inst Technol ABIT Cuttack 753014 Odisha India Gauhati Univ Dept Informat Technol Gauhati 781014 Assam India

In recent days, cardiovascular disease (CVD) has been considered one of the significant causes of morbidity and mortality in the world. Arrhythmia, acute myocardial infarction (AMI), and other cardiac illnesses have always been serious disorders that put patients' lives in danger. Different studies have shown that substantial features play a foremost role in enhancing the machine learning (ML) technique's performance. Moreover, the dataset was imbalanced, and the researchers could not utilize any model to deal with this issue. Thus, the objective is to discover the substantial features and solve the class imbalance problem that can boost accuracy and lead to better predicting cardiovascular patient survivors. In this proposed work, a novel SMOTE-based hybrid deep learning (SMOTE-HDL) network is proposed for predicting the patient's survivors of cardiovascular disease. At first, the data acquisition stage collects the input data from the dataset and then pre-processes using data normalization and missing value imputation. In addition, the class imbalance problem is solved by an oversampling technique named the synthetic minority oversampling technique (SMOTE). To extract the substantial features, an attention-based perceptive long short-term extraction network (APLSTEN) is employed. The process of feature selection is accomplished through an adaptive coati optimization algorithm (ACOA). Finally, the selected features are served to a Kepler-optimized deep stacked recurrent network (KDSRN) for attaining enhanced classification. The proposed SMOTE-HDL network is implemented in the Python platform using the Heat-failure-clinical-records dataset and assessed the performance in response to dissimilar evaluation criteria. The maximum prediction accuracy obtained by the proposed SMOTE-HDL is 95.52%, superior to the existing classifiers.

关键词： Cardiovascular disease Synthetic minority oversampling technique Missing value imputation Coati optimization algorithm Kepler optimization stacked autoencoder

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