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Radar Signal Intra-Pulse Modulation Recognition Based on convolutional denoising autoencoder and Deep convolutional Neural Network

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IEEE ACCESS 2019年 7卷 112339-112347页

作者： Qu, Zhiyu Wang, Wenyang Hou, Changbo Hou, Chenfan Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China

Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is -9 dB.

关键词： Radar signal recognition Cohen class time frequency distribution convolutional denoising autoencoder deep convolutional neural network

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Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 311卷 124015页

作者： Pu, Hongbin Yu, Jingxiao Luo, Jie Paliwal, Jitendra Sun, Da -Wen South China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Peoples R China South China Univ Technol Acad Con orary Food Engn Guangzhou Higher Educ Mega Ctr Guangzhou 510006 Peoples R China Guangzhou Univ Guangzhou Higher Educ Mega Ctr Guangdong Key Lab Earthquake Engn & Appl Tech Guangzhou 510006 Peoples R China Univ Coll Dublin Natl Univ Ireland Agr & Food Sci Ctr Food Refrigerat & Computerized Food Technol FRCFT Dublin Ireland Univ Manitoba Dept Biosyst Engn Winnipeg MB R3T 2N2 Canada

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time -domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise -to -signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second -order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1 -score of 85.87% performed better than the original first -order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

关键词： Infested rice grains Sitophilus oryzae Terahertz time-domain spectroscopy convolutional denoising autoencoder Data reconstruction

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RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035111-035111页

作者： Yao, Xuejian Zhu, Junjun Jiang, Quansheng Yao, Qin Shen, Yehu Zhu, Qixin Suzhou Univ Sci & Technol Sch Mech Engn Suzhou 215009 Peoples R China

Remaining useful life (RUL) prediction of rolling bearing plays an important role in maintaining the safety of the equipment. However, the data collected from industrial scene often contains noises, which affects the RUL prediction precision of rolling bearing. To overcome the above problem, a data-driven scheme for RUL prediction of rolling bearing is proposed based on convolutional denoising autoencoder (CDAE) and bidirectional long short-term memory network (Bi-LSTM). In the proposed method, the vibration signal is directly used as input of the prognostics network model. Then, a denoising network model based on CDAE is built to reduce the effect of noise. Through stacking the convolutional autoencoder, the noise component is automatically removed from the raw data. Finally, the network model based on Bi-LSTM is established to extract the high-dimensional degradation characteristics of bearing and estimate the RUL of the rolling bearing. The experimental results on the Xi'an Jiaotong University bearing dataset show that the proposed method has satisfied performance of RUL prediction.

关键词： remaining useful life prediction rolling bearing convolutional denoising autoencoder bidirectional LSTM

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Robust correlation mapping of train-induced stresses for high-speed railway bridge using convolutional denoising autoencoder

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2023年第2期22卷 832-845页

作者： Niu, Jin Li, Zhonglong Zhuo, Yi Di, Hao Wei, Jianfeng Wang, Xin Guo, Yapeng Li, Shunlong Harbin Inst Technol Sch Transportat Sci & Engn Huanghe Rd Harbin 150090 Heilongjiang Peoples R China China Railway Design Corp Tianjin Peoples R China

Train-induced stresses in different monitoring points not only reflects local mechanical characteristics of the structural components but also has inherent spatiotemporal correlations in the high-speed railway bridges. Mapping correlations among the stress responses can assist recognizing train-induced stress pattern and lay foundation for structural health diagnosis. However, correlation mapping and feature extraction of structural responses rely heavily on the data integrity, the pre-constructed model may be out of action due to data acquisition/transmission error, sensor faults, etc., commonly exists in the structural health monitoring system. Considering the characteristics of various incomplete train-induced stresses, this study presents a robust correlation mapping of incomplete data to complete data using one-dimensional convolutional denoising autoencoder. Stacked convolutional layers are employed as encoder to extract robust spatiotemporal feature of incomplete stresses, and transposed convolutional layers served as decoder to reconstruct denoised and complete stresses. In the training strategy, various incomplete data conditions, where stress data are lost continuously or discretely with different missing rates, are considered as training samples, making the established correlation mapping robust, accurate, and adaptive. The application on a high-speed railway truss bridge demonstrates that the proposed method can robustly reconstruct the complete stress data under different data loss conditions. The method can also be employed to assess the importance of any sensor combinations to the monitoring item, which shed light on the maintenance of sensor network.

关键词： Train-induced stress high-speed railway bridge convolutional denoising autoencoder spatiotemporal correlation robust data mining

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Radar Signal Waveform Recognition Based on convolutional denoising autoencoder

Radar Signal Waveform Recognition Based on Convolutional Den...

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7th International Conference on Communications, Signal Processing, and Systems (CSPS)

作者： Liu, Zhaolei Mao, Xiaojie Deng, Zhian Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China Harbin Engn Univ Coll Informat & Telecommun Harbin 150001 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

To solve the problem of the low recognition rate of the existing methods at low signal-to-noise ratio (SNR), we propose a novel method of radar signal waveform recognition. In this method, we extract the time-frequency images (TFIs) of radar signals through Cohen class time frequency distribution. Then, we introduce convolutional denoising autoencoder (CDAE) to denoise and repairs the TFIs. Finally, we build a convolutional neural network (CNN) to identify the TFIs of radar signals. Simulation experiment shows that the proposed method can identify 12 kinds of radar signal waveforms, and the overall probability of successful recognition (PSR) is 95.4% when the SNR is -7 dB.

关键词： Radar signal recognition Cohen class time frequency distribution convolutional denoising autoencoder convolutional neural network

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Facial Expression Recognition Based on convolutional denoising autoencoder and XGBoost 8

Facial Expression Recognition Based on Convolutional Denoisi...

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IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Yang, Jun Zhang, Damin Pan, Zhiyuan Liu, Dong Chen, Juanmin Guizhou Univ Coll Big Data & Informat Engn Guiyang Guizhou Peoples R China

ISBN: (纸本)9781538681787

In order to solve the insufficiency of feature extraction in traditional facial expression recognition process and further improve the classification accuracy, a deep learning method combining the convolutional denoising autoencoder and XGBoost model is proposed in this paper. In the early stage of feature extraction, convolutional autoencoder is used to fully learn the high-dimensional complex feature data and reduce the nonlinear dimension. On this basis, noise is introduced into the original image to enhance the robustness and generalization ability of the model. In the later classification process, XGBoost classifier is used to classify the extracted features. Experiments are performed on JAFFE and CK+ facial expression recognition datasets, the results show that this method has better recognition accuracy than other comparison methods.

关键词： facial expression recognition feature extraction convolutional denoising autoencoder XGBoost classifier

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Multivariate Time Series as Images: Imputation Using convolutional denoising autoencoder 1

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18th International Symposium on Intelligent Data Analysis (IDA)

作者： Al Safi, Abdullah Beyer, Christian Unnikrishnan, Vishnu Spiliopoulou, Myra Otto von Guericke Univ Fak Informat Postfach 4120 D-39106 Magdeburg Germany

ISBN: (数字)9783030445843

ISBN: (纸本)9783030445843;9783030445836

Missing data is a common occurrence in the time series domain, for instance due to faulty sensors, server downtime or patients not attending their scheduled appointments. One of the best methods to impute these missing values is Multiple Imputations by Chained Equations (MICE) which has the drawback that it can only model linear relationships among the variables in a multivariate time series. The advancement of deep learning and its ability to model non-linear relationships among variables make it a promising candidate for time series imputation. This work proposes a modified convolutional denoising autoencoder (CDA) based approach to impute multivariate time series data in combination with a preprocessing step that encodes time series data into 2D images using Gramian Angular Summation Field (GASF). We compare our approach against a standard feed-forward Multi Layer Perceptron (MLP) and MICE. All our experiments were performed on 5 UEA MTSC multivariate time series datasets, where 20 to 50% of the data was simulated to be missing completely at random. The CDA model outperforms all the other models in 4 out of 5 datasets and is tied for the best algorithm in the remaining case.

关键词： convolutional denoising autoencoder Gramian Angular Summation Field MICE MLP. Imputation Time series

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Assessment of flame stability through a convolutional denoising autoencoder and statistical analysis

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COMBUSTION AND FLAME 2023年第Part2期258卷

作者： Han, Zhezhe Tang, Xiaoyu Hossain, Md. Moinul Xu, Chuanlong Nanjing Inst Technol Sch Informat & Commun Engn Nanjing 211167 Peoples R China Univ Kent Sch Engn Canterbury CT2 7NT Kent England Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China

Flame stability assessment is essential for optimizing combustion operation and improving combustion quality. However, an accurate and reliable assessment of stability is difficult, heavily relying on prior expert knowledge and massive labeled data. This study proposes a novel method for flame stability assessment through flame images and deep learning techniques. In this method, the deep image features are extracted by an unsupervised convolutional denoising autoencoder (CDAE), and then quantitatively analyzed by a stability index. In particular, the CDAE introduces a new loss function composed of denoising coding constraints and reconstruction similarity to improve its training efficiency. The stability index is established based on clustering analysis and statistical analysis of the deep image features, with a numerical interval of [0, 1]. The effectiveness of the proposed method is verified by the flame images obtained from ethylene-air diffusion combustion conditions. Results show that the proposed method extracts representative flame features accurately and quantifies the flame stability with strong robustness and generalization ability. (c) 2023 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

关键词： Flame stability Flame image convolutional denoising autoencoder Statistical analysis Stability index

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A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年 63卷 102225-102225页

作者： Dasan, Evangelin Panneerselvam, Ithayarani Sethu Inst Technol Dept Comp Sci & Engn Kariapatti India

Typical IoT based e-health scenarios use resource constrained wearable device to facilitate ubiquitous long-term monitoring for chronic conditions like cardiovascular disease (CVD). Electrocardiogram (ECG) is an efficient indicator to diagnose patients with CVD. In wearable technology, the signal transmission cost is high and the observed ECG signal is likely to be contaminated with noise. To amend this, an efficient lightweight signal compression scheme is designed to reduce the signal size before transmitting it and thereby reducing the transmission cost and allow long-term monitoring. In this paper, an edge based novel approach is proposed by combining convolutional denoising autoencoder (CDAE) and long short-term memory (LSTM) for ECG signal compression. A single layered LSTM network is added to the end of encoder section of the CDAE, instead of adding several convolutional filters and pooling layers. In which, the number of trainable parameters of the model are reduced and in turn lessen the computation time. Also, the LSTM network learns the order dependencies between the data that helps to reconstruct the data from its compressed form. In the meantime, the proposed algorithm denoises the signal as it employs denoising autoencoder architecture. The experiments are conducted on ECG signal taken from MIT-BIH Arrhythmias Database. The experimental result shows that the proposed method is efficient by achieving compression ratio of 64 with better reconstruction quality score of 15.61 which is higher than state-of-the-art methods. As well the proposed method is lightweight when compared with baseline methods CDAE and stacked autoencoder in terms of computation cost.

关键词： convolutional denoising autoencoder Long short-term memory Wearable device Electrocardiogram Signal compression Energy efficiency

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Automatic Fabric Defect Detection with a Multi-Scale convolutional denoising autoencoder Network Model

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SENSORS 2018年第4期18卷 1064-1064页

作者： Mei, Shuang Wang, Yudan Wen, Guojun China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China

Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

关键词： fabric defect detection unsupervised learning deep neural network convolutional denoising autoencoder Gaussian pyramid

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