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Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded deep autoencoder

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第11期24卷 3203-3214页

作者： Tsai, Kun-Hsi Wang, Wei-Chien Cheng, Chui-Hsuan Tsai, Chan-Yen Wang, Jou-Kou Lin, Tzu-Hao Fang, Shih-Hau Chen, Li-Chin Tsao, Yu Med Dept Imediplus Inc Taipei 11529 Taiwan Natl Taiwan Univ Hosp Dept Pediat Taipei 11529 Taiwan Acad Sinica Biodivers Res Ctr Taipei 11529 Taiwan Yuan Ze Univ Dept Elect Engn Taoyuan 131 Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei 11529 Taiwan

Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases. To reach accurate diagnoses, a device must be able to recognize heart and lung sounds from various clinical situations. However, the recorded chest sounds are mixed by heart and lung sounds. Thus, effectively separating these two sounds is critical in the pre-processing stage. Recent advances in machine learning have progressed on monaural source separations, but most of the well-known techniques require paired mixed sounds and individual pure sounds for model training. As the preparation of pure heart and lung sounds is difficult, special designs must be considered to derive effective heart and lung sound separation techniques. In this study, we proposed a novel periodicity-coded deep auto-encoder (PC-DAE) approach to separate mixed heart-lung sounds in an unsupervised manner via the assumption of different periodicities between heart rate and respiration rate. The PC-DAE benefits from deep-learning-based models by extracting representative features and considers the periodicity of heart and lung sounds to carry out the separation. We evaluated PC-DAE on two datasets. The first one includes sounds from the Student Auscultation Manikin (SAM), and the second is prepared by recording chest sounds in real-world conditions. Experimental results indicate that PC-DAE outperforms several well-known separation works in terms of standardized evaluation metrics. Moreover, waveforms and spectrograms demonstrate the effectiveness of PC-DAE compared to existing approaches. It is also confirmed that by using the proposed PC-DAE as a pre-processing stage, the heart sound recognition accuracies can be notably boosted. The experimental results confirmed the effectiveness of PC-DAE and its potential to be used in clinical applications.

关键词： Heart Lung Acoustics Neurons Decoding Convolution Informatics Blind monaural source separation deep autoencoder deep neural networks heart sound lung sound phonocardiogram periodic analysis

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Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2020年第1期40卷 440-453页

作者： Raja, P. M. Siva Rani, Antony Viswasa Amrita Coll Engn & Technol Comp Sci & Engn Erachakulam Tamil Nadu India

In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues from the brain. In the brain image, the size of a tumor can be varied for different patients along with the minute details of the tumor. It is a difficult task to diagnose and classify the tumor from numerous images for the radiologists. This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach. Initially, the pre-processing stage is performed using the non-local mean filter for denoising purposes. Then the BFC (Bayesian fuzzy clustering) approach is utilized for the segmentation of brain tumors. After segmentation, robust features such as, informationtheoretic measures, scattering transform (ST) and wavelet packet Tsallis entropy (WPTE) methods are used for the feature extraction process. Finally, a hybrid scheme of the DAE (deep autoencoder) based JOA (Jaya optimization algorithm) with a softmax regression technique is utilized to classify the tumor part for the brain tumor classification process. The proposed scheme is implemented in a MATLAB environment. The simulation results are conducted by the BRATS 2015 database which proved that the proposed approach obtained the high classification accuracy (98.5 %) when compared to other state-of-art methods. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Brain tumor segmentation Brain tumor classification deep autoencoder Bayesian fuzzy clustering MRI image

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Automatic Chinese Short Answer Grading with deep autoencoder 1

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19th International Conference on Artificial Intelligence in Education (AIED)

作者： Yang, Xi Huang, Yuwei Zhuang, Fuzhen Zhang, Lishan Yu, Shengquan Beijing Normal Univ Beijing Adv Innovat Ctr Future Educ Beijing 100875 Peoples R China Sunny Educ Inc Beijing 100102 Peoples R China Beijing Univ Chem Technol Beijing 100029 Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9783319938462

ISBN: (纸本)9783319938462;9783319938455

Short answer question is a common assessment type of teaching and learning. Automatic short answer grading is the task of automatically scoring short natural language responses. Most previous autograders mainly rely on target answers given by teachers. However, target answers are not always available. In this paper, a deep autoencoder based algorithm for automatic short answer grading is presented. The proposed algorithm can be built without expressly defining target answers, and learn the lower-dimensional representation of student responses. For the sake of reducing the influence of data imbalance, we introduce the expectation regularization term of label ratio into the model. The experimental results demonstrate the effectiveness of our proposed method.

关键词： Automatic grading Short Answer deep autoencoder Text classification

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Health Assessment for Crane Pumps based on Vehicle Tests using deep autoencoder and Metric Learning

Health Assessment for Crane Pumps based on Vehicle Tests usi...

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Xiao, Dengyu Huang, Yixiang Qin, Chengjin Shi, Haotian Liu, Chengliang Shan, Zenghai Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Xuzhou Construct Machinery Grp Co Ltd Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781538683576

Pump is one of the key components in a crane, which once fails will severely hurt the reliability of the hydraulic system and cause great loss. Therefore, accurate, reliable and effective crane pump health assessment must be performed. However, the research about pump health assessment still stays at the stage of bench tests, which have the limited help for the realworld pump health prognosis. In this paper, to evaluate crane pump health status and avoid the issue above, the real-world vehicle tests of several cranes with different service years are performed to acquire the pump signals during the cranes' actual operations. deep autoencoder (DAE), a kind of unsupervised learning approach, which possesses the capacity to learn meaningful representations from raw signal, is used reduce the data dimension before they are sent to Mahalanobis-Taguchi System in metric learning. Mahalanobis distance (MD) is utilized to reveal the performance degradation and assess the health condition. Performances of other feature learning methods such as statistical features, EMD, MLP, CNN are tested and contrasted. Results show that the proposed approach achieves the best performance.

关键词： Health assessment crane pumps deep autoencoder metric learning

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Unsupervised Facial Image Occlusion Detection with deep autoencoder 11

Unsupervised Facial Image Occlusion Detection with Deep Auto...

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11th International Conference on Digital Image Processing (ICDIP)

作者： Wang Xu-dong Wei Hong-quan Li Shao-mei Gao Chao Huang Rui-yang Natl Digital Switching Syst Engn & Technol R&D Ct Zhengzhou 450002 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still a challenging problem. Although there are many works to solve the problem of obscuring the face, the occlusion is still a challenge in face recognition. To overcome this issue, firstly we should detect the occlusion position in the facial images. We construct a robust self-encoding machine to solve the occlusion detection problem in face images and uses synthetic occlusion data for training. We evaluated our method under various synthetic occlusion face images. Experiments show that our method can effectively detect various types of occlusion masks in an unsupervised manner and has better robustness to the occlusion categories.

关键词： Face Recognition Occlusion Detection deep autoencoder

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AE5-SSIM: A Novel Unsupervised Tinfoils Defect Detection Model with deep autoencoder 16

AE5-SSIM: A Novel Unsupervised Tinfoils Defect Detection Mod...

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16th IEEE International Conference on Signal Processing (ICSP)

作者： Zhang, Fanghui Zhang, Linna Zhang, Damin Huang, Yansen Kan, Shichao Cen, Yigang Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Guizhou Univ Sch Mech Engn Guiyang Peoples R China Guizhou Univ Coll Bigdata & Informat Engn Guiyang Peoples R China Guizhou Lianjian Civil Engn Qual Testing Monitori Guiyang Guizhou Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (数字)9781665460569

ISBN: (纸本)9781665460569

Tinfoils defect detection plays an important role in the field of industrial manufacturing. Human visual evaluation is not only time-consuming and labor-consuming but also subjective and error-prone. There were some defect detection algorithms with deep learning proposed to deal with this problem. However, most of the existing algorithms need to be trained by large numbers of normal and abnormal training samples. It is difficult to collect and mark a lot of abnormal samples, thus limiting the usage of these methods. In this paper, we propose a novel unsupervised tinfoils defect detection model with deep autoencoder, called AE 5-SSIM (structural similarity-based reconstruction autoencoder, whose encoder and decoder networks have five sub-encoder blocks and five sub-decoder blocks, respectively), to break the constraints of limited abnormal training samples on algorithms. We only use the normal areas of tinfoils samples to train the AE 5-SSIM model. After trained, this model can reconstruct the input tinfoils images to normal images. Finally, combining the input image and the reconstructed image, a classifier is designed to classify the defects. Experimental results on our public dataset proved the efficiency and effectiveness of the proposed algorithm, which achieved 95.33% AUC for the tinfoils defect detection.

关键词： Anomaly detection deep autoencoder Structural similarity The tinfoils Unsupervised learning

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An Outlier Detection Approach Based on WGAN-Empowered deep autoencoder 9

An Outlier Detection Approach Based on WGAN-Empowered Deep A...

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IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Huang, Yunxin Xu, Hongzuo Wang, Xiaodong Wu, Zhiyue Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781728111902

Modelling normal data is one of the major challenges in outlier detection. deep learning has been proven to be effective in modelling underlying distributions of input training data. However, the existing deep learning-based methods normally focus on how to alleviate the negative impact from the presence of outliers when they model the full dataset. Besides, insufficient size of training data also leads to unsatisfactory training of deep networks. This paper proposes a WGAN-empowered deep autoencoder-based Outlier detection approach (GAEO for short) which presents a novel way to directly and effectively model reliable normal data. GAEO first obtains initial normal data by a proposed initial distance-based outlier scoring function, then constructs a WGAN network to perform normal data augmentation to obtain adequate training samples for subsequent normal data modelling. The deep autoencoder is then trained to discover and yield distributions and patterns of normal data, and the reconstruction error is defined as outlierness of each data object. In our experiments, we investigate the effectiveness of our method GAEO compared with three state-of-the-art outlier detectors on ten real-world datasets, and discuss the impact of the parameters in GAEO. We show that our method GAEO significantly outperforms its contenders by 4% to 20% AUC improvement.

关键词： outlier detection deep autoencoder deep learning GAN

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Fault Detection of Elevator Systems Using deep autoencoder Feature Extraction 13

Fault Detection of Elevator Systems Using Deep Autoencoder F...

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13th IEEE International Conference on Research Challenges in Information Science (IEEE RCIS)

作者： Mishra, Krishna Mohan Krogerus, Tomi R. Huhtala, Kalevi J. Tampere Univ Unit Automat Technol & Mech Engn Tampere Finland

ISBN: (纸本)9781728148441

In this research, we propose a generic deep autoencoder model for automated feature extraction from the raw sensor data. Extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data is used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved 100% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperform the results using existing features. Existing features are also classified with random forest to compare results. deep autoencoder random forest provides better results due to the new deep features extracted from the dataset when compared to existing features. Our model provides good classification and is robust against overfitting characteristics. This research will help various predictive maintenance systems to detect false alarms, which will reduce unnecessary visits of service technicians to installation sites.

关键词： Elevator System deep autoencoder Fault Detection Feature Extraction Random Forest

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Semantic Retrieval of Personal Photos using a deep autoencoder Fusing Visual Features with Speech Annotations Represented as Word/Paragraph Vectors 16

Semantic Retrieval of Personal Photos using a Deep Autoencod...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Lu, Hung-tsung Liou, Yuan-ming Lee, Hung-yi Lee, Lin-shan Natl Taiwan Univ Grad Inst Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781510817906

It is very attractive for the user to retrieve photos from a huge collection using high-level personal queries (e.g. "uncle Bill's house"), but technically very challenging. Previous works proposed a set of approaches toward the goal assuming only 30% of the photos are annotated by sparse spoken descriptions when the photos are taken. In this paper, to promote the interaction between different types of features, we use the continuous space word representations to train a paragraph vector model for the speech annotation, and then fuse the paragraph vector with the visual features produced by deep Convolutional Neural Network (CNN) using a deep autoencoder (DAE). The retrieval framework therefore combines the word vectors and paragraph vectors of the speech annotations, the CNN-based visual features, and the DAE-based fused visual/speech features in a three-stage process including a two-layer random walk. The retrieval performance was significantly improved in the preliminary experiments.

关键词： image retrieval speech annotation word representation paragraph vector convolutional neural network deep autoencoder random walk fused features

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Matrix Factorization and deep autoencoder based Clustering Scheme for Large-scale UAV Networks 97

Matrix Factorization and Deep Autoencoder based Clustering S...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Fang, Jiaolan Wang, Chan Li, Rongpeng Wei, Hanyu Zhao, Minjian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350311143

In recent years, unmanned aerial vehicle (UAV) ad-hoc networks have achieved rapid development due to their autonomy and high reliability. Typically, clustering is widely adopted to reduce the degradation of network performance in large-scale UAV networks. However, due to the limited channel resources and complex communication environment, it becomes challenging to obtain complete information needed for clustering from remote nodes. In addition, the high dimension and nonlinear relationships of data also make large-scale clustering diffcult. Therefore, in this paper, we propose a noval distributed clustering scheme. First, a matrix factorization (MF) based time series algorithm is proposed to predict and complement the incomplete information. Second, we adopt deep autoencoder to wisely incorporate the non-linear relationship of information needed for clustering. Finally we extract features as input to k-means to obtain clustering results. Simulation results demonstrate the effectiveness of the proposed clustering scheme.

关键词： UAV ad-hoc networks clustering scheme matrix factorization deep autoencoder

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