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Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions

ADVANCES IN MECHANICAL ENGINEERING 2019年第12期11卷

作者： Che, Changchang Wang, Huawei Fu, Qiang Ni, Xiaomei Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat 29 Jiang Jun St Nanjing 211106 Jiangsu Peoples R China

Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating conditions. To obtain robust feature representation, the denoising autoencoder is used to denoise and reduce dimension of unlabeled rolling bearing signals. For those unlabeled target domain signals, a feature matching method based on multi-kernel maximum mean discrepancies between source domain and target domain is adopted to get enough labeled target domain samples. Then, these rolling bearing signals are converted to multi-dimensional graph samples and fed into a convolutional neural network model for fault diagnosis. To improve the generalization of convolutional neural network under variable operating conditions, we combine model-based transfer learning with feature-based transfer learning to initialize and optimize the convolutional neural network parameters. The effectiveness of the proposed method is validated through several comparative experiments of Case Western Reserve University data. The results demonstrate that the proposed method can learn features adaptively from noisy data and increase the accuracy rate by 2%-8% comparing with other models.

关键词： denoising autoencoder convolutional neural network multi-kernel maximum mean discrepancy transfer learning fault diagnosis rolling bearing

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Biomedical ontology alignment: an approach based on representation learning

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JOURNAL OF BIOMEDICAL SEMANTICS 2018年第1期9卷 21页

作者： Kolyvakis, Prodromos Kalousis, Alexandros Smith, Barry Kiritsis, Dimitris Ecole Polytech Fed Lausanne Route Cantonale CH-1015 Lausanne Switzerland Univ Appl Sci HES SO Western Switzerland Carouge Business Informat Dept Lausanne Switzerland SUNY Buffalo Dept Philosophy 104 Pk Hall Buffalo NY 14260 USA SUNY Buffalo Dept Biomed Informat 104 Pk Hall Buffalo NY 14260 USA

Background: While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. Results: An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. Conclusions: Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.

关键词： Ontology matching Semantic similarity Sentence embeddings Word embeddings denoising autoencoder Outlier detection

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Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Almukadi, Wafa Sulaiman Alrowais, Fadwa Saeed, Muhammad Kashif Yahya, Abdulsamad Ebrahim Mahmud, Ahmed Marzouk, Radwa Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah Saudi Arabia Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Univ Northern Border Coll Comp & Informat Technol Informat Technol Dept Rafha Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents' movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

关键词： Fall detection Computer vision Deep feature fusion Pelican optimization algorithm Deep learning denoising autoencoder

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Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network

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SENSORS 2023年第14期23卷 6303页

作者： Zhang, Xinghui Fan, Xiumei Yu, Shunyuan Shan, Axida Men, Rui Xian Univ Technol Dept Automat & Informat Engn Xian 710048 Peoples R China Ankang Univ Coll Elect & Informat Engn Ankang 725000 Peoples R China Baotou Teachers Coll Sch Informat Sci & Technol Baotou 014030 Peoples R China

Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current sche

关键词： intelligent transportation signalized intersections multi-objective optimization NSGA-III denoising autoencoder

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An Improved Deep Canonical Correlation Fusion Method for Underwater Multisource Data

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IEEE ACCESS 2020年 8卷 146300-146307页

作者： Song, Kuiyong Wang, Nianbin Zhang, Yun Harbin Engn Univ Coll Comp Sci & Technol Harbin 150000 Heilongjiang Peoples R China Hulunbuir Vocat Tech Coll Dept Informat Engn Hulunbuir 021000 Peoples R China

In complex underwater environments, the single mode of a single sensor cannot meet the precision requirement of object identification, and multisource fusion is currently the mainstream research approach. Deep canonical correlation analysis is an efficient feature fusion method but suffers from problems such as not strong scalability and low efficiency. Therefore, an improved deep canonical correlation analysis fusion method is proposed for underwater multisource sensor data containing noise. First, a denoising autoencoder is used for denoising and to reduce the data dimension to extract new feature expressions of raw data. Second, given that underwater acoustic data can be characterized as 1-dimensional time series, a 1-dimensional convolutional neural network is used to improve the deep canonical correlation analysis model, and multilayer convolution and pooling are implemented to decrease the number of parameters and increase the efficiency. To improve the scalability and robustness of the model, a stochastic decorrelation loss function is used to optimize the objective function, which reduces the algorithm complexity from O(n(3)) to O(n(2)). The comparison experiment of the proposed algorithm and other typical algorithms on MNIST containing noise and underwater multisource data in different scenes shows that the proposed algorithm is superior to others regardless of the efficiency or precision of target classification.

关键词： Convolutional neural network deep canonical correlation analysis denoising autoencoder multisource fusion underwater data

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Auto-Encoder based Recommendation Algorithm Combining Item Types

Frontiers in Economics and Management

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Frontiers in Economics and Management 2021年第3期2卷 321-327页

作者： Tianci Wang

Traditional collaborative filtering algorithm relies on score matrix to generate prediction, fails to mine potential features related to item genres, which leads to a low recommendation accuracy. A denoising autoencoder based collaborative filtering recommendation algorithm combining item types is proposed. Firstly, combining item score data and genres data, the potential features of the items are extracted by denoising autoencoder. Then the prediction is generated by the collaborative filtering algorithm. Experiments on MovieLens dataset show that the new algorithm can mine the potential features of item genres comprehensively, and improve the recommendation accuracy.

关键词： denoising autoencoder Collaborative Filtering Items with Same Genres User Preference

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Learning and visualizing chronic latent representations using electronic health records

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BIODATA MINING 2022年第1期15卷 18-18页

作者： Chushig-Muzo, David Soguero-Ruiz, Cristina Bohoyo, Pablo de Miguel Mora-Jimenez, Inmaculada Rey Juan Carlos Univ Dept Signal Theory & Commun Telemat & Comp Syst Madrid Spain Univ Hosp Fuenlabrada Madrid Spain

Background Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods We propose the use of the denoising autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting. Results To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients. Conclusio

关键词： denoising autoencoder Chronic diseases Diabetes Hypertension Clustering Patient representation Synthetic patient Health status trajectory

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A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks

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BRIEFINGS IN BIOINFORMATICS 2023年第3期24卷 bbad111-bbad111页

作者： Wang, Xin-Fei Yu, Chang-Qing You, Zhu-Hong Li, Li-Ping Huang, Wen-Zhun Ren, Zhong-Hao Li, Yue-Chao Wei, Meng-Meng Xijing Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Longdong Univ Coll Agr & Forestry Qingyang Peoples R China Xijing Univ Sch Informat Engn Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China

Motivation: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy ***: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and denoising autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed.

关键词： circRNA-miRNA interaction circRNA denoising autoencoder structural similarity functional similarity

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Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data

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SENSORS 2019年第5期19卷 1168-1168页

作者： Park, Sebin Gil, Myeong-Seon Im, Hyeonseung Moon, Yang-Sae Kangwon Natl Univ Dept Comp Sci Chuncheon Si 24341 Gangwon Do South Korea

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user's experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.

关键词： kalman filtering measurement noise sensor data filtering accuracy wavelet transform denoising autoencoder

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Robust Spoofed Speech Detection with Denoised I-vectors

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2023年第4期36卷 1553-1561页

作者： Disken, Gokay Adana Alparslan Turkes Sci & Technol Univ Elect Elect Engn Dept TR-01250 Adana Turkiye

Spoofed speech detection is recently gaining attention of the researchers as speaker verification is shown to be vulnerable to spoofing attacks such as voice conversion, speech synthesis, replay, and impersonation. Although various different methods have been proposed to detect spoofed speech, their performances decrease dramatically under the mismatched conditions due to the additive or reverberant noises. Conventional speech enhancement methods fail to recover the performance gap, hence more advanced techniques seem to be necessary to solve the noisy spoofed speech detection problem. In this work, denoising autoencoder (DAE) is used to obtain clean estimates of i-vectors from their noisy versions. ASVspoof 2015 database is used in the experiments with five different noise types, added to the original utterances at 0, 10, and 20 dB signal-to-noise ratios (SNR). The experimental results verified that the DAE provides a more robust spoof detection, where the conventional methods fail.

关键词： denoising autoencoder Speaker verification Spoof detection

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