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A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第3期20卷 5223-5242页

作者： Yang, Jing Xie, Guo Yang, Yanxi Li, Qijun Yang, Cheng Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.

关键词： regulable resampling multilevel recovery sparse autoencoder rolling bearing fault diagnosis

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Deep feature learning for cover song identification

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第22期76卷 23225-23238页

作者： Fang, Jiunn-Tsair Day, Chi-Ting Chang, Pao-Chi Ming Chuan Univ Dept Elect Engn 5 Deming Rd Taoyuan 33348 Taiwan Natl Cent Univ Dept Commun Engn 300 Jhongda Rd Taoyuan 32001 Taiwan

The identification of a cover song, which is an alternative version of a previously recorded song, for music retrieval has received increasing attention. Methods for identifying a cover song typically involve comparing the similarity of chroma features between a query song and another song in the data set. However, considerable time is required for pairwise comparisons. In this study, chroma features were patched to preserve the melody. An intermediate representation was trained to reduce the dimension of each patch of chroma features. The training was performed using an autoencoder, commonly used in deep learning for dimensionality reduction. Experimental results showed that the proposed method achieved better accuracy for identification and spent less time for similarity matching in both covers80 dataset and Million Song Dataset as compared with traditional approaches.

关键词： Cover song Deep learning Music retrieval sparse autoencoder

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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.Alsoufi Maheyzah Md Siraj Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed Faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a sparse autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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Feature extraction and physical interpretation of melt pressure during injection molding process

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2018年 261卷 50-60页

作者： Zhou, Xundao Zhang, Yun Mao, Ting Ruan, Yufei Gao, Huang Zhou, Huamin Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mold Technol 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Melt pressure is crucial in injection molding. A variety of pressure sensors have been installed in injection molding machines and molds to collect melt-pressure information. Many methods have been developed to extract the features of melt pressure. However, challenges exist for these feature extraction methods because they are, inevitably, extremely dependent on manual operation. In this study, an unsupervised feature extraction method using a sparse autoencoder is proposed to extract the features of melt pressure during injection molding. An injection molding model was applied to reinterpret the network structure of a sparse autoencoder to better understand the network structure. The feature curve, which is defined as the curve of weights between input and hidden units, was proved to be a significant indicator to measure how melt compression causes variations in injection pressure. Over 10,000 shots were conducted to verify the proposed feature extraction method. The experimental and simulation results show that the feature curve effectively extracts switching points (gate and velocity-pressure switchover), important indicators (viscosity index, holding pressure, and peak pressure) in the injection molding process and shows how the geometric features of a part affect the melt flow.

关键词： Feature extraction Feature physical interpretation sparse autoencoder Injection molding process

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Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Jul.期129卷 212-225页

作者： Gong, Maoguo Yang, Hailun Zhang, Puzhao Xidian Univ Minist Educ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Ternary change detection Deep learning Synthetic aperture radar Representation learning sparse autoencoder Convolutional neural networks

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From Seeing to Recognising-An Extended Self-Organizing Map for Human Postures Identification

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7899-7906页

作者： He, Xin Zielinska, Teresa Dutta, Vibekananda Matsumaru, Takafumi Sitnik, Robert Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu 8080135 Japan Warsaw Univ Technol WUT Fac Power & Aeronaut Engn PL-00665 Warsaw Poland Warsaw Univ Technol Fac Mechatron PL-00661 Warsaw Poland

The letter presents a dedicated method for recognizing human postures using classification and clustering options. The ultimate goal of the research is to recognise human actions based on posture sequences. Such a task imposes expectations on the developed method. For this purpose, a sparse autoencoder combined with a Self-Organized Map (SOM) is proposed. SOM is equipped with an additional layer of post-labeling or clustering. This entire structure is called the extended SOM. Two task-oriented modifications are applied to improve SOM performance - a dedicated angular distance measure and a neighbourhood function for updating the SOM weights. The research contribution is the concept of extended SOM, which is trained using unlabeled data and classifies or clusters the human postures. The sparse autoencoder preserves the characteristics of the data while reducing its dimensionality. Better classification efficiency of the developed method is demonstrated compared to other representative methods. Ablation studies illustrate how the introduced modifications improve classification results. The developed method is characterised by good resolution in distinguishing postures. A discussion of the concept's usefulness is provided at the end of the letter.

关键词： Human postures improved self-organizing-map sparse autoencoder neighbourhood function

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Learning-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop With Sequencing Flexibility

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6663-6675页

作者： Lin, ChengRan Cao, ZhengCai Zhou, MengChu Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA

This work considers an extended version of flexible job-shop problem from a postprinting or semiconductor manufacturing environment, which needs a directed acyclic graph rather than a linear order to describe the precedences among operations. To obtain its reliable and high-quality schedule in a reasonable time, a learning-based cuckoo search (LCS) algorithm is presented. In it, cuckoo search is selected as an optimizer. To produce promising solutions in a high-dimensional solution space, a sparse autoencoder is introduced to compress a high-dimensional solution into an informative low-dimensional one. It extends the application area of autoencoder-embedded evolutionary optimization methods into combinational optimization by developing an improved one-hot encoding method. Then, in order to reveal the linkages among decision variables and enhance the explore ability of the proposed method, a factorization machine (FM) is used, for the first time, to capture the relevant and complementary features of population. Hence, a parallel framework involving three co-evolved subpopulations is constructed. The first one is an autoencoder embedded subpopulation, the second one is assisted by an FM, and the last one undergoes a regular iteration process. To balance the exploration and exploitation of the proposed framework and avoid unnecessary computation, a reinforcement learning algorithm is used to adaptively adjust the proportion of subpopulations and tune parameters of each subpopulation iteratively. Numerical simulations with benchmarks are performed to compare it with CPLEX, some classical heuristics, and several recently developed methods. The results shows that it well outperforms them.

关键词： Cuckoo search (CS) factorization machine (FM) job shop scheduling reinforcement learning (RL) sparse autoencoder

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A Physics-Guided MFL Deformed Defect Recovery Method

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 2113-2124页

作者： Jiang, Lin Zhang, Huaguang Liu, Jinhai Xiao, Qi Shen, Xiangkai Xu, Hang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. Note to Practitioners-The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a

关键词： Feature extraction Pipelines Training Deformable models Testing Deformation Numerical models Deformed defect recovery physical features sparse autoencoder pre-trained model MFL

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Deep learning of chroma representation for cover song identification in compression domain

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2018年第3期29卷 887-902页

作者： Fang, Jiunn-Tsair Chang, Yu-Ruey Chang, Pao-Chi Ming Chuan Univ Dept Elect Engn 5 Deming Rd Taoyuan 33348 Taiwan Natl Cent Univ Dept Commun Engn 300 Jhongda Rd Taoyuan 32001 Taiwan

Methods for identifying a cover song typically involve comparing the similarity of chroma features between the query song and another song in the data set. However, considerable time is required for pairwise comparisons. In addition, to save disk space, most songs stored in the data set are in a compressed format. Therefore, to eliminate some decoding procedures, this study extracted music information directly from the modified discrete cosine transform coefficients of advanced audio coding and then mapped these coefficients to 12-dimensional chroma features. The chroma features were segmented to preserve the melodies. Each chroma feature segment was trained and learned by a sparse autoencoder, a deep learning architecture of artificial neural networks. The deep learning procedure was to transform chroma features into an intermediate representation for dimension reduction. Experimental results from a covers80 data set showed that the mean reciprocal rank increased to 0.5 and the matching time was reduced by over 94% compared with traditional approaches.

关键词： Cover song Music retrieval sparse autoencoder Descriptor Advanced audio coding

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A Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction Prediction

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CURRENT BIOINFORMATICS 2024年第4期19卷 341-351页

作者： Wei, Meng-Meng Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Lei-Wang, Lei- Xijing Univ Sch Informat Engn Xian Peoples R China Longdong Univ Coll Agr & Forestry Qingyang Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Guangxi Acad Sci Guangxi Key Lab Human Machine Interact & Intellige Nanning Peoples R China

Background LncRNA is not only involved in the regulation of the biological functions of protein-coding genes, but its dysfunction is also associated with the occurrence and progression of various diseases. Various studies have shown that an in-depth understanding of the mechanism of action of lncRNA is of great significance for disease treatment. However, traditional wet testing is time-consuming, laborious, expensive, and has many subjective factors which may affect the accuracy of the *** Most of the methods for predicting lncRNA-protein interaction (LPI) rely on a single feature, or there is noise in the feature. To solve this problem, we proposed a computational model, CSALPI based on a deep neural *** Firstly, this model utilizes cosine similarity to extract similarity features for lncRNA-lncRNA and protein-protein, denoising similar features using the sparse autoencoder. Second, a neighbor enhancement autoencoder is employed to enforce neighboring nodes to be represented similarly by reconstructing the denoised features. Finally, a Light Gradient Boosting Machine classifier is used to predict potential *** To demonstrate the reliability of CSALPI, multiple evaluation metrics were used under a 5-fold cross-validation experiment, and excellent results were achieved. In the case study, the model successfully predicted 7 out of 10 disease-associated lncRNA and protein *** The CSALPI can be an effective complementary method for predicting potential LPIs from biological experiments.

关键词： lncRNA-protein interactions lncRNA protein cosine similarity sparse autoencoder neighbor enhancement autoencoder

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