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Method of extracting gear fault feature based on stacked autoencoder

JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8765-8769页

作者： Liu, Shuo Liu, Yulong Gu, Yuhai Xu, Xiaoli Beijing Informat Sci & Technol Univ Key Lab Electromech Measurement & Control Beijing Peoples R China

Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, deep learning techniques have been gradually applied to feature extraction and pattern recognition, and the features of feature extraction and fault diagnosis in complex working environments have shown certain advantages. This study is based on stacked autoencoder under deep learning model, and improve training network performance by modified activation function. Through the network training before and after the experiment done, and to extract the fault feature data comparison in testing, improving network after activation function to extract fault features showed a greater advantage, can be a very good application in practical fault feature extraction.

关键词： mechanical engineering computing fault diagnosis gears feature extraction neural nets learning (artificial intelligence) practical fault feature extraction fault features network training modified activation function training network performance deep learning model complex working environments pattern recognition deep learning techniques changeable condition complicated condition different transmission systems stacked autoencoder gear fault feature

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Prediction of potential miRNA-disease associations based on stacked autoencoder

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BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbac021-bbac021页

作者： Wang, Chun-Chun Li, Tian-Hao Huang, Li Chen, Xing China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China China Univ Min & Technol Xuzhou Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Inst Bioinformat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Big Data Res Ctr Xuzhou 221116 Jiangsu Peoples R China

In recent years, increasing biological experiments and scientific studies have demonstrated that microRNA (miRNA) plays an important role in the development of human complex diseases. Therefore, discovering miRNA-disease associations can contribute to accurate diagnosis and effective treatment of diseases. Identifying miRNA-disease associations through computational methods based on biological data has been proven to be low-cost and high-efficiency. In this study, we proposed a computational model named stacked autoencoder for potential MiRNA-Disease Association prediction (SAEMDA). In SAEMDA, all the miRNA-disease samples were used to pretrain a stacked autoencoder (SAE) in an unsupervised manner. Then, the positive samples and the same number of selected negative samples were utilized to fine-tune SAE in a supervised manner after adding an output layer with softmax classifier to the SAE. SAEMDA can make full use of the feature information of all unlabeled miRNA-disease pairs. Therefore, SAEMDA is suitable for our dataset containing small labeled samples and large unlabeled samples. As a result, SAEMDA achieved AUCs of 0.9210 and 0.8343 in global and local leave-one-out cross validation. Besides, SAEMDA obtained an average AUC and standard deviation of 0.9102 +/- /-0.0029 in 100 times of 5-fold cross validation. These results were better than those of previous models. Moreover, we carried out three case studies to further demonstrate the predictive accuracy of SAEMDA. As a result, 82% (breast neoplasms), 100% (lung neoplasms) and 90% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by databases. Thus, SAEMDA could be a useful and reliable model to predict potential miRNA-disease associations.

关键词： microRNA disease association prediction stacked autoencoder pretraining fine-tuning

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Effective Insect Recognition Using a stacked autoencoder with Maximum Correntropy Criterion

Effective Insect Recognition Using a Stacked Autoencoder wit...

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International Joint Conference on Neural Networks

作者： Yu Qi Goktug T. Cinar Vinicius M. A. Souza Gustavo E. A. P. A. Batista Yueming Wang Jose C. Principe Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China

ISBN: (纸本)9781479919611

Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environment is extremely important. Recently, an intelligent trap that uses laser sensors was presented to control the population of target insects. The device could record and analyze sensor signals when an insect passes through the trap and make quick decisions whether to catch it or not. The effectiveness of the trap relies on the correct choice of classification algorithm to perform the insect detection. In this paper, we propose to use a deep neural network with maximum correntropy criterion (MCC) for reliable classification of insects in real-time. Experimental results show that, deep networks are effective for learning stable features from brief insect passage signals. By replacing the mean square error cost with MCC, the robustness of autoencoders against noise is improved significantly and robust features could be learned. The method is tested on five species of insects and a total of 5325 passages. High classification accuracy of 92.1 % is achieved. Compared with previously applied methods, better classification performance is obtained using only 10% of the computation time. Therefore, our method is efficient and reliable for online insect detection.

关键词： Effective Insect Recognition stacked autoencoder Maximum Correntropy Criterion Insecta Traps pest insects Laser sensors Sorting algorithm Humanities beneficial insect MCC Genes computational time PASSAGE

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Electricity price forecast based on stacked autoencoder in spot market environment

Electricity price forecast based on stacked autoencoder in s...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Ya Zou Mengfu Tu Xianliang Teng Rongzhang Cao Wei Xie Power Grid Dispatching&Control Technology Branch Company NARI TECHNOLOGY DEVELOPMENT CO.LTD Nanjing China NARI RESEARCH INSTITUTE NARI TECHNOLOGY DEVELOPMENT CO.LTD Nanjing China

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

Artificial neural network method is a common method for short-term electricity price forecasting. However, when the amount of input and output data is large, the training speed will be slow, and it is easy to fall into local extreme values or even the result is difficult to converge. In view of this, the paper proposes a deep learning model based on stacked autoencoder (SAE) to predict electricity price. This paper analyzes the factors affecting electricity price, proposes an algorithm based on stacked autoencoder model, and uses MATLAB tools to predict electricity price in PJM power market. The comparison between SAE algorithm and BP algorithm is carried out in the example. The results show that the prediction results based on SAE model are more accurate. The deep learning model has better ability to express the objective function than the shallow model, and can effectively solve the problem of traditional neural network training difficulties.

关键词： Electricity price factor Electricity price forecasting Deep learning stacked autoencoder Electricity pricing Electricity Prices algorithms Objective function

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stacked-autoencoder-based model for COVID-19 diagnosis on CT images

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APPLIED INTELLIGENCE 2021年第5期51卷 2805-2817页

作者： Li, Daqiu Fu, Zhangjie Xu, Jun Nanjing Univ Informat Sci Technol Sch Comp & Software Nanjing 210044 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

With the outbreak of COVID-19, medical imaging such as computed tomography (CT) based diagnosis is proved to be an effective way to fight against the rapid spread of the virus. Therefore, it is important to study computerized models for infectious detection based on CT imaging. New deep learning-based approaches are developed for CT assisted diagnosis of COVID-19. However, most of the current studies are based on a small size dataset of COVID-19 CT images as there are less publicly available datasets for patient privacy reasons. As a result, the performance of deep learning-based detection models needs to be improved based on a small size dataset. In this paper, a stacked autoencoder detector model is proposed to greatly improve the performance of the detection models such as precision rate and recall rate. Firstly, four autoencoders are constructed as the first four layers of the whole stacked autoencoder detector model being developed to extract better features of CT images. Secondly, the four autoencoders are cascaded together and connected to the dense layer and the softmax classifier to constitute the model. Finally, a new classification loss function is constructed by superimposing reconstruction loss to enhance the detection accuracy of the model. The experiment results show that our model is performed well on a small size COVID-2019 CT image dataset. Our model achieves the average accuracy, precision, recall, and F1-score rate of 94.7%, 96.54%, 94.1%, and 94.8%, respectively. The results reflect the ability of our model in discriminating COVID-19 images which might help radiologists in the diagnosis of suspected COVID-19 patients.

关键词： COVID-19 diagnosis Computed tomography Deep learning stacked autoencoder

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Feature learning using stacked autoencoder for Multimodal Fusion, Shared and Cross Learning on Medical Images

Feature learning using Stacked Autoencoder for Multimodal Fu...

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IEEE Bombay Section Signature Conference (IBSSC)

作者： Zeeshan Ul Islam Vikas Singh Nishchal K. Verma Mechanical Engineering Visvesvarya National Institute of Technology Nagpur India Indian Institute of Technology Kanpur India

ISBN: (纸本)9781538674765

The analysis of medical images and to find meaningful patterns in it is a cumbersome task, even with the use of techniques of Computer Vision when the dataset is very large. In such a situation deep learning is a handy tool, because of its ability to learn and extract meaningful patterns and features from the images. The use of multiple modalities of training data to train system has been in practice for conventional machine learning algorithms. Here, in this paper, we are going to present a Deep Learning based architecture for extraction of features from large training set of medical images. The deep learning model is tested against conventional techniques by performing Multimodal fusion, Shared Learning and Cross Learning on it. It was found out that Deep Learning model performs superior than the conventional techniques in multimodal fusion and shared learning settings.

关键词： Deep Learning stacked autoencoder Multimodal Fusion Shared Learning Cross Modal Learning images Traditional technology Learning Fusion reactions Fusion, biological FUSION fusion (melting) Medical imaging Medical computer vision

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Batch Process Monitoring Based on Multi-stage Fourth Order Moment stacked autoencoder

Batch Process Monitoring Based on Multi-stage Fourth Order M...

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第32届中国控制与决策会议

作者： Jin Chen Wang Pu Wang Kai Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked autoencoder(M-FOM-SAE) is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.

关键词： Batch process Fault monitoring non-Gaussian Multi-stage stacked autoencoder

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A stacked autoencoder Method for the PAPR Reduction in VLC Systems

A Stacked Autoencoder Method for the PAPR Reduction in VLC S...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Lili Hao Chengdong Li School of Information and Electrical Engineering Shandong Jianzhu University Jinan China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

The visible light communication (VLC) system can provide data communication function as well as lighting. It is widely used in automated and sustainable smart farming for its energy efficiency, reliability, security without degrading the crops growth compared to radio frequency communication. A novel peak to average power ratio (PAPR) reduction method is applied based on stacked autoencoder network in VLC systems. The deep learning network is trained by a combined loss function. Simulation results reveal that the VLC system achieves a distinct PAPR reduction and shows an improved bit error ratio performance. The system can be easily implemented and controlled in the existing infrastructure. This research provides a basis for the feasibility of deep learning theory in the design of optical communication system.

关键词： visible light communication stacked autoencoder peak-to-average power ratio Peak to average power ratio variable length codes Visible light communication OPTICAL COMMUNICATION SYSTEMS

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Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation

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GEOCARTO INTERNATIONAL 2022年第7期37卷 2031-2054页

作者： Ahmadi, Seyyed Ali Mehrshad, Nasser Arghavan, Seyyed Mohammadali Univ Birjand Fac Elect & Comp Engn Birjand Iran Kharazmi Univ Dept Elect & Comp Engn Tehran Iran

Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including extended multi-attribute profiles and multiscale Gabor features, with sparse stacked autoencoder (GEAE). GEAE stacks the spatial and spectral information to form the fused features. Also, GEAE generates virtual samples using weighted average of available samples for expanding the training set so that many parameters of DL network can be learned optimally in limited labelled samples situations. Therefore, the similarity between samples is determined with distance metric learning to overcome the problems of Euclidean distance-based similarity metrics. The experimental results on three HSIs datasets demonstrate the effectiveness of the GEAE in comparison to some existing classification methods.

关键词： Classification hyperspectral image limited labelled samples deep learning stacked autoencoder Gabor feature extended multi-attribute profile

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Towards Control of EEG-based Robotic Arm using Deep Learning via stacked Sparse autoencoder

Towards Control of EEG-based Robotic Arm using Deep Learning...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Idowu, Oluwagbenga Paul Fang, Peng Li, Xiangxin Xia, Zeyang Xiong, Jing Li, Guanglin Chinese Acad Sci Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen 518055 Peoples R China Shenzhen Engn Lab Neural Rehabil Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728103778

Recently, an EEG-based robotic arm control has offered a key solution to the problem of high level amputation or severe neuromuscular damage. Several attempt to use EMG-based pattern recognition ( PR) failed due to insufficient myoelectric signals from the residual limb to perform control functions. The EEG activity recorded from human scalp is used to control the movement of a robotic arm. This can either be achieved when the arm is attached to or separated from the amputee stump by interfacing the brain directly to the robotic arm through the brain-machine interface ( BMI). To build an intelligent robotic ( or prosthetics) system that will manipulate object seamlessly with multiple degrees of freedom ( DoF), it is required that a robust learning algorithm which is able to control the prosthetic arm while interacting with the environment should be implemented. However, the conventional machine learning approach of using handcrafted features to design a robot controller that can perform multiple task is not a feasible option. Hence, we propose a robust learning control which is based on unsupervised learning algorithm of deep autoencoder. We applied stacked autoencoder to generate our features, and softmax layer is then used to classify five different motor imagery tasks. The proposed method produced an overall accuracy of 98.9% across the four amputees recruited for the experiments. Our algorithm shows a better performance when compared with the state-of-the art classifiers. Thus, the proposed results demonstrates the possibility of providing better control performance for EEG-based prosthesis.

关键词： Human robot interaction Artificial intelligence in robotics Brain machine interface EEG-based robotic arm Deep learning stacked autoencoder

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