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13th Symposium on Neural Networks and Applications (NEUREL)

作者： Bobic, Vladislava Tadic, Predrag Kvascev, Goran Univ Belgrade Sch Elect Engn Bul Kralja Aleksandra 73 Belgrade 11120 Serbia

ISBN: (纸本)9781509015306

In this paper, two neural network based methods were implemented for recognition of images showing 10 hand gestures. Images were available from 24 subjects and captured on two different backgrounds and with several space orientations. Firstly, Histogram of Oriented Gradients method was applied for feature extraction and training was performed with multilayer feedforward neural network with backpropagation algorithm. Within the second method, sparse autoencoder with 5 hidden layers and decreasing number of neurons was implemented. For both methods it was examined how number of descriptors influences the accuracy of classification and found relationship was used to determine best performing case. Both classification methods achieved accuracy of about 92.5%, by using the similar number of estimated parameters.

关键词： Hand gesture recognition Histogram of Oriented Gradients sparse autoencoder

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Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry 8

Predictive maintenance based on anomaly detection using deep...

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8th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Davari, Narjes Veloso, Bruno Ribeiro, Rita P. Pereira, Pedro Mota Gama, Joao LIAAD INESC TEC Porto Portugal Univ Portucalense LIAAD INESC TEC Porto Portugal Univ Porto LIAAD INESC TEC Porto Portugal

ISBN: (纸本)9781665420990

Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. This study proposes a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto by deep learning based on a sparse autoencoder (SAE) network that efficiently detects abnormal data and considerably reduces the false alarm rate. Several analog and digital sensors installed on the APU system allow the detection of behavioral changes and deviations from the normal pattern by analyzing the collected data. We implemented two versions of the SAE network in which we inputted analog sensors data and digital sensors data, and the experimental results show that the failures due to air leakage problems are predicted by analog sensors data while other types of failures are identified by digital sensors data. A low pass filter is applied to the output of the SAE network, and a sequence of abnormal data is used as an alarm for the APU system failure. Performance indicators of the SAE network with digital sensors data, in terms of F1 Score, Recall, and Precision, are respectively, about 33.6%, 42%, and 28% better than those of the SAE network with analog sensors data. For comparison purposes, we also implemented a variational autoencoder (VAE). The results show that SAE performance is better than that of VAE by 14%, 77%, and 37% respectively, for Recall, Precision and F1 Score.

关键词： Failure detection Predictive maintenance Air production unit sparse autoencoder Time series

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Deep and Self-taught Learning for Protein Accessible Surface Area Prediction 15

Deep and Self-taught Learning for Protein Accessible Surface...

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15th International Conference on Frontiers of Information Technology (FIT)

作者： ul Hassan, Fahad Minhas, Fayyaz ul Amir Afsar DI Khan Inst Nucl Med & Radiotherapy Dera Ismail Khan Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Biomed Informat Res Lab Islamabad Pakistan

ISBN: (纸本)9781538635674

ASA captures the degree of burial or surface accessibility of a protein residue. It is a very important indicator of the behavior of amino acids within a protein as well. It can be used to find protein interactions, interfaces, folding states, etc. Calculation of the ASA requires the presence of the structure of the protein. However, structure determination for proteins is expensive and requires significant technical effort. As a consequence, the prediction of ASA is a very important and fundamental problem in Bioinformatics and Proteomics. In this work, we have investigated self-taught machine learning methods along with deep neural network to predict the residue level accessible surface area (ASA) of a protein. We have found that deep learning neural networks can predict the ASA of the residues in a protein accurately. Furthermore, the proposed deep learning based method does not require the use of computationally demanding features such as the position specific scoring matrix (PSSM) which have been used in previous works. A simple Blosum62 matrix based position dependent representation of amino acids in a sequence window gives comparable performance. This is particularly attractive for proteome wide prediction of ASA. We have used various self-taught learning schemes for obtaining an optimal feature representation from unlabeled data. These include a sparse and regularized autoencoder neural network and a dictionary based learning scheme. We have used unlabeled data from the protein universe in an attempt to improve the feature representation. We have also evaluated the performance of a stochastic gradient based predictor of accessible surface area for different feature representations.

关键词： Accessible Surface Area Deep Learning Self-Taught Learning sparse autoencoder Dictionary Learning Stochastic Subgradient Optimization based Regressor

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A Novel Deep Model for Image Recognition 5

A Novel Deep Model for Image Recognition

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5th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhu, Ming Wu, Yan Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781479932795

In this paper we propose a hybrid deep network for image recognition. First we use the sparse autoencoder(SAE) which is a method to extract high-level feature representations of data in an unsupervised way, without any manual feature engineering, and then we perform the classification using the deep belief networks(DBNs), which consist of restricted Boltzmann machine(RBM). Finally, we implement some comparative experiments on image datasets, and the results show that our methods achieved better performance when compared with neural network and other deep learning techniques such as DBNs.

关键词： deep belief network sparse autoencoder image recognition

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An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

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GEOENVIRONMENTAL DISASTERS 2020年第1期7卷 1-16页

作者： Nam, Kounghoon Wang, Fawu Shimane Univ Dept Earth Sci 1060 Nishikawatsu Cho Matsue Shimane 6908504 Japan

BackgroundLandslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme *** prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%).ConclusionsThis study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment.

关键词： Stacked autoencoder sparse autoencoder Support vector machine Random forest Landslide susceptibility

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Deep Learning-Based Classification of Massive Electrocardiography Data

Deep Learning-Based Classification of Massive Electrocardiog...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Lin Zhou Yan Yan Xingbin Qin Chan Yuan Dashun Que Lei Wang Information and Communication Engineering WuHan University of Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781509001668

Classification is the basis of electrocardiography(ECG) *** the last decades,a large number of methods were proposed to deal with the classification of ECG *** this paper a kind of deep learning method is introduced into ECG beats *** create a classifier with stacked sparse autoencoder(SAE),and then combine the softmax regression with the SAE networks to consummate the classifier,from which we can get higher accuracy in the classification *** the deep networks architecture,we use the stacked sparse autoencoder to get high-level *** results with the MIT-BIH ECG dataset confirmed that classifier build in this autoencoder based deep networks method perform better in the classical classification problem.

关键词： electrocardiography beats classification sparse autoencoder softmax regression deep learning

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Machine Learning Anomaly Detection in Large Systems 52

Machine Learning Anomaly Detection in Large Systems

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IEEE AUTOTESTCON

作者： Murphree, Jerry DRS Technol Huntsville AL 35808 USA

ISBN: (纸本)9781509007899

We have a need for methods to efficiently determine the health of a system. Diagnostics and prognostics determine system heath through analysis of data from sensors. Anomalies in the data can help us determine if there is a failure or a pending failure. There are common statistical methods to detect anomalies in individual measurements. For systems with many measurements, the anomalies may occur as specific combinations of values. Large systems have various associated states and modes which define the valid measurements. The amount of data to analyze grows very quickly as the system becomes more complex. In recent years techniques have been developed to address large data analysis. Machine Learning encompasses a broad selection of tools to optimize a statistical model of the data. These tools include supervised learning techniques, such as linear regression and logistic regression, in which training data exists to tune the model. Unsupervised learning, such as clustering, is used to explore data which does not have a defined output label associated with inputs data. Standard approaches to training supervised learning systems require a large sample of positive and negative outcome data. Some uses of machine learning involve data where there are very few cases of negative outcomes. There are machine learning algorithms defined as Anomaly Detection which are designed to deal with this type of data. Simple algorithms include Gaussian Distribution Analysis, which assumes independence in distributions of data. Large Systems with anomalies defined in the dependent combinations of data require either a manual creation of combinations of independent variables, or Multivariate Gaussian Distribution Analysis, which does not scale well for large systems. A further complication is the mixture of linear and discrete data. Neural Networks are a type of learning system which has been applied to each of the individual needs addressed above. This paper describes an approach to anomal

关键词： Machine Learning Anomaly Detection Large Systems sparse autoencoder Neural Networks

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Abnormal Event Detection using Recurrent Neural Network

Abnormal Event Detection using Recurrent Neural Network

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2nd International Conference on Computer Science and Applications (CSA)

作者： Zhou, Xu-gang Zhang, Li-qing Shanghai Jiao Tong Univ Dept Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781479999613

In this paper, we introduce a simple but novel model to detect abnormal event in surveillance video using sparse autoencoder and recurrent neuron network. In this model, we first train a sparse autoencoder to extract features and use a sequence of temporal continuous features to train a recurrent neuron network to predict the subsequent features. We classify the frame as normal and abnormal based on the prediction error of recurrent neuron network. Experimental result on a crowd activity dataset verifies the effectiveness of our model and the implication of recurrent neural networks in abnormal detection is also discussed.

关键词： abnormal event detection sparse autoencoder recurrent neuron network

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Hybrid evolutionary approach for Devanagari handwritten numeral recognition using Convolutional Neural Network 6

Hybrid evolutionary approach for Devanagari handwritten nume...

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6th International Conference on Smart Computing and Communications (ICSCC)

作者： Trivedi, Adarsh Srivastava, Siddhant Mishra, Apoorva Shukla, Anupam Tiwari, Ritu ABV Indian Inst Informat Technol Soft Comp & Expert Syst Lab Gwalior 474015 India

In recent years, deep learning has been extensively used in both supervised and unsupervised learning problems. Among the deep learning models, CNN has outperformed all others for object recognition task. Although CNN achieves exceptional accuracy, still a huge number of iterations and chances of getting stuck in local optima makes it computationally expensive to train. Genetic Algorithm is a metaheuristic approach inspired by the theory of natural selection and has been used for solving both bounded and unbounded optimization problems by a large success. To handle these issues, we have developed a hybrid deep learning model using Genetic Algorithm and L-BFGS method for training CNN. To test our model, we have taken the Devanagari handwritten numeral dataset. Our results show that GA assisted CNN produces better results than non-GA assisted CNN. This study concludes that evolutionary technique can be used to train CNN more efficiently. (C) 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications.

关键词： Convolutional Neural Network Genetic Algorithm sparse autoencoder

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PAN-SHARPENING BASED ON MULTILEVEL COUPLED DEEP NETWORK 38

PAN-SHARPENING BASED ON MULTILEVEL COUPLED DEEP NETWORK

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Cai, Wanting Xu, Yang Wu, Zebin Liu, Hongyi Qian, Ling Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Robot Res Inst Co Ltd Nanjing 211135 Jiangsu Peoples R China Lianyungang E Port Informat Dev Co Ltd Lianyungang 222042 Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781538671504

Pan-sharpening is a common image-fusion method. To improve the quality of fused images, a multilevel deep learning Pan-sharpening method is proposed in this paper. In the training phase, we introduce Coupled sparse Denoising Autoencorder (CSDA) to reconstruct high-Resolution (HR) multispectral (MS) image from low-Resolution (LR) MS image and HR Panchromatic (Pan) image. CSDA has four networks including LM-HP network, HR-MS network, feature mapping network and fine-tuning network. The hidden features in LM-HP network and HR-MS network as well as the mapping function between the two features are learned through joint optimization. In LM-HP and HR-MS networks, the hidden features of image patch pairs are extracted by the sparse autoencoder. A sparse denoising autoencoder is used to build the nonlinear mapping between the extracted features. In the testing phase, the LR-MS and HR-Pan images patches are fed to the CSDA network to reconstruct the fused HR-MS image. The experimental results show that the proposed method is better than the traditional pans-sharpening methods.

关键词： Pan-sharpening deep learning sparse autoencoder multilevel coupled networks

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