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检索条件"主题词=sparse AutoEncoder"

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Compressive Sensing and autoencoder Based Compressed Data Aggregation for Green IoT Networks

Compressive Sensing and Autoencoder Based Compressed Data Ag...

引用

IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Zhang, Mingqiang Zhang, Haixia Yuan, Dongfeng Zhang, Minggao Shandong Prov Key Lab Wireless Commun Technol Jinan 250100 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China

ISBN: (纸本)9781728109626

In cellular Internet of Things networks, massive access and highly dynamic traffic of machine type communication devices may cause network congestion, heavy energy consumption or even service unavailability. To reduce the amount of data transmission and improve the efficiency of data aggregation in sporadic machine type communications, we develop a two-stage compressed data aggregation scheme by jointly utilizing compressive sensing and sparse autoencoder. A deep compressive sensing network (DCSNet) is designed by using deep learning method to reconstruct signals from compressive measurements. Experimental results demonstrate that the proposed scheme can effectively reduce the data traffic, achieve higher reconstruction accuracy in different sparse conditions of signal and brake the obstacles that compressive sensing method has a large reconstruction error even when the measured data is small.

关键词： Data aggregation compressive sensing sparse autoencoder deep learning Internet of things

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Training Large Scale Deep Neural Networks on the Intel Xeon Phi Many-core Coprocessor 28

Training Large Scale Deep Neural Networks on the Intel Xeon ...

引用

28th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Jin, Lei Wang, Zhaokang Gu, Rong Yuan, Chunfeng Huang, Yihua Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781479941162

As a new area of machine learning research, the deep learning algorithm has attracted a lot of attention from the research community. It may bring human beings to a higher cognitive level of data. Its unsupervised pre-training step allows us to find high-dimensional representations or abstract features which work much better than the principal component analysis (PCA) method. However, it will face problems when being applied to deal with large scale data due to its intensive computation from many levels of training process against large scale data. The sequential deep learning algorithms usually can not finish the computation in an acceptable time. In this paper, we propose a many-core algorithm which is based on a parallel method and is used in the Intel Xeon Phi many-core systems to speed up the unsupervised training process of sparse autoencoder and Restricted Boltzmann Machine (RBM). Using the sequential training algorithm as a baseline to compare, we adopted several optimization methods to parallelize the algorithm. The experimental results show that our fully-optimized algorithm gains more than 300-fold speedup on parallelized sparse autoencoder compared with the original sequential algorithm on the Intel Xeon Phi coprocessor. Also, we ran the fully-optimized code on both the Intel Xeon Phi coprocessor and an expensive Intel Xeon CPU. Our method on the Intel Xeon Phi coprocessor is 7 to 10 times faster than the Intel Xeon CPU for this application. In addition to this, we compared our fully-optimized code on the Intel Xeon Phi with a Matlab code running on single Intel Xeon CPU. Our method on the Intel Xeon Phi runs 16 times faster than the Matlab implementation. The result also suggests that the Intel Xeon Phi can offer an efficient but more general-purposed way to parallelize the deep learning algorithm compared to GPU. It also achieves faster speed with better parallelism than the Intel Xeon CPU.

关键词： Deep learning Unsupervised learning Deep architecture sparse autoencoder Restricted Boltzmann Machine Parallel algorithm Intel Xeon Phi Many-core

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Open-Circuit Fault Diagnosis of Z-Source Inverter via Deep Neural Network

Open-Circuit Fault Diagnosis of Z-Source Inverter via Deep N...

引用

Chinese Automation Congress (CAC)

作者： Song, Baoye Nie, Shiju Li, Xuewen Xu, Lin Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

ISBN: (纸本)9781728176871

In this paper, issues of open-circuit fault diagnosis are solved for Z-source-inverter via the deep-sparse-autoencoder, which is one type of the various deep neural networks. Firstly, the preliminary of Z-source-inverter is introduced briefly. On the basis of the introduction of Z-source inverter, MATLAB/Simulink is utilized for the development of the open-circuit fault model of the Z-source inverter, and the output voltages of diverse fault cases are recorded for the training and test of the deep neural network based fault diagnosis. In the end, the superiority and effectiveness of the presented fault diagnosis approach can be verified by several comparisons with other methods on simulation experiments.

关键词： deep neural network sparse autoencoder Z-source inverter open-circuit fault fault diagnosis

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A Protocol-based Intrusion Detection System using Dual autoencoders 21

A Protocol-based Intrusion Detection System using Dual Autoe...

引用

21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Huang, Yu-Lun Hung, Ching-Yu Hu, Hsiao-Te Natl Yang Ming Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn Hsinchu Taiwan

ISBN: (纸本)9781665458139

This paper proposes a dual autoencoder-based Intrusion Detection System (duAE-IDS) for the ever-changing network attacks. duAE-IDS is a protocol-based IDS, which divides traffic by its application-layer protocol. duAE-IDS determines the traffic's abnormality by considering both the criteria and the application-layer protocol. The criteria are obtained by training our neural network model (duAE model) with traffic containing only one type of application-layer protocol. duAE-IDS represents each traffic flow with 67 features with eight new features for TCP traffic to improve detection accuracy. duAE-IDS uses two sparse autoencoders and one 1D CNN to extract features from traffic for every application-layer protocol. We conduct several experiments to prove the abilities and flexibilities of duAE-IDS. We prove that duAE-IDS trained with the known datasets can reach an F1-score of 0.87 for detecting attack traffic in an unknown network. We can run duAE-IDS in any network without pre-collecting the traffic of the network.

关键词： Intrusion Detection Feature Extraction autoencoder sparse autoencoder

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An Effective Multi-classification Method for NHL Pathological Images

An Effective Multi-classification Method for NHL Pathologica...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Jiang, Huiyan Li, Zhongkuan Li, Siqi Zhou, Fucai Northeastern Univ Software Coll Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sino Dutch Biomed & Informat Engn Sch Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538666500

Accurate classification on pathological images is a significant research focus such as for non-Hodgkin lymphomas (NHL). To this end, this paper proposes a hierarchical classification model based on the labels' statistics for three NHL pathological images, including chronic lymphocytic leukemia (CLL), follicular lymphoma (FL) and mantle cell lymphoma (MCL). First, each pathological image is converted onto the grayscale channel and then divided into 130 non-overlapped patches with 100 x 100 pixels. Next, the sparse autoencoder (SAE), an unsupervised feature extraction method, is utilized to learn the representations of all patches and meanwhile texture features are extracted on these patches which are considered as the hand-craft features. Following this process, we can obtain a 680-dimension feature set. Finally, a hierarchical classification model trained by these 680-dimension features is applied to classify NHL as CLL, FL and MCL, where the label of each NHL pathological image is determined via the output labels of its 130 patches. The experimental results and comparisons demonstrate the advantages of the proposed hierarchical classification model.

关键词： Lymphoma pathological images sparse autoencoder Feature extraction Hierarchical classification

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SAR Automatic Target Recognition Based on a Visual Cortical System

SAR Automatic Target Recognition Based on a Visual Cortical ...

引用

6th International Congress on Image and Signal Processing (CISP)

作者： Ni, Jia Cheng Xu, Yue Lei Air Force Engn Univ Inst Aeronaut & Astronaut Engn Xian Peoples R China

ISBN: (纸本)9781479927630

Human Vision system is the most complex and accurate system. In order to extract better features about Synthetic Aperture Radar (SAR) targets, a SAR automatic target recognition (ATR) algorithm based on human visual cortical system is proposed. This algorithm contains three stages: (1) Image preprocessing (we use a Kuan filter to do the enhancement and an adaptive Intersecting Cortical Model (ICM) to do the segmentation) (2) Feature extraction using a sparse autoencoder. (3) Classification using a softmax regression classifier. Experiment result of MSTAR public data shows a better performance of recognition.

关键词： SAR Object Recognition Visual Cortex ICM sparse autoencoder

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Deep Learning-Based Recognition of Underwater Target

Deep Learning-Based Recognition of Underwater Target

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IEEE International Conference on Digital Signal Processing (DSP)

作者： Cao, Xu Zhang, Xiaomin Yu, Yang Niu, Letian Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

ISBN: (纸本)9781509041657

Underwater target recognition remains a challenging task due to the complex and changeable environment. There have been a huge number of methods to deal with this problem. However, most of them fail to hierarchically extract deep features. In this paper, a novel deep learning framework for underwater target classification is proposed. First, instead of extracting features relying on expert knowledge, sparse autoencoder (AE) is utilized to learn invariant features from the spectral data of underwater targets. Second, stacked autoencoder (SAE) is used to get high-level features as a deep learning method. At last, the joint of SAE and softmax is proposed to classify the underwater targets. Experiment results with the received signal data from three different targets on the sea indicated that the proposed approach can get the highest classification accuracy compared with support vector machine (SVM) and probabilistic neural network (PNN).

关键词： Deep learning Underwater target recognition sparse autoencoder Stacked autoencoder

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A Deep Learning and Softmax Regression Fault Diagnosis Method for Multi-Level Converter 11

A Deep Learning and Softmax Regression Fault Diagnosis Metho...

引用

IEEE 11th International Symposium on Diagnostics for Electrical Machines Power Electronics and Drives (SDEMPED)

作者： Xin, Bin Wang, Tianzhen Tang, Tianhao Shanghai Maritime Univ Shanghai Peoples R China

ISBN: (纸本)9781509004096

With the single-tube and double-tube fault of seven-level converter, this paper presents a new way to learn the faults feature based on the deep neural network of sparse autoencoder. sparse autoencoder is an unsupervised learning method, it can learn the feature information of the fault data according to training. The feature information is used to train the softmax classifier by softmax regression to realize the aim of classification. Comparing with the traditional neural network of BP neural network, the experimental results show that the method to classify the fault of seven level converter based on deep neural network of sparse autoencoder can achieve higher accuracy.

关键词： fault diagnosis deep neural network seven-level converter sparse autoencoder softmax classifier

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Receptive Field Resolution Analysis in Convolutional Feature Extraction

Receptive Field Resolution Analysis in Convolutional Feature...

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13th International Symposium on Communications and Information Technologies (ISCIT) - Communication and Information Technology for New Life Style Beyond the Cloud

作者： Phaisangittisagul, Ekachai Chongprachawat, Rapeepol Kasetsart Univ Fac Engn Dept Elect Engn Bangkok 10900 Thailand Kasetsart Univ Grad Sch Bangkok 10900 Thailand

ISBN: (纸本)9781467355803;9781467355780

Instead of introducing new learning algorithm for solving complex classification tasks, many research groups in machine learning have focused on creating a good feature representation. In addition, labeled data is often difficult and expensive to obtain sufficiently large amount of data. So, learning features from unlabeled data is proposed since unlabeled data is much easier to obtain than the labeled data. In this work, a highlevel feature representation is created by a sparse autoencoder with convolutional extraction. A sparse autoencoder is an unsupervised feedforward neural network that is trained to predict the input itself and has been widely used for learning good feature representation. A major advantage of this feature extraction approach not only provides good feature representation for higherlevel tasks but also can scale up to large images. However, there are several parameters that require careful selection to obtain high performance. The main objective in this work is to present a detailed analysis on the effect of receptive field resolution in handwritten classification based on MNIST database. In the experiment, the results show that receptive field resolution is one of the critical parameters to achieve state-of-the-art performance.

关键词： high-level feature labeled data MNIST dataset receptive field resolution sparse autoencoder unlabeled data

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Automatic Recognition of ISAR Images Based on Deep Learning

Automatic Recognition of ISAR Images Based on Deep Learning

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CIE International Conference on Radar (RADAR)

作者： He, Xingyu Tong, Ningning Hu, Xiaowei Air Force Engn Univ Air & Missile Def Coll Xian Peoples R China

ISBN: (纸本)9781509048281

The problem of target classification of non-cooperative airplane using inverse synthetic aperture radar (ISAR) images is studied. ISAR image's variation with the radar imaging view makes the classification difficult. The point scatter models of five different airplanes are built. Deep learning methods have been shown to be able to classify images in a variety of disciplines with a high level of accuracy. As an effective deep learning algorithm, sparse autoencoder (SA) learning algorithm can automatically learn features from unlabeled data. In this paper, the SA algorithm is used to extract the features of the ISAR images. The extracted features are served as the input of the softmax classifier and then the recognition and classification of ISAR images are achieved. Simulation results verify the effectiveness and superiority of the proposed method.

关键词： ISAR images deep learning recognition sparse autoencoder softmax

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