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6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Ju, Yao Guo, Jun Liu, Shuchun East China Normal Univ Comp Ctr Dept Shanghai Peoples R China

ISBN: (纸本)9781467392006

In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse autoencoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse autoencoder to learn the features of the data. Secondly, we use SVM to classify. Many experimental results show that compared with SVM, our proposed method can improve the classification rate. In particular, it can effectively deal with large-scale data sets.

关键词： sparse autoencoder unsupervised learning deep learning SVM

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sparse autoencoder Based Manifold Analyzer Model of Multi-Angle Target Feature

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IEEE ACCESS 2020年 8卷 153250-153263页

作者： Chen, Xiuyuan Peng, Xiyuan Li, Jun-Bao Huo, Chaoying Harbin Inst Technol Harbin 150080 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China Sci & Technol Electromagnet Scattering Lab Beijing 100039 Peoples R China

Automatic target recognition (ATR) has always been an important research topic, and the performance is affected by feature extraction. High-resolution range profiles (HRRP) contains structural information of target from different angles. Existing target recognition algorithms are mostly adopt the supervised learning and verify the validity through classification accuracy. However, these methods cannot satisfy the information acquisition such as correlation, composition analysis and attitude analysis of unlabeled or uncoordinated targets. Therefore, we are committed to researching new intelligent feature extraction and analysis method. In this article, a novel learning framework is proposed to realize the angle correlation feature extraction and feature analysis of multi-angle HRRP targets. A multi-layer sparse autoencoder is applied to extract HRRP features and the extracted features are mapped to the low-dimensional space by manifold learning method to find the correlation distribution of HRRP data. The effectiveness of the proposed framework is demonstrated by experiments with simulated and measured data. The experimental results show that the framework realizes the extraction of angle-invariant features, and the analysis of distribution relationship between HRRP data and angles. The research results of the correspondence between the assembly and the related geometry provide the basis and possibility for further identification and composition determination.

关键词： ATR feature extraction HRRP manifold analyzer sparse autoencoder

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Hand Gesture Recognition using sparse autoencoder-based Deep Neural Network Based on Electromyography Measurements 2

Hand Gesture Recognition using Sparse Autoencoder-based Deep...

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Conference on Nano-, Bio-, Info-Tech Sensors, and 3D Systems II

作者： Wang, Yucheng Wang, Chunhui Wang, Zhonghui Wang, Xiaojie Li, You Chinese Acad Sci Inst Adv Mfg Technol Hefei Inst Phys Sci Changzhou 213164 Peoples R China China Astronauts Res & Training Ctr Natl Key Lab Human Factors Engn Beijing 100094 Peoples R China Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Jiangsu Peoples R China

ISBN: (纸本)9781510616912

Hand gesture recognition has recently grown as a powerful technical means in human-machine interaction field for control the appliances such as in home automation. However, the accuracy recognition of diverse hand gestures is still in the early stage for real-world application. In this paper, we present a new gesture recognition framework which is capable of classifying ten different hand gestures based on the input signals from surface electromyography (sEMG) sensors. The multi-channel signals of a hand motion are simultaneously captured and transmitted to a PC via Bluetooth wireless protocol. The proposed recognition framework composes of three main steps: gesture sequence segmentation, feature extraction by sparse autoencoder, and deep neural network (DNN) based classification. The advantage of the proposed approach is the automated abstract feature extraction based on sparse autoencoder method. Combined with the DNN classification technique, we could achieve a better recognition performance tested on the dataset consisting of ten types of hand gestures compared with other classification methods.

关键词： Hand gesture recognition sparse autoencoder Deep neural network EMG

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The Effect of Different Hidden Unit Number of sparse autoencoder 27

The Effect of Different Hidden Unit Number of Sparse Autoenc...

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27th Chinese Control and Decision Conference (CCDC)

作者： Xu, Qingyang Zhang, Li Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Shandong Peoples R China

ISBN: (纸本)9781479970162

sparse autoencoder is the fundamental part in some deep architecture. The hidden layer output is the compression of the input data which gives a better representation of the input than the original raw input. However, the determination of hidden unit number is always experiential. In this paper, the different hidden unit number is discussed. The weight of sparse autoencoder will learn the digital number outline of the handwriting instead of pen strokes when the hidden unit number is smaller. The weight can learn the pen strokes of the handwriting when the hidden unit number is larger.

关键词： sparse autoencoder Different hidden unit number Backpropagation MNIST database

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POSTER: Wi-Fi Indoor Positioning Based on sparse autoencoder and Deep Belief Network 24

POSTER: Wi-Fi Indoor Positioning Based on Sparse Autoencoder...

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24th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

作者： Cui, Xuerong Lou, Jinyang Li, Juan Jiang, Bin Li, Shibao Liu, Jianhang China Univ Petr East China Coll Oceanog & Space Informat Qingdao Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9798350331653

In order to reduce the influence of the complexity and diversity of indoor environment on traditional localization methods and to more effectively use Wi-Fi fingerprint data to position an object, an indoor localization algorithm based on sparse autoencoder(SAE) and Deep Belief Network(DBN) was proposed, the SAE-DBN model, was proposed. In this method, the SAE first extracts the depth features of the training data, and identifies the objects from different experimental areas. Then, the DBN model of the corresponding area is used to accurately position the objects. The simulation results show that compared with the traditional Wi-Fi positioning method and some existing improved algorithms, the proposed Wi-Fi positioning method has higher accuracy and stability, and the average positioning accuracy is 1.13 m.

关键词： sparse autoencoder Deep belief network Wi-Fi positioning fingerprint positioning

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Asymmetry Analysis with sparse autoencoder in Mammography 16

Asymmetry Analysis with Sparse Autoencoder in Mammography

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8th International Conference on Internet Multimedia Computing and Service (ICIMCS)

作者： Yang, Dongxiao Wang, Ying Jiao, Zhicheng Xidian Univ Sch Elect Engn VIPS Lab Xian 710071 Peoples R China

ISBN: (纸本)9781450348508

With the development of medical image processing methods, computer-aided detection systems sprang up, which could assist the diagnosis of radiologists, reducing fatigue, and greatly improve the accuracy and efficiency of early detection. However, most researches concentrated on mass detection methods by unilateral mammography, ignoring the relationship between left and right mammograms that the bilateral breast tissues were symmetric by physiology. However, radiologists often preferred to take images of both sides into consideration to judge if there were lesions. In this paper, we proposed an asymmetric analysis framework which employed bilateral mammograms together. Generally speaking, an improved bilateral region matching method that control points extracted automatically based on shape context was used to align the corresponding regions of bilateral mammograms. Meanwhile, sparse autoencoder was introduced into asymmetric analysis to learn a network between normal and its corresponding normal regions. For giving one side of bilateral regions, it would output a reconstruction vector of another form through the trained network. A similarity was computed between the output and the corresponding region of input, therefore a reasonable result was given.

关键词： Bilateral asymmetric analysis region matching multi-scale sliding window sparse autoencoder breast mass

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A sparse autoencoder Based Adversarial Open Set Domain Adaptation Model for Fault Diagnosis of Rotating Machinery 3

A Sparse Autoencoder Based Adversarial Open Set Domain Adapt...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Liu, Zhao-Hua Jiang, Lin-Bo Wei, Hua-Liang Wang, Chang-Tong Lv, Ming-Yang Chen, Lei School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom Harbin Electric Corporation Wind Power Co. Ltd Xiangtan China

ISBN: (纸本)9781665492812

Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: conventional domain adaptation approach only works under the 'closed set' assumption that all test classes are known at training time. In practice, a more realistic situation is 'open set', i.e., knowledge is incomplete in the training process, resulting in unknown classes during the testing. In this paper, a sparse autoencoder based adversarial open set domain adaptation (SAOSDA) model is proposed for rotating machinery fault diagnosis under open set scenarios, which can recognize the unknown faults and detect the known faults under different working conditions. This model utilizes adversarial learning to reduce the discrepancies between source samples and known target samples and reject the unknown target samples simultaneously. Experimental results of the actual bearing dataset verify the superiority and effectiveness of this method. © 2022 IEEE.

关键词： adversarial learning deep learning domain adaptation fault diagnosis open set recognition rotating machinery sparse autoencoder unknown faults

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An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第1期31卷 015005-015005页

作者： Zhao, Ke Jiang, Hongkai Li, Xingqiu Wang, Ruixin Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

The effective fault diagnosis of rolling bearings is of great importance in guaranteeing the normal operation of rotating machinery. However, measured rolling bearing vibration signals are highly nonlinear and interrupted by background noise, making it hard to obtain the representative fault features. Based on this, an optimal fault diagnosis method is proposed in this paper to accurately and steadily diagnose rolling bearing faults. The proposed method primarily contains the following stages. Firstly, a gated recurrent unit and a sparse autoencoder are constructed as a novel hybrid deep learning model to directly and effectively mine the fault information of rolling bearing vibration signals. Secondly, the key parameters of the constructed model are optimized by the grey wolf optimizer algorithm to achieve better diagnosis performance. Finally, the features obtained by the constructed model are input into the classifier to get the final diagnosis results. The proposed method is validated using the experimental and practical engineering bearing data and the results confirm that the diagnosis performance of the developed method is more effective and robust than other methods.

关键词： rolling bearing fault diagnosis hybrid deep learning model gated recurrent unit sparse autoencoder grey wolf optimizer

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ASD-SAENet: A sparse autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 654315页

作者： Almuqhim, Fahad Saeed, Fahad Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://***/pcdslab/ASD- SAENet.

关键词： ASD fMRI autoencoder sparse autoencoder ABIDE deep-learning classification diagnosis

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Sustainable subway indoor air quality monitoring and fault-tolerant ventilation control using a sparse autoencoder-driven sensor self-validation

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SUSTAINABLE CITIES AND SOCIETY 2020年 52卷

作者： Loy-Benitez, Jorge Li, Qian Nam, KiJeon Yoo, ChangKyoo Kyung Hee Univ Coll Engn Dept Environm Sci & Engn Yongin 446701 South Korea

Sensors providing measurements for monitoring and control of indoor air quality (IAQ) are key components of the ventilation systems in subway stations. However, faulty sensors due to harsh ambient conditions may deliver incorrect information triggering misinterpretation;causing energy waste or IAQ deterioration. This paper presents a holistic online framework for sensor self-validation in a subway station based on a sparse autoencoder (AE) architecture. The sensor self-validation procedure consists of sensor fault detection, faulty sensor identification, and faulty sensor reconstruction. First, the AE-based detection rate between 44% and 100%. Then, the faulty sensor identification was conducted through an AE-sensor validity index (SVI AE ). The faulty sensor reconstruction was conducted by the AE structure and evaluated with several performance metrics. Finally, the sustainability and fault-tolerance aspects of this framework were verified through mathematical modeling of the ventilation system;showing the effects of the faulty and reconstructed sensors on energy consumption and public health.

关键词： Fault-tolerant ventilation control Sensor self-validation sparse autoencoder Subway transportation systems IAQ monitoring

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