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Proposing a Fuzzy soft-max-based classifier in a hybrid deep learning architecture for human activity recognition

IET BIOMETRICS 2022年第2期11卷 171-186页

作者： Shakerian, Reza Yadollahzadeh-Tabari, Meisam Rad, Seyed Yaser Bozorgi Islamic Azad Univ Dept Comp Engn Babol Branch Babol Iran

Human Activity Recognition (HAR) is the process of identifying and analysing activities performed by a person (or persons). This paper proposes an efficient HAR system based on wearable sensors that uses deep learning techniques. The proposed HAR takes the advantage of staking Convolutional Neural Network and Long Short-Term (LSTM), for extracting the high-level features of the sensors data and for learning the time-series behaviour of the abstracted data, respectively. This paper proposed a Fuzzy soft-max classifier for the dense layer which classifies the output of LSTM Blocks to the associated activity classes. The authors' decision for proposing this classifier was because sensor data related to the resembling human activities, such as walking and running or opening door and closing door, are often very similar to each other. For this reason, the authors expect that adding fuzzy inference power to the standard soft-max classifier will increase its accuracy for distinguishing between similar activities. The authors were also interested in considering a post-processing module that considers activity classification over a longer period. Using the proposed Fuzzy soft-max classifier and by the post-processing technique, the authors were able to reach the 97.03 and 85.1 rates of accuracy for the PAMAP2 and Opportunity dataset, respectively.

关键词： deep learning Fuzzy Human Activity Recognition (HAR) soft-max classifier

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Turn-to-turn short circuit of motor stator fault diagnosis in continuous state based on deep auto-encoder

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IET ELECTRIC POWER APPLICATIONS 2019年第10期13卷 1598-1606页

作者： Wang, Botao Shen, Chuanwen Xu, Kexing Zheng, Tingting Xi An Jiao Tong Univ Sch Elect Engn Xianning West Rd Xian Shaanxi Peoples R China

In this study, a turn-to-turn short circuit of motor stator fault diagnosis system based on deep auto-encoder and soft-max classifier is proposed. It is also considered in the proposed system that an unbalanced power supply has similar current characteristics and is possible to be misidentified. The determination of neural network parameters and their effect in training are given systematically, which improves the performance compared with traditional auto-encoder. The proposed fault diagnosis system can map the preprocessed three-phase motor current signals to a two-dimensional vector, corresponding to certain area of the feature plane to identify different fault types. The proposed system is verified via simulation experiment using MATLAB and physical experiment using a motor in laboratory. The conclusion illustrates that when signals originating from discrete motor speed, load, and fault severity are used in training, the fault diagnosis is still effective in continuous state. The accuracy is 89% for speed verification and 95% for load verification in simulation and is above 99% in the physical experiment.

关键词： fault diagnosis stators neural nets learning (artificial intelligence) induction motors turn-to-turn short circuit continuous state deep auto-encoder motor stator fault diagnosis system soft-max classifier similar current characteristics traditional auto-encoder three-phase motor current signals different fault types discrete motor speed fault severity

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Assessment of PD severity in gas-insulated switchgear with an SSAE

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第4期11卷 423-430页

作者： Tang, Ju Jin, Miao Zeng, Fuping Zhang, Xiaoxing Huang, Rui Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Province Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China State Grid Shandong Elect Power Co Shandong Elect Power Res Inst Jinan 250002 Peoples R China

Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas-insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes. The output feature that is almost similar to the input PD information is produced in the model. The features extracted from PD data are then fed into a soft-max classifier to be classified into one of four defined PD severity states. In addition, unsupervised greedy layer-wise pre-training and supervised fine-tuning are utilised to train the SSAE network during evaluation. Results of testing and simulation analysis show that the features extracted by the SSAE model effectively characterise PD severity. The performance of the SSAE model, which possesses an average assessment accuracy of up to 92.2%, is better than that of the support vector machine algorithm based on statistical features. According to the tested number of SSAE layers and features and the training sample size, the SSAE model possesses good expansibility and can be useful in practical applications.

关键词： partial discharge measurement gas insulated switchgear statistical analysis neural nets learning (artificial intelligence) encoding feature extraction computerised instrumentation PD severity assessment gas-insulated switchgear SSAE partial discharge severity assessment discharge time discharge amplitude deep-learning neural network model stacked sparse autoencoder feature extraction soft-max classifier unsupervised greedy layer-wise pre-training method supervised fine-tuning method support vector machine algorithm

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Application of Sparse auto-encoder in Handwritten Digit Recognition 18

Application of Sparse auto-encoder in Handwritten Digit Reco...

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International Symposium on Big Data and Artificial Intelligence (ISBDAI)

作者： Zhou, Kaihong Qiao, Xinxin Shi, Jingkai Guilin Univ Technol Coll Mech & Control Engn Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)9781450365703

Deep learning and non-supervised learning methods have a wide range of applications in image feature extraction. This article uses MATLAB to train a deep neural network to classify handwritten digital pictures. The deep neural network is formed by stacking multiple sparse auto-encoders, training the data in an unsupervised manner, initializing the weights of the network, and then fine-tuning the network with a reciprocal propagation algorithm. Finally, the images is classified using the soft-max classifier. Sparse reduces the number of dimensions effectively, and the back propagation algorithm is optimized on the cost function, leading to the accuracy rate has been greatly improved, and completing the classification of handwritten numbers.

关键词： MATLAB Sparse auto-encoder MNIST handwritten digits soft-max classifier

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Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis Using Dropout Rate Improved Deep Sparse AutoEncoder 3

Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis U...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Wang, Botao Ma, Baohui Xu, Kexing Zheng, Tingting Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China State Key Lab Large Elect Drive Syst & Equipment Tianshui 741020 Gansu Peoples R China

ISBN: (纸本)9781538645093

Motor is one of the most frequently used machines in industry. Ensuring the reliability of motor and timely identifying the fault type of motor can guarantee the properly working and prevent the great loss. In this paper, a turn-to-turn short circuit of motor stator and unbalance power supply fault diagnosis system based on Deep Sparse Auto-Encoder and soft-max classifier is proposed. The selection of neural network parameters and their influence are systematic given. A new method where dropout rates of Sparse Auto-Encoder are various in each layer is adopted to increase the stability and convergence speed of training process. Finally, the proposed system is applied to an experiment on a motor in laboratory. The conclusion shows the ability to identify the fault type of motor at the continuous state that the accuracy reaches 100%, when only the data from motor at discrete state point are used in training, which makes the system extensible and promising.

关键词： Fault Diagnosis Sparse Auto-Encoder soft-max classifier Parameter Selection and Optimization

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Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis in Continuous State Based on Deep Auto-Encoder

Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis i...

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IEEE International Power Electronics and Application Conference and Exposition (IEEE PEAC)

作者： Wang, Botao Xu, Kexing Zheng, Tingting Shen, Chuanwen Xi An Jiao Tong Univ Sch Elect Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781538660546

The motor is one of the most commonly used equipment in the industry. It is necessary to ensure the reliability of the motor, and identify the type of motor fault in time to ensure the normal operation of the motor and reduce the loss. In this paper, a turn-to-turn short circuit of motor stator and unbalance power supply fault diagnosis system based on Deep Auto-Encoder and soft-max classifier is proposed. The influence of neural network parameters on the training process and the choice of parameters are given. The proposed fault diagnosis system can map the motor state to a 2-dimension vector, corresponding to different area of a plane to identify different fault type. Finally, the proposed system is verified by experiment on a motor in laboratory. The conclusion shows the ability to identify the fault type of motor at the continuous state that the accuracy is above 99.5%, when only the data from motor at discrete state point are used in training, which makes the system extensible and promising.

关键词： Fault Diagnosis Auto-Encoder soft-max classifier Parameter Selection Continuous State

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Multiple classifiers-Based Feature Fusion for RGB-D Object Recognition

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2017年第5期31卷 1750014-1750014页

作者： Wu, Yan Li, Jiqian Bai, Jing Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

RGB-D-based object recognition has been enthusiastically investigated in the past few years. RGB and depth images provide useful and complementary information. Fusing RGB and depth features can significantly increase the accuracy of object recognition. However, previous works just simply take the depth image as the fourth channel of the RGB image and concatenate the RGB and depth features, ignoring the different power of RGB and depth information for different objects. In this paper, a new method which contains three different classifiers is proposed to fuse features extracted from RGB image and depth image for RGB-D-based object recognition. Firstly, a RGB classifier and a depth classifier are trained by cross-validation to get the accuracy difference between RGB and depth features for each object. Then a variant RGBD classifier is trained with different initialization parameters for each class according to the accuracy difference. The variant RGB-D-classifier can result in a more robust classification performance. The proposed method is evaluated on two benchmark RGB-D datasets. Compared with previous methods, ours achieves comparable performance with the state-of-the-art method.

关键词： RGB-D object recognition feature fusing combination of multiple classifiers soft-max classifier

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