Convolutional neural network architecture for beam instabilities identification in Synchrotron Radiation Systems as an anomaly detection problem

Piekarski, Michał; Jaworek-Korjakowska, Joanna; Wawrzyniak, Adriana; Gorgon, Marek Boguslaw

doi:10.1016/j.measurement.2020.108116

Cited by 21 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, CNNs models [ 42 ] are the most frequently used in biomedical signals classification for anomaly detection due to its high classification accuracy. In this sense, several biomedical signals-based CNNs studies [ 43 , 44 , 45 ] have been suggested for anomaly detection tasks using various architectures such as CNN, visual geometry group network (VGGNet), Residual Network (ResNet), Dense Net, Inception Net, etc. In the present study, a CNN architecture is developed to classify the drowsy or awakeness states of each participant using an Emotiv EPOC

headset.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

View full text Add to dashboard Cite

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

show abstract

headset.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…To a certain degree, CNN benefits from the weight sharing mechanism of the convolutional layers and can reduce the number of training parameters. Now, more CNN structures with better generalization capabilities have been developed and applied in various fields, such as LeNet-5 [ 46 ], AlexNet [ 46 , 47 ], Vgg [ 48 ], and so on.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

show abstract

“…In contrast, deep learning methods are superior to these techniques and manual examination. Convolutional neural networks (CNNs) with multiple convolutional layers are typically used in deep learning feature extraction [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

et al. 2021

View full text Add to dashboard Cite

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

show abstract

Convolutional neural network architecture for beam instabilities identification in Synchrotron Radiation Systems as an anomaly detection problem

Cited by 21 publications

References 19 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Contact Info

Product

Resources

About