A softmax classifier for high-precision classification of ultrasonic similar signals

Gao, Fei; Li, Bing; Chen, Lei; Shang, Zhongyu; Wei, Xiang; He, Chen

doi:10.1016/j.ultras.2020.106344

Cited by 46 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the network is initialized, the reverse fine-tuning is realized on the basis of label data. Softmax can be used as classifier [23].…”

Section: Art Feature Extraction Modelingmentioning

confidence: 99%

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Luo

2021

Security and Communication Networks

View full text Add to dashboard Cite

In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data. Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data. On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function. Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed. Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities.

show abstract

“…After the network is initialized, the reverse fine-tuning is realized on the basis of label data. Softmax can be used as classifier [23].…”

Section: Art Feature Extraction Modelingmentioning

confidence: 99%

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Luo

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The last unit in the CNN is the classification layer, which is applied to transform the output of the last FC layer into the probability that the input belongs to a given class. For multiclass classification, the softmax classifier [47] with cross-entropy loss [48] is extensively used.…”

Section: ) Classification Modulementioning

confidence: 99%

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

Rojhani

Passafiume

Sadeghibakhi

et al. 2023

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

Deep neural networks (DNNs) have become a relevant subject in the classification of radio frequency signals and remote sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN training and the effort that making experimental measurements requires. Hence, the quality and quantity of data used for the training and testing of models are crucial for effective classifier development. The training dataset should cover a wide range of cases that synthesize the actual scenarios being classified. This work proposes a novel data augmentation method based on a deterministic model to generate a simulated dataset of radar micro-Doppler signatures suitable for unmanned aerial vehicle (UAV) target classification, without requiring measurement data. It is shown that the DNN trained using the properly generated model-based data offers improved classification accuracy performance. Resultsare presented for a two-class classification of the number of UAV motors using a 77-GHz frequency-modulated continuouswave (FMCW) automotive radar system. The effectiveness of the proposed methodology is proven: a classification accuracy of 78.68% is achieved using a convolutional neural network (CNN) trained using the synthetic dataset, while an accuracy of 66.18% is achieved by using a typical signal processing data augmentation method on a limited measured dataset.

show abstract

“…Due to the multi-label aspect of our case, a cross-entropy loss function for regularization is used. As an output activation function, the softmax function is used because of the multi-class aspect of our classification task [47,48]. The mathematical formula of the activation function used is defined as follows:…”

Section: D) Detailed Architecture Of the Proposed Cov2netmentioning

confidence: 99%

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Moujahid¹,

Cherradi²,

Gannour³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Due to the rapid propagation characteristic of the Coronavirus (COV-ID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper, we propose a novel Convolutional Neural Network (CNN) model ( COV2Net) that can detect COVID-19 virus by analyzing the X-ray images of suspected patients. This model is trained on a dataset containing thousands of X-ray images collected from different sources. The model was tested and evaluated on an independent dataset. In order to approve the performance of the proposed model, three CNN models namely Mobile-Net, Residential Energy Services Network (Res-Net), and Visual Geometry Group 16 (VGG-16) have been implemented using transfer learning technique. This experiment consists of a multi-label classification task based on X-ray images for normal patients, patients infected by COVID-19 virus and other patients infected with pneumonia. This proposed model is empowered with Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-Cam++ techniques for a visual explanation and methodology debugging goal. The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

show abstract

A softmax classifier for high-precision classification of ultrasonic similar signals

Cited by 46 publications

References 20 publications

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Contact Info

Product

Resources

About