Feature Extraction of Cultural Gene Image Based on PCA Method

Wu, Chenming; Qi, Guanglei; Zhao, Haiying; Chen, Zhijiang

doi:10.1109/iccea50009.2020.00189

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…erefore, all three methods are important to the DRSAE model, and none of them are indispensable. e DRSAE model in this paper was compared with the typical linear dimension reduction methods, such as principal component analysis (PCA) [28] model, and the nonlinear dimension reduction methods, such as AE [29] and SAE [30] models by using parameter settings in Table 1, and SVM classi er was used to classify data. e feature extraction time and classi cation time of each model are shown in Table 4.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness

Bai

Zhang

et al. 2022

Security and Communication Networks

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In wide-area distributed scenarios, it is particularly important to carry out information security situational awareness for the air traffic management (ATM) system with integrated air-ground structure. The operation data of the communication, navigation and surveillance (CNS) equipment of ATM system have the characteristics of multi-dimension, complexity, and strong correlation. In the process of situation awareness feature extraction, there are problems such as poor model accuracy, weak feature expression ability, and low classification performance. A feature association algorithm is designed to solve the above problems. Based on this algorithm, a deep-related sparse autoencoder (DRSAE) model based on improved sparse autoencoder is established. In DRSAE model, L1 regularization and Kullback–Leibler divergence (KLD) sparsity terms are used to penalize the parameters of the encoder network, and the quantity of hidden layers is increased to allow the model to optimize the global encoder network by iteratively training a single encoder. Moreover, the proposed DRSAE model and other feature extraction models such as principal component analysis (PCA), autoencoder (AE), and sparse autoencoder (SAE) are compared and evaluated by using the support vector machine (SVM) classifier. Compared with other feature extraction models, it is found that the proposed DRSAE model has good robustness in feature extraction of ATM system, and the obtained features have strong expression ability, which enhances the classification performance of the model and is convenient for situation awareness.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness

Bai

Zhang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…In the process of training RNN model, some useless data in the matrix will bring noise, which will affect the calculation result and increase the calculation cost. Therefore, the PCA method [30] was introduced to reduce the dimension of the data and explore some potential characteristic variables.…”

Section: Methodsmentioning

confidence: 99%

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

Chen

Qureshi

Geng

2022

IET Signal Processing

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The research of visibility detection in foggy days is of great significance to both road traffic and air transport safety. Based on the meteorological and video data collected from an airport, a deep Recurrent Neural Network (RNN) model was established in this study to predict the visibility. First, the Fourier Transform was used to extract feature variables from video data. Then, the Principal Component Analysis method was used to reduce the dimension of features. After that, 462 sets of sample data include image features, air pressure, temperature and wind speed, were used as inputs to train the RNN model. By comparing the predicted results with the actual visibility data as well as some other stateof-the-art methods, it can be found that the proposed model makes up for the deficiency of models based only on meteorological or image data, and has higher accuracy in different grades of visibility. With considering the meteorological data, the accuracy of RNN model is improved by 18.78%. Besides, with aids of correlation analysis, the influence of the meteorological factors on the predicted visibility was analysed, for fog at night, temperature is the dominant factor affecting visibility.

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