scholarly journals A New GNSS-R Altimetry Algorithm Based on Machine Learning Fusion Model and Feature Optimization to Improve the Precision of Sea Surface Height Retrieval

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiang Wang ◽  
Wei Zheng ◽  
Fan Wu ◽  
Aigong Xu ◽  
Huizhong Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Extraction Method ◽  
Weighted Average ◽  
Leading Edge ◽  
Sea Surface ◽  
Fusion Model ◽  
Feature Extraction Method ◽  
Fusion Feature ◽  
New Machine

The global navigation satellite system reflectometer (GNSS-R) can improve the observation and inversion of mesoscale by increasing the spatial coverage of ocean surface observations. The traditional retracking method is an empirical model with lower accuracy and condenses the Delay-Doppler Map information to a single scalar metric cannot completely represent the sea surface height (SSH) information. Firstly, to use multi-dimensional inputs for SSH retrieval, this paper constructs a new machine learning weighted average fusion feature extraction method based on the machine learning fusion model and feature extraction, which takes airborne delay waveform (DW) data as input and SSH as output. R2-Ranking method is used for weighted fusion, and the weights are distributed by the coefficient of determination of cross validation on the training set. Moreover, based on the airborne delay waveform data set, three features that are sensitive to the height of the sea surface are constructed, including the delay of the 70% peak correlation power (PCP70), the waveform leading edge peak first derivative (PFD), and the leading edge slope (LES). The effect of feature sets with varying levels of information details are analyzed as well. Secondly, the global average sea surface DTU15, which has been corrected by tides, is used to verify the reliability of the new machine learning weighted average fusion feature extraction method. The results show that the best retrieval performance can be obtained by using DW, PCP70 and PFD features. Compared with the DTU15 model, the root mean square error is about 0.23 m, and the correlation coefficient is about 0.75. Thirdly, the retrieval performance of the new machine learning weighted average fusion feature extraction method and the traditional single-point re-tracking method are compared and analyzed. The results show that the new machine learning weighted average fusion feature extraction method can effectively improve the precision of SSH retrieval, in which the mean absolute error is reduced by 63.1 and 59.2% respectively, and the root mean square error is reduced by 63.3 and 61.8% respectively; The correlation coefficient increased by 31.6 and 44.2% respectively. This method will provide the theoretical method support for the future GNSS-R SSH altimetry verification satellite.

EEG based emotion recognition using fusion feature extraction method

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27057-27074 ◽  
Author(s):  
Qiang Gao ◽  
Chu-han Wang ◽  
Zhe Wang ◽  
Xiao-lin Song ◽  
En-zeng Dong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Fusion Feature

scholarly journals Near-field Sensors with Machine Learning for Breast Tumor Detection

2018 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Maged A. Aldhaeebi ◽  
Thamer S. Almoneef ◽  
Omar M. Ramahi
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Breast Tumor ◽  
Extraction Method ◽  
Near Field ◽  
Tumor Detection ◽  
Electromagnetic Properties ◽  
Feature Extraction Method ◽  
The Difference ◽  
Breast Tissues

In this work, we propose the use of an electrically small novel antenna as a probe combined with a classification algorithm for nearfield microwave breast tumor detection. The resonant probe ishighly sensitive to the changes in the electromagnetic properties of the breast tissues such that the presence of the tumor is estimatedby determining the changes in the magnitude and phase responseof the reflection coefficient of the sensor. The Principle Component placed at the middle of the probe as shown in Fig. 1. The mainAnalysis (PCA) feature extraction method is applied to emphasize the difference in the probe responses for both the healthy and thetumourous cases . We show that when a numerical realistic breast with and without tumor cells is placed in the near field of the probe, the probe is capable of distinguishing between healthy and tumorous tissue. In addition, the probe is able to identify tumors with various sizes placed in single locations.

Research on computer network information security based on improved machine learning

2020 ◽  
pp. 1-12
Author(s):  
Yu Guangxu
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Information Security ◽  
Extraction Method ◽  
Situational Awareness ◽  
Rapid Development ◽  
Selection Method ◽  
Feature Extraction Method ◽  
Network Information ◽  
Awareness System

The 21st century is an era of rapid development of the Internet. Internet technology is widely used in various fields. With the rapid development of network, the importance of network information security is also highlighted. The traditional network information security technology has been difficult to ensure the security of network information. Therefore, we mainly study the application of machine learning feature extraction method in situational awareness system. A feature selection method based on machine learning is proposed to extract situational features.By analyzing whether the background of network information is safe or not, and according to the current research situation at home and abroad and the trend of Internet development, this paper tries out the practical application of machine learning feature extraction method in a certain perception system. Based on the above points, a selection method based on machine learning is proposed to extract situational features. The accuracy and timeliness of situational awareness system detection are seriously affected by the high dimension, noise and redundant features of massive network traffic data.Therefore, it is of great value to further study network intrusion detection technology on the basis of machine learning.

scholarly journals Electronic Tongue Recognition with Feature Specificity Enhancement

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 772 ◽  
Author(s):  
Tao Liu ◽  
Yanbing Chen ◽  
Dongqi Li ◽  
Tao Yang ◽  
Jianhua Cao
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Extraction Method ◽  
Sensor Array ◽  
Electronic Tongue ◽  
Learning Models ◽  
Learning Methods ◽  
Feature Extraction Method ◽  
Machine Learning Methods ◽  
Machine Learning Models

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

scholarly journals An Improved Feature Extraction Approach for Web Anomaly Detection Based on Semantic Structure

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zishuai Cheng ◽  
Baojiang Cui ◽  
Tao Qi ◽  
Wenchuan Yang ◽  
Junsong Fu
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Language Processing ◽  
Web Application ◽  
Extraction Method ◽  
Domain Knowledge ◽  
Semantic Structure ◽  
Feature Extraction Method ◽  
Web Attacks

Anomaly-based Web application firewalls (WAFs) are vital for providing early reactions to novel Web attacks. In recent years, various machine learning, deep learning, and transfer learning-based anomaly detection approaches have been developed to protect against Web attacks. Most of them directly treat the request URL as a general string that consists of letters and roughly use natural language processing (NLP) methods (i.e., Word2Vec and Doc2Vec) or domain knowledge to extract features. In this paper, we proposed an improved feature extraction approach which leveraged the advantage of the semantic structure of URLs. Semantic structure is an inherent interpretative property of the URL that identifies the function and vulnerability of each part in the URL. The evaluations on CSIC-2020 show that our feature extraction method has better performance than conventional feature extraction routine by more than average dramatic 5% improvement in accuracy, recall, and F1-score.

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