Feature Extraction Algorithm Using a Correlation Coefficient Combined With the VMD and Its Application to the GPS and GRACE

Shen, Yifan; Zheng, Wei; Yin, Wenjie; Xu, Aigong; Zhu, Huizhong

doi:10.1109/access.2021.3049118

Cited by 21 publications

(7 citation statements)

References 53 publications

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“…Wearable sensor-based attitude monitoring system, a wearable-based portable motion state monitoring method, is not limited by the monitoring area, and because it is a portable device, therefore, it is easy to carry, and the elderly can decide where they want to go according to their own ideas, and at the same time, they can also let medical staff or relatives know their own motion status. When the motion parameters change, the current motion posture of the human body can be judged by algorithms [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

Zhang¹,

Cheng²

2022

Journal of Sensors

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In order to solve the problem of low accuracy of human posture recognition during motion, the author proposes a human posture recognition and detection method based on multiple sensors. The method uses acceleration sensor, angular velocity sensor, and single-chip microcomputer to collect data; uses time domain and frequency domain analysis methods to analyze the collected data; and then uses Bayesian classifier to classify and identify the current motion posture of the human body. The obtained results are as follows: in the traditional method, with the increase of subjects, the accuracy of the classification method continues to decline; when the number of subjects reaches 50, the accuracy rate is only 60%; in the experiment, the zero crossing times of the acceleration signal axis are selected, and the axis area of the angular velocity signal is used as the characteristic item to distinguish the two different attitudes of going upstairs and going downstairs; the discrimination can reach about 90%. The authors combine the data collected by the acceleration sensor and the angular velocity sensor to perform feature extraction and classification, and the classification accuracy can reach more than 95%. It is proved that the method proposed by the authors can perform human gesture recognition very quickly and has a high accuracy rate.

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Section: Literature Reviewmentioning

confidence: 99%

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

Zhang¹,

Cheng²

2022

Journal of Sensors

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“…Now, the variable equation is based on the body shape "sub," and the three-level index is set as the variable. Because the dimensions of the index are inconsistent, and the change direction is inconsistent, the original test value of the index should be converted first; that is, the score should be made according to the national physical fitness measurement standard, and the variable will be changed from the original variable to the score variable, which is expressed by "s + three-level index name"; for example, "s body fat rate" represents the score of body fat rate [19]. All three-level indicators represent the setting of conditional parameters of "s + three-level indicator name" of different ages, which can be represented by "if then" in STELLA software.…”

Section: Evaluation Algorithm Of National Physique Datamentioning

confidence: 99%

Feature Extraction of National Physical Fitness Data Based on Data Mining

Huang¹,

Li²,

Ren³

2022

Mathematical Problems in Engineering

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In order to better understand and optimize the national physical fitness, this paper puts forward the national physical fitness data change feature extraction method based on data mining, uses the decision tree and association rule data mining algorithm to collect the national physical fitness data in recent years, constructs the database to realize the effective data management, and uses the data mining algorithm to construct the physical fitness change feature evaluation index. Finally, through experiments, it is confirmed that the national physique data change feature extraction method based on data mining has high effectiveness in the process of practical application. It can better understand the national physique change trend and put forward targeted suggestions for national physique health optimization.

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“…In addition, the Crustal Movement Observation Network of China (CMONOC) was established about 10 years ago, which makes it possible to obtain the crustal deformation over mainland China [38,39]. The GNSS datasets provided by CMONOC have been widely utilized to analyze crustal deformation and surface loading [22,40,41]. However, the distribution of GNSS stations is uneven due to harsh geo-climatic conditions, which dramatically limits the application of GNSS for TWSA inversion [3].…”

Section: Introductionmentioning

confidence: 99%

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

et al. 2022

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Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.

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Feature Extraction Algorithm Using a Correlation Coefficient Combined With the VMD and Its Application to the GPS and GRACE

Cited by 21 publications

References 53 publications

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

Feature Extraction of National Physical Fitness Data Based on Data Mining

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

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