Dynamic Curve Fitting and BP Neural Network With Feature Extraction for Mobile Specific Emitter Identification

Zhang, Qin; Guo, Yu; Song, Zhengyu

doi:10.1109/access.2021.3060794

Cited by 23 publications

(14 citation statements)

References 16 publications

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“…The comparison results are shown in Table 1. It can be seen from the experimental results in Table 1 that the difference between the number of different buildings extracted by this method and the actual number of buildings is small, while the difference between the number of different buildings extracted by depth learning method and PLS method and the actual number of buildings is large [20]. The experimental results verify the extraction accuracy of this method again.…”

Section: Results Analysismentioning

confidence: 52%

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Zhang¹,

Wan

2022

Journal of Sensors

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In order to solve the problem of complex extraction caused by large feature dimension of remote sensing data, this paper proposes a dimension compression extraction method of urban building land remote sensing data under BIM Technology. Firstly, the remote sensing data is imported into the BIM model for lightweight processing to obtain the element information required for urban construction land and then analyze the urban construction land data, extract the key elements of BIM Technology through semantic filtering, and use the triangulation method to transform the remote sensing data into the triangulation model that can be processed by GIS model. Finally, the random projection method is used to reduce the dimension and compress the remote sensing data, and the remote sensing data extraction of urban construction land is realized through dictionary learning, vocabulary coding, and feature extraction. The experimental results show that the accuracy of extracting different land use types by this method is more than 99%, while the accuracy of extracting different land use types by depth learning method and PLS method is less than 98.5%. In addition, the signal-to-noise ratio of the image extracted by this method is significantly higher than that by depth learning method and PLS method. Conclusion. This method can effectively compress and extract the urban construction land in the remote sensing data, and the extraction accuracy of remote sensing data is high. It provides a technical basis for the approval of urban construction planning. It has the advantages of simple feature extraction and effective differentiation of ground objects.

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Section: Results Analysismentioning

confidence: 52%

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Zhang¹,

Wan

2022

Journal of Sensors

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“…Another related concept is MLP, which has multiple hidden layers. MLP emphasizes the structure of the neural network, while the BP neural network adopts the BP algorithm to adjust the network weight on the premise of the multilayer network [14]. In most cases, MLP will use the BP algorithm for weight adjustment, so they generally refer to the same network.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests.

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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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Dynamic Curve Fitting and BP Neural Network With Feature Extraction for Mobile Specific Emitter Identification

Cited by 23 publications

References 16 publications

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Intelligent Storage Data Classification System Based on the BP Neural Network

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

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