A dual channel and node mobility based cognitive approach to optimize wireless networks in coal mines

Javaid, Fawad; Wang, Anyi; Sana, Muhammad Usman; Hassan, Hafiz Zameer ul; Ashraf, Imran

doi:10.1016/j.jksuci.2022.02.016

Cited by 1 publication

(1 citation statement)

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“…The detection sensor control device selects an ultrasonic sensor and a force detection sensor. When the detection sensor receives a signal, the amplitude of the higher harmonic operating frequency of the detection sensor is first determined by means of the operating frequency and its integral part multiples by means of Kalman filtering [22]. By then continuing to subtract the measured received signal from the original received signal, it is possible to obtain the noise component of the unstable received signal including the reception ineffective signal and the noise received signal.…”

Section: Kalman Filter Iterative Optimization Algorithmmentioning

confidence: 99%

Embedded Highway Health Maintenance System Based on Digital Twin Superposition Model

Lei,

Li,

Huang

2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development. OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management. METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal. RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution. CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.

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Section: Kalman Filter Iterative Optimization Algorithmmentioning

confidence: 99%