Mine Internet of Things Data Collection Algorithm Based on Neural Network and Rough Set

Wang, Bin-Guo; Du, Xuedong; Hong, Yu

doi:10.1166/jctn.2015.4671

Cited by 4 publications

(3 citation statements)

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“…When t = 3 2 T , MC K −1 is fully charged again by MC K and turns to its second charging cycle. As we can see, there is a time lag of 1 2 T between MC K −1 and MC K .…”

Section: Properties Of Distributed Cooperative Wireless Chargingmentioning

confidence: 99%

“…The Mine IoT includes three layers: a physical layer, transmission layer and application layer. With the large number of sensors in the physical layer deployed in mine roadways, the Mine IoT can gather useful data, such as humidity, temperature, gas and wind speed [3], [4]. The data can then be transmitted to a data center through an integrated transmission platform of industrial Ethernets and hybrid wireless networks [5].Based on the collected information, many applications have been developed, such as a Supervisory Control and Data Acquisition (SCADA) system, Mine Safety and Health Administration (MSHA) system and Device Health Detection and Predictive (DHDEP) maintenance system [2], [6].…”

Section: Introductionmentioning

confidence: 99%

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Distributed Cooperative Wireless Charging for the Mine Internet of Things

Yang

Zhang

et al. 2019

IEEE Access

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Existing wireless charging methods for one-dimensional space are not suitable for coal mine roadways due to the crowding problem of mobile chargers (MCs). To solve this problem, we propose a novel method called distributed cooperative wireless charging, in which a coal mine roadway segment is divided into sections, and each MC is responsible for charging the nodes in its own section. From time t = (K − 1/2)T , all the MCs of a segment start to work simultaneously, where K is the number of sections and T is the charging cycle. When the steady state is reached, distributed cooperative wireless charging is the fastest method to finish a round of charging. To guarantee the practicality of the algorithm, we also provide the method to determine the MC battery capacities and the roadway section lengths. The numerical simulations indicate that the proposed algorithm provides better charging efficiency than the existing onedimensional charging methods in coal mine roadways.INDEX TERMS Mine Internet of Things, cooperative wireless charging, mobile charger, energy usage effectiveness, coal mine roadway.

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“…When t = 3 2 T , MC K −1 is fully charged again by MC K and turns to its second charging cycle. As we can see, there is a time lag of 1 2 T between MC K −1 and MC K .…”

Section: Properties Of Distributed Cooperative Wireless Chargingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Cooperative Wireless Charging for the Mine Internet of Things

Yang

Zhang

et al. 2019

IEEE Access

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show abstract

“…The neural network can process a large amount of information to reduce the amount of data, and it can also analyze some qualitative data. It has been widely used in data fusion, breaking the traditional reasoning mode, making data fusion technology no longer limited to strict logical reasoning and accurate calculation, and overcoming many problems that cannot be solved by traditional fusion technology [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Wang

2020

Symmetry

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Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

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Prediction method of mine gas emission based on complex neural work optimized by Wolf pack algorithm

Wang

et al. 2018

Systems Science & Control Engineering

Self Cite

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In view of the local extreme problem of the gradient descent algorithm, which makes the working face of mine gas emission prediction uncertainly, this paper combined Wolf pack algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model. The WPA shows good global convergence and computational robustness in the solving process of complex high-dimensional functions. Working face in a coal mine as a case, this paper selects seven factors as input variables of the mine gas emission prediction, uses training data to mature prediction model and adopted it to predict six group gas emission data. Research results show that the mean absolute percentage value of the complex neural network model which has been optimized by WPA is 0.06%, the root mean square error value is 0.0191, the mean absolute error value is 0.0175 and the equal coefficient value is 0.9979. The prediction results are very close to the real value, and the change trend is highly consistent with the actual situation.

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Mine Internet of Things Data Collection Algorithm Based on Neural Network and Rough Set

Cited by 4 publications

References 0 publications

Distributed Cooperative Wireless Charging for the Mine Internet of Things

Distributed Cooperative Wireless Charging for the Mine Internet of Things

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Prediction method of mine gas emission based on complex neural work optimized by Wolf pack algorithm

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