Wireless Sensor Network nodes correlation method in coal mine tunnel based on Bayesian decision

Chen, Wei; Jiang, Xiaorong; Li, Xin; Gao, Jing; Xiaoli, Xu; Ding, Shifei

doi:10.1016/j.measurement.2013.04.018

Cited by 24 publications

(18 citation statements)

References 21 publications

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“…Only a few studies are about coal mining. References [3,[27][28][29][30] have studied the energy consumption problem of the coal mine, but all of them did not consider the big data technology and only use single-sink structure. Some novel algorithm is proposed in [31,32]: in [31] a model for underground mines is generated by adopting a performancebased approach; in [32] a green MAC algorithm is proposed for smart home sensor networks.…”

Section: Related Studiesmentioning

confidence: 99%

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Chen

Wei

2018

Scientific Programming

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More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

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Section: Related Studiesmentioning

confidence: 99%

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Chen

Wei

2018

Scientific Programming

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“…Here, ( ) P D is a normalized constant irrelevant to structure and ( | ) P D S is the boundary likelihood (Wei and Xiaorong et al, 2013). In this way, what is needed to confirm the posterior distribution of network structure is to calculate the boundary likelihood of the data for every possible structure.…”

Section: Bayesian Network Model and Constitutionmentioning

confidence: 99%

A Handwritten Digit Recognition Method Based on Bayesian Network

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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Handwritten digit recognition is a classical pattern recognition problem and it has significant research and application value. Bayesian network is an effective tool to apply probability and statistics into uncertainty reasoning and data analysis of complicated system. On the basis of the analysis of the classification and connection weight design method of Bayesian network model, this paper learns and researches the structure learning and parameter learning method of Bayesian network in both complete data and incomplete data, proposes the Bayesian network classifier to be applied in handwritten digit recognition and builds the Bayesian network model for handwritten digit model. This network model uses the training sample set, identifies the topological structure of Bayesian network by integrating as much prior knowledge as possible and determine the conditional probability table for various variables in the given network structure. The experiment results proves that the Bayesian network classifier built in this paper has excellent effect and the sample training classifier improves the classification efficiency to a large extent and it can accurately reject the counter-example and reduce the false accept rate.

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“…WSNs are being utilized increasingly in critical applications and consequently many researchers have chosen to study this area (Chen et al, 2013;Funck and Guhmann, 2014;Huang et al, 2015;Yu et al, 2015). A WSN consists of a number of self-powered devices that can sense and communicate with other devices for the purpose of gathering local information to make global 14 decisions about a physical environment (Olariu and Xu 2005).…”

Section: Application In Wireless Sensor Networkmentioning

confidence: 99%

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

2016

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Wireless Sensor Network nodes correlation method in coal mine tunnel based on Bayesian decision

Cited by 24 publications

References 21 publications

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

A Handwritten Digit Recognition Method Based on Bayesian Network

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

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