Artificial Intelligence Powered Mobile Networks: From Cognition to Decision

Luo, Guiyang; Yuan, Quan; Li, Jinglin; Wang, Shangguang; Yang, Fangchun

doi:10.1109/mnet.013.2100087

Cited by 107 publications

(36 citation statements)

References 15 publications

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“…It is assumed that there is a smart platform [24][25][26] to coordinate network elements with its operator. This platform is based on Telecommunications equipment [27][28][29]. In this condition, it can be used optimization formulation for the proposed scheme.…”

Section: Rcgtep Modelmentioning

confidence: 99%

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Seyyedi

Rashid

Akbari

et al. 2022

IET Generation Trans & Dist

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Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient‐constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching‐learning‐based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6‐Bus and IEEE 89‐Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network.

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Section: Rcgtep Modelmentioning

confidence: 99%

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Seyyedi

Rashid

Akbari

et al. 2022

IET Generation Trans & Dist

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show abstract

“…The master node plays the coordination function in Zigbee wireless sensor network. The main node is the convergence node of the network, and the physical device used is the full-function device FFD, which can communicate with other devices in the network through software design [16][17][18]. In Zigbee wireless sensor network, the slave node uses the simplified function device RFD, which can complete the communication function with the master node.…”

Section: Design Of Zigbee Wireless Sensor Network Modulementioning

confidence: 99%

Node Location Method of Ecological Environment Monitoring Network Based on Zigbee

2022

Discrete Dynamics in Nature and Society

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In view of the poor initialization performance of ecological environment monitoring network node location, a method of ecological environment monitoring network node location based on Zigbee is proposed. The node data collection model of ecological environment monitoring network is built based on Zigbee, and the performance is stable, which is more suitable for the node location of the ecological environment monitoring network; it is hoped that this study can provide reliable value reference and help for the future ecological studies. Through the installation of different types of sensors, the data of the ecological environment monitoring network nodes are automatically collected and sent to the server. The static weight coefficient of the collected data of the ecological environment monitoring network nodes is modified. According to the modified results, the ecological environment monitoring network is modified by DV-HOP positioning algorithm. The nodes of the ecological environment monitoring network are located by the three-way positioning method. The experimental results show that the initialization performance of this method is better, the accuracy is about 98%, and it is stable. It is more suitable for the node location of ecological environment monitoring network, which mainly includes ZigBee wireless sensor network module, embedded ARM, and Linux.

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“…According to the method described above, the vector form is used to express the features of the portal information, the evaluation function is used to select the features of the portal information, reduce the dimension of the portal information, and delete the redundant data in the portal information; according to feature similarity, the features of the portal information are clustered to determine the target function of portal information retrieval, and constraints are used [31][32][33][34].…”

Section: Implementation Of Web Portal Information Retrievalmentioning

confidence: 99%

Fast Retrieval Method of Portal Information Based on a Chaotic Genetic Algorithm

Zhao

Tai

2022

Mathematical Problems in Engineering

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The traditional retrieval method cannot respond to the influence of the change in the portal website’s information characteristics, resulting in low efficiency. In this regard, a fast information retrieval method based on a chaotic genetic algorithm is proposed. According to the relevant theory of association rules, the correlation between information data of dynamic portal websites is calculated; different portal website information is retrieved based on the Markov model output; a chaotic genetic algorithm is used to fuse different portal website information. The information data constructs a decision tree for rapid retrieval of portal information, uses the vector form to express the characteristics of portal information, and finally realizes the rapid retrieval of portal information. The experimental results show that the designed method takes up to 15 ms when the sample complexity is high, which shows that the designed method has high efficiency and is of great significance in practical applications.

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Artificial Intelligence Powered Mobile Networks: From Cognition to Decision

Cited by 107 publications

References 15 publications

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Node Location Method of Ecological Environment Monitoring Network Based on Zigbee

Fast Retrieval Method of Portal Information Based on a Chaotic Genetic Algorithm

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