An Intelligent Network-Warning Model with Strong Survivability

The importance of network selection for wireless networks, is to facilitate users with various personal wireless devices to access their desired services via a range of available radio access networks. The inability of these networks to provide broadband data service applications to users poses a serious challenge in the wireless environment. Network Optimization has therefore become necessary, so as to accommodate the increasing number of users’ service application demands while maintaining the required quality of services. To achieve that, the need to incorporate intelligent and fast mechanism as a solution to select the best value network for the user arises. This paper provides an intelligent network selection strategy based on the user- and network-valued metrics to suit their preferences when communicating in multi-access environment. A user-driven network selection strategy that employs Multi-Access Service Selection Vertical Handover Decision Algorithm (MASS-VHDA) via three interfaces; Wi-Fi, WiMAX and LTE-A is proposed, numerically evaluated and simulated. The results from the performance analysis demonstrate some improvement in the QoS and network blocking probability to satisfy user application requests for multiple simultaneous services.

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The ultimate intelligent network?

Electronics & Communications Engineering Journal ◽

10.1049/ecej:19900025 ◽

1990 ◽

Vol 2 (3) ◽

pp. 88

Author(s):

S. Piggott

Keyword(s):

Intelligent Network

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The introduction of France Telecom's intelligent network

10.1109/iss.1990.770122 ◽

2005 ◽

Cited By ~ 2

Author(s):

R. Kung ◽

J.C. Martin ◽

P. Collet ◽

M. Lapierre

Keyword(s):

Intelligent Network

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The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

Sustainability ◽

10.3390/su13020566 ◽

2021 ◽

Vol 13 (2) ◽

pp. 566

Author(s):

Nelly Florida Riama ◽

Riri Fitri Sari ◽

Henita Rahmayanti ◽

Widada Sulistya ◽

Mohamad Husein Nurrahmat

Keyword(s):

Path Analysis ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Coastal Flooding ◽

Coastal Areas ◽

Public Acceptance ◽

Community Attitudes ◽

Early Warning Model ◽

Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

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Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽

10.3390/sym13061082 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1082

Author(s):

Fanqiang Meng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Early Warning ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coal Face ◽

Early Warning Model ◽

Working Face ◽

The Neural Network ◽

Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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