The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the need...
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ISBN:
(纸本)9781479927173
The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the needs and guide the deployment of WLAN, that isn't a easy issue. It will waste the investment if we deploy the WLAN in improper places. This paper proposes a solution which can collect customer feedback with the help of smart phone client software and affinity propagation algorithm is applied to determine which region should be deploy first according to giant user feedback from that client software. Actual results show that the method is feasible and effective. It can significantly improve the accuracy of deployment of WLAN and efficiency of operations.
As the demand of ITS for real-time traffic flow parameters increasing, the mobile terminal positioning technology which uses mobile base stations to obtain information such as position and velocity mobile phones on th...
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ISBN:
(纸本)9781467344975
As the demand of ITS for real-time traffic flow parameters increasing, the mobile terminal positioning technology which uses mobile base stations to obtain information such as position and velocity mobile phones on the road, is getting more and more important. In this paper, the number of vehicles and number of mobile terminals fitting is compared with the mobile terminal relative distance regression. as two traffic flow parameters extraction methods. The result shows that the first method suits large range extraction while the second one on the contrary. Furthermore, a optimization method for mobile terminal relative distance regression is presented. It is proved that MAPE has been improved 2% after optimization. The conclusions of the study can be used in more accurate trafficflow parameter extraction in the case of different path length.
The main challenge facing us in the design and conception of Wireless Sensor Networks (WSNs) is to find the best way to extend their life span. The clustering algorithm is a key technique used to increase the scalabil...
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ISBN:
(纸本)9781467363730
The main challenge facing us in the design and conception of Wireless Sensor Networks (WSNs) is to find the best way to extend their life span. The clustering algorithm is a key technique used to increase the scalability and life span of the network in general. In this paper, we have proposed an energy aware cluster head selection algorithm, for heterogeneous wireless sensor networks, by modifying the manner in which the nodes transmit their sensing data;In addition we introduce the node's remaining energy so as to compute election probabilities. Finally, simulation results show that the proposed algorithm increases the life span of the whole network and performs better than LEACH and than EEHC according to the metric: first node dies.
Power industry is one of the important fields in the application of big data technology. Power big data is generated in every link of power production and contains rich commercial and social values. It is necessary to...
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Power industry is one of the important fields in the application of big data technology. Power big data is generated in every link of power production and contains rich commercial and social values. It is necessary to implement the analysis of electric power user behavior based on big data technology. This paper presents a comprehensive study on the analysis of power user behavior based on big data. The characteristics and application challenges of electric power big data are first introduced, followed by the extraction of power user side big data processing mode. Finally, this paper focuses on the main methods of data mining and analysis and discusses the clustering analysis algorithms to make better analysis of electric power user behavior.
With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method,...
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With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras’ high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.
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