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In wireless communications, massive multiple-input multiple-output (MIMO) uplink systems in which the base station is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO uplink systems are an attractive technology for next-generation wireless systems and for green communications. However, phase...
The information of electricity demand forecasting is a base for energy generation enterprise to develop electricity supply system. The purpose of this study is to develop a monthly electricity forecasting model in order to predict electricity demand for energy management. The proposed approach to monthly electricity demand time series forecasting model, describes the trend of the electricity demand...
Electricity demand forecasting is an important tool for private enterprise to develop electricity supply system. The purpose of this study is to develop monthly electricity forecasting model in order to predict future electricity demand for energy management. The influence of the weather factors such as temperature and humidity are diluted in an overall value that represents the total monthly electricity...
Ground surface settlement prediction is very important to identify potential damage incurred to adjacent structures. However, the traditional prediction methods, such as empirical formulae, are inaccurate because most of them do not take into consideration all the relevant factors. In this paper, the relevance vector machine (RVM) is introduced to predict the unseen data. Thus we focus on the use...
Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems (ITS). Based on statistical learning theory, support vector machine (SVM) has better generalization performance and can guarantee global minima for given training data. However, the good generalization performance of SVM highly depends on the construction of kernel function. An effective multi-scale...
Accurate traffic flow forecasting is the key to the development of intelligent transportation systems (ITS). However, the classical forecasting method using the support vector regression (SVR) based on RBF kernel does not support online learning and has the problems of information loss, long processing time, low robustness and so on. An effective Marr Wavelet kernel which we combine the wavelet theory...
Identifying and predicting the situation of traffic flow play an important role in traveler information broadcast and real-time traffic control. In this paper, a short-term traffic flow prediction model based on the parallel self-scaling quasi-Newton (SSPQN) neural network is presented. In this method, a set of parallel search directions are generated at the beginning of each iteration. Each of these...
Accurate traffic flow forecasting is key to the development of intelligent transportation systems (ITS). The support vector regression (SVR) method is employed for traffic flow forecasting and the comparative results between SVR and BP model using real traffic data of SCOOT system in Dalian city is also presented in this paper. Since support vector machines have better generalization performance and...
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