Earticle

Internet is a huge source of information. The growth of information and unstructured and semi structured nature of web information, cause some challenges for people to find their potential useful information for long-time needs. Hence, the implementation of automated tools selecting and evaluating information is necessary. Information filtering could be seen as a solution to this problem. It allows us to automatically filter out the unwanted content of the information. In this paper we first define the analytical architecture of the web information filtering system and second we suggest a systematic framework to classify web information filtering structures. We hope this proposed framework will lead to empirical and technical comparison of web information filtering structures and development of more efficient structures at future.

There are strong randomness in wind power, its output is not controllable. Large scale wind power grid-connection to dispatch system brings new uncertainty. This paper constructs the wind-fire random optimal model based on combined MIPSO algorithm and achieves the multi-objective optimizations on the minimization of power cost and pollution emissions of thermal power units. Firstly, wind power outputs can be forecasted by utilizing BP neural network algorithm. Then random multi-objective optimal solution is obtained through MIPSO algorithm. Finally, multi-objective optimal scheduling is illustrated by using the IEEE-30 node. The results show that the combination arithmetic solution set uniform distribution, predict accuracy and qualified rate were 0.9962 and 0.7651, using MIPSO algorithm is out of the solution of the highest degree of coordination, get each time specific unit output of the plan, according to the calculation results make corresponding power generation plan, to access a fairly large wind power dispatch system has the actual reference value, so the proposed method is feasible and effective.

The prediction of chaotic time series is an important research issue. To improve the prediction accuracy, a hybrid approach called WNN-PSO is proposed, which based on the self-learning ability of wavelet neural network, whose parameters are optimized by particle swarm optimization. The WNN-PSO method has higher prediction accuracy, fast convergence, and heightens the ability of jumping the local optimums. The experiment results of the prediction for chaotic time series show the feasibility and effectiveness of the proposed method. Compared with wavelet neural network and BP neural network, the proposed method are superior to them. Finally, the WNN-PSO is applied to predict the life energy consumption of china in our lives.

Cooperative communication is a novel class of wireless communication technique which wireless nodes help each other relay information to the intended destination. Orthogonal frequency division multiplexing (OFDM) has become a popular technique for the high data rate and performance. The integration of OFDM and cooperative communication in next generation wireless leads to the design of an efficient and reliable wireless network. A mathematical model for cooperative MIMO-OFDM system has been developed in the present study. Simulation results are provided to evaluate the BER and Text message performance of proposed system. It is observed that a quite satisfactory system performance is achieved in cooperative MIMO-OFDM with STBC under AAF relay for the MMSE detection scheme. It has been estimated from the simulation study that the performance of the cooperative MIMO-OFDM communication system improves with the increasing of SNR.

Traditional web service discovery composition technology is hard to get adapted to dynamic, flexible modern business workflow. Due to the advantage of Cloud computing, dynamic, distributed, heterogeneous and autonomous nature to solve this problem provides a new way of thinking. This paper proposes a cloud computing environment that supports dynamic application service composition model, and in order to meet the real dynamic workflow environment, proposed cloud computing Web Service workflow management system CCB_WSFMS.

The objective of this paper is to investigate the enhancement of damping the low frequency oscillations via tuning of static synchronous compensator (STATCOM) damping controller. The problem of STATCOM based damping controller is formulated as an optimization problem according with the time domain-based objective function which is solved by a quantum-behaved particle swarm optimization (QPSO) technique that has fewer parameters and stronger search capability than the classical PSO. The performance of the designed controller is demonstrated through nonlinear time-domain simulation and some performance indices studies in comparison with designed CPSO, genetic algorithm (GA) and phase compensation based STATCOM controllers.