More and more microgrid projects are put into operation and completed, and the load data are becoming more and more multidimensional and massive. This requires effective classification of load data. Most of the tradit...More and more microgrid projects are put into operation and completed, and the load data are becoming more and more multidimensional and massive. This requires effective classification of load data. Most of the traditional processing methods are based on neural network to classify the grid data. However, with the development of microgrid, the traditional neural network algorithm is having a hard time meeting the requirement of the classification and operation of massive microgrid data. In this paper, the back propagation neural network (BPNN) algorithm is parallelized based on the traditional reverse neural network algorithm. Multiple algorithms are applied for data learning, for example, the combined application of extreme learning algorithm and simulated annealing algorithm, artificial fish swarm algorithm and other evolutionary algorithms. The input variables in BPNN are optimized in the network training process. After adding the algorithm fitness evaluation function, the combined algorithm of improved back propagation neural network algorithm came out. It is most in line with the real-time data of power grid by means of root mean square error. This result could provide data support and theoretical basis for load management, microgrid optimization, energy storage management and electricity price modeling of microgrid.展开更多
Brazil has introduced a referendum regarding the prohibition of firearm commerce and propaganda arguments have invoked socially and personally driven issues in the promotion of voting in favor of and against firearm c...Brazil has introduced a referendum regarding the prohibition of firearm commerce and propaganda arguments have invoked socially and personally driven issues in the promotion of voting in favor of and against firearm control, respectively. Here, we used different techniques to study the brain activity associated with a voter’s perception of the truthfulness of these arguments and their influence on voting decisions. Low-resolution tomography was used to identify the possible different sets of neurons activated in the analysis of the different types of propaganda. Linear correlation was used to calculate the amount information H(ei) provided to different electrodes about how these sets of neurons enroll themselves to carry out this cognitive analysis. The results clearly showed that vote decision was not influenced by arguments that were introduced by propaganda, which was typically driven by specific social or self-interest motives. However, different neural circuits were identified in the analysis of each type of propaganda argument, independently of the declared vote (for or against the control) intention.展开更多
文摘More and more microgrid projects are put into operation and completed, and the load data are becoming more and more multidimensional and massive. This requires effective classification of load data. Most of the traditional processing methods are based on neural network to classify the grid data. However, with the development of microgrid, the traditional neural network algorithm is having a hard time meeting the requirement of the classification and operation of massive microgrid data. In this paper, the back propagation neural network (BPNN) algorithm is parallelized based on the traditional reverse neural network algorithm. Multiple algorithms are applied for data learning, for example, the combined application of extreme learning algorithm and simulated annealing algorithm, artificial fish swarm algorithm and other evolutionary algorithms. The input variables in BPNN are optimized in the network training process. After adding the algorithm fitness evaluation function, the combined algorithm of improved back propagation neural network algorithm came out. It is most in line with the real-time data of power grid by means of root mean square error. This result could provide data support and theoretical basis for load management, microgrid optimization, energy storage management and electricity price modeling of microgrid.
文摘Brazil has introduced a referendum regarding the prohibition of firearm commerce and propaganda arguments have invoked socially and personally driven issues in the promotion of voting in favor of and against firearm control, respectively. Here, we used different techniques to study the brain activity associated with a voter’s perception of the truthfulness of these arguments and their influence on voting decisions. Low-resolution tomography was used to identify the possible different sets of neurons activated in the analysis of the different types of propaganda. Linear correlation was used to calculate the amount information H(ei) provided to different electrodes about how these sets of neurons enroll themselves to carry out this cognitive analysis. The results clearly showed that vote decision was not influenced by arguments that were introduced by propaganda, which was typically driven by specific social or self-interest motives. However, different neural circuits were identified in the analysis of each type of propaganda argument, independently of the declared vote (for or against the control) intention.
