BaMgAl_(10)O_(17):Eu blue phosphors were synthesized and the effect of dopingE^(3+) and Nd^(3+) ions in the phosphor on the luminescent properties was investigated. When thecontent of Er^(3+) and Nd^(3+) ions is small...BaMgAl_(10)O_(17):Eu blue phosphors were synthesized and the effect of dopingE^(3+) and Nd^(3+) ions in the phosphor on the luminescent properties was investigated. When thecontent of Er^(3+) and Nd^(3+) ions is small, the phosphor remains single phase and the luminescentintensity of Eu^(2+) increases effectively. When Er^(3+) is doped, the shape of the excitationspectrum of the phosphor in the UV (ultraviolet) region remains unchanged. As Nd^(3+) is doped inthe phosphor, the location and intensity of the two excitation peaks, and the emission intensityratio excited by corresponding UV change dramatically owing to the alternation of crystal fieldsplitting and level barycenter of 4f^6 5d configuration of Eu^(2+) ion.展开更多
This paper presents a coupled neural network, called output-threshold coupled neural network (OTCNN), which can mimic the autowaves in the present pulsed coupled neural networks (PCNNs), by the construction of mutual ...This paper presents a coupled neural network, called output-threshold coupled neural network (OTCNN), which can mimic the autowaves in the present pulsed coupled neural networks (PCNNs), by the construction of mutual coupling between neuron outputs and the threshold of a neuron. Based on its autowaves, this paper presents a method for finding the shortest path in shortest time with OTCNNs. The method presented here features much fewer neurons needed, simplicity of the structure of the neurons and the networks, and large scale of parallel computation. It is shown that OTCNN is very effective in finding the shortest paths from a single start node to multiple destination nodes for asymmetric weighted graph, with a number of iterations proportional only to the length of the shortest paths, but independent of the complexity of the graph and the total number of existing paths in the graph. Finally, examples for finding the shortest path are presented.展开更多
Gene selection (feature selection) is generally pertormed in gene space(feature space), where a very serious curse of dimensionality problem always existsbecause the number of genes is much larger than the number of s...Gene selection (feature selection) is generally pertormed in gene space(feature space), where a very serious curse of dimensionality problem always existsbecause the number of genes is much larger than the number of samples in gene space(G-space). This results in difficulty in modeling the data set in this space and the lowconfidence of the result of gene selection. How to find a gene subset in this case is achallenging subject. In this paper, the above G-space is transformed into its dual space,referred to as class space (C-space) such that the number of dimensions is the verynumber of classes of the samples in G-space and the number of samples in C-space isthe number of genes in G-space. it is obvious that the curse of dimensionality in C-spacedoes not exist. A new gene selection method which is based on the principle of separatingdifferent classes as far as possible is presented with the help of Principal ComponentAnalysis (PCA). The experimental results on gene selection for real data set areevaluated with Fisher criterion, weighted Fisher criterion as well as leave-one-out crossvalidation, showing that the method presented here is effective and efficient.展开更多
文摘BaMgAl_(10)O_(17):Eu blue phosphors were synthesized and the effect of dopingE^(3+) and Nd^(3+) ions in the phosphor on the luminescent properties was investigated. When thecontent of Er^(3+) and Nd^(3+) ions is small, the phosphor remains single phase and the luminescentintensity of Eu^(2+) increases effectively. When Er^(3+) is doped, the shape of the excitationspectrum of the phosphor in the UV (ultraviolet) region remains unchanged. As Nd^(3+) is doped inthe phosphor, the location and intensity of the two excitation peaks, and the emission intensityratio excited by corresponding UV change dramatically owing to the alternation of crystal fieldsplitting and level barycenter of 4f^6 5d configuration of Eu^(2+) ion.
文摘This paper presents a coupled neural network, called output-threshold coupled neural network (OTCNN), which can mimic the autowaves in the present pulsed coupled neural networks (PCNNs), by the construction of mutual coupling between neuron outputs and the threshold of a neuron. Based on its autowaves, this paper presents a method for finding the shortest path in shortest time with OTCNNs. The method presented here features much fewer neurons needed, simplicity of the structure of the neurons and the networks, and large scale of parallel computation. It is shown that OTCNN is very effective in finding the shortest paths from a single start node to multiple destination nodes for asymmetric weighted graph, with a number of iterations proportional only to the length of the shortest paths, but independent of the complexity of the graph and the total number of existing paths in the graph. Finally, examples for finding the shortest path are presented.
文摘Gene selection (feature selection) is generally pertormed in gene space(feature space), where a very serious curse of dimensionality problem always existsbecause the number of genes is much larger than the number of samples in gene space(G-space). This results in difficulty in modeling the data set in this space and the lowconfidence of the result of gene selection. How to find a gene subset in this case is achallenging subject. In this paper, the above G-space is transformed into its dual space,referred to as class space (C-space) such that the number of dimensions is the verynumber of classes of the samples in G-space and the number of samples in C-space isthe number of genes in G-space. it is obvious that the curse of dimensionality in C-spacedoes not exist. A new gene selection method which is based on the principle of separatingdifferent classes as far as possible is presented with the help of Principal ComponentAnalysis (PCA). The experimental results on gene selection for real data set areevaluated with Fisher criterion, weighted Fisher criterion as well as leave-one-out crossvalidation, showing that the method presented here is effective and efficient.
