Complex of global functional network as the core of consciousness

Finding the neural basis of consciousness is a challenging issue, and it is still inconclusive where the core of consciousness is distributed in the brain. The global neuronal workspace theory (GNWT) emphasizes the role of the frontoparietal regions, whereas the integrated information theory (IIT) argues that the posterior part of the brain is the core of consciousness. IIT has proposed “main complex” as the core of consciousness in a dynamic system, which is a set of elements that the information loss in a hierarchical partition approach is the largest among that of all its supersets and subsets. However, no experimental study has reported the core of consciousness using the main complex for actual brain activity. This study estimated the main complex of brain dynamics using a functional MRI. The whole-brain fMRI data of eight conditions (seven tasks and a rest state) were divided into multiple elements based on network atlases, and the main complex of the dynamic system was estimated for each condition. It is assumed that, if there is a set of elements in the complex that are common to all conditions, the set is likely to contain the core of consciousness. Executive control, salience, and dorsal/ventral attention networks were commonly included in the main complex across all conditions, implying that these networks are responsible for the core of consciousness. This finding is consistent with the GNWT, as these networks are across the prefrontal and parietal regions.

A New Edge Betweenness Measure Using a Game Theoretical Approach: An Application to Hierarchical Community Detection

In this paper we formally define the hierarchical clustering network problem (HCNP) as the problem to find a good hierarchical partition of a network. This new problem focuses on the dynamic process of the clustering rather than on the final picture of the clustering process. To address it, we introduce a new hierarchical clustering algorithm in networks, based on a new shortest path betweenness measure. To calculate it, the communication between each pair of nodes is weighed by the importance of the nodes that establish this communication. The weights or importance associated to each pair of nodes are calculated as the Shapley value of a game, named as the linear modularity game. This new measure, (the node-game shortest path betweenness measure), is used to obtain a hierarchical partition of the network by eliminating the link with the highest value. To evaluate the performance of our algorithm, we introduce several criteria that allow us to compare different dendrograms of a network from two point of view: modularity and homogeneity. Finally, we propose a faster algorithm based on a simplification of the node-game shortest path betweenness measure, whose order is quadratic on sparse networks. This fast version is competitive from a computational point of view with other hierarchical fast algorithms, and, in general, it provides better results.

A New Edge betweenness Measure Using a Game Theoretical Approach : An Application to Hierarchical Community Detection

In this paper we formally define the hierarchical clustering network problem (HCNP) as the problem to find a good hierarchical partition of a network. This new problem focuses on the dynamic process of the clustering rather than on the final picture of the clustering process. To address it, we introduce a new hierarchical clustering algorithm in networks, based on a new shortest path betweenness measure. To calculate it, the communication between each pair of nodes is weighed by the importance of the nodes that establish this communication. The weights or importance associated to each pair of nodes are calculated as the Shapley value of a game, named as the linear modularity game. This new measure, (the node-game shortest path betweenness measure), is used to obtain a hierarchical partition of the network by eliminating the link with the highest value. To evaluate the performance of our algorithm, we introduce several criteria that allow us to compare different dendrograms of a network from two point of view: modularity and homogeneity. Finally, we propose a faster algorithm based on a simplification of the node-game shortest path betweenness measure, whose order is quadratic on sparse networks. This fast version is competitive from a computational point of view with other hierarchical fast algorithms, and, in general, it provides better results.

A Line-Context Based Object Recognition Method

The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be handled. The first is obtaining clean edges and the other is organizing those edges into a structured form so that they can be manipulated easily. We apply a bio-inspired orientation detection algorithm because it can output a fairly clean set of lines, and all lines are in the form of vectors instead of pixels. This line representation is efficient. We decompose them into several slope-depended layers and then create a hierarchical partition tree to record their geometric distribution. Based on the similarity of trees, a rough classification of objects can be realized. However, for an accuracy recognition, we design a moment-based measure to describe the detail layout of lines in a layer and then re-describe image by Hu's moment invariants. The experimental results suggest that the representation efficiency enabled by simple cell's neural mechanism and application of multi-layered representation schema can simplify the complexity of the algorithm. This proves that line-context representation greatly eases subsequent shape-oriented recognition.

Optimization of Voltage and Reactive Power in Regional Power Network Based on MCR

Along with the increasing use of cables in power grid and the increasing ration of distributed power sources’ synchronization, such as small hydropower’s synchronization, increasing the reactive power transmission on the line, make it difficult to achieve the balance of reactive hierarchical partition. Take a certain region’s power grid for actual examples, after the installation of magnetic control reactor (MCR), using immune genetic algorithm (IGA) to coordinate the capacity of magnetic control reactor and the existing reactive power resources, the results show that the magnetic control reactor does much good to absorb the system’s excessive reactive power and limit the voltage’s increasing.

Application of Reactive Power Optimization in Power Grid in AVC

Automatic voltage control(AVC) is the highest form of current power grid voltage and reactive power control,during the implementation of AVC, the whole network reactive power optimization isthe core and foundation. Thispaper researches and discuses the application of reactive power optimization inpower grid AVC. In the traditional reactive power optimization, the reactivepower limits of synchronous generators are fixed. In this paper, thesynchronous generator PQ operating limits change with external conditions,thus establishes reactive power optimization model in accordance with therequirements of AVC. Thispaper presents reactive power optimization method based on the principle ofpartition. The method decomposes the system to several partitions. Eachpartition separately optimized, thus reduces the system scale.And the convergence of the algorithm, the calculation speed and the discretevariable processing etc. improve. At the same time, this method reflects theclassification, hierarchical, partition, characteristics of coordinated controlof AVC.