Improvement of cluster-based WSN protocol using fuzzy logic

<span>A wireless sensor network is a collection of wireless nodes with sensor devices that can collect data from the real world. This is because sensor nodes usually use limited-powered batteries. Therefore, if the battery on the sensor node is exhausted, the node will no longer be available. If the battery on some nodes is discharged, the sensor network will not work properly. To maintain sensor network system, there are many wireless sensor network protocols to increase energy efficiency of nodes. One of the energy-efficient methods is cluster-based protocols. These protocols divide the sensor fields into clusters and send and receive data between nodes. Thus, depending on how the cluster is constructed, the network's lifetime may be reduced or increased. Cluster-based protocols cannot always be optimal cluster configurations. These problems have been improved using fuzzy logic. In general, fuzzy logic is used to elect cluster heads based on node residual energy, node concentration and node centrality. However, it is possible that nodes close to each other at a high density area are elected as cluster heads. In this paper, we propose a method to consider the number of adjacent cluster heads instead of Node Concentration to improve the problem.</span>

Research on Unequal Grouping of Parallel Compensation Capacity in Substation

For the problem of unequal grouping of parallel compensation capacity in substation, this paper proposes a new optimization method based on curve segmentation and clustering. Firstly, calculate reactive power demand curve by transformer parameters and load curve, partition this curve into several segments. Then cluster these segmentation results into K clusters by using modified FCM (fuzzy C-means clustering) algorithm, in which K means the number of capacitor groups. Take the difference between two adjacent cluster centers as the capacity of each group. Furthermore, study the relationship between segment number and grouping centers in order to get the steady grouping results. After the grouping plan is determined, take the nine-area figure as control strategy. Finally, simulating with an equivalent practical power grid and load profile, the results show both the availability and rationality of this method that power loss is less and power factor is higher compared with the equal grouping method when capacitor is divided into 3 groups.

Optimized K-means (OKM) clustering algorithm for image segmentation

This paper presents the optimized K-means (OKM) algorithm that can homogenously segment an image into regions of interest with the capability of avoiding the dead centre and trapped centre at local minima phenomena. Despite the fact that the previous improvements of the conventional K-means (KM) algorithm could significantly reduce or avoid the former problem, the latter problem could only be avoided by those algorithms, if an appropriate initial value is assigned to all clusters. In this study the modification on the hard membership concept as employed by the conventional KM algorithm is considered. As the process of a pixel is assigned to its associate cluster, if the pixel has equal distance to two or more adjacent cluster centres, the pixel will be assigned to the cluster with null (e. g., no members) or to the cluster with a lower fitness value. The qualitative and quantitative analyses have been performed to investigate the robustness of the proposed algorithm. It is concluded that from the experimental results, the new approach is effective to avoid dead centre and trapped centre at local minima which leads to producing better and more homogenous segmented images.

Cohesinopathy mutations disrupt the subnuclear organization of chromatin

In Saccharomyces cerevisiae, chromatin is spatially organized within the nucleus with centromeres clustering near the spindle pole body, telomeres clustering into foci at the nuclear periphery, ribosomal DNA repeats localizing within a single nucleolus, and transfer RNA (tRNA) genes present in an adjacent cluster. Furthermore, certain genes relocalize from the nuclear interior to the periphery upon transcriptional activation. The molecular mechanisms responsible for the organization of the genome are not well understood. We find that evolutionarily conserved proteins in the cohesin network play an important role in the subnuclear organization of chromatin. Mutations that cause human cohesinopathies had little effect on chromosome cohesion, centromere clustering, or viability when expressed in yeast. However, two mutations in particular lead to defects in (a) GAL2 transcription and recruitment to the nuclear periphery, (b) condensation of mitotic chromosomes, (c) nucleolar morphology, and (d) tRNA gene–mediated silencing and clustering of tRNA genes. We propose that the cohesin network affects gene regulation by facilitating the subnuclear organization of chromatin.