We introduce a RBFs mesheless method of lines that decomposes theinterior and boundary centers to obtain the numerical solution of the timedependent PDEs. Then, the method is applied with an Adaptive algorithmto obtain the numerical solution of one dimensional problems. We show thatin the problems in which the solutions contain region with rapid variation, the Adaptive RBFs methods are successful so that the PDE solution can beapproximated well with a small number of basis functions. The method isdescribed in detail, and computational experiments are performed for one-dimensional Burgers' equations.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by Adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Different types of contact, including contact between node pairs, any-contact of Nodes, and contacts of the entire network, are used to characterize social relations in mobile social networks. Different modes of routing, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for Nodes, improves the performance of these networks. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in routing applications, it causes to use of the distribution of one type of contact for any mode of routing. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of routing. We propose a routing algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.

Dynamic behavior of continuous systems, such as beams and plates under the application of moving concentrated loads, is an important issue in engineering. In this study, a meshfree method is presented for the analysis of the dynamic response of thick plates under the influence of concentrated moving loads. The displacement field is based on the third-order shear deformation theory. In this numerical method, the field variables are interpolated only by using Nodes distributed purposively in the computational domain. Since there is no conectivity between the Nodes, it is possible to add Nodes in the areas of application of the force. Another feature of the proposed method is the use of the radial point interpolation method (RPIM) shape functions which possess the Kronecker delta function property and therefore satisfies the essential boundary conditions easily. Also, due to the high density of nodal points in the vicinity of the point of application of the load, the background decomposition method (BDM) is used in order to achieve a high accuracy with appropriate speed. In this paper, Nodes are re-arranged in the path of the moving load Adaptively which leads to high accuracy and speed of the final solution. To validate the proposed method, the obtained results are compared with the analytical solutions.

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One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust Adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust Adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

The notion of strong arcs in a fuzzy graph was introduced by Bhutani and Rosenfeld in [1] and fuzzy end Nodes in the subsequent paper [2] using the concept of strong arcs. In Mordeson and Yao [7], the notion of "degrees" for concepts fuzzified from graph theory were defined and studied. In this note, we discuss degrees for fuzzy end Nodes and study further some properties of fuzzy end Nodes and fuzzy cut Nodes.

Wireless Sensor Networks need primal information like location of the Nodes to start working and sending environment data. One method for setting a sensor network in a battlefield is to drop them from helicopter or to fire from artillery. It will cost a lot to Equip all network Nodes with GPS. So to reduce these costs, only a few of Nodes called anchor Nodes are equipped with gps. Location of Nodes are estimated by using belief propagation algorithm with measuring distance between Nodes (with use of recieved signal strength) and location information of anchor Nodes. Since the cost of anchor Nodes is reletively high, their number is important. also position of anchor Nodes affects algorithms error. In this paper, by using multi-objective algorithms with two objectives of error reduction and anchor Nodes number reduction, position and number of anchor Nodes are determined. results show that by using this methond, a few solutions are provided which are better than similar algorithms in terms of error reduction and energy consumption.