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.

In this paper we will present an ADAPTIVE wavelet scheme to solve the generalized Stokes problem. Using divergence free wavelets, the problem is transformed into an equivalent matrix vector system, that leads to a positive definite system of reduced size for the velocity. This system is solved iteratively, where the application of the infinite stiffness matrix, that is sufficiently compressible, is replaced by an ADAPTIVE approximation. Finally we prove that this ADAPTIVE method has optimal computational complexity, that is it recovers an approximate SOLUTION with desired accuracy at a computational expense that stays proportional to the number of terms in a corresponding wavelet-best N-term approximation.

An ADAPTIVE unstructured grid generation scheme is introduced to use finite volume (FV) and finite element (FE) formulation to solve the heat equation with singular boundary conditions. Regular grids could not achieve accurate SOLUTION to this problem. The grid generation scheme uses an optimal time complexity frontal method for the automatic generation and Delaunay triangulation of the grid points. The algorithm is incremental, so it is the most appropriate for an ADAPTIVE solver. Using ADAPTIVE grids, the SOLUTION is refined to get enough accuracy in all grid points. Two schemes are applied for the SOLUTION of the equations to show the flexibility of the ADAPTIVE grid scheme. First a cell-vertex finite volume formulation is used. Then, for the FE scheme, using linear shape functions, a set of linear equations are solved explicitly, with over relaxation. A sequence of adaptation is applied and appropriate number of grid points are introduced in finite predetermined formats to the existent elements, till convergence in the SOLUTION is observed. A post process is used to smooth the distribution of the set of nodes. This procedure is applied to a few study cases to show that the method is convergent, and produces accurate SOLUTION even in the case of singular boundary conditions.

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.

In practice, obtaining the global optimum for the economic dispatch (ED) problem with ramp rate limits and prohibited operating zones is presents difficulties. This paper presents a new and efficient method for solving the economic dispatch problem with non-smooth cost functions using a Fuzzy ADAPTIVE Genetic Algorithm (FAGA). The proposed algorithm deals with the issue of controlling the exploration and exploitation capabilities of a heuristic search algorithm in which the real version of Genetic Algorithm (RGA) is equipped with a Fuzzy Logic Controller (FLC) which can efficiently explore and exploit optimum SOLUTIONs. To validate the results obtained by the proposed FAGA, it is compared with a Real Genetic Algorithm (RGA). Moreover, the results obtained by FAGA and RGA are also compared with those obtained by other approaches reported in the literature. It was observed that the FAGA outperforms the other methods in solving the power system economic load dispatch problem in terms of quality, as well as convergence and success rates.

In this paper, an ADAPTIVE physics-based method is developed for solving wave motion problems in one dimension (i.e., wave propagation in strings, rods and beams). The SOLUTION of the problem includes two main parts. In the first part, after discretization of the domain, a physics-based method is developed considering the conservation of mass and the balance of momentum. In the second part, ADAPTIVE points are determined using the wavelet theory. This part is done employing the Deslauries-Dubuc (D-D) wavelets. By solving the problem in the first step, the domain of the problem is discretized by the same cells taking into consideration the load and characteristics of the structure. After the first trial SOLUTION, the D-D interpolation shows the lack and redundancy of points in the domain. These points will be added or eliminated for the next SOLUTION. This process may be repeated for obtaining an ADAPTIVE mesh for each step. Also, the smoothing spline fit is used to eliminate the noisy portion of the SOLUTION. Finally, the results of the proposed method are compared with the results available in the literature. The comparison shows excellent agreement between the obtained results and those already reported.

In this study, we numerically solve a class of two-point boundary-value-problems with a Riemann-Liouville-Caputo fractional derivative, where the SOLUTION might contain a weak singularity. Using the shooting technique based on the secant iterative approach, the boundary value problem is first transformed into an initial value problem, and the initial value problem is then converted into an analogous integral equation. The functions contained in the fractional integral are finally approximated using linear interpolation. An ADAPTIVE mesh is produced by equidistributing a monitor function in order to capture the singularity of the SOLUTION. A modified Gronwall inequality is used to establish the stability of the numerical scheme. To show the effectiveness of the suggested approach over an equidistributed grid, two numerical examples are provided.

In this paper, an ADAPTIVE implementation of Finite Element Method, FEM, is presented which is based on the discretization of the SOLUTION region according to calculation errors. The method is applied to the SOLUTION of Laplace and Poisson equations arising in static electromagnetic problems. It is shown that the efficiency of the proposed ADAPTIVE method is superior to that of the standard application of the FEM.

Background: Nowadays, the use of mechanical ventilation devices has a vital use in the treatment of patients hospitalized in intensive care units, while different modes of mechanical ventilation devices had different outcomes, thus this study aimed at comparing two modes of mechanical ventilation, that is the outcome of pressure regulated volume-controlled (PRVC) and ADAPTIVE support ventilation (ASV) on hemodynamic changes and time taken to separate the patient from the device in the intensive care unit. Methods: This study is a single-blind randomized clinical trial conducted in Alzahra hospital during 2018-2019. The research population of the study included 74 patients admitted to the intensive care unit who were selected by convenience sample. The patients were ramdomly divided into two groups. The device parameters were adjusted on PRVC mode for the first group and ASV mode for the second group. The data were compared between two groups using. Data were analyzed using Chi-square tests, T-test, and repeated measures of ANOVA and ANCOVA. Findings: There was no significant difference between the two groups at different intervals in terms of systolic and diastolic blood pressure and heart rate, pH and HCO3, RAMSY score. The analysis of variance with repeated observations showed that both, the group and time had significant effects on SPO2 and PCO2. The average length of time connected to the mechanical ventilation device and the duration of hospitalization in the ASV group was significantly lower than the PRVC Group. Conclusion: The ASV mode more than PRVC mode decreased the length of stay and need for ventilation of hospitalized patients in intensive care units.