Despite the fact that the quality of forecasts from numerical weather prediction (NWP) models has increased in recent years, yet exact forecast of precipitation is a difficult and challenging task. In order to obtain more accurate precipitation forecasts, efforts have been made to improve the models, formulations, and the accuracy of the initial conditions. One important alternative is to improve the model output via postprocessing.In this paper, the WRF model was applied for a six month period from 1 November 2008 to 30 April 2009 with two nests using 45 and 15 Km grid. The model outputs were then postprocessed for 24-hour precipitation forecasts for 205 synoptic stations over Iran using two methods of the moving average (MA) and the best easy systematic estimator (BES). Data for the first three months were used for training and the rest of data were used for the test and comparison. Statistical scores including degree of mass balance (DMB), mean absolute error (MAE) and its corresponding skill score were calculated for both direct and postprocessed outputs.Results showed that both methods improve the direct outputs of the model. The MA method decreased MAE for different stations from 5 to 50 percent. The mean of MAE decrease for all stations was about %25. In the BES method the average value of MAE for all stations is around 13 percent.

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.

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.

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.

In this paper, an innovative Adaptive output feedback control scheme is proposed for general multi-input multi-output (MIMO) plants with unknown parameters in a regulation task; such that the outputs of the plant converge to zero as well as the control gains remain uniformly bounded. First an Adaptive observer is designed to estimate the state variables and system parameters by using the inputs and outputs of the plant. Then a linear combination of the estimated states by Adaptive control gains is used to design a robust Adaptive controller. Some theorems are given to show the convergence of the modeling errors and the control gains. The proposed controller is used to control a two degree of freedom robot manipulator such that the robot moves from any initial configuration to zero position. Simulation results exhibit the effectiveness of the proposed scheme to control the robot manipulator with different initial conditions and parameter perturbations.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an Adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

Nonlinear static methods are simplified procedures in which the problem of evaluating the maximum expected response of a MDOF system for a specified level of earthquake motion is replaced by response evaluation of its equivalent SDOF system. The common features of these procedures are the use of pushover analysis to characterize the structural system. In pushover analysis both the force distribution and the target displacement are based on the assumptions that the response is controlled by the fundamental mode and that the mode shape remains unchanged after the structure yields. Therefore, the invariant force distributions does not account for the change of load patterns caused by the plastic hinge formation and changes in the stiffness of different structural elements. That could have some effects in the outcome of the method depending on different structural parameters. This paper introduces an Adaptive pushover analysis method to improve the accuracy of the currently used pushover analysis in predicting the seismic-induced dynamic demands of the structures. Comparison of the common pushover analyses, Adaptive pushover analyses and time-history analyses performed for a number of multiple-bay, short and high-rise steel structures, demonstrates the efficiency of the proposed method.

Backpropagation algorithm performs gradient descent only in the weight space of a network with fixed topology. A very small network cannot learn the problem well, and a very larger network will lead to overfitting and poor generalization performance. Algorithms that can find an appropriate network architecture automatically are thus highly desirable. The algorithms that are introduced by researchers can be classified into five major groups. Pruning algorithms, constructive algorithms, hybrid algorithms, evolutionary algorithms, and learning automata based algorithms. Meybodi and Beigy introduced the first learning automata based algorithms, called survival algorithm. This algorithm produces networks with low complexity and high generalization. Survival algorithm by turning off and on the weights, tries to find the most important weights. At the beginning, all weights of the network are on and contribute to learning. The on weights, whose absolute values are less than a threshold value, are penalized and those, whose absolute value are larger than another threshold value, are rewarded. The on weights, whose absolute values lie between these two threshold values, neither rewarded, nor penalized. The values of these two thresholds are determinative and have considerable effect on the performance of the survival algorithm. Determination of the values of these thresholds is not an easy task and usually is determined by trial and error or using past experience. In this paper, we propose a method for adaptation of these two threshold values. The proposed method have been tested on number of problems and shown through simulations that the network generated by the survival algorithm when threshold values are adapted has lesser number of weights and neurons, comparing to the network generated by the first version of the algorithm reported earlier. Experimentation shows that the Adaptive survival algorithm has nearly the same degree of generalization as the non-Adaptive version.