At present, functional verification represents the most expensive part of the digital systems design. Moreover, different problems such as: clock synchronization, code compatibility, simulation automation, new design ...
详细信息
At present, functional verification represents the most expensive part of the digital systems design. Moreover, different problems such as: clock synchronization, code compatibility, simulation automation, new design methodologies, proper use of coverage metrics, among others represent challenges in this area. The automated test vector generation is involved in these problems. In this work, an automated functional test sequences generation for digital systems based on the use of coverage models and a binary particle swarm optimization algorithm with a reinitialization mechanism (BPSOr) is described. Also, a comparison with other meta-heuristic algorithms such as: Genetic algorithms (GA) and pseudo-random generation is presented using different fitness functions, coverage models and devices under verification. The main strategy is based on the combination of the simulation and meta-heuristic algorithms to test the device behavior through the generation of test vector sequences. According to the results, the proposed test generation method represents a good alternative to increase the functional coverage during the automated functional verification of block-level digital systems verification.
particleswarmoptimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values ar...
详细信息
particleswarmoptimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [1, 1], and Gauss distribution with mean 0 and variance 1 (U [0, 1], U [1, 1] and G (0, 1)), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [1, 1] or G (0, 1) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [1, 1] or G (0, 1) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.
Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam,...
详细信息
Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particleswarmoptimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.
In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of smart energy, the use of such devices is becoming e...
详细信息
In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of smart energy, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using particle swarm optimization algorithms. All calculations were performed in MATLAB.
Gravitational search algorithm (GSA) is a swarm intelligence heuristic optimizationalgorithm based on the law of gravitation. Aiming at the disadvantage of poor local search ability and slow convergence speed in stan...
详细信息
Gravitational search algorithm (GSA) is a swarm intelligence heuristic optimizationalgorithm based on the law of gravitation. Aiming at the disadvantage of poor local search ability and slow convergence speed in standard GSA, four improved GSA-PSO hybrid algorithm are proposed by introducing a small constant updating strategy in order to enhance the update ability of velocity, acceleration factor and the optimal individual location, where PSO strategy was used to optimize the position and velocity of the GSA. Through simulation experiments on typical test functions to verify its performance, the simulation results show that the optimal setup of GSA parameters can improve the convergence rate of the algorithm and improve the accuracy of the solution.
Electric load prediction is an important decision tool in area of electricity economy. Recently researchers have presented innovative models to improve the forecasting accuracy of short-term electricity series, which ...
详细信息
Electric load prediction is an important decision tool in area of electricity economy. Recently researchers have presented innovative models to improve the forecasting accuracy of short-term electricity series, which is valuable in allowing both consumers and electric power sector to make effective planning. This study proposed novel combing optimization model to improve the precision of electric load forecasting and called SSPM. First, taken the advantage of linear prediction for the seasonal autoregressive integrated moving average (SARIMA) model and non-linear prediction for the support vector machines (SVM) model to combine a new model. Next, the produce results by SARIMA model is regarded as linear component and used SVM model for correcting the residual from SARIMA as non-linear component of forecasting results. Third, in order to show the dynamic relationship of linear and non-linear components, the weight variable of alpha(1) and alpha(2) are proposed that optimized by particleswarmoptimization (PSO) algorithm with lower error of fitness function, the combining model is applied in the daily electric load data at New South Wales (NSW) in Australia. The experimental results indicate that the proposed optimization model obtains better performance of precise and stability than models of SARIMA and SVM respectively, outperform than conventional artificial neural network (ANN). Although the novel model is applied to electric load forecasting in this paper, it has more scopes for application in a number of areas to gain improvement of forecast accuracy in complex time series.
During the power ascension, the operating point is based on core power, core flow, control rod pattern, and concentrations of fission product, such as xenon. The thermal limits and fuel conditioning at the operating p...
详细信息
During the power ascension, the operating point is based on core power, core flow, control rod pattern, and concentrations of fission product, such as xenon. The thermal limits and fuel conditioning at the operating point should meet the constraints. ASCENTB is an automatic power ascension path searching program for boiling water reactors. The control rod movement is searched for by the particleswarmoptimization (PSO) algorithm. The operating points of one control rod withdrawal sequence are based on the PSO1 or PSO2 strategy. The results of ASCENTS for two selected cycles are comparable with the power plant records. (C) 2016 Elsevier Ltd. All rights reserved.
The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the ...
详细信息
The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with particleswarmoptimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by particleswarmoptimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by particle swarm optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control-particleswarmoptimization speed controller being obtained.
Accurately determining the fluxes of mass and energy between land and the atmosphere is important for understanding regional climates and hydrological cycles. In numerical modeling, the parameterization of a turbulent...
详细信息
Accurately determining the fluxes of mass and energy between land and the atmosphere is important for understanding regional climates and hydrological cycles. In numerical modeling, the parameterization of a turbulent flux is usually based on Monin-Obukhov similarity theory (MOST). According to this theory, it is necessary to simultaneously calculate the empirical similarity parameters beta(m), beta(h), gamma(m), and gamma(h), the aerodynamic roughness (z(om)) and the thermal roughness (z(T)). However, it is difficult to solve a simultaneous set of nonlinear equations for these six parameters. In this study, a new method was introduced to solving this problem. Using measurements from Maqu Station in the source region of the Yellow River, this study employed the artificial intelligence particleswarmoptimization (PSO) algorithm to calibrate the parameters relating to the turbulent flux in the surface layer. We concluded that the differences in the sensible heat and momentum fluxes between the calculations that used the calibrated parameters and the measurements were rather small and that their correlation coefficients were relatively high. The results suggested that PSO algorithm is a feasible approach which can be applied in MOST parameter estimation. (C) 2016 The Author(s). Published by Elsevier B.V.
A particleswarmoptimization(TVPSO) algorithm with time varying parameters is proposed to improve the performance of particleswarmoptimization(PSO) algorithm by two improvements. Aiming at the fact general PSO algo...
详细信息
A particleswarmoptimization(TVPSO) algorithm with time varying parameters is proposed to improve the performance of particleswarmoptimization(PSO) algorithm by two improvements. Aiming at the fact general PSO algorithms have the disadvantages of falling into local optima caused by linearly decreased inertia weight. TVPSO uses the related properties of the trigonometric function to improve the dynamic changes of inertia weight along With Time. The inertia weight maintains a large value in the initial stage, and decreases gradually and reaches a small value at the end. Thus, the global search capability and convergence performance were improved;In order to cope with changes in inertia weight, learning factors also change with time. TVPSO and the other latest particle swarm optimization algorithms are tested on 10 functions at the same time. Experimental results show that TPSO has faster search speed and stronger global search capabilities.
暂无评论