System identification of force transducers for dynamic measurements using particle swarm optimization

A method of system identification for force transducers against the oscillation force is developed. In this method, force transducers are equipped with an additional top mass and excited by a facility with the sine mechanism. Particle swarm optimization (PSO) algorithm is employed to identify the parameters of the derived mathematical models. For improving the convergence speed of PSO, exponential transformation is introduced to the fitness function. Subsequently, numerical simulations and experiments are carried out, and consistent results demonstrate that the identification method proposed in this investigation is feasible and efficient for estimating the transfer functions from sinusoidal force calibration measurements

Array Pattern Synthesis Using a Digital Position Shift Method

Considering all possible steering directions for beam scanning, a digital position shift method (DPSM) is presented to minimize the Peak Sidelobe Level (PSL) by searching the best position solution for every sensor and calculating the pattern with position offset factor. For the truly minimum PSL, digital position shift with optimal amplitude (DPSOA) is considered simultaneously for beam scanning. For searching the best solution to the two methods, constrained conditions for position shift range and amplitude range are described. The method of feedback particle swarm optimization (FPSO) is presented to obtain a large searching space and fast convergence in local space with refined solution. Numerical examples show that the optimized results by DPSM and DPSOA in all steering directions can be used in beam scanning for its digital realization. When compared with the other techniques published in the literature, especially the steering direction close to endfire direction, this method has lower PSL when the main beam width is maintained

One more look on visualization of operation of a root-finding algorithm

Many algorithms that iteratively find solution of an equation require tuning. Due to the complex dependence of many algorithm’s elements, it is difficult to know their impact on the work of the algorithm. The article presents a simple root-finding algorithm with self-adaptation that requires tuning, similarly to evolutionary algorithms. Moreover, the use of various iteration processes instead of the standard Picard iteration is presented. In the algorithm’s analysis, visualizations of the dynamics were used. The conducted experiments and the discussion regarding their results allow to understand the influence of tuning on the proposed algorithm. The understanding of the tuning mechanisms can be helpful in using other evolutionary algorithms. Moreover, the presented visualizations show intriguing patterns of potential artistic applications

Visual Analysis of Dynamics Behaviour of an Iterative Method Depending on Selected Parameters and Modifications

There is a huge group of algorithms described in the literature that iteratively find solutions of a given equation. Most of them require tuning. The article presents root-finding algorithms that are based on the Newton-Raphson method which iteratively finds the solutions, and require tuning. The modification of the algorithm implements the best position of particle similarly to the particle swarm optimisation algorithms. The proposed approach allows visualising the impact of the algorithm's elements on the complex behaviour of the algorithm. Moreover, instead of the standard Picard iteration, various feedback iteration processes are used in this research. Presented examples and the conducted discussion on the algorithm's operation allow to understand the influence of the proposed modifications on the algorithm's behaviour. Understanding the impact of the proposed modification on the algorithm's operation can be helpful in using it in other algorithms. The obtained images also have potential artistic applications

An Equivalent Point-Source Stochastic Model of the NGA-East Ground-Motion Models and a Seismological Method for Estimating the Long-Period Transition Period TL

This dissertation deals with the stochastic simulation of the Next Generation Attenuation- East (NGA-East) ground-motion models and a proposing a new method of calculating the long-period transition period parameter, TL, in the seismic building codes. The work of this dissertation is carried out in two related studies. In the first study, a set of correlated and consistent seismological parameters are estimated in the in Central and Eastern United States (CEUS) by inverting the median 5%-damped spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs). These seismological parameters together form a point-source stochastic GMM. Magnitude-specific inversions are performed for moment magnitude ranges Mw 4.0-8.0, rupture distances Rrup = 1-1000 km and periods T = 0.01-10s, and National Earthquake Hazard Reduction Program site class A conditions. In the second study, the long-period transition period parameter TL is investigated, and an alternate seismological approach is used to calculate it. The long-period transition period parameter is utilized in the determination of the design spectral acceleration of long-period structures. The estimation of TL has remained unchanged since its original introduction FEMA 450-1/2003; The calculation is loosely based on a correlation between modal magnitude Mw and TL that does not account for different seismological parameters in different regions of the country. This study will calculate TL based on the definition of corner period, and will include two seismological parameters, the stress parameters Δσ and crustal velocity in the source region β, in its estimation. The results yield a generally more conservative (or longer) estimation of TL than the estimation that is currently used in engineering design standards

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms