A novel characterization method of piezoelectric composite material based on particle swarm optimization algorithm

Sun, Xuan; Li, Shiyang; Dun, Xiangming; Li, Deyue; Li, Tangan; Guo, Rongjing; Yang, Ming

doi:10.1016/j.apm.2018.08.015

Cited by 24 publications

(8 citation statements)

References 11 publications

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“…The global group movement is an evolution process to achieve the optimal solution by solving space problems from disorder to an orderly process. The main strong advantages of the PSO algorithm are easy and simple to implement, and it has few parameters that need to adjusted [19].…”

Section: Parameter Identification Of the Modified Bouc-wen Model By Umentioning

confidence: 99%

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

Fang

Jia

et al. 2019

Micromachines

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The piezoelectric actuator (PA) is one of the most commonly used actuators in a micro-positioning stage. But its hysteresis non-linearity can cause error in the piezo-actuated stage. A modified Bouc–Wen model is presented in this paper to describe the hysteresis non-linearity of the piezo-actuated stage. This model can be divided into two categories according to the input frequency: rate-independent type and rate-dependent type. A particle swarm optimization method (PSO) is employed to identify these parameters of the Bouc–Wen hysteresis model. An inverse model feedforward compensator is established based on the modified Bouc–Wen model. The fuzzy proportional-integral-derivative (PID) controller combined with the feedforward compensator is implemented to the piezo-actuated stage. The experimental results indicate that the proposed control strategy can compensate for the hysteresis phenomenon.

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Section: Parameter Identification Of the Modified Bouc-wen Model By Umentioning

confidence: 99%

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

Fang

Jia

et al. 2019

Micromachines

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“…The simulation experiment and its application in the code division multiple access also prove its high efficiency [21]. Sun et al proposed a characterization method for the high-loss piezoelectric composite material based on the particle swarm optimization algorithm [22]. Shahbeig et al proposed a hybrid algorithm to determine the development of the most relevant involved genes in breast cancer.…”

Section: Particle Swarm Optimizermentioning

confidence: 99%

Improvement of flattenability using particle swarm optimizer for surface unfolding in bolus shaping

Peng

Ingleby

et al. 2020

SN Appl. Sci.

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Bolus, formed by a sheet of material, covers on tumors of patient skins to generate the desired dose distribution in a process of the high-energy radiotherapy. The existing methods of bolus shaping use a manual process to cut the commercial material into the shape, which can cause air gaps between the patient skin surface and bolus in the reduced effect of cancer care treatment. A method of automatic bolus shaping can improve the accuracy and reduce air gaps of bolus shaping, which uses a process of first segmenting 3D patient skin surface models into several patches and then unfolding the patches into 2D patterns for material cutting before folding the cut sheet into the bolus. However, air gaps between the bolus and patient skin surface cannot be directly evaluated to find the bolus accuracy. This paper proposes a method to improve model flattenability and evaluate the air gaps using a particle swarm optimizer (PSO). A 3D surface shape is unfolded only if the air gap and surface flattenability meet the requirement of accuracy. Otherwise, the surface will be segmented into several patches to improve the flattenability and reduce air gaps. The objective and constraint are identified to search for an optimal solution for the 3D surface with a high flattenability. A strategy of the dimension reduction is proposed for the selection of local nodes on a meshed surface to increase the searching efficiency of surface flattenability. A local node selection based PSO (L-PSO) method is developed to search for the optimal solution. The proposed method is verified in two case studies of bolus shaping.

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“…Los diversos problemas de optimización existentes en la industria y la necesidad de encontrar soluciones de manera rápida e eficaz y no necesariamente óptima, ha generado el desarrollo de los algoritmos heurísticos, estos han sido probados en diferentes escenarios de la industria y la tecnología tal como el problema de enrutamiento y asignación de longitud de onda en redes ópticas de transporte de datos (Ardjmand et al, 2019;Salehpoor et al, 2019;Sun et al, 2019). En redes de transporte óptico, se han ensayado diversas heurísticas evolutivas como simulated annealing, tabu search entre otros (Tsenov et al, 2008), y las heurísticas bioinspiradas (HBI) como Ant, Firefly, Bat, entre otros (Wen, 2017;Xin-She et al, 2015).…”

Section: Introductionunclassified

Entropía topológica: nuevo enfoque para el testeo de algoritmos heurísticos en redes ópticas

2022

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El presente estudio muestra la incorporación de un indicador para el testeo de algoritmos de enrutamiento en escenarios topológicamente equivalentes. El indicador está basado en el desorden topológico que muestra la matriz de enlaces en su diagonal secundaria, dado que en redes ópticas en general son simétricas respecto de la diagonal principal. Se establecen comparaciones para escenarios de diferente número de nodos, pero manteniendo el mismo desorden topológico, lo cual permite probar los algoritmos de enrutamiento con mayor precisión en las simulaciones. Las heurísticas son comparadas desde la operatividad en redes de alto tráfico, sin embargo, no se realizan estudios de la operatividad temporal del algoritmo mientras se aumenta progresivamente la carga nodal, manteniendo la complejidad topológica. Este nuevo indicador es denominado Entropía Topológica, que permite la comparación de los algoritmos en condiciones de equivalencia topológica. En conclusión, se muestra el funcionamiento de la metodología para diferentes estados entrópicos, lo que permite comparar diferentes algoritmos y su tiempo de respuesta.

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A novel characterization method of piezoelectric composite material based on particle swarm optimization algorithm

Cited by 24 publications

References 11 publications

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

Improvement of flattenability using particle swarm optimizer for surface unfolding in bolus shaping

Entropía topológica: nuevo enfoque para el testeo de algoritmos heurísticos en redes ópticas

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