scholarly journals Particle Swarm Optimization Algorithm-Based Design Method for Ultrasonic Transducers

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 715
Author(s):  
Dongdong Chen ◽  
Jianxin Zhao ◽  
Chunlong Fei ◽  
Di Li ◽  
Yuanbo Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Equivalent Circuit ◽  
Design Method ◽  
Particle Swarm ◽  
Equivalent Circuit Model ◽  
Pso Algorithm ◽  
Circuit Model ◽  
Center Frequency ◽  
Swarm Optimization ◽  
And Performance

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1−x)O3 (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

Quantitative Feedback Theory control design using particle swarm optimization method

2011 ◽  
Vol 34 (4) ◽  
pp. 463-476 ◽  
Author(s):  
Hazem I Ali ◽  
Samsul Bahari B Mohd Noor ◽  
SM Bashi ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Design Procedure ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Quantitative Feedback Theory ◽  
Swarm Optimization ◽  
Servo Actuator ◽  
Actuator System ◽  
And Performance

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

scholarly journals A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Cancan Yi ◽  
Yong Lv ◽  
Zhang Dang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Pso Algorithm ◽  
Potential Method ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Mode Decomposition

Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

scholarly journals Speed control of DC motor using fractional order PID controller based on particle swarm optimization

2021 ◽  
Vol 22 (3) ◽  
pp. 1345
Author(s):  
Ghassan A. Sultan ◽  
Amer F. Sheet ◽  
Satar M. Ibrahim ◽  
Ziyad K. Farej
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Pid Controller ◽  
High Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Performance Improvements ◽  
Controlling System ◽  
And Performance

Due to the required different speeds and important role of direct current (DC) motors in laboratories, production factories and industrial application, speed controlling of these motors becomes an essential matter for proper operation with high efficiency and performance accuracy. This paper presents a new speed controlling technique that is based on particle swarm optimization (PSO) algorithm in the optimization process of the parameters for the fractional order proportional–integral–derivative (FOPID) controller. The FOPID is an advanced and modern controlling system in which the two more added parameters (the derivative μ and integral λ orders) are fractional rather than integer. Through the process of minimizing the fitness functions, the obtained results show that the designed controller system can excellently set the best controller parameters due to the fractions of these additional parameters. With respect to the PSO-PID controller, the simulation results for the proposed PSO-FOPID controller show performance improvements of 14%, 21%, 24.5%, 78%, and 19.3% in the values of the parameters Kp, Ki, Kd, Tr, and Ts respectively.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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