scholarly journals An Experimental Comparison of Swarm Optimization Based Abrupt Motion Tracking Methods

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75383-75394 ◽  
Author(s):  
Huanlong Zhang ◽  
Xiujiao Zhang ◽  
Yan Wang ◽  
Kunfeng Shi ◽  
Jianwei Zhang ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Experimental Comparison ◽  
Swarm Optimization

Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization

2017 ◽  
Vol 24 (06) ◽  
pp. 1740001 ◽  
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Response Times ◽  
Particle Swarm ◽  
Strategic Decision ◽  
Experimental Comparison ◽  
Selection Algorithm ◽  
Interaction Operator ◽  
Swarm Optimization ◽  
View Selection

A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.

Particle swarm optimization-tuned adaptive fuzzy output feedback motion tracking control of piezo-positioning mechanism with unknown hysteresis

2019 ◽  
Vol 41 (10) ◽  
pp. 2897-2908 ◽  
Author(s):  
Mohsen Hasanpour Naseriyeh ◽  
Adeleh Arabzadeh Jafari ◽  
Mehrnoosh Zaeifi ◽  
Seyed Mohammad Ali Mohammadi
Keyword(s):  
Particle Swarm Optimization ◽  
Output Feedback ◽  
Motion Tracking ◽  
Tracking Error ◽  
Particle Swarm ◽  
Small Neighborhood ◽  
Output Feedback Control ◽  
Fuzzy Logic Systems ◽  
Swarm Optimization ◽  
Adaptive Fuzzy

This paper considers the problem of observer-based adaptive fuzzy output feedback control for a piezo-positioning mechanism with unknown hysteresis. In this paper, fuzzy logic systems (FLSs) are used to estimate the unknown nonlinear functions, and also Nussbaum function is utilized to overcome the unknown direction hysteresis. Based on the Lyapunov method, the control scheme is constructed by using the backstepping and adaptive technique. In order to better control performance in reducing tracking error, the particle swarm optimization (PSO) algorithm is utilized for tuning the controller parameters. Proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded (SGUUB) and the tracking error can converge to a small neighborhood of the origin. Finally, the simulation results are provided to demonstrate the effectiveness and robustness of the proposed approach.

scholarly journals A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Sanjay Saini ◽  
Dayang Rohaya Bt Awang Rambli ◽  
M. Nordin B. Zakaria ◽  
Suziah Bt Sulaiman
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Motion Tracking ◽  
Particle Swarm ◽  
Search Space ◽  
Human Motion ◽  
Video Sequences ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Human Motion Tracking

Automatic human motion tracking in video sequences is one of the most frequently tackled tasks in computer vision community. The goal of human motion capture is to estimate the joints angles of human body at any time. However, this is one of the most challenging problem in computer vision and pattern recognition due to the high-dimensional search space, self-occlusion, and high variability in human appearance. Several approaches have been proposed in the literature using different techniques. However, conventional approaches such as stochastic particle filtering have shortcomings in computational cost, slowness of convergence, suffers from the curse of dimensionality and demand a high number of evaluations to achieve accurate results. Particle swarm optimization (PSO) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space. This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking. An attempt is made to provide a guide for the researchers working in the field of PSO based human motion tracking from video sequences. Additionally, the paper also presents the performance of various model evaluation search strategies within PSO tracking framework for 3D pose tracking.

scholarly journals Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2367
Author(s):  
Hugo Yañez-Badillo ◽  
Francisco Beltran-Carbajal ◽  
Ruben Tapia-Olvera ◽  
Antonio Favela-Contreras ◽  
Carlos Sotelo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dynamic Systems ◽  
Tracking Control ◽  
Motion Tracking ◽  
Swarm Optimization ◽  
B Spline ◽  
Control Scheme ◽  
Taylor Series Expansions ◽  
Artificial Neural

Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.

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