Your search
Results 4 resources
-
Image reconstruction for industrial applications based on Electrical Capacitance Tomography (ECT) has been broadly applied. The goal of image reconstruction based ECT is to locate the distribution of permittivity for the dielectric substances along the cross-section based on the collected capacitance data. In the ECT-based image reconstruction process: (1) the relationship between capacitance measurements and permittivity distribution is nonlinear, (2) the capacitance measurements collected during image reconstruction are inadequate due to the limited number of electrodes, and (3) the reconstruction process is subject to noise leading to an ill-posed problem. Thence, constructing an accurate algorithm for real images is critical to overcoming such restrictions. This paper presents novel image reconstruction methods using Deep Learning for solving the forward and inverse problems of the ECT system for generating high-quality images of conductive materials in the Lost Foam Casting (LFC) process. Here, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models were implemented to predict the distribution of metal filling for the LFC process-based ECT. The recurrent connection and the gating mechanism of the LSTM is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors. Experimental results showed that the presented ECT-LSTM-RNN model is highly reliable for industrial applications and can be utilized for other manufacturing processes. © 2013 IEEE.
-
Meta-heuristic search algorithms were successfully used to solve a variety of problems in engineering, science, business, and finance. Meta-heuristic algorithms share common features since they are population-based approaches that use a set of tuning parameters to evolve new solutions based on the natural behavior of creatures. In this paper, we present a novel nature-inspired search optimization algorithm called the capuchin search algorithm (CapSA) for solving constrained and global optimization problems. The key inspiration of CapSA is the dynamic behavior of capuchin monkeys.The basic optimization characteristics of this new algorithm are designed by modeling the social actions of capuchins during wandering and foraging over trees and riverbanks in forests while searching for food sources. Some of the common behaviors of capuchins during foraging that are implemented in this algorithm are leaping, swinging, and climbing. Jumping is an effective mechanism used by capuchins to jump from tree to tree. The other foraging mechanisms exercised by capuchins, known as swinging and climbing, allow the capuchins to move small distances over trees, tree branches, and the extremities of the tree branches. These locomotion mechanisms eventually lead to feasible solutions of global optimization problems. The proposed algorithm is benchmarked on 23 well-known benchmark functions, as well as solving several challenging and computationally costly engineering problems. A broad comparative study is conducted to demonstrate the efficacy of CapSA over several prominent meta-heuristic algorithms in terms of optimization precision and statistical test analysis. Overall results show that CapSA renders more precise solutions with a high convergence rate compared to competitive meta-heuristic methods. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
-
The performance of any meta-heuristic algorithm depends highly on the setting of dependent parameters of the algorithm. Different parameter settings for an algorithm may lead to different outcomes. An optimal parameter setting should support the algorithm to achieve a convincing level of performance or optimality in solving a range of optimization problems. This paper presents a novel enhancement method for the salp swarm algorithm (SSA), referred to as enhanced SSA (ESSA). In this ESSA, the following enhancements are proposed: First, a new position updating process was proposed. Second, a new dominant parameter different from that used in SSA was presented in ESSA. Third, a novel lifetime convergence method for tuning the dominant parameter of ESSA using ESSA itself was presented to enhance the convergence performance of ESSA. These enhancements to SSA were proposed in ESSA to augment its exploration and exploitation capabilities to achieve optimal global solutions, in which the dominant parameter of ESSA is updated iteratively through the evolutionary process of ESSA so that the positions of the search agents of ESSA are updated accordingly. These improvements on SSA through ESSA support it to avoid premature convergence and efficiently find the global optimum solution for many real-world optimization problems. The efficiency of ESSA was verified by testing it on several basic benchmark test functions. A comparative performance analysis between ESSA and other meta-heuristic algorithms was performed. Statistical test methods have evidenced the significance of the results obtained by ESSA. The efficacy of ESSA in solving real-world problems and applications is also demonstrated with five well-known engineering design problems and two real industrial problems. The comparative results show that ESSA imparts better performance and convergence than SSA and other meta-heuristic algorithms.
-
Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.