• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1083-1097
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• 2018

This paper presents a novel range-free localization algorithm based on quantum particle swarm optimization. The proposed algorithm is capable of estimating the distance between two non-neighboring sensors for multi-hop heterogeneous wireless sensor networks where all nodes' communication ranges are different. Firstly, we construct a new cumulative distribution function of expected hop progress for sensor nodes with different transmission capability. Then, the distance between any two nodes can be computed accurately and effectively by deriving the mathematical expectation of cumulative distribution function. Finally, quantum particle swarm optimization algorithm is used to improve the positioning accuracy. Simulation results show that the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes for heterogeneous wireless sensor networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3347-3358
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• 2024

Addressing the challenge of identifying an appropriate set of material and irradiation parameters for accurate simulation models using crystal plasticity finite element method (CPFEM), this study proposes a novel two-stage method for nano-indentation modeling of ion-irradiated face-centered cubic (FCC) materials. It includes implementing the strain-gradient crystal plasticity (SGCP) theory with irradiation effects and the calibration of simulation parameters using the particle swarm optimization (PSO) algorithm with experimental data. The proposed method consists of two stages: establishing CPFEM without irradiation effects in stage 1 and modeling irradiation effects based on CPFEM in stage 2. Modeling the nano-indentation test of ion-irradiated stainless steel 304 (SS304) using real experimental data is conducted to evaluate the efficiency of the proposed method. The accuracy of the calibration method using PSO is verified through comparisons between simulation and experimental results for force-indentation depth and hardness-indentation depth relationships under both unirradiated and irradiated conditions. Moreover, effect of ion-irradiation on the mechanical behavior during the nano-indentation of single crystal SS304 is also examined to demonstrate that the proposed method is a powerful approach for nano-indentation modeling of ion-irradiated FCC single crystals using SGCP theory and the PSO algorithm.

• Journal of Magnetics
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• v.19 no.4
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• pp.378-384
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• 2014

Optimization design and robust design are significant measures for improving the performance and reliability of electromagnetic devices (EMDs, specifically refer to relays, contactors in this paper). However, the implementation of the above-mentioned design requires substantial calculation; consequently, on the premise of guaranteeing precision, how to improve the calculation speed is a problem that needs to be solved. This paper proposes a new method for establishing an approximate model for the EMD. It builds a relationship between the input and output of the EMD with different coil voltages and air gaps, by using a user-defined interpolating function. The coefficient of the fitting function is determined based on a quantum particle swarm optimization (QPSO) method. The effectiveness of the method proposed in this paper is verified by the electromagnetic force calculation results of an electromagnetic relay with permanent magnet.

• Computers and Concrete
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• v.30 no.2
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• pp.127-141
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• 2022

In this paper, a numerical procedure is proposed for achieving the minimum cost design of reinforced concrete polygonal column cross-sections under compression and biaxial bending. A methodology is developed to integrate the metaheuristic algorithm Quantum Particle Swarm Optimization (QPSO) with an algorithm for the evaluation of the strength of reinforced concrete cross-sections under combined axial load and biaxial bending, according to the design criteria of Brazilian Standard ABNT NBR 6118:2014. The objective function formulation takes into account the costs of concrete, reinforcement, and formwork. The cross-section dimensions, the number and diameter of rebar and the concrete strength are taken as discrete design variables. This methodology is applied to polygonal cross-sections, such as rectangular sections, rectangular hollow sections, and L-shaped cross-sections. To evaluate the efficiency of the methodology, the optimal solutions obtained were compared to results reported by other authors using conventional methods or alternative optimization techniques. An additional study investigates the effect on final costs for an alternative parametrization of rebar positioning on the cross-section. The proposed optimization method proved to be efficient in the search for optimal solutions, presenting consistent results that confirm the importance of using optimization techniques in the design of reinforced concrete structures.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2134-2143
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• 2018

This work introduces a fault diagnosis method for transformer based on self-powered radio frequency identification (RFID) sensor tag and improved Hilbert-Huang transform (HHT). Consisted by RFID tag chip, power management circuit, MCU and accelerometer, the developed RFID sensor tag is used to acquire and wirelessly transmit the vibration signal. A customized power management including solar panel, low dropout (LDO) voltage regulator, supercapacitor and corresponding charging circuit is presented to guarantee constant DC power for the sensor tag. An improved band restricted empirical mode decomposition (BREMD) which is optimized by quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to deal with the raw vibration signal. Compared with traditional methods, this improved BREMD method shows great superiority in reducing mode aliasing. Then, a promising fault diagnosis approach on the basis of Hilbert marginal spectrum variations is brought up. The measured results show that the presented power management circuit can generate 2.5V DC voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.8m in the test environment. Furthermore, the measurement results indicate the promising performance of fault diagnosis for transformer.