Dynamic Weight Particle Swarm Optimization Research Articles

Wavelet-ARMA-Based Short-Term Drift Calibration of IMU Position on Human Lower Limbs

Position calibration in the inertial motion capture (Mocap) system is a crux to guarantee the correct calculation of human posture. Due to muscle movement and clothes slippage during motion, the position of inertial measurement units (IMUs) installed on the human body generates displacements. Aiming at the drift error caused by IMUs’ position shift in the short term, this paper proposes a wavelet-ARMA prediction model. Three different wavelet bases, Haar, Daubechies, and Coiflet, are selected to combine with the ARMA model to predict angle sequences, among which the Db4 wavelet-ARMA model with the best prediction performance, choosing the angle sequence predicted by the model and the measurement sequence to set up an error model, and using the Gauss-Newton (GN) and the dynamic weight particle swarm optimization (DWPSO) algorithms to realize IMUs’ position calibration. The experimental results show that the wavelet-ARMA model has better prediction performance than the ARMA, BP, and LSTM models with the value of the calibrated RMSE decreasing. The results confirm that the model proposed in this paper can accurately predict the changing trend of joint angle sequences on human lower limbs during gait motion and automatically calibrate the short-term drift error formed in the process of gait motion.

Effects of Dynamic IMU-to-Segment Misalignment Error on 3-DOF Knee Angle Estimation in Walking and Running.

The inertial measurement unit (IMU)-to-segment (I2S) alignment is an important part of IMU-based joint angle estimation, and the accurate estimation of the three degree of freedom (3-DOF) knee angle can provide practical support for the evaluation of motions. In this paper, we introduce a dynamic weight particle swarm optimization (DPSO) algorithm with crossover factor based on the joint constraint to obtain the dynamic alignment vectors of I2S, and use them to perform the quaternion-based 3-DOF knee angle estimation algorithm. The optimization algorithm and the joint angle estimation algorithm were evaluated by comparing with the optical motion capture system. The range of 3-DOF knee angle root mean square errors (RMSEs) is 1.6°-5.9° during different motions. Furthermore, we also set up experiments of human walking (3 km/h), jogging (6 km/h) and ordinary running (9 km/h) to investigate the effects of dynamic I2S misalignment errors on 3-DOF knee angle estimation during different motions by artificially adding errors to I2S alignment parameters. The results showed differences in the effects of I2S misalignment errors on the estimation of knee abduction, internal rotation and flexion, which indicate the differences in knee joint kinematics among different motions. The IMU to thigh misalignment error has the greatest effect on the estimation of knee internal rotation. The effect of IMU to thigh misalignment error on the estimation of knee abduction angle becomes smaller and then larger during the two processes of switching from walking to jogging and then speeding up to ordinary running. The effect of IMU to shank misalignment error on the estimation of knee flexion angle is numerically the largest, while the standard deviation (SD) is the smallest. This study can provide support for future research on the accuracy of 3-DOF knee angle estimation during different motions.

Synthesis of sparse square arrays with high beam collection efficiency under minimum element spacing constraints

AbstractThis letter presents a large spacing nonuniform distribution, uniform excitation sparse square array model to maximize beam collection efficiency (BCE) and minimize sidelobe level outside the collection area (CSL) of the transmitting antenna in microwave wireless power transmission (MWPT) systems. We improve the position update parameters in the dynamic weight particle swarm optimization (DWPSO) algorithm by adding multiobjective learning features (MODWPSO). We use MODWPSO to optimize the elements' positions to maximize BCE and minimize CSL and discuss the effect of the minimum element spacing value on BCE and CSL. By comparing the array performance with a typical array model, it can be concluded that the proposed large spacing sparse nonuniform distribution, uniform excitation square array model has higher BCE, lower CSL, and fewer costs.

Performance Optimization of Polarized MIMO Relay Channels Based on the COST 2100 Channel Model

Polarization selectivity has a great impact on multiple-input–multiple-output (MIMO) relay channels. In this letter, the channel capacity of MIMO relay channels is analyzed, revealing the polarization dependency of multipath components. Polarization optimization schemes are proposed, including a regular optimization and a dynamic weight particle swarm optimization (DWPSO) for MIMO relay channels. The performance analysis shows that the DWPSO can significantly improve the link capacity of MIMO relay channels based on the COST 2100 channel model. This study indicates that it is necessary to optimize the polarization direction of MIMO relay channels.

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Abstract Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.

Joint Constraints Based Dynamic Calibration of IMU Position on Lower Limbs in IMU-MoCap.

The position calibration of inertial measurement units (IMUs) is an important part of human motion capture, especially in wearable systems. In realistic applications, static calibration is quickly invalid during the motions for IMUs loosely mounted on the body. In this paper, we propose a dynamic position calibration algorithm for IMUs mounted on the waist, upper leg, lower leg, and foot based on joint constraints. To solve the problem of IMUs’ position displacement, we introduce the Gauss–Newton (GN) method based on the Jacobian matrix, the dynamic weight particle swarm optimization (DWPSO), and the grey wolf optimizer (GWO) to realize IMUs’ position calibration. Furthermore, we establish the coordinate system of human lower limbs to estimate each joint angle and use the fusion algorithm in the field of quaternions to improve the attitude calibration performance of a single IMU. The performances of these three algorithms are analyzed and evaluated by gait tests on the human body and comparisons with a high-precision IMU-Mocap reference device. The simulation results show that the three algorithms can effectively calibrate the IMU’s position for human lower limbs. Additionally, when the degree of freedom (DOF) of a certain dimension is limited, the performances of the DWPSO and GWO may be better than GN, when the joint changes sufficiently, the performances of the three are close. The results confirm that the dynamic calibration algorithm based on joint constraints can effectively reduce the position offset errors of IMUs on upper or lower limbs in practical applications.

Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems

Aiming at the problem of low accuracy of reliability prediction, a back propagation neural network (BPNN) model is developed. In the process of reliability prediction, a dynamic weight particle swarm optimization-based sine map (SDWPSO) method including a novel inertial weight update strategy is developed. This new strategy introduced a linear decreasing parameter in the sine-map, which enables particles to perform a fine search at a very low speed in the later stage of the search and greatly improves the convergence speed of the algorithm. Furthermore, a hybrid model named SDWPSO-BPNN is created to improve the reliability prediction accuracy in engineering systems. The proposed SDWPSO approach is compared with four algorithms using fourteen benchmark functions to verify the effectiveness. The experimental results indicate that SDWPSO has a better search ability than the other algorithms. Then, the hybrid SDWPSO-BPNN is applied to predict the reliability of turbocharger and industrial robot systems, respectively. The obtained results manifest that the SDWPSO-BPNN is more powerful than that of SVM and ANN methods for reliability prediction in engineering.

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied for the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. Moreover, the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the pre-existing algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.

Hypercube optimization based global solution in numerical benchmark and sensor localization

An original learning algorithm for solving global numerical optimization problems is proposed. The proposed algorithm is strong stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The hypercube optimization algorithm includes the initialization and evaluation process, and searching space process. The designed HO algorithm is tested on specific benchmark functions. The comparative performance analysis have made against with other approaches of dynamic weight particle swarm optimization and self-adaptive differential evolution algorithm. Convergence characteristics of self-adaptive differential evolution algorithm has deliver the much better functional value in compare to dynamic weight based particle swarm optimization.

Chaotic dynamic weight particle swarm optimization for numerical function optimization

Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, PSO has problems with premature convergence and easy trapping into local optimum solutions. In order to overcome these deficiencies, a chaotic dynamic weight particle swarm optimization (CDW-PSO) is proposed. In the CDW-PSO algorithm, a chaotic map and dynamic weight are introduced to modify the search process. The dynamic weight is defined as a function of the fitness. The search accuracy and performance of the CDW-PSO algorithm are verified on seventeen well-known classical benchmark functions. The experimental results show that, for almost all functions, the CDW-PSO technique has superior performance compared with other nature-inspired optimizations and well-known PSO variants. Namely, the proposed algorithm of CDW-PSO has better search performance.