Ming Li

Abstract: The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.

Abstract: Particle filter algorithm is a filtering method which uses Monte Carlo idea within the framework of Bayesian estimation theory. It approximates the probability distribution by using particles and discrete random measure which is consisted of their weights, it updates new discrete random measure recursively according to the algorithm. When the sample is large enough, the discrete random measure approximates the true posteriori probability density function of the state variable. The particle filter algorithm is applicable to any non-linear non-Gaussian system. But the standard particle filter does not consider the current measured value, which will lead to particles with non-zero weights become less after some iterations, this results in particle degradation; re-sampling technique was used to inhibit degradation, but this will reduce the particle diversity, and results in particle impoverishment. To overcome the problems, this paper proposed a new particle filter which introduced genetic algorithm and particle swarm optimization algorithm. The new algorithm is called intelligent particle filter (IPF). Driving particles move to the optimal position by using particle swarm optimization algorithm, thus the numbers of effective particles was increased, the particle diversity was improved, and the particle degradation was inhibited. Replace the re-sampling method in traditional particle filter by using the choice, crossover and mutation operation of the genetic algorithm, avoiding the phenomenon of impoverishment. Simulation results show that the new algorithm improved the estimation accuracy significantly compare with the standard particle filter.

Abstract: Aiming at the difficulty of distinguishing texture feature in facial expressions recognition, this paper put forward a LGBP facial expression recognition algorithm based on the character blocking and sparse representation The main contents are training the block images of different categories expression images and extracting the LGBP features of each sub-block We construct a over-complete dictionary from which we get the discrepancy vector of each sub-block using sparse representation Through finding the minimum residual vectors to achieve the recognition of different facial expressions To some extent, the experimental results based on the JAFFF and Cohn-kanade facial expression database show that this algorithm can effectively overcome the influence of texture feature differences and have higher recognition rate

Abstract: The traditional searching scheme of independent component analysis (ICA) is based on gradient algorithm. And a learning step size is required beforehand. It couldn’t resolve the problem of convergence. To overcome the drawback, an improved particle swarm optimization (PSO) is applied to ICA algorithm. Firstly, the dynamic inertia weight which is based on evolution speed and aggregation degree is introduced into PSO. And then, Based on the analysis of ICA, a fitness function of PSO was defined. Finally, the detailed algorithm was given by using improved PSO. Based on TIMIT corpus and Noise-92 database, the experiments were implemented. The results indicate that the performance of DPSO-ICA algorithm is superior to the traditional FastICA for processing mixed noisy speech signals. Keywords-speaker signal; Independent Component Analysis; Particle Swarm Optimation; FastICA

Abstract: Blind Source Separation?BSS?is a recently addressed speech signal processing method, the traditional searching scheme use gradient-based algorithm; However, the convergence of it often depends on choosing of a learning step, it couldn’t resolve the problem of lower velocity of convergence. To overcome the drawback, an efficient BSS algorithm based on improved Particle Swarm Optimization (PSO) is presented. We introduce evolution speed and aggregation degree to update dynamic inertia weight in PSO. Then define fitness function of PSO based on BSS. Finally, the detail algorithm of BSS is presented. Experimental results on mixed voice signal indicate that the established algorithm of PSO can quickly and effectively get optimal resolution to BSS.

Abstract: Particle filters are of great concern in a large variety of engineering fields such as robotics, statistics or automatics. Recently, it has developed among Prognostics and Health Management (PHM) applications for diagnostics and prognostics. According to some authors, it has ever become a state-of-the-art technique for prognostics. Nowadays, around 50 papers dealing with prognostics based on particle filters can be found in the literature. However, no comprehensive review has been proposed on the subject until now. This paper aims at analyzing the way particle filters are used in that context. The development of the tool in the prognostics׳ field is discussed before entering the details of its practical use and implementation. Current issues are identified, analyzed and some solutions or work trails are proposed. All this aims at highlighting future perspectives as well as helping new users to start with particle filters in the goal of prognostics.

Abstract: Particle Filters (PFs) have received increasing attention by researchers from different disciplines including the hydro-geosciences, as an effective tool to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation using the PFs in hydrology has evolved since 2005 from the PF-SIR (sampling importance resampling) to PF-MCMC (Markov Chain Monte Carlo), and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and MCMC, the so-called EPFM. In this framework, the prior distribution undergoes an evolutionary process based on the designed mutation and crossover operators of GA. The merit of this approach is that the particles move to an appropriate position by using the GA optimization and then the number of effective particles is increased by means of MCMC, whereby the particle degeneracy is avoided and the particle diversity is improved. In this study, the usefulness and effectiveness of the proposed EPFM is investigated by applying the technique on a conceptual and highly nonlinear hydrologic model over four river basins located in different climate and geographical regions of the United States. Both synthetic and real case studies demonstrate that the EPFM improves both the state and parameter estimation more effectively and reliably as compared with the PF-MCMC.

Abstract: Feature selection is the problem of finding the minimum number of features among a redundant feature space which leads to the maximum classification performance. In this paper, we have proposed a novel feature selection method based on mathematical model of interaction between grasshoppers in finding food sources. Some modifications were applied to the grasshopper optimization algorithm (GOA) to make it suitable for a feature selection problem. The method, abbreviated as GOFS is supplemented by statistical measures during iterations to replace the duplicate features with the most promising features. Several publicly available datasets with various dimensionalities, number of instances, and target classes were considered to evaluate the performance of the GOFS algorithm. The results of implementing twelve well-known and recent feature selection methods were presented and compared with GOFS algorithm. Comparative experiments indicate the significance of the proposed method in comparison with other feature selection methods.

Abstract: The energy harvesting (EH) from unused natural waste energy sources is common nowadays because of rising power demand. The sources have the potential of producing micro to milliwatts power depending on the ambient conditions. Many researchers have been concentrating on micro-level energy harvesting to provide power to the micro-devices in a remote area. The concept leads to a drastic reduction in cost. Once the structure is established, it can generate electricity with minimal cost or effort like renewable sources. This paper reviews the two key areas of the piezoelectric energy harvesting system (PEHS), namely, mechanical and electronic approaches, developed by several researchers. From the thorough review, it is realized that the existing technologies more or less can capable to EH by using the piezoelectric elements; however, the consistency and stability of the systems are not up to the mark yet. In this study, vibration-based PEHS has been considered for the application of the optimization technique to enhance its performance. This review has been focused on numerous challenges and recommendations for next-generation EH by utilizing vibration-based piezoelectric elements.

Abstract: Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).