Lithium-ion batteries (LIBs) are widely adopted in electric vehicles and electronic equipments due to their high energy density, low self-discharge rate, and long cycle life. However, LIBs suffer from capacity fade with cyclic usage. Prediction of remaining useful life (RUL) is therefore essential for ensuring the reliability and safety of LIBs. This paper proposes a bat-based particle filter (PF) algorithm using a semi-empirical model to predict RUL. The semi-empirical model is adopted as the capacity degradation model. Model parameters are then updated by the bat-based PF algorithm. The prediction results of the proposed prediction approach are compareed with PF methods with different resampling algorithms. Simulation results show that the proposed prediction approach can achieve better performance in RUL prediction.

Magnetic resonance elastography (MRE) offers a noninvasive solution to visualize the mechanical properties of soft tissue, but the study suffers from expensive magnetic resonance scanning. Moreover, translating MRE wave images into soft tissue elasticity is a nontrivial issue for clinical professionals and healthcare practitioners. An interactive system-OpenMRE-is thus developed with the aid of ImageJ for numerical MRE study. It is comprised of two comparatively independent toolkits, namely MREA for simulation and MREP for interpretation. MREA mainly deals with the forward problem of MRE, and provides a numerical platform to determine the propagation and distribution of specially designed elastic wave. It is possible to numerically study some state-of-the-art paradigms including multisource and multifrequency MRE. The resultant wave images are interpretable in MREP that is designed for the inverse problem of MRE. It consists of the algorithms for phase unwrapping, directional filtering, and elasticity reconstruction. In a word, OpenMRE offers the MRE community a convenient and well-functioning system for interactive MRE study.

Block principal component analysis with ℓ1-norm (BPCA-L1) has demonstrated its effectiveness in a lot of visual classification and data mining tasks. However, the greedy strategy for solving the ℓ1-norm maximization problem is prone to being struck in local solutions. In this paper, we propose a BPCA with nongreedy ℓ1-norm maximization, which obtains better solutions than BPCA-L1 with all the projection directions optimized simultaneously. Other than BPCA-L1, the new algorithm has been evaluated against some popular principal component analysis (PCA) algorithms including PCA-L1 and 2-D PCA-L1 on a variety of benchmark data sets. The results demonstrate the effectiveness of the proposed method.

In the paper, we propose an adaptive variable strategy Pareto differential evolution algorithm for multi-objective optimization (AVSPDE). It is different from the general adaptive DE methods which are regulated by variable parameters and applied in single-objective area. Based on the real-time information from the tournament selection set (TSS), there are two DE variants to switch dynamically during the run, in which one aims at fast convergence and the other focus on the diverse spread The theoretical analysis and the digital simulation show the presented method can achieved better performance.

Echo state networks (ESN) is a novel time series model stemming from RNN. The reservoir of ESN provides a rich set of dynamics whose weighted combination can approximate teacher signal effectively. Its excellent predicting capability in deterministic system has been proved by several benchmarks. Yet analyzing an input-output system using ESN has not discussed. In the paper a new I/O model is presented to address both input and output series as the observation of systems which comprise a teacher vector. Learning the vector by ESN can establish the mapping from input to output and predict the system output on the basis of new input. Though learning only the output series can also predict the unknown quantity, repeating simulations demonstrate that our model can restrain the instability of network state and improve the predicting performance. Such model gives us new choice to analyze input-output system.

High computational burden in solving quadratic programming problem is a major obstacle when we apply model predictive control to industrial process. Recurrent neural networks offer a new quadratic programming optimization approach due to its parallel computational performance. In this paper, we present a new architecture of solving model predictive control (MPC) problem based on one layer recurrent neural network. We give algorithm of model predictive control based on recurrent neural network and prove convergence property of one layer recurrent neural network at each sample step. Two examples demonstrate the effectiveness and efficient of the proposed recurrent neural network based MPC. Simulation results show that this approach can utilize fast converge property and the parallel computation ability of recurrent neural network and apply to real-time industrial process control.