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The state of health (SOH) of lithium-ion batteries (LIBs) needs to be accurately estimated to ensure the safety and stability of electric vehicles (EVs) while in operation. In this paper, we proposed a SOH estimation method based on Improved Aquila Optimizer (IAO) and Support Vector Regression (SVR) to achieve an accurate estimation of SOH. During the charging and discharging phases of the battery, we analyzed the trends in current, voltage, and energy, then extracted four features. We used the Kendall coefficient and gray relational grade to prove that features and SOH were highly correlated. On the other hand, IAO was used to optimize the penalty factor and kernel function parameters of the SVR to further improve the generalization and mapping ability. The proposed method was verified under different operating conditions using the CACLE battery data set; the results show that high accuracy can be achieved in SOH estimation via IAO–SVR, and the estimation error of mean MAE is remaining within 2%. [ABSTRACT FROM AUTHOR]

Spectral unmixing is an important part of hyperspectral image processing. In recent years, constrained nonnegative matrix factorization (CNMF) has been successfully applied for unmixing without the pure-pixel assumption and the result is physically meaningful. However, traditional CNMF algorithms always have two limitations: 1) Most of them are based on gradient methods and usually get trapped in a local optimum. 2) As they adopt static penalty function as the constraint handling method, it's difficult to choose a proper regularization parameter that can balance the tradeoff between reconstruction error and constraint well, which leads to the decreased accuracy. In this paper, we introduce particle swarm optimization (PSO) combined with two types of progressive constraint handling approaches for spectral unmixing in the framework of CNMF. A basic method called high-dimensional double-swarm PSO (HDPSO) algorithm is first proposed. It divides the original high-dimension problem into a series of easier subproblems and adopts two interactive swarms to search endmembers and abundances, respectively. Then, adaptive PSO (APSO) and multiobjective PSO algorithms are proposed by respectively incorporating adaptive penalty function and multiobjective optimization approaches into HDPSO. Experiments with both simulated data and real hyperspectral images are used to compare these methods with traditional algorithms and results validate that the proposed methods give better performance for spectral unmixing. [ABSTRACT FROM PUBLISHER]

Endmember extraction from hyper spectral data is an important procedure of hyper spectral unmixing. Nonnegative Matrix Factorization (NMF) has been widely used in the last few years for endmember extraction without assuming the presence of pure pixels. Many methods that incorporate different types of constraints into the NMF objective function have been proposed to accurately extract endmembers. However, traditional constrained NMF algorithms generally have two limitations. First, controlling the tradeoff between the accurate reconstruction and constraint well through the fixed penalty coefficient is difficult. Second, most traditional methods are usually trapped in a local optimum that renders the global optimum difficult to find. To overcome these constraints, we present a novel method called the High-dimension Adaptive Particle Swarm Optimization (HAPSO) for endmember extraction based on the minimum distance constrained NMF (MDC-NMF) scheme. HAPSO enhances the global search ability through PSO. Two key improvements-the high-dimensional PSO and adaptive penalty coefficient method based on swarm information-are considered. The standard PSO algorithm particularly suffers from the " curse of dimensionality" , such that it is more likely to plunge into local optima as the dimensionality of the search space increases. To overcome this problem, high-dimensional PSO divides the complex high-dimensional constrained NMF problem into several simple low-dimensional sub problems according to the characteristics of objective function and hyper spectral data. Thus, each particle in the swarm can search for increasingly accurate positions in a detailed manner that significantly improves the accuracy of the results. Furthermore, particle information, such as the positions and feasibility of the PSO algorithm, can be easily applied to balance the search bias between objective functions and constraints. Thus, this study proposes an adaptive penalty coefficient method according to the proportion of feasible solutions in the swarm, instead of the fixed penalty coefficient. The penalty coefficient particularly increases when the proportion of feasible solutions is low in the preliminary stage and decreases as the proportion of feasible solutions increases. The proposed HAPSO algorithm, MDC-NMF, minimum volume constrained NMF, and vertex component analysis are compared. Both synthetic and real data are considered. For the synthetic data, different numbers of endmembers and signal-to-noise ratio are considered. Real hypers pectral data, including AVIRIS and HYDICE images are used. Results demonstrate that the proposed HAPSO algorithm outperforms other algorithms, it extracts more accurate endmembers and causes less reconstruction error. HAPSO is a method that applies PSO for endmember extraction based on the MDC-NMF scheme. The proposed method can effectively overcome the disadvantages of the traditional constrained NMF endmember extraction algorithms and solve high-dimensional and adaptive penalty coefficient problems for NMF endmember extraction. We will consider the influence of many endmembers that can decrease accuracy in future work. More reasonable and effective constraint handling approaches should be studied, although an adaptive penalty coefficient method performs well. The algorithm accuracy should also be further improved because not all endmembers are perfectly extracted by the proposed algorithm in the experiments. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) is the first step in hyperspectral data unmixing. N-FINDR is one of the most commonly used EE algorithms. Nevertheless, its computational complexity is high, particularly, for a large data set. Following a parallel version of N-FINDR, i.e., P-FINDR, further improvements are presented in this paper. First, generic endmember re-extraction operation (GERO) and multiple search paths are introduced such that multiple endmembers are extracted in parallel. Second, by making full use of the advantages of the proposed algorithms, two extended schemes, i.e., extended mapping rule and multiple-stage GERO are presented, which can reduce synchronous cost and provide steady parallel performance. In experiments, the proposed algorithms have been quantitatively evaluated. The results demonstrate that they can outperform the conventional parallel computing and do not degrade the quality of EE. [ABSTRACT FROM PUBLISHER]