Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power systems.The Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems.Particle Filter(PF)is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability.However,there are problems that particle weights cannot be updated in the prediction stage and particles degradation.To settle these issues,an innovative technique of F-distribution PF and Kernel Smoothing(FPFKS)algorithm is proposed.In the prediction stage,the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time.Meanwhile,a first-order independent Markov capacity degradation model is established.Moreover,the kernel smoothing algorithm is integrated into PF,so that the variance of the parameters of capacity degradation model keeps invariant.Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Liion batteries.

The Application of Kernel Smoothing to Time Series Data

There are already a lot of models to fit a set of stationary time series, such as AR, MA, and ARMA models. For the non-stationary data, an ARIMA or seasonal ARIMA models can be used to fit the given data. Moreover, there are also many statistical softwares that can be used to build a stationary or non-stationary time series model for a given set of time series data, such as SAS, SPLUS, etc. However, some statistical softwares wouldn＇t work well for small samples with or without missing data, especially for small time series data with seasonal trend. A nonparametric smoothing technique to build a forecasting model for a given small seasonal time series data is carried out in this paper. And then, both the method provided in this paper and that in SAS package are applied to the modeling of international airline passengers data respectively, the comparisons between the two methods are done afterwards. The results of the comparison show us the method provided in this paper has superiority over SAS＇s method.

A Note on the Optimality of Generalized Cross-validation Bandwidth Selection in Partially Linear Models with Kernel Smoothing Estimator

The issue of selection of bandwidth in kernel smoothing method is considered within the context of partially linear models, hi this paper, we study the asymptotic behavior of the bandwidth choice based on generalized cross-validation （CCV） approach and prove that this bandwidth choice is asymptotically optimal. Numerical simulation are also conducted to investigate the empirical performance of generalized cross-valldation.

Multilinear commutators of BMO functions and multilinear singular integrals with non-smooth kernels

This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequalities for this kind of commutators are established.

Commutators of Weighted Lipschitz Functions and Multilinear Singular Integrals with Non-Smooth Kernels

This paper is devoted to studying the commutators of the multilinear singular integral operators with the non-smooth kernels and the weighted Lipschitz functions. Some mapping properties for two types of commutators on the weighted Lebesgue spaces, which extend and generalize some previous results, are obtained.

Improved kernel gradient free-smoothed particle hydrodynamics and its applications to heat transfer problems

Kernel gradient free-smoothed particle hydrodynamics （KGF-SPH） is a modified smoothed particle hydrodynamics （SPH） method which has higher precision than the conventional SPH. However, the Laplacian in KGF-SPH is approximated by the two-pass model which increases computational cost. A new kind of discretization scheme for the Laplacian is proposed in this paper, then a method with higher precision and better stability, called Improved KGF-SPH, is developed by modifying KGF-SPH with this new Laplacian model. One-dimensional （1D） and two-dimensional （2D） heat conduction problems are used to test the precision and stability of the Improved KGF-SPH. The numerical results demonstrate that the Improved KGF-SPH is more accurate than SPH, and stabler than KGF-SPH. Natural convection in a closed square cavity at different Rayleigh numbers are modeled by the Improved KGF-SPH with shifting particle position, and the Improved KGF-SPH results are presented in comparison with those of SPH and finite volume method （FVM）. The numerical results demonstrate that the Improved KGF-SPH is a more accurate method to study and model the heat transfer problems.

Weighted estimates for commutators of singular integral operators with non-smooth kernels

In this paper, we establish the boundedness of commutators of singular integral operators with non-smooth kernels on weighted Lipschitz spaces Lipβ,ω. The condition on the kernel in this paper is weaker than the usual pointwise HSrmander condition.

Toeplitz Operator Related to Singular Integral with Non-Smooth Kernel on Weighted Morrey Space

Let T1 be a singular integral with non-smooth kernel or ±I, let T2 and T4 be the linear operators and let T3 = ± I. Denote the Toeplitz type operator by Tb = T1M^b Ia T2 ＋ T3IaMbT4,where M^bf = bf, and Ib is the fractional integral operator. In this paper, we investigate the boundedness of the operator Tb on the weighted Morrey space when b belongs to the weighted BMO space.

基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别

针对多基地水下小目标分类识别问题,本文提出了一种基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别方法 .对水下目标多角度散射信号提取6种典型的具有信息互补性和关联性的特征,提出一种随机森林(Random Forest,RF)和最小冗余最大相关(minimum Redundancy and Maximum Relevance,mRMR)相结合的特征选择方法(RF-mRMR),得出综合的特征重要性排序结果 .通过实验得出分类模型所需的最优特征子集,达到降低数据处理复杂度和提高目标分类结果的目的 .为了捕捉到数据中的高阶结构,在联合稀疏表示模型的基础上,使用核函数将线性不可分的特征数据映射到高维核特征空间.为了充分挖掘稀疏重构后包含在残差波段中的有用信息,使用指数平滑公式对具有一定意义的残差信息进行再利用,最后由核特征空间下的最小误差准则判定目标的类别.应用本文提出的方法对4类目标的海试数据进行识别,结果表明,相较于其他7种对比算法,本文提出的改进方法具有更好的分类性能,而且大多数情况下,本文提出的算法在双基地声呐模式下具有比单基地声呐更高的识别准确率和更低的虚警率.

基于生成对抗网络的二次屏柜交直流侵入信号检测方法

变电站继电保护二次屏柜接线端子存在因人为操作失误或检测设备故障导致的交直流侵入情况,针对该情况特有的数据样本数量少、质量差的问题,文中提出基于生成对抗网络和主成分分析的侵入信号检测方法。采用高斯核平滑对接线端子电压、电流、频率等数据进行预处理,滤除干扰,再将清洗后的数据经过生成对抗网络进行扩增,以对交直流侵入信号进行主成分分析和故障检测识别。所提出方法识别准确率达到95%,实现了对二次屏柜接线端子小样本故障数据的精确检测。

海量数据下光滑分位数回归聚合估计

计算机内存限制和分位数回归损失函数的不光滑性,对海量数据下分位数回归的研究提出了挑战。文章将聚合估计方程与核卷积光滑方法相结合,提出了一种在分布式环境下基于估计方程的光滑分位数的回归聚合估计算法(DSCQR)。理论研究表明,在分块数K满足一定条件时,聚合估计量与全样本估计量具有相同的渐近性质。模拟和实证研究结果表明,所提方法和已有研究提出的DCQR方法相比,在计算速度上具有显著优越性,且在K满足一定条件时具有相当的稳健性。

基于数据筛选及平滑处理的风电功率曲线建模方法研究

风电功率曲线是评估风力发电机组性能及发电能力的一项重要依据,但同一风速下的风电功率往往存在较大范围的波动,这给风电功率曲线的建模带来了很大的困难。针对传统风电功率曲线建模方法精度不理想的问题,提出了一种新型的风电功率曲线建模方法。采用核密度估计法对数据做筛选处理;采用基于时间序列平滑的预处理方法对输入风速进行平滑处理;利用小波神经网络实现风速与功率的非线性映射功率曲线建模。通过风电功率曲线建模实例的对比分析,结果表明该方法具有很好的优越性,能取得更高的建模精度。

面向长尾分布问题的猪只上方体况研究

针对猪只养殖的无人化、智能化问题,本文对传统猪只体况评分方法做出改进,提出一种通过上方进行猪只体况评分的方法,并使用线性插值法作为新的体况评分方式。以Resnet-50网络为基础,引入卷积核注意力机制模块增强对不同体况猪只的特征提取。加入标签平滑算法以及特征平滑分布算法以解决猪只上方体况中存在的长尾分布问题。此外,使用猪场采集的数据制作猪只上方体况数据集,实验结果表明,改进后网络在测试中可达到85.5%的准确率,较原网络提升了3.5%;平均绝对误差和均方误差分别为42.7%和28.5%,较原网络分别降低12.7%和18.7%。通过实验验证猪只上方体况研究的可行性以及所使用方法在提升模型处理长尾分布问题上的有效性。

学习理论中核函数逼近的Jackson型不等式

从微分算子角度理解核函数空间,借助经典Fourier变换研究核函数逼近问题.应用Fourier乘子算子和算子半群定义了一种光滑模,证明其与一种基于微分算子的K-泛函的等价性,由此给出了刻画核函数逼近收敛性的Jackson不等式.进一步证明,如果微分算子为Riesz势算子或Bessel势算子,逼近的收敛性可以转化为卷积算子逼近.特别地,给出了再生核Hilbert空间逼近的一种上界估计.

Mapping Seismic Hazard for Canadian Sites Using Spatially Smoothed Seismicity Model

The estimated seismic hazard based on the delineated seismic source model is used as the basis to assign the seismic design loads in Canadian structural design codes.An alternative for the estimation is based on a spatially smoothed source model.However,a quantification of differences in the Canadian seismic hazard maps(CanSHMs)obtained based on the delineated seismic source model and spatially smoothed model is unavailable.The quantification is valuable to identify epistemic uncertainty in the estimated seismic hazard and the degree of uncertainty in the CanSHMs.In the present study,we developed seismic source models using spatial smoothing and historical earthquake catalogue.We quantified the differences in the estimated Canadian seismic hazard by considering the delineated source model and spatially smoothed source models.For the development of the spatially smoothed seismic source models,we considered spatial kernel smoothing techniques with or without adaptive bandwidth.The results indicate that the use of the delineated seismic source model could lead to under or over-estimation of the seismic hazard as compared to those estimated based on spatially smoothed seismic source models.This suggests that an epistemic uncertainty caused by the seismic source models should be considered to map the seismic hazard.

典型居民温控负荷建模及聚合特性研究

智能电网的发展为温控负荷参与电网调度提供硬件支撑。为研究温控负荷为电网提供负荷调度的容量特性,首先根据居民典型温控负荷的工作特性,建立可准确描述单个温控负荷工作原理的物理模型,并在此基础上通过核平滑技术建立了小区温控负荷的聚合模型,进而求得聚合负荷功率需求与相关参数的变化特性。最后对小区温控负荷的可调度容量做了相关特性分析,算例结果验证了所述方法的有效性。

一种新的含噪混沌信号降噪算法

该文针对低信噪比、非高斯加性噪声和混沌动力学系统参数未知的含噪混沌信号降噪问题,提出了一种基于粒子滤波(Particle Filtering,PF)的降噪新算法。该算法将混沌信号和动力学系统中的未知参数作为一个多维状态矢量,利用PF方法递推计算多维状态矢量的联合后验概率分布,进而实现了对混沌信号的最优估计。对于混沌信号轨道分离过快所导致的退化问题,提出了有效的解决方法,并利用核平滑和自回归(Auto-Regression,AR)模型建模的方法分别实现了非时变以及时变参数的递推估计。仿真实验的结果表明,与现有的降噪方法相比,该文提出的新算法能够更加有效地抑制含噪混沌信号中的加性噪声。

基于电网动态特性的发电机主导参数辨识方法

为提高发电机动态参数辨识的准确性,提出一种基于电网动态特性的发电机主导参数辨识方法。首先,根据实测量对发电机进行解耦,构建了包含动态参数的同步发电机的离散非线性状态空间模型,据此给出了参数核平滑算法(kernel smoothing,KS)和祖先采样粒子吉布斯算法(particle Gibbs with ancestor sampling,PGAS),并将两种算法结合得到KS-PGAS算法;然后,基于Morris筛选方法,使用灵敏度因子判别动态参数变化对输出的影响程度,并筛选出第一摆最大功率与阻尼比两个特征量相对应的主导参数;最后,使用KS-PGAS算法先对第一摆最大功率对应的主导参数修正,再利用修正后的主导参数对阻尼比对应的主导参数进行修正。仿真结果证明了所提算法的有效性和优越性。

采用动态特征选择的中文情感识别研究

针对中文情感识别中特征空间稀疏度和冗余度较高的特点,从集成学习视角出发,提出一种基于动态特征选择机制的情感识别方法.该方法首先采用核平滑方法构建特征子集划分的维度分布和特征空间的重要度分布,然后根据这两种分布函数,自适应划分特征空间,形成多个不同粒度的子空间,然后训练对应的基分类器,最后使用多数投票法的融合策略构造集成识别模型.在校园BBS评论数据上与其他基准算法进行对比实验,结果表明该算法在查全率和查准率等方面均优于其他算法,有效地提高了情感识别的准确性和鲁棒性.

变系数EV模型系数参数的一步核估计

利用核函数法和广义最小二乘法给出了一般变系数EV模型系数参数的估计,得到了估计的强相合性.模拟计算表明估计的效果优良.