ISG-FHEV等效燃油消耗最小控制策略

针对室内复杂环境下火灾识别准确率会降低的问题,提出了一种改进的粒子群算法优化支持向量机参数进行火灾火焰识别的方法。首先在 颜色空间进行火焰图像分割,对获得的火焰图像进行预处理并提取相关特征量;其次采用PSO算法搜索SVM的最优核参数和惩罚因子,并在PSO算法中加入变异操作和非线性动态调整惯性权值的方法,加快了搜索SVM最优参数的精度和速度;然后将提取的火焰各个特征量作为训练样本输入SVM模型进行训练,并建立参数优化后的SVM分类器模型;最后将待测试样本输入SVM模型进行分类识别。算法的火灾识别准确率达到94.09%,分类效果明显优于其他分类算法。仿真结果表明,改进的PSO优化SVM算法提高了火焰识别的准确率和实时性,算法的自适应性更强,误判率更低。     

A high order energy preserving scheme for the strongly coupled nonlinear Schrōdinger system

A high order energy preserving scheme for a strongly coupled nonlinear Schrōdinger system is roposed by using the average vector field method. The high order energy preserving scheme is applied to simulate the soliton evolution of the strongly coupled Schrōdinger system. Numerical results show that the high order energy preserving scheme can well simulate the soliton evolution, moreover, it preserves the discrete energy of the strongly coupled nonlinear Schrōdinger system exactly.     

An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine

Based on the techniques of Hilbert–Huang transform (HHT) and support vector machine (SVM), a noise-based intelligent method for engine fault diagnosis (EFD), so-called HHT–SVM model, is developed in this paper. The noises of a sample engine under normal and several fault states are first measured and denoised by using the wavelet packet threshold method to initially lower the noise level with negligible signal distortion. To extract fault features of the engine, then, the HHT is selected and applied to the measured noise signals. A nine-dimensional vector, which consists of seven intrinsic mode functions (IMFs) from the empirical mode decomposition (EMD), maximum value of HHT marginal spectrum and its corresponding frequency component, is specified to represent each engine fault feature. Finally, an optimal SVM model is established and trained for engine failure classification by using the fault feature vectors of the noise signals. Cross-validation results show that the proposed noise-based HHT–SVM method is accurate and effective for engine fault diagnosis. Due to outstanding time–frequency characteristics and pattern recognition capacity of the HHT and SVM, the newly proposed HHT–SVM can be used to deal with both the stationary and nonstationary signals, and even the transient ones. In the view of applications, the HHT–SVM technique may be suggested not only to detect the abnormal states of vehicle engines, but also to be extended to other fields for failure diagnosis in engineering.     

Evolution of emergent C-points, L-lines, and C-lines in free space

Polarization singularities,which emerge from the incoherent superposition of two vector electric fields with the same frequency,and their evolution in free space are studied analytically and illustrated by numerical examples.It is shown that there exist C-points,L-lines,in particular,C-lines in incoherently superimposed two-dimensional wavefields.Usually,the C-lines are unstable and disappear during the free-space propagation.The motion,pair creation-annihilation process of the emergent C-points,as well as the distortion of the L-lines may take place,and the degree of polarization of the emergent C-points varies upon propagation and may be less than 1.     

Photoproduction of Vector Meson ø off Deuteron in QCD Inspired Model

Based on the quark-gluon contents of nucleon and strongly believing that the force mediators, Pomeron and its counterpart in the conventional approach of Regge theory, for high energy diffractive process would be the tensor glueball and Odderonrespectively, we discuss photo-production of vector meson ø off the deuteron at energy less than 3 GeV in the QCD inspired model in which the quark gluon degrees of freedom and glueball, Odderon exchange are taken into account. A calculation isperformed for γ+D → ø+D, and the theoretical predictions of the differential cross section dσ ^γD/dt, are presented and compared with available experimental data. Our QCD inspired model reproduces data quite well in the whole range of the experimental measurements up to |t|≌ 0.4~GeV. Our results can be used toextract γn →øn data, which cannot be measured in experiment.     

基于拓扑结构的碱金属化合物摩尔磁化率的支持向量回归研究

基于经典电动力学导出的表征简单离子磁化率的磁性点价g_i所构建的分子磁性连接性指数?^mF及45种碱金属化合物的摩尔磁化率χ_m的实测数据集,利用粒子群寻优的支持向量回归（SVR）方法,建立了基于⁰F和¹F的碱金属化合物χ_m的预测模型,并与基于多元线性回归（MLR）模型的计算结果进行了比较.结果显示,基于9次交叉验证的SVR模型预测的平均绝对误差、平均相对误差绝对值以及均方根误差均比MLR模型小,表明SVR模型的回归预测能力优于MLR.研究表明,磁性连接性指数^mF是一种合适的分子描述符,SVR是一种预测碱金属化合物χ_m的有效方法. 关键词： 碱金属化合物 摩尔磁化率 支持向量回归 预测     

用隆格-楞兹矢量推导玻尔公式

利用隆格一楞兹矢量这一守恒量,在经典力学的框架中推导了量子力学中的玻尔能级公式,这样可以避开复杂的积分,使求解过程大大简化.     

电磁场的复张量表述

在电磁场的三维复矢量表述基础之上获得对应的四维复张量表述形式,同时又考虑磁荷流密度并引入双势法,将电磁场复张量表述推广到同时包含电荷与磁荷流密度的情形并获得了复张量麦克斯韦方程组.     

利用同步荧光光谱快速鉴别潲水油

为快速鉴别潲水油,采用三维同步荧光光谱结合平行因子法解析潲水油的特征波长差(Δλ),并利用支持向量机建立潲水油鉴别模型。结果表明,潲水油的特征Δλ为60 nm;特征Δλ下的样品原始同步荧光光谱经过主成分分析提取5个主成分,以径向基函数(RBF)为核函数,利用网格搜索和6-fold交叉验证优化建模参数,得到惩罚因子C=512、核参数g=0.5,该条件下建立的模型对训练集和预测集的判别率均达到100%。采用同步荧光光谱可以快速、准确地鉴别潲水油。     

基于高光谱成像的生鲜猪肉细菌总数预测建模方法研究

生鲜猪肉中细菌总数(TVC)超标会直接危害大众,为此研究验证高光谱成像技术结合相应的建模方法预测生鲜猪肉中TVC的可行性。针对非线性、小样本问题, 以及光谱维和空间维的大数据量问题,在综合比较偏最小二乘回归(PLSR)、人工神经网络(ANNs)和最小二乘支持向量机(LS-SVM)3种建模方法的基础上,最终选取了LS-SVM方法组建模型。3种建模方法综合比较的结果表明,LS-SVM同时兼顾了训练精度和泛化能力两方面的性能,使其都能做到最优,与标准平板菌落计数法所检测TVC的决定系数分别为0.987 2和0.942 6,校正均方根误差和预测标准均方根误差分别为0.207 1和0.217 6,其建模性能优于其他方法。研究结果表明,高光谱成像技术结合LS-SVM预测建模方法可作为快速、非破坏预测生鲜猪肉TVC的有效手段。