移动车载识别的两种解法及其试验验证

大多数移动车载识别方法最终转化为线性方程组 ,其不同的求解方法往往给出不同的识别精度。利用频时域法由桥梁响应识别桥面移动车载 ,重点比较了伪逆解法和奇异值解法的识别结果 ,并讨论了几个主要参数对识别结果的影响。结果表明 ,使用奇异值解法能够明显提高频时域法的识别精度。

离子束加工中驻留时间的求解模型及方法(英文)

驻留时间求解问题是离子束加工中的关键问题.通常,离子束加工过程可以描述为一个包含驻留时间的二维卷积方程,理论上通过反卷积即可以求解出驻留时间.然而,反卷积问题是一个病态问题,所以驻留时间一般较难很好地求解出.为了解决这个问题,介绍了一个离散的线性模型——CEH模型,分析了该模型的优点.提出应用截断奇异值分解法(TSVD)来求解CEH模型;深入分析了该方法的优点,并利用“L-曲线”分析了驻留误差和加工量之间的关系以及用“L-曲线”对CEH模型中去除点和驻留点的不同取法进行了评价.仿真结果表明,CEH模型和TS-VD方法对于求解光学镜面离子束加工中的驻留时间很有效.

The analysis on IP signals in TEM response based on SVD

During transient electromagnetic method （TEM） exploration of a copper mine, we detected the late-channel TEM signal reversal phenomenon （a voltage change from positive to negative） caused by the influence of the induced polarization （IP） effect, which affects the depth and precision of the TEM detection. The conventional inversion method is inefficient because it is difficult to process the data. In this paper, the Cole-Cole model is adopted to analyze the effect of Dc resistivity, chargeability, time constant, and frequency exponent on the TEM response in an homogeneous half space model. Singular Value Decomposition （SVD） is used to invert the measured TEM data, and the Dc resistivity, chargeability, time constant and frequency exponent were extracted from the measured TEM data in the mine area. The extracted parameters are used for interpreting the detection result as a supplement. This reveals why the TEM data acquired in the area has a low resolution. It was found that the DC resistivity and time constant do not significantly change the results, however, the chargeability and frequency exponent have a significant effect. Because of these influences, the SVD method is more accurate than the conventional method in the apparent resistivity profile. The area of the copper mine is confined accurately based on the SVD inverted data. The conclusion has been verified by drill and is identical to the practical geological situation.

Study on algorithms of low SNR inversion of T_2 spectrum in NMR

The method of regularization factor selection determines stability and accuracy of the regularization method. A formula of regularization factor was proposed by analyzing the relationship between the improved SVD and regularization method. The improved SVD algorithm and regularization method could adapt to low SNR. The regularization method is better than the improved SVD in the case that SNR is below 30 and the improved SVD is better than the regularization method when SNR is higher than 30. The regularization method with the regularization factor proposed in this paper can be better applied into low SNR （5〈SNR） NMR logging. The numerical simulations and real NMR data process results indicated that the improved SVD algorithm and regularization method could adapt to the low signal to noise ratio and reduce the amount of computation greatly. These algorithms can be applied in NMR logging.

STRUCTURE OPTIMIZATION STRATEGY OF NORMALIZED RBF NETWORKS

Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.

柔性薄壁大部件数字化装配调姿算法研究

采用定位器托架混合调姿定位平台,提出了一种飞机薄壁柔性大部件数字化调姿方法。首先,采用奇异值分解法(SVD)计算机翼位姿,通过运动学逆解得到定位器各轴的运动状态。其次,根据飞机大部件易变形的特点,提出了一种消除位姿计算误差的建立飞机部件坐标系的方法,实现快速高精度调姿。最后,将该方法应用于飞机机翼的调姿,经测量调姿精度满足装配要求。

两个串补元件对电力系统影响的探究与分析

随着输电电压的升高、输电距离的增长,电力系统中安装的串联型柔性交流输电(Flexible AC Transmission,FACTS)装置日渐增多,如何抑制多个串联型FACTS元件间的交互影响并保证其协调运行就变得尤为重要。文章运用奇异值分解法(SVD)理论计算分析了多个串补元件同时投入运行时元件之间存在的交互影响,并利用PSCAD搭建仿真模型,进行了仿真验证。仿真结果表明,元件间交互影响造成的电压波动和传输功率不稳定对系统产生了不利的影响,并利用matlab进行傅立叶变换对稳定后的母线电压进行了谐波分析。

GPS测量中病态性问题的初探

重点研究奇异值分解法(SVD)和奇异改进型岭估计法(SVDRE)解决病态问题的思想、途径、对关键问题的处理、适用范围等,得出了这两种方法能解决法方程病态性问题,且算法简便、易懂,有较强应用价值的结论。

Short-term photovoltaic power prediction using combined K-SVD-OMP and KELM method

For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.

Wavelet matrix transform for time-series similarity measurement

A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace,and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example,the experimental results show that the proposed method has low dimension of feature vector,the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method,the sensitivity of proposed method is 1/3 as large as that of plain wavelet method,and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.

A blind watermarking algorithm based on DWT and SVD

This paper presents a new digital image blind watermarking algorithm based on combination of discrete wavelet transform （DWT） and singular value decomposition （SVD）. First of all, we make wavelet decomposition for the original image and divide the acquired low frequency sub-band into blocks. Then we make singular value decomposition for each block and embed the watermark information in the largest singular value by quantitative method. The watermark can be extracted without the original image. The experimental results show that the algorithm has a good imperceptibility and robustness.

基于线性约束的快速点云配准方法研究

三维视景仿真中,目标对象的重构需要多组测量数据进行配准,提高点云的配准速度和精度是点云配准的关键。因此,针对经典ICP配准算法存在计算量大、点状特征提取精度低的特点,文章结合改进的S-ICP算法对目标函数进行优化求解,同时在S-ICP算法基础上对初始旋转平移参数进行优化改进,最终得到更为精确的配准。实验结果表明,与经典ICP以及S-ICP算法相比,文章算法在配准速度和精度方面都有明显提高,能够实现点云的快速、准确配准。

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.

A Generalized Upper and Lower Solution Method for Singular Discrete Boundary Value Problems

This paper presents new existence results for singular discrete boundary value problems. In particular our nonlinearity may be singular in its dependent variable and is allowed to change sign.

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.

Interdecadal variations of surface winds over China marginal seas

Long term variability in the surface winds over the marginal seas of China is examined with a dominant-mode singular value decomposition method. Both interannual and interdecadal patterns are found to be seasonally and spatially dependent, with reanalyses and satellite remote sensing data yielding highly consistent results. The study reveals that summer monsoon winds over the East China Sea experienced an interdecadal weakening in the late 1960s and began a persistent recovery in 2005. The study also shows gradual weakening of the winter monsoon in the southern South China Sea by more than 2m/s since the 1960s, with corroboration from coastal climate stations in Borneo. This phenomenon has not been reported in previous monsoon studies.

Simultaneous (M,N) Singular Value Decomposition of Matrices

A necessary and sufficient condition for the existence of simultaneous (M,N)singular value decomposition of matrices is given.Some properties about the weighted partial ordering are discussed with the help of the decomposition.

Matched Field Localization Based on CS-MUSIC Algorithm

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning.The signal matrix is calculated through the SVD(Singular Value Decomposition) of the observation matrix.The observation matrix in the sparse mathematical model is replaced by the signal matrix,and a new concise sparse mathematical model is obtained,which means not only the scale of the localization problem but also the noise level is reduced;then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS(Compressive Sensing) method and MUSIC(Multiple Signal Classification) method.The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots,and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large,which will be proved in this paper.

AN ALGORITHM FOR DICTIONARY GENERATION IN SPARSE REPRESENTATION

The K-COD (K-Complete Orthogonal Decomposition) algorithm for generating adaptive dictionary for signals sparse representation in the framework of K-means clustering is proposed in this paper,in which rank one approximation for components assembling signals based on COD and K-means clustering based on chaotic random search are well utilized. The results of synthetic test and empirical experiment for the real data show that the proposed algorithm outperforms recently reported alternatives: K-Singular Value Decomposition (K-SVD) algorithm and Method of Optimal Directions (MOD) algorithm.

Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.