Stability of GM(1,1) power model on vector transformation

The morbidity problem of the GM（1,1） power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM（1,1） power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM（1,1） power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM（1,1） power model.

Effect of Vector Density and Competence on Macromolecular Vector Transformation Efficiency

To research the effects of vector quantity and competence on the positive cloning rate,with a known gene sequence but in the absence of DNA template,we artificially designed 26 primers to synthesize a target gene of 835 bp in vitro using overlapping PCR technique. The whole experiment design with two factors and six levels( 36 combinations) was applied to study the effects of the vector density and competent cells on the macromolecular vector transformation efficiency. Based on the 1 500 ng target gene,the vector density grades were designed( 50,100,150,200,250,300 ng),and then the recombinant plasmids were transformed into Top10F',DH5,Stbl3,Epi400,JM108,SCSI. Results showed that the positive cloning rates of different vector amount from big to small were in the order of 200,250,300,150,100 and 50 ng.The maximum positive cloning rate of 200 ng reached 75%; and the average value was 28. 5%. The positive cloning rates of different competent cells from big to small were in the order of stbl3,Top10F',DH5,JM108,Epi400 and SCSI. Stbl3 was higher than other competent cells under any vector density,and its average positive cloning rate was 42. 4%. Both the vector density and competent cells had significant effects on the macromolecular vector transformation efficiency. The optimal combination was C4 with 200 ng vector density and Stbl3,the positive cloning rate of which could reach 75%.

FLOW AROUND A CIRCULAR CYLINDER USING A FINITE-VOLUME TVD SCHEME BASED ON A VECTOR TRANSFORMATION APPROACH

Flows around a circular cylinder displaying an unsteady vortex shedding process at the Reynolds numbers of 1000,3900 and 1×104 are studied using a finite-volume Total Variation Diminishing（TVD） scheme for solving the Unsteady Reynolds-Averaged Navier-Stokes（URANS） equations.An Elemental Velocity Vector Transformation（EVVT） approach is proposed for the local normal and tangential velocity transformation at the interfaces of main and satellite elements.The presented method is validated by comparing with the available experimental data and numerical results.It is shown that the two-dimensional TVD finite volume method with the Renormalization Group（RNG） turbulence model can be used to determine hydrodynamic forces and captures vortex shedding characteristics very well.

Comparison of Nonlinear Local Lyapunov Vectors with Bred Vectors, Random Perturbations and Ensemble Transform Kalman Filter Strategies in a Barotropic Model

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors （BVs） are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector （NLLV） scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more com- ponents in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random pertur- bation （RP） technique, and the BV method, as well as its improved version--the ensemble transform Kalman filter （ETKF） method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

Expression Vector Construction and Genetic Transformation of <i>Paeonia lactiflora</i>Gibberellin 20-Oxidase Gene

GA20-oxidase (GA20ox) gene encodes a key enzyme in gibberellins (GAs) biosynthesis pathway. Previously, we have cloned a PlGA20ox gene (GeneBank accession number: KU886552) from Paeonia lactiflora. To further reveal its function, we constructed an expression vector in the present study and then transformed it into Arabidopsis thaliana plants by floral dip method. The transgenic plants exhibited an early bolting, increased height and improved vegetative growth. These results provided efficient vector tool and functional information of PlGA20ox for future gene engineering in peony.

Ignition Pattern Analysis for Automotive Engine Trouble Diagnosis Using Wavelet Packet Transform and Support Vector Machines

Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.

Expression Vector Construction and Genetic Transformation of <i>Rosa rugosa β</i>-l,3-Glucanase Gene (<i>RrGlu</i>)

In order to lay a foundation for researching the function of Rosa rugose (R. rugosa) RrGlu gene, the RrGlu gene was amplified from the styles of R. rugosa “Tanghong”, a gene expression vector named PBI121-RrGlu was constructed and the vector was introduced into tobacco with the agrobacterium-mediated method. PCR results showed that the RrGlu gene was integrated into the tobacco genome.

Support Vector Regression for Bus Travel Time Prediction Using Wavelet Transform

In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.

张量时间序列预测T-Transformer模型

张量时间序列呈现了多个时间序列随着时间变化协同演化的特征,张量时间序列预测已成为一个重要的问题。针对张量时间序列的高维特征,提出了基于Transformer的张量时间序列预测方法T-Transformer,将张量运算和Transformer共同集成到一个统一的框架中。该方法用张量表示的高阶时间序列,运用张量的切片和向量化,将张量时间序列转换成向量,将该向量经过编码后输入到Transformer模型,得到张量时间序列预测值。在三个公开数据集上进行的实验表明,与基准方法相比较,所提出的方法取得了更好的预测效果。

Application of Slantlet Transform Based Support Vector Machine for Power Quality Detection and Classification

Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the production company. The deregulation of electricity markets has made the industry become more competitive and distributed. Thus, a higher demand on reliability and quality of services will be required by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was constructed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy condition is investigated in this paper.

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.

Invariance of the Electromagnetic Field Vectors Obtained in Course of the Lorentz Transformation Characteristic for the Relativistic Theory

The invariance of several new component electromagnetic-field vectors with respect to the Lorentz transformation has been demonstrated in the paper. The formalism of the classical relativistic mechanics has been applied in examining both the time-square variable of the field, as well as the square-values of the position coordinates of a moving particle.

Construction of expression vector and transformation of FpDREB2A gene into Robinia pseudoacacia 'Idaho' mediated with Agrobacterium tumefaciens

The transcription factor gene FpDREB2A of Fraxinus pennsylvanica Marsh var. subintegerrima （Vahl.） Fern was con- structed into the higher plant expression vector pBin438 and transformed into Robinia pseudoacacia ‘Idaho＇ by Agrobacterium tu- mefaciens GV3101. Callus was screened with G418. Morphogenesis of shoots and roots of Idaho locust transformed genes was car- ried out on antibiotic media. The transformed plants were verified by PCR and Southern blotting tests that the FpDREB2A gene had been inserted into the genome DNA of Idaho locust.

基于Transformer与Vector Loss模块的椎骨Cobb角点定位网络

目的:通过定位脊椎骨角点计算Cobb角度数。方法:使用神经网络的方法进行脊椎骨角点坐标的定位,通过嵌入Transformer与Vector Loss模块解决了在清晰度不高、拍摄角度不同的X光图像上计算Cobb角度数精确不高的问题。结果:在MICCAI 2019公开脊椎挑战赛数据集中,本文方法的平均对称百分比(SMAPE)高达9.01。相较于最新的方法,该方法在SMAPE值上提高了1.80。结论:本文所提出的算法嵌入Transformer与Vector Loss模块,与现有的诸多算法相比,具有较高的准确率和稳健性,可以辅助临床医生选择适合患者的治疗方案。

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Machinery Condition Prediction Based on Support Vector Machine Model with Wavelet Transform

Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.

Image Coding Using Wavelet Transform and EntropyConstrained Vector Quantization with Quadtree Structure Vectors

This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with

ON THE DECOMPOSITION OF COMPLEX VECTOR SPACES AND THE JORDAN CANONICAL FORM OF COMPLEX LINEAR TRANSFORMATIONS

New objects characterizing the structure of complex linear transformations areintroduced. These new objects yield a new result for the decomposition of complexvector spaces relative to complex lrnear transformations and provide all Jordan basesby which the Jordan canonical form is constructed. Accordingly, they can result in thecelebrated Jordan theorem and the third decomposition theorem of space directly. and,moreover, they can give a new deep insight into the exquisite and subtle structure ofthe Jordan form. The latter indicates that the Jordan canonical form of a complexlinear transformation is an invariant structure associated with double arbitrary. choices.