Surface Modification of Biomimetic PLGA-(ASP-PEG) Matrix with RGD-Containing Peptide:a New Non-Viral Vector for Gene Transfer and Tissue Engineering

RGD-containing peptide （ K16-GRGDSPC） , characterized as non-viral gene vectors, was fabricated to modify the surface of PLGA-[ASP- PEG] matrix, which offered the foundation for gene transfer with porous matrix of gene activated later. Peptide was synthesized and matrix was executed into chips A, B and chip C. Chip C was regarded as control. Chips A and B were reacted with cross-linker. Then chip A was reacted with peptide. MS and HPLC were ased to detect the .14W and purity of peptide. Sulphur, existing on the surface of biomaterials, was detected by XPS. The purity of un-reacted peptide in residual solution was detected by a spectrophotometer. HPLC shows that the peptide purity was 94%- 95% , and MS shows that the MW was 2 741. 3307. XPS reveals that the binding energy of sulphur was 164 eV and the ratio of carbon to sulphur （C/S） was 99. 746 ：0. 1014 in reacted chip A. The binding energy of sulphur in reacted chip B was 164 eV and 162 eV, C/ S was 99.574：0.4255, aM there was no sulphur in chip C. Peptide was manufactured and linked to the surface of biomimetic and 3-D matrix, which offered the possibilities for gene transfer and tissue engineering with this new kind of non-viral gene vector.

Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine

Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.

Raster to Vector Conversion for Engineering Drawings:Survey and Prospect

Recent development and recognition methods of raster to vector conversion for engineering drawings are presented. The advantages and disadvantages of all existing models are analyzed. Some research challenges and future directions are discussed.

NOVEL WEIGHTED LEAST SQUARES SUPPORT VECTOR REGRESSION FOR THRUST ESTIMATION ON PERFORMANCE DETERIORATION OF AERO-ENGINE

A thrust estimator with high precision and excellent real-time performance is needed to mitigate perfor- mance deterioration for future aero-engines. A weight least squares support vector regression is proposed using a novel weighting strategy. Then a thrust estimator based on the proposed regression is designed for the perfor- mance deterioration. Compared with the existing weighting strategy, the novel one not only satisfies the require- ment of precision but also enhances the real-time performance. Finally, numerical experiments demonstrate the effectiveness and feasibility of the proposed weighted least squares support vector regression for thrust estimator. Key words ： intelligent engine control; least squares ; support vector machine ; performance deterioration

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.

Time-variant reliability analysis of three-dimensional slopes based on Support Vector Machine method

In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

Modelling of modern automotive petrol engine performance using Support Vector Machines

Modern automotive petrol engine performance is significantly affected by effective tune-up. Current practice of engine tune-up relies on the experience of the automotive engineer, and tune-up is usually done by trial-and-error method and then the vehicle engine is run on the dynamometer to show the actual engine performance. Obviously the current practice involves a large amount of time and money, and then may even fail to tune up the engine optimally because a formal performance model of the engine has not been determined yet. With an emerging technique, Support Vector Machines (SVM), the approximate per- formance model of a petrol vehicle engine can be determined by training the sample engine performance data acquired from the dynamometer. The number of dynamometer tests for an engine tune-up can therefore be reduced because the estimated engine performance model can replace the dynamometer tests to a certain extent. In this paper, the construction, validation and accuracy of the model are discussed. The study showed that the predicted results agree well with the actual test results. To illustrate the significance of the SVM methodology, the results were also compared with that regressed using multilayer feedforward neural networks.

Data fusion for fault diagnosis using multi-class Support Vector Machines

Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

Least Squares Support Vector Machine Based Real-Time Fault Diagnosis Model for Gas Path Parameters of Aero Engines

Least squares support vector machine （LS-SVM） is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern search method. Finally, by decoding aircraft communication addressing and reporting system （ACARS） report, a real-time cruise data set is acquired, and the diagnosis model is adopted to process data. In contrast to the radial basis function （RBF） neutral network, LS-SVM is more suitable for real-time diagnosis of gas turbine engine.

Multiclassification algorithm and its realization based on least square support vector machine algorithm

As a new type of learning machine developed on the basis of statistics learning theory, support vector machine （SVM） plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifter. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM（LS-SVM） and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-dassiftcation is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and twospirals to measure the performance of the classifier.

Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network

The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of machinery and equipment,and the identification of faults is a prerequisite.Therefore,the fault identification of internal combustion engines is one of the important directions of current research.In order to further improve the accuracy of the fault recognition of internal combustion engines,this paper takes a certain type of internal combustion engine as the research object,and constructs a support vector machine and a fuzzy neural network fault recognition model.The binary tree multi⁃class classification algorithm is used to determine the priority,and then the fuzzy neural network is verified.The feasibility of the model is proved through experiments,which can quickly identify the failure of the internal combustion engine and improve the failure processing efficiency.

Novel Real-Time Seam Tracking Algorithm Based on Vector Angle and Least Square Method

Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,is still dominant in automobile industry,which is less flexible when welding objects or situation change.A novel real-time algorithm consisting of seam detection and generation is proposed to track seam.Using captured 3D points,space vectors were created between two adjacent points along each laser line and then a vector angle based algorithm was developed to detect target points on the seam.Least square method was used to fit target points to a welding trajectory for seam tracking.Furthermore,the real-time seam tracking process was simulated in MATLAB/Simulink.The trend of joint angles vs.time was logged and a comparison between the off-line and the proposed seam tracking algorithm was conducted.Results show that the proposed real-time seam tracking algorithm can work in a real-time scenario and have high accuracy in welding point positioning.

Lantiq宣布可实现全系统级噪声消除的VDSL2 Vectoring芯片

领特科技公司（Lantiq）近日宣布：该公司已首次实现其VINAX IVE1000，一款实现系统级串扰抵消的芯片的商用发货。一般线卡级的Vectoring虽然符合G．vector标准，但只有实现系统级的Vectoring，如Lanticl全新的Vectoring Engine芯片所提供的功能，才能够达到两倍以上的数据传输速率和覆盖距离，以满足运营商的下一代网络需求。

Lantiq发布可实现全系统级噪声消除的VDSL2 Vectoring芯片

2012年1月，宽带接入和家庭网络技术供应商领特科技公司（Lantiq）首次实现其VINAXTMIVE1000，一款实现系统级串扰抵消的（System Level Vectoring Engine）芯片的商用发货。一般线卡级的Vectoring虽然符合G．vector标准。但只有实现系统级的Vectoring。如Lantiq全新的Vectoring Engine芯片所提供的功能。才能够达到两倍以上的数据传输速率和覆盖距离。以满足运营商的下一代网络需求。

Weight Analysis of Impact Factors of Interbedded Anti-Inclined Slopes Block-Flexure Toppling Based on Support Vector Regression

Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock(IAR)slopes,and seriously threatens the construction of hydropower infrastructure.In this study,we first investigated the Lean Reservoir area’s geological setting and the Linda landslide’s characteristics.Then,uniform design and random design were used to design 110 training datasets and 31 testing datasets,respectively.Afterwards,the toppling response was obtained by using the discrete element code.Finally,support vector regression was used to obtain the influence weights of 21 impact factors.The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96%and 17.28%,respectively,which are much greater than the other 19 impact factors within the research range.For the primary impact factors,the influence weight is sorted from large to small as slope geometry parameters,joints parameters,and rock mechanics parameters.Joints parameters,especially the geometric parameters,cannot be ignored when evaluating the stability of IAR slopes.Through numerical simulation,it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

基于要素稠度的电子海图矢量瓦片组合构建方法

为进一步提升基于WebGIS技术的电子海图在前端的加载效率,对海图矢量瓦片的划分方法进行深入研究。在考虑要素空间分布稠度的基础上,提出一种海图瓦片的组合格网划分方法。在四叉树划分过程中引入矢量瓦片数据阈值将其改造成非平衡四叉树,作为瓦片划分的第1阶段,在瓦片划分至单瓦片最大数据量小于数据阈值后,通过融合变异系数的二叉树对瓦片进行第2阶段的划分。将组合格网法、原始均匀格网法和改进均匀格网法生成的矢量瓦片数据与试验结果进行对比。研究结果表明,在瓦片第3~第9层级范围内,相对于2种均匀格网法,基于要素稠度的组合格网法生成的矢量瓦片在同一层级的标准差更小,矢量要素在瓦片上的分布更加均衡,在同一视窗范围内,组合格网法生成的瓦片加载时间更短。

基于表面辐射声信号的柴油机进气及齿轮故障诊断

利用声振信号进行发动机故障诊断过程中,部分故障激励仅在发动机表面特定位置的振动中有较强响应,振动测点要求高,需要接触测量,部分场景难以实现.为此,提出了一种以表面辐射声为媒介、以自适应变分模态提取(adaptive variational mode extraction,AVME)进行预处理的柴油机进气故障和齿轮故障诊断方法.开展了某直列六缸重型柴油机的进气滤清器堵塞、气门间隙异常和正时齿轮损伤3类故障状态的台架试验,获取了不同故障程度下发动机表面辐射噪声.基于改进的AVME方法,实现噪声信号本征模函数(intrinsic mode function,IMF)的最优分解,通过计算IMF与原信号间的互相关系数,提取高相关IMF构成故障诊断输入.经预处理后,声信号故障特征得到有效增强,再输入到麻雀搜索算法优化支持向量机模型(support vector machine model optimized by sparrow search algorithm,SSA-SVM),进行特征参量和模型参数协同优化可以获得更好的诊断精度.试验验证表明,无需在半消声室测试,仅使用单通道声信号对3类11种程度的进气系统和齿轮故障进行诊断,前端噪声准确率最高(98.89%),顶部噪声准确率最低(88.78%);使用前、顶、后三通道噪声数据后,诊断精度可提升至99.57%.研究结论为基于声信号等非接触测量的发动机故障诊断提供了参考.

基于支持向量机的油气生产复杂系统信息物理攻击识别方法

在数据驱动的复杂油气生产系统中,存在故障数据干扰攻击识别的问题,忽视系统内部可能存在的故障数据对攻击检测的影响,则难以及时防御攻击或解决故障。因此,为了提高复杂油气生产系统中信息物理攻击检测的准确性,提出了一种基于支持向量机的无向图联合检测方法。首先,对复杂油气生产系统中的关键传感器拓扑化形成无向图,建立传感器之间的连接关系并捕捉数据交互。然后,利用支持向量机检测传感器系统异常原因,并选择接收站低压泵及接收站储罐系统作为示例验证,前者的准确率、精确度、召回率和F1分数均高于99%,后者F1分数高于99%,其余均高于97%。与传统方法K均值聚类相比,本方法具有更高的准确性、鲁棒性和完整性,有助于防范攻击和生产事故,保障油气生产系统的安全。