论高职创新创业教育的矛盾关系与回归向度

高职创新创业教育的矛盾是新时代高等职业教育发展中创新型人才培养面临的挑战与困境的高度概括,矛盾具体体现在职业教育理念、人才培养模式、学生需求发展、教育管理边界、质量评估要求等五个领域。因此,应该从回归以人为本、回归教育实践、回归时代诉求三个向度来破解当前高职创新创业教育中的矛盾,理顺继承与创新、目的与手段、整理与部分的系统关系,提升创新型人才培养水平,完善和构建高质量创新创业的育人体系。

RESEARCH ON COLOR CONSTANCY UNDER OPEN ILLUMINATION CONDITIONS

Color information plays a key role in the research fields of object recognition and image retrieval. However, the actual color varies by the conditions of illumination, especially the open natural daylight. Four different color constancy schemes are proposed in the paper to minimize the effects of open illumination conditions. (1) The color constancy scheme based on the image statistics is proposed, which includes the color cast detection and removal. (2) The color constancy scheme based on the color temperature curve is proposed, which combines Gaussian model with linear fitting to estimate color temperature curve. (3) The color constancy scheme based on the double exposure theory is proposed, which is able to reproduce a color image under typical illumination. (4) According to the concepts of supervised learning, the supervised color constancy scheme is proposed. The transformation of color values from unknown illumination to typical illumination is solved by improved Support Vector Regression (SVR).

Flatness intelligent control via improved least squares support vector regression algorithm

To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.

Fuzzy Support Vector Regression Model of 4-CBA Concentration for Industrial PTA Oxidation Process

In the past few years, support vector machines (SVMs) have been applied to many fields, such as pattern recognition and data mining, etc. However there still exist some problems to be solved. One of them is that the SVM is very sensitive to outliers or noises because of over-fitting problem. In this paper, a fuzzy support vector regression (FSVR) method is presented to deal with this problem. Strategies based on k nearest neighbor (kNN) and support vector data description (SVDD) are adopted to set the fuzzy membership values of data points in FSVR.The proposed FSVR soft sensor models based on kNN and SVDD are employed to predict the concentration of 4-carboxy-benzaldehyde (4-CBA) in purified terephthalic acid (PTA) oxidation process. Simulation results indicate that the proposed method indeed reduces the effect of outliers and yields higher accuracy.

Multi-scale prediction of MEMS gyroscope random drift based on EMD-SVR

To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.

Estimation of tensile strength of ductile iron friction welded joints using hybrid intelligent methods

A hybrid intelligent method for evaluation of near optimal settings of friction welding process parameters of ductile iron was presented, The optimization of welding parameters was carried out in automatic cycle with the use of support vector regression （SVR）, genetic algorithm （GA） and imperialist competitive algorithm （ICA）. The method suggested was used to determine welding process parameters by which the desired tensile strength was obtained in the friction welding of ductile iron. The highest tensile strength （TS） of 256.93 MPa was obtained using SVR plus GA method for the following friction welding parameters： heating force 40 kN, heating time 300 s and upsetting force 10.12 kN. The samples were welded by friction and subjected to the tensile strength test. The optimized values obtained by means of these hybrid techniques were compared with the experimental results. The application of hybrid intelligent methods allowed to increase the tensile strength joints from 211 to 258 MPa for the friction welder ZT-14 type.