科创中国

针对传统SVM主动学习中批量采样方法的不足,提出了动态可行域划分算法.从特征空间与参数空间的对偶关系入手,深入分析SVM主动学习的本质,将特征空间中对样本的标注视为参数空间中对可行域的划分;通过综合利用当前分类模型和先前标注样本两方面信息,动态地优化可行域划分方案,以确保选取的样本对模型改进的价值,最终实现更为高效的选择性采样.实验结果表明,基于动态可行域划分的SVM主动学习算法能够显著提高所选样本的信息量,从而能够在有限的标注代价下大幅提高其分类性能.

Drill sampling has been widely employed as an effective way to acquire deep samples in extraterrestrial exploration. A novel sampling method, namely, flexible-tube coring, was adopted for the Chang'e mission to acquire drilling cores without damaging stratification information. Since the extraterrestrial environment is uncertain and different from the terrestrial environment, automated drill sampling missions are at risk of failure. The principles of drilling and coring for the lunar subsurface should be fully tested and verified on earth before launch. This paper proposes a test-bed for conducting the aforementioned experiments on earth. The test-bed comprises a rotary-percussive drilling mechanism, penetrating mechanism, drilling medium container, and signal acquisition and control system. For granular soil, coring experiments indicate that the sampling method has a high coring rate greater than 80%. For hard rock, drilling experiments indicate that the percussive frequency greatly affects the drilling efficiency. A multi-layered simulant composed of granular soil and hard rock is built to test the adaptability of drilling and coring. To tackle complex drilling media, an intelligent drilling strategy based on online recognition is proposed to improve the adaptability of the sampling drill. The primary features of this research are the proposal of a scheme for drilling and coring a test-bed for validation on earth and the execution of drilling experiments in complex media.

The porcelain fracture caused by low metal-ceramic bond strength is a critical issue in porcelain fused to metal(PFM)restorations.Surface roughening methods,such as sand blasting,acid etching and alkaline degreasing for the metal matrix are used to increase bond strength.However,the metal matrix of PFM processed by selective laser melting(SLM)has natural rough surface.To explore the effect of the original roughness on metal-ceramic bond strength,two groups of specimen are fabricated by SLM.One group of specimen surface is polished smooth while another group remains the original rough surface.The dental porcelain is fused to the specimens'surfaces according to the ISO 9693:1999 standard.To gain the bond strength,a three-point bending test is carried out and X ray energy spectrum analysis(EDS),scanning electron microscope(SEM)are used to show fracture mode.The results show that the mean bond strength is 116.5±16 MPa of the group with rough surface(Ra=17.2),and the fracture mode is cohesive.However,when the surface is smooth(Ra=3.8),the mean bond strength is 74.5 MPa 5 MPa and the fracture mode is mixed.The original surface with prominent structures formed by the partly melted powder particles,not only increases surface roughness but also significantly improves the bond strength by forming strong mechanical lock effect.Statistical analysis(Student's t-test)demonstrates a significant difference(p<0.05)of the mean value of bond strength between the two groups.The experiments indicate the natural rough surface can enhance the metal-ceramic bond strength to over four times the minimum value(25 MPa)of the ISO 9693:1999 standard.It is found that the natural rough surface of SLM-made PFM can eliminate the porcelain collapse defect produced by traditional casting method in PFM restorations.

Parallel robots with SCARA(selective compliance assembly robot arm) motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error's influence on the moving platform's pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.

As a promising technique, surrogate-based design and optimization(SBDO) has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function(SEO-SRBF). Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of the material volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.

将金精矿粉样品直接置于石英舟中,在高纯氧气氛中燃烧,释放出汞,与齐化管中的金形成金汞齐,于900℃热释放出汞蒸汽,用直接测汞仪法测定汞的含量.测定结果的相对标准偏差为0.28%~1.57%(n=6),方法检出限为1.0pg/kg,加标回收率为95.7%~117.4%.用该法对4种土壤标准样品进行了测定,测定结果与标准值相符.该方法适合于金精矿粉中微量汞的测定.