科创中国

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.

介绍了快速傅立叶变换在苹果形状识别中的应用,首先需要将苹果图像的边缘像素进行极坐标转换,经过FFT变换后的数据较多,因此还需采用主成份分析法对其进行处理,得到能够描述苹果形状的两个主成份值.

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.

根据河北省旱情试验站长系列监测资料,利用系统分析的方法,研究了试验站土壤墒情变化规律,建立了基于旱情试验站的单站增墒、退墒模型,揭示了河北省平原区土壤墒情的变化规律和演变趋势,有力促进了墒情、旱情预报和抗旱减灾工作,对中国北方类似地区相关应用具有重要的参考价值.

根据冀北士石山区永定河流域多年来水土保持综合治理的模式和经验,在介绍和总结水土保持生态修复实用技术的基础上,分析了该区域水土保持生态修复中存在的主要问题,结合区域水土保持治理情况,对永定河流域水土保持生态修复技术与研究提出建议.

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.

水土流失的预测是开发建设项目水土保持方案的重要工作之一,也是难点之一.目前在方案编制中,受时间、实测条件和费用等限制,大部分的水土保持方案在进行水土流失预测时都采取工程类比法.类比法在应用于工程上时,有直观、快速的优点,但相对来说不够严谨,论据不够有力.本文探讨了运用类比法的五个标准,来确保类比的准确性,进而提高预测结果的可信性.

头屯河水库除险加固工程建设造成新增水土流失,对该项工程建设期可能产生水土流失的特点和形式进行了分析,结合头屯河水库除险加固工程可行性研究报告和现场查勘情况,进行综合分析确定.采用土壤侵蚀模数法,运用经验公式进行水土流失量的估算,从而预测出库区内新增水土流失量,初步分析确定了头屯河水库除险加固工程水土保持的重点区域,为今后水库的水土保持综合治理提供了科学的决策依据.

介绍了变频调速技术的节能原理,阐述了基于PLC和变频器控制的恒压供水特点,完成了一台变频器控制四台水泵实现恒压供水的具体要求,设计了硬件控制回路和软件编程方法.实际运行证明,变频恒压供水系统控制简单、可靠性高、保护功能完善、节能效果明显.

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.