盐酸介质中咪唑离子液体对铜的缓蚀作用

以1-甲基咪唑、1,4-丁烷磺内酯和浓硫酸为原料,用乙醚洗涤合成了1-甲基-3-(4-硫酸基丁基)咪唑硫酸盐(4-BMIM)缓蚀剂。采用动电位极化和交流阻抗技术研究了4-BMIM在5%HCl溶液中对铜的缓蚀性能及作用机理。实验结果表明:缓蚀效率随着缓蚀剂浓度的增加先增大后降低,当浓度为0.03mol/L时,缓蚀效率最高;同一浓度下,随着温度的升高缓蚀效率降低。动电位极化表明咪唑离子的加入对铜的阴、阳极腐蚀过程均有抑制作用,是混合型缓蚀剂。热力学计算结果表明咪唑离子液体吸附在铜表面,其吸附机制为自发进行的物理吸附,并且在铜/溶液界面的吸附遵循Langmuir吸附等温式。     

超声导波频散曲线反演皮质骨参数的研究

皮质骨参数的变化可以反映骨质的健康状况,因此文章提出了一种频散能量匹配算法来反演皮质骨参数。首先,通过频散特性方程计算出预定义参数下的频散曲线数据库;其次,利用时域有限差分算法建立皮质骨声场模型,将仿真的时域信号通过功率谱估计得到频散能量信息,并与数据库进行匹配,通过分析匹配结果的能量从而得到皮质骨的厚度、纵波速度和横波速度。仿真结果显示：与理论值相比,厚度的平均相对误差为3.8%,纵波速度与横波速度的平均相对误差分别为0.6%、0.9%。对三组离体牛胫骨皮质骨进行反演,离体实验结果显示：与真实值相比,厚度相对误差为4.9%,且实验频散曲线与反演得到的理论频散曲线吻合。因此文中所提出的反演算法可以有效获取皮质骨的厚度,横波速度和纵波速度,从而为评价骨质的健康状况提供可靠的依据。     

Transient response of Bouc–Wen hysteretic system under random excitation via RBFNN method

Hysteresis normally exhibited by mechanical systems and materials is so prevalent that its response prediction under random excitation has been extensively investigated for decades. Nevertheless, the transient solution of the response, which is crucial for assessing the system’s reliability, is still a challenging topic that requires additional development. To this regard, this work proposes a semi-analytical method using the radial basis function neural network (RBFNN) to attain the transient probability density distribution of the randomly excited Bouc–Wen system. Specifically, the trial solution of the corresponding FPK equation is configured as the RBFNN with undetermined time-varying weight coefficients. By discretizing the time derivative with the Euler difference method, a loss function with time recurrence is derived and minimized to yield the time-varying optimal weight coefficients through the optimization method. Additionally, an optimized sampling strategy is adopted to reduce the burden of calculation. Finally, the Bouc–Wen hysteretic systems with softening and hardening nonlinearity are considered to investigate the performance of the adopted technique. The numerical results have shown that the evaluation process of the probability density functions(PDFs) can be captured well with sufficient accuracy and efficiency. The proposed efficient sampling technique can provide considerable efficiency improvement for the medium dimension system. The work of the paper will contribute to the reliability design of hysteretic structures in engineering.     

Analyzing CAD competence with univariate and multivariate learning curve models

Understanding how learning occurs, and what improves or impedes the learning process is of importance to academicians and practitioners; however, empirical research on validating learning curves is sparse. This paper contributes to this line of research by collecting and analyzing CAD (computer-aided design) procedural and cognitive performance data for novice trainees during 16-weeks of training. The declarative performance is measured by time, and the procedural performance by the number of features used to construct a design part. These data were analyzed using declarative or procedural performance separately as predictors (univariate), or a combination of declarative or procedural predictors (multivariate). Furthermore, a method to separate the declarative and procedural components from learning curve data is suggested.     

Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images

In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results.

高分辨率超声Lamb波频散曲线测量与板厚估计

超声Lamb波在工业无损检测与 评价中具有广阔的应用前景。但受制于超声导波多模式频散与混叠的影响,如何实现高分辨率超声导波的模式分离与频散曲线提取,进而定量分析材料的健康状况一直是研究的热点与难点。本文旨在将现代信号处理中广泛采用的谱估计技术应用于超声Lamb波阵列信号分析,并通过提取频散曲线定量测量板厚度。以经典参数谱估计法中的Yule-Walker法与Burg法为例,实验测量和分析了3 mm,4 mm和 5 mm厚铝板中的宽带多模式Lamb波信号,准确地提取了铝板中的宽带Lamb波频散曲线,并比较了两种经典谱估计方法与二维傅氏变换法的性能,最终实现了铝板厚度估计。     

基于非均匀系数的水库移民收入恢复评价

水利水电工程征地移民搬迁后的生产生活恢复,不仅是实现移民规划目标的重点内容,也是保持安置区社会稳定的重要因素。水库移民搬迁后的收入恢复水平是评价移民工程搬迁安置效果的一个重要指标。移民收入的评价,除收入水平之外,还涉及到贫富差距等共同富裕问题。针对南水北调中线丹江口库区试点移民的收入恢复,在抽样与跟踪调查获取数据的基础上,分析了移民收入的整体恢复水平,并利用非均匀系数对收入的差距进行了研究。