Logarithmic decrease of adhesion force with lateral dynamic revealed by an AFM cantilever at different humidities

Adhesion forces between a tipless cantilever and an Au film were determined to investigate the influence of lateral velocity by recording force curves with an atomic force microscope at 20%–90% relative humidities. The sample was moved laterally, forth and back, with a frequency of 0.001–100 Hz and scan distances of 0.8, 8, and 80 μm to achieve a velocity ranging over 7 orders of magnitude. Experimental results show that at low lateral velocities (between 1.6 nm/s and 1–10 μm/s), the adhesion force either increases or decreases or remains stable with the lateral velocity without a certain characteristic trend. However, after a critical velocity, the adhesion force decreases logarithmically with the lateral velocity (between 1–10 and 16,000 μm/s). The decreasing magnitude can be as large as 97.3% of the maximum adhesion force. This decrease is well-explained by the contact time dependence of water bridges formed by capillary condensation.     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

Dynamic response of ferrofluidic deformable mirrors using elastomer membrane and overdrive techniques

The experimental results obtained with a ferrofluidic deformable mirror controlled by electro-magnet actuators are presented here. Using a step input through a single actuator, we obtained a steady-state settling time of 100?ms; however, different combinations of overdrive inputs can be used to decrease it to 25?ms. A new technique which consists of laying down an elastomer membrane, coated with an aluminum film, on the ferrofluid is also discussed. By adding the membrane on the ferrofluid, it further decreases the time response by a factor of 2. Furthermore, the thin aluminum layer improves the reflectivity of the mirror. Finally, using the membrane and the overdrive techniques combined, the time response is improved by a factor of 20. Numerical simulations show that ferrofluidic mirrors using membranes and improved electronics should reach settling times of the order of a millisecond. Presumably, even lower settling times could be possible.     

建筑固废对GRC 性能影响研究

本文对建筑固废制备GRC进行了研究,通过物理性能、力学性能和抗冻性三方面对再生GRC进行性能表征,同时探讨了建筑固废的取代掺量对再生GRC各项性能的影响。试验结果如下:采用喷射成型工艺,再生GRC的力学性能在废混凝土10%,废尾矿10%和废砖1%取代掺量下达到最优,而后随着掺量的增加力学性能逐渐降低。建筑固废的掺入对再生GRC的物理性能没有明显影响。25次冻融循环后再生GRC没有发现冻融破坏现象,且质量损失在0.2%以下。     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.     

基于决策树的堆芯物理参数预测研究

反应堆堆芯核设计涉及大量方案的搜索与详细计算，缩短方案搜索时间有利于提高核设计效率。数据挖掘技术通过对大量数据进行学习与模式识别，可实现核设计方案物理参数的快速预测，更快地筛选出可行的备选堆芯方案。本文基于数据挖掘的决策树4种算法：C4.5、RepTree、Random Forest及Random Tree，在计算时以燃料富集度、含可燃毒物燃料棒数量及含量作为自变量，以寿期内k_eff不均匀系数偏差（KUCD）、径向功率不均匀系数偏差（RPNCD）、径向中子通量不均匀系数偏差（RFNCD）、堆芯寿期（CL）作为目标函数，构成目标函数符合度（CPF），利用大量已知核设计参数的组件及堆芯设计方案作为数据挖掘训练集，构建数据挖掘模型，并用于对未知核设计参数的组件方案集合（测试集）进行CPF快速预测。结果表明，4种算法利用训练集构建数据挖掘模型的时间在0.6 s以内，各算法的交叉验证精度均在0.7以上，其中C4.5算法对CPF预测精度最高；对测试集方案的核设计参数预测中，单个方案的预测时间均在0.9 s以内，而Random Forest算法对CPF等于4的预测效果最好。     

读懂衡器的最大允许误差

本文主要讨论衡器的检定分度数能否大于传感器的最大检定分度数nmax,这是一直困扰大家和争论不休的问题。作者由分析最大允许误差mpe本质入手,指出mpe是人为规定的误差,不是传感器实际的物理特性。通过理论分析得出的对单只传感器的衡器,n≤nmax和v≥vmin的误差限原则是不能违背的。对于四只传感器的衡器,不受上述误差限原则限制。     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.