In-time motion adjustment in laser cladding manufacturing process for improving dimensional accuracy and surface finish of the formed part

This paper presents in-time motion adjustment in laser cladding manufacturing process as a means to improve dimensional accuracy and surface finish of the built part. Defects occurring during laser cladding degrade the part quality such as dimensional accuracy and surface finish. In this paper, in-time motion adjustment strategy was presented to remedy and eliminate defects occurring during laser cladding to improve the dimensional accuracy and surface finish. Based on the relationship between the motion of laser head relative to the growing part and other parameters in effects on clad profile, the laser traverse speed, stand-off distance and laser approach orientation to the existing clad layer were adjusted by instructions from a close-loop control system in real time to remedy and eliminate defects. The results of the experiments verified the effects of in-time motion adjustment on dimensional accuracy and surface finish.     

Selection of meaningless steady noise for masking of speech

This paper focuses on masking speech with meaningless steady noise as a way of realizing a comfortable sound environment. As a basis for research, meaningless steady noise at minimum sound pressure levels for masking of male or female meaningful speech is considered, based on psychological experiments using a method of adjustment. From the results, band-limited pink noise can be selected as the most effective noise for masking of speech. In the case of speech with a lower sound pressure level, the sound pressure level of the meaningless steady noise needs to be a little higher.     

COPART:一种面向约束条件的自适应软硬件划分算法

本文介绍一种将自适应算法、遗传算法和列表调度算法结合起来应用于软硬件划分问题的算法COPARTART，并提出了一种基于约束条件的开销系数自适应调整方法，该方法所获得的划分结果能够良好体现设计目标。     

A bartlett-type adjustment for the likelihood ratio statistic with an ordered alternative

For testing the equality of normal variances with an increasing alternative, under the null hypothesis the likelihood ratio test statistic is asymptotically distributed as a mixture of chi-squared distributions. In this paper a Bartlett-type adjustment is proposed to improve the approximation of the null distribution of the likelihood ratio test statistic with an ordered alternative.     

透射电镜薄样品倾转过程中的附加旋转及其计算

模拟透射电镜双倾台进行样品位向调整过程,推导出反映样品倾转前后其合成倾转轴(即共有菊池线对的法线)方向变化规律的计算公式,称为附加旋转角计算公式。指出,实现样品位向调整的双倾操作,等效于样品绕其合成倾转轴的倾转及该倾转轴绕Z轴(平行入射束方向)的旋转之和。利用双倾台对薄膜样品进行的系列倾转实验表明,由附加旋转角公式计算的附加旋转角和实测值相一致。还根据双倾操作过程导出了合成倾转角的计算公式,它可用于判断样品位向调整的准确度。     

Aperture-Adjustable and Repairable Metal-Based MOFs Catalysis Membrane for Water Purification

Precise adjustment of the pore size, damage repair, and efficient cleaning is all challenges for the wider application of inorganic membranes. This study reports a simple strategy of combining dry-wet spinning and electrosynthesis to fabricate stainless-steel metal–organic framework composite membranes characterized by customizable pore sizes, targeted reparability, and high catalytic activity for membrane cleaning. The membrane pore size can be precisely customized in the range of 14–212 nm at nanoscale, and damaged membranes can be repaired by targeted treatment in 120 s. In addition, advanced oxidation processes can be used to quickly clean the membrane and achieve 98% flux recovery. The synergistic actions of the membrane matrix and the selective layer increase the adsorption energy of active sites to oxidant, shorten the electron transfer cycle, and enhance the overall catalytic performance. This study can provide a new direction for the development of advanced membranes for water purification and high-efficiency membrane cleaning methods.     

Meta-transfer-adjustment learning for few-shot learning

Deep neural network models with strong feature extraction capacity are prone to overfitting and fail to adapt quickly to new tasks with few samples. Gradient-based meta-learning approaches can minimize overfitting and adapt to new tasks fast, but they frequently use shallow neural networks with limited feature extraction capacity. We present a simple and effective approach called Meta-Transfer-Adjustment learning (MTA) in this paper, which enables deep neural networks with powerful feature extraction capabilities to be applied to few-shot scenarios while avoiding overfitting and gaining the capacity for quickly adapting to new tasks via training on numerous tasks. Our presented approach is classified into two major parts, the Feature Adjustment (FA) module, and the Task Adjustment (TA) module. The feature adjustment module (FA) helps the model to make better use of the deep network to improve feature extraction, while the task adjustment module (TA) is utilized for further improve the model’s fast response and generalization capabilities. The proposed model delivers good classification results on the benchmark small sample datasets MiniImageNet and Fewshot-CIFAR100, as proved experimentally.     

反馈调节的局部正则化鲁棒光流估计

提出了一种引入反馈调节机制的新局部鲁棒光流场计算思想.该思想结合局部法和全局法的特点,首先划分整场为有公共边界的子块,定义距离来判断每个点的子块归属,然后在每个子块内进行光流约束和平滑约束正则化的优化计算.将光流约束中的光强残差作为控制参量,衡量光流准确度的峰值信噪比作为输出,经反馈处理,调节控制参量使光流估计准确度趋向最优.实验结果表明,这种新的结合反馈调节机制局部光流场计算法能使光流准确度获得提升.     

激光光栅干涉技术的微位移测量研究

为提升精密测量的精度与量程,提出了激光光栅干涉技术的微位移测量方法.通过集成双光栅微位移测量方法以2 组近似正交相位差的光栅交替选择并累积高灵敏度测量区域,扩大测量量程,且在测量过程中避免激光光束存在抖动问题,通过频率调解法实行外差干涉信号处理,转变待测物理量的信息为调相或调频信号,实现测量的高分辨率.检测结果表明,该...     

双目视觉中的角点检测算法研究

为了实现双目视觉中的特征提取与立体匹配,在研究现有角点检测算法的基础上,提出一种改进的Harris角点检测算法。该算法首先采用图像分块和邻近角点剔除的策略,实现了阈值的自适应调节,并保证角点分布的均匀和避免角点聚簇的产生。结合Forstner检测算法,将精度提高到亚像素级。编程计算结果验证了该方法的准确性和稳定性。