基于时域频域分析结合的调谐激光吸收光谱检测参数优化

随着调谐激光吸收光谱(tunable laser absorption spectroscopy, TLAS)技术在气体检测中的应用越来越广泛,二次谐波信号的质量与检测参数紧密相关,因而分析检测参数的优化方法很有意义。本文根据谱线预处理中的滤波参数、系统采样时间、锁相放大器的时间常数对信号的影响以及参数间的联系,总结检测参数的选取规律。根据滤波原理和不同浓度下信号的均方根误差(root mean squared error,RMSE)值选择合适的滤波阶数和窗宽。选择信噪比(signal-to-noise ratio,SNR)、RMSE、信号与噪声频域幅度之比(ratio of amplitude in frequency domain of signal to noise,f_SNR)3种评价指标的曲线变化趋势进行时频分析,得到最佳采样周期数为30,结合实验系统具体参数可计算最佳采样时间。通过信号主频带与截止频率的关系和滤波效果选择合适的时间常数。综合分析3个检测参数并总结选取方法,可提高二次谐波信号的质量。本文提出的参数选取方法对提高二次谐波在实际应用中的准确度有...     

Blood cell counting based on U-Net++ and YOLOv5

Clinical information about a variety of disorders is available through blood cell counting, which is usually done by manual methods. However, manual methods are complex, time-consuming and susceptible to the subjective experience of inspectors. Although many efforts have been made to develop automated blood cell counting algorithms, the complexity of blood cell distribution and the highly overlapping nature of some red blood cells (RBCs) remain significant challenges that limit the improvement of analytical accuracy. Here, we proposed an end-to-end method for blood cell counting based on deep learning. Firstly, U-Net++ was used to segment the whole blood cell image into several regions of interest (ROI), and each ROI contains only one single cell or multiple overlapping cells. Subsequently, YOLOv5 was used to detect blood cells in each ROI. Specifically, we proposed several strategies, including fine classification of RBCs, adaptive adjustment for non-maximal suppression (NMS) threshold and blood cell morphology constraints to improve the accuracy of detection. Finally, the detection outcomes for each ROI were combined and superimposed. The results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells, with a 98.18% accuracy rate for blood cell counting.     

Phase unwrapping based on deep learning in light field fringe projection 3D measurement

Phase unwrapping is one of the key roles in fringe projection three-dimensional (3D) measurement technology. We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measurement based on deep learning. A multi-stream convolutional neural network (CNN) is proposed to learn the mapping relationship between camera array light filed wrapped phases and fringe orders of the expected central view, and is used to predict the fringe order to achieve the phase unwrapping. Experiments are performed on the light field fringe projection data generated by the simulated camera array fringe projection measurement system in Blender and by the experimental 3×3 camera array light field fringe projection system. The performance of the proposed network with light field wrapped phases using multiple directions as network input data is studied, and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated.     

基于太赫兹布拉格光纤的表面生物传感器

空心布拉格光纤在传感领域应用极为广泛。本文提出了一种基于带有缺陷层的太赫 兹(terahertz,THz)空心布拉格光纤的表面生物传感器,并对该传感器在太赫兹频段的细菌种类检测性能进行 分析。在太赫兹布拉格光纤中,高折射率光敏树脂层和低折射率空气层以纤芯为中心周期性 排列,在纤芯内壁沉积分析物引入缺陷模式。本文对提出的传感器性能进行了数值研究。仿 真结果表明,该传感器有很高的纤芯功率比,限制损耗在0.3 THz和0.45 TH z频 段有损耗峰,能得到可识别的特征频率,此特征频率用于细菌种类检测。当频率大于0.7 TH z时,该传感器可实现厚度无关的表面生物传感。本文所提出的基于太赫兹空心布拉格光纤 的 表面生物传感器可用于检测细菌种类,在微生物检测方面有很大的应用潜力。     

An improved U-Net for cell confluence estimation

Cell confluence is an important metric to determine the growth and the best harvest time of adherent cells. At present, the evaluation of cell confluence mainly relies on experienced labor, and thus it is not conducive to the automated cell culture. In this paper, we proposed an improved U-Net algorithm (called DU-Net) for the segmentation of adherent cells. First, the general convolution was replaced by the dilated convolution to expand the receptive fields for feature extraction. Then, the convolutional layers were combined with the batch normalization layers to reduce the dependence of the network on initialization. As a result, the segmentation accuracy and F1-score of the proposed DU-Net for adherent cells with low confluence (<50%) reached 96.94% and 93.87%, respectively, and for those with high confluence (≥50%), they reached 98.63% and 98.98%, respectively. Further, the paired t-test results showed that the proposed DU-Net was statistically superior to the traditional U-Net algorithm.     

云雾干扰条件下激光引信高斯分解测距方法

在云雾影响下,脉冲激光引信存在着虚警概率高、测距精度差的问题,这些问题是制约激光引信全天候工作的主要问题。为了降低云雾对于全波形采样激光引信的影响,提出一种基于高斯分解的回波脉冲处理方法,采用高斯模型将回波信号分解成单独高斯脉冲的形式,根据波形特征分辨出真实目标回波与云雾后向散射回波。从理论仿真和实验上对比了该方法与数字互相关方法的性能差异,结果表明:在能见度小于4 m的云雾条件下,数字互相关法测距精度恶化到1.6 m,而高斯分解法能够实现0.18 m的测距精度,优于互相关处理方式。该方法为激光引信在云雾环境下高精度测距提供了新的数据处理思路。     

POI recommendation based on a multiple bipartite graph network model

The Journal of Supercomputing - In location-based social network platforms, the point-of-interest(POI) recommendation is an essential function to serve users. The existing POI recommendation...     

会展设计中的交互技术应用分析

目的研究交互技术在会展设计中的应用。方法通过对世博会经典展馆与专业会展设计的分析,对交互技术与会展设计之间的关系与应用方式进行研究,并以此对会展设计与交互技术应用的趋势作出展望。结论在以世博会为代表的会展活动与设计中,交互与虚拟现实技术的应用改变了传统的展示方式,不但强调和突显了观众的主体作用,而且也扩大了信息的承载量,提高了展示的艺术效果与效率。     

中空涤纶/不锈钢/竹炭保温透湿材料的开发及性能研究

通过花式捻线方法将中空涤纶、不锈钢长丝和竹炭纤维三种功能性纤维并捻获得复合纱线,利用纱线强度、毛羽和实际捻度测试,得到最佳的纱线纺制参数。并采用最佳工艺参数纺制的包绕纱线进行平纹织物织造,对其织物透湿性、远红外发射率以及保温性能进行测试表征,最终制备一种可用于防寒服的新型保温透湿织物。结果表明,缠绕捻度为上捻度200,下捻度0时,纺制的包芯纱具有最佳捻度。中空涤纶/不锈钢/竹炭织物透湿率为5 184g/(m2·24h),达到透湿织物透湿要求的2倍以上。一至四层织物的远红外发射率均在70%及以上,符合远红外发射率具有功能性结果的范围。织物克罗值达到7.355clo,高于极低温作业穿着克罗值51.6%,可满足极低温环境下的织物保温要求。织物保温率为62.43%,相比于棉、毛等传统纺织原料,织物保温率可以提升2倍以上。     

利用氧等离子体预处理增强聚对苯二甲酸乙二醇酯/聚吡咯导电复合薄膜的界面粘附性

首先对聚对苯二甲酸乙二醇酯(PET)薄膜进行氧等离子体预处理,然后通过原位化学氧化聚合法使得吡咯单体在PET薄膜表面沉积聚合,制备了聚对苯二甲酸乙二醇酯/聚吡咯(PET-PPy)复合膜。通过原子力显微镜、X射线光电子能谱仪、ζ电位测试对经不同时长的氧等离子体预处理的PET薄膜进行表征;利用扫描电子显微镜、耐磨性实验和表面电阻测试对PET-PPy复合膜磨损前后的表面形貌和电阻进行分析与表征。结果表明,氧等离子体预处理显著增强了PET-PPy复合膜界面粘附性。氧等离子体预处理使PET薄膜表面粗糙度增大、电负性显著增强。特别地,当预处理时间为120s时,薄膜表面电负性明显增强,此时PET-PPy复合膜磨损后的电阻值变化最小,表明PET薄膜与PPy功能层界面粘附强度得到明显增强。