弹簧摆动力学行为演示仪

在非线性保守系统中,弹簧摆结构简单,运动特性较为典型.本文通过拉格朗日方程对弹簧摆系统进行理论分析解得运动方程,并在径向和切向将运动分解,运用图像处理技术设计制作了弹簧摆演示仪和实验软件,对运动轨迹与时序图进行可视化.软件绘制的弹簧摆轨迹与理论结果具有较好的一致性.     

高光谱影像的鲜桃可溶性固形物含量预测模型

可溶性固形物含量(SSC)是决定鲜桃风味和品质的重要成分。高光谱影像的特征提取为无损检测可溶性固形物含量提供了数据基础和方法路径。先前的研究表明,基于多光谱、荧光谱、近红外光谱、电子鼻的水果内部品质评估取得较好的结果。但是,由于缺少多特征融合,从而限制了水果品质的精准估测。为此,提出了一种基于堆栈自动编码器-粒子群优化支持向量回归(SAE-PSO-SVR)模型预测鲜桃可溶性固形物含量。首先,利用高光谱影像提取光谱信息、空间信息及空-谱融合信息。其次,设置普适性堆栈自动编码器(SAE)提取光谱信息、空间信息及空-谱融合信息的深层特征。最后,将深层特征作为粒子群优化支持向量回归(PSO-SVR)模型的输入数据进行鲜桃可溶性固形物含量的预测。其中,对于光谱信息作为输入的SAE模型,设计了453-300-200-100-40, 453-350-250-150-50, 453-350-250-100-60的三个隐含层结构。对于空间信息作为输入的SAE模型,设计了894-700-500-300-50, 894-650-350-200-80, 894-800-700-500-100的三个隐含层结构。对于融合信息作为输入的SAE模型,设计了1347-800-400-200-40, 1347-750-550-400-100, 1347-700-500-360-150的三个隐含层结构。实验结果表明,对于输入数据分别为光谱信息、空间信息及融合信息的SAE模型,结构为453-300-200-100-40, 894-800-700-500-100和1347-750-550-400-100的模型效果较好,而且基于融合信息的模型预测精度明显优于基于光谱信息或者图像信息的模型。为了验证模型的普适性,利用结构为1347-750-550-400-100的SAE模型提取融合信息的深层特征估测不同品种鲜桃的可溶性固形物含量并进行可视化。结果表明,基于结构为1237-650-310-130的SAE-PSO-SVR模型预测效果最好(R2=0.873 3, RMSE=0.645 1)。因此,所提出的SAE-PSO-SVR模型提高了鲜桃可溶性固形物含量的估计精度,为鲜桃的其他成分检测提供了技术支撑。     

气泡在超声场中绕圈运动的高速摄影及其图像分析

借助高速摄影和图像分析技术对首次发现的附壁气泡的绕圈现象进行了实验研究,重点研究游移气泡的运动轨迹、附壁气泡的布阵过程、气泡的来源以及气泡的振动细节.研究发现游移绕圈气泡的运动轨迹呈现出不稳定、不规则、不光滑的特点.阵列气泡源于游移气泡,而游移气泡变成阵列气泡的方式主要是通过合并增大体积,从而减小所受的Bjerknes力,降低活性的方式实现的.游移气泡源于ALF(acoustic lichtenberg figure)空化云中大量空泡的合并,使以径向振动为主的空泡逐渐过渡到以表面波动为主的气泡.阵列气泡在Bjerknes力的作用下呈现出规则的表面波动,而体积更小受力更大的游移空泡的表面完全失稳,呈现极不规则的形貌,并对附近阵列气泡的表面波动产生影响.阵列气泡呈现出十分规则的排布,相邻阵列气泡之间的振动相位是相反的,表现为相互排斥.     

基于Faster-RCNN和Level-Set的桥小脑角区肿瘤自动精准分割

桥小脑角区（CPA）肿瘤的精准分割在手术治疗、放疗中有重要影响,本文结合更快速区域卷积神经网络（Faster-RCNN）和水平集（Level-Set）方法对CPA肿瘤的自动分割进行了研究.首先,采集317名CPA肿瘤患者的T₁WI-SE序列磁共振图像,使用基于Faster-RCNN主干网络VGG16提取特征,结合区域建议网络（RPN）进行学习训练,建立带有CPA肿瘤位置信息的定位模型,再应用Level-Set对肿瘤进行精准分割.本文对比了不同CPA肿瘤区域勾画范围对分割结果产生的影响,并以精确率、召回率、均值平均精度值（mAP）和戴斯系数（Dice系数）等指标评估了模型定位和分割的性能.实验结果表明,结合Faster-RCNN和Level-Set建立的模型能更有效对CPA肿瘤进行精准分割,减轻临床医生的负担,并提升治疗效果.     

Micron-sized particle separation with standing surface acoustic wave—Experimental and numerical approaches

Traditional cell/particle isolation methods are time-consuming and expensive and can lead to morphology disruptions due to high induced shear stress. To address these problems, novel lab-on-a-chip-based purification methods have been employed. Among various methods introduced for the separation and purification of cells and synthetics particles, acoustofluidics has been one of the most effective methods. Unlike traditional separation techniques carried out in clinical laboratories based on chemical properties, the acoustofluidic process relies on the physical properties of the sample. Using acoustofluidics, manipulating cells and particles can be achieved in a label-free, contact-free, and highly biocompatible manner. To optimize the functionality of the platform, the numerical study should be taken into account before conducting experimental tests to save time and reduce fabrication expenses. Most current numerical studies have only considered one-dimensional harmonic standing waves to simulate the acoustic pressure distribution. However, one-dimensional simulations cannot calculate the actual acoustic pressure distribution inside the microchannel due to its limitation in considering longitudinal waves. To address this limitation, a two-dimensional numerical simulation was conducted in this study. Our numerical simulation investigates the effects of the platform geometrical and operational conditions on the separation efficiency. Next, the optimal values are tested in an experimental setting to validate these optimal parameters and conditions. This work provides a guideline for future acoustofluidic chip designs with a high degree of reproducibility and efficiency.     

Agglomeration of particles by a converging ultrasound field and their quantitative assessments

The acoustic radiation force resulting from acoustic waves have been extensively studied for the contact-free generation of organized patterning arrays. The precise arrangement of microscopic objects clustered at the pressure nodes is critical to the development of functional structures and patterned surfaces. However, the size of the clusters is restricted by the saturation limit of the acoustic nodes. Here, we present a bulk acoustic wave (BAW) platform, which employs a two-dimensional acoustic wave to propel particles of various sizes. Experimentally, when particles are large, significant acoustic energy is scattered and partly absorbed by the matched layers in front of the sensors. The acoustic radiation force from a convergent acoustic pressure field agglomerates the large polystyrene (PS) particles towards the central region instead of the pressure nodes. The parametric analysis has been performed to assess the transition in the particles from clustering at the organized nodal arrays to agglomerating in the central region, which is a function of particle size, particle concentration, and load voltage. Statistically, the particles can agglomerate with a cluster ratio greater than 70%, and this ratio can be improved by increasing the load power/voltage supplied to the transducers. With its ability to perform biocompatible, label-free, and contact-free self-assembly, this concept offers a new possibility in the fabrication of colloidal layers, the recreation of tissue microstructure, the development of organoid spheroid cultures, the migration of microorganisms, and the assembly of bioprinting materials.     

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.     

基于激光点云多条件约束的相机检校方法

为了克服相机检校对二维/三维检校场的依赖,提出一种基于激光点云多条件约束的相机检校方法。该方法通过对相机获取的多视影像进行光束法平差获得初始相机参数;利用影像点云与其最邻近的激光点云之间的位置关系,以共线方程为基础模型,建立多条件约束的相机检校数学模型;使用不等式约束的最小二乘方法平差迭代解算相机参数。将本文方法与基于三维控制场的检校精度进行了实验对比分析,结果表明本文方法与基于三维控制场的检校精度相当,两者反投影平均误差相差小于0.1 pixel,验证了本文方法在没有传统检校场的情况下进行相机检校的可行性。