Evaluating Optical Quality of a Bifocal Soft Contact Lens in Near Vision Using a Shack–Hartmann Wavefront Aberrometer

To assess optical characteristics of bifocal soft contact lenses (BCLs) in use, we measured wavefront aberrations of human eyes, of eyes with a monofocal soft contact lens (MCL), and of eyes with a BCL. Modulation transfer functions (MTFs), Strehl ratios, and simulated images for far and near vision were produced with the measured aberrations. High order aberrations of subject 1 were significantly smaller than those of subject 2 (t-test, P = 0.001). We found that wearing the BCL improved the optical quality of an eye in subject 1, expressed as the horizontal MTF from 2 to 48 cycles per degree (cpd) for near vision and the Strehl ratio (t-test, P = 0.009 for Strehl ratio). But we did not find the same effect in subject 2. This difference may be due to the difference in the aberrations of the eyes of the two subjects.     

论化学学科在世界一流大学建设中的作用*

化学是世界科学发展史上的中心科学,是人类探索未知世界的基本手段,是人类的核心素养和知识体系中必不可少的组成部分。化学学科推动了人类文明进程和科学的形成演化,在世界一流大学形成进程中发挥了至关重要的作用。在新一轮科技革命和高等教育新工科建设的背景下,化学将继续发挥四大基础科学的支柱作用,因此在建设世界一流大学的过程中应不断加强和完善化学学科建设和人才培养体系。世界高等教育的发展证明:一流大学必须有强大的化学学科,能够提供一流的化学相关课程和培养体系,这对人才培养质量起着至关重要的作用。世界一流大学建设中,无论出现什么样的新趋势、新理念,无论评价导向如何变化,化学为基础科学和中心科学的地位不容撼动,在人才培养质量中的核心课程地位不容撼动。     

机器视觉在光学加工检测中的应用

随着自动化控制领域的飞速发展,机器视觉技术日渐成熟,并且在各个领域赢得了越来越广泛的应用。机器视觉系统通过对图像信息的处理,用机器代替人眼实现了各种测量和判断。本综述简要介绍了机器视觉技术的概念和机器视觉系统的主要组成部分,阐述了机器视觉关键技术的发展现状,着重分析了机器视觉技术在光学加工检测领域,特别是在光学抛光检测过程中实现自动化的相关应用,最后展望了机器视觉在光学加工检测领域未来的发展趋势。     

一种运动检测算法在无人机视频遥感上的应用

提出了一种基于灰度投影与位平面匹配的图像全局运动估计算法,并将其应用于无人机图像稳定系统中.算法采用灰度投影获取全局运动,用网格检测和灰度切分图精确匹配的方法获取更准确的运动数据.由于算法中使用自适应性的灰度切分层选择和基于灰度投影的运动估计缩小搜索区域,使算法效率在空间复杂度和时间复杂度上得到提高,具有较好的实时性和对影像的适应性.     

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

This paper addresses the problem of frequency stability prediction (FSP) following active power disturbances in power systems by proposing a vision transformer (ViT) method that predicts frequency stability in real time. The core idea of the FSP approach employing the ViT is to use the time-series data of power system operations as ViT inputs to perform FSP accurately and quickly so that operators can decide frequency control actions, minimizing the losses caused by incidents. Additionally, due to the high-dimensional and redundant input data of the power system and the O(N²) computational complexity of the transformer, feature selection based on copula entropy (CE) is used to construct image-like data with fixed dimensions from power system operation data and remove redundant information. Moreover, no previous FSP study has taken safety margins into consideration, which may threaten the secure operation of power systems. Therefore, a frequency security index (FSI) is used to form the sample labels, which are categorized as “insecurity”, “relative security”, and “absolute security”. Finally, various case studies are carried out on a modified New England 39-bus system and a modified ACTIVSg500 system for projected 0% to 40% nonsynchronous system penetration levels. The simulation results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on normal, noisy, and incomplete datasets in comparison with eight machine-learning methods.     

An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints

In image classification with Deep Convolutional Neural Networks (DCNNs), the number of parameters in pointwise convolutions rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Existing studies demonstrated that a subnetwork can replace pointwise convolutional layers with significantly fewer parameters and fewer floating-point computations, while maintaining the learning capacity. In this paper, we propose an improved scheme for reducing the complexity of pointwise convolutions in DCNNs for image classification based on interleaved grouped filters without divisibility constraints. The proposed scheme utilizes grouped pointwise convolutions, in which each group processes a fraction of the input channels. It requires a number of channels per group as a hyperparameter Ch. The subnetwork of the proposed scheme contains two consecutive convolutional layers K and L, connected by an interleaving layer in the middle, and summed at the end. The number of groups of filters and filters per group for layers K and L is determined by exact divisions of the original number of input channels and filters by Ch. If the divisions were not exact, the original layer could not be substituted. In this paper, we refine the previous algorithm so that input channels are replicated and groups can have different numbers of filters to cope with non exact divisibility situations. Thus, the proposed scheme further reduces the number of floating-point computations (11%) and trainable parameters (10%) achieved by the previous method. We tested our optimization on an EfficientNet-B0 as a baseline architecture and made classification tests on the CIFAR-10, Colorectal Cancer Histology, and Malaria datasets. For each dataset, our optimization achieves a saving of 76%, 89%, and 91% of the number of trainable parameters of EfficientNet-B0, while keeping its test classification accuracy.     

高功率微波对家兔视觉电生理功能及组织结构的影响

采用高功率电磁波直接辐照家免全身后,即刻分别测试家兔的视网膜电流图（Electro-retinogram,ERG）;随后即刻取材,包埋固定,分别在光学显微镜和透射电镜下观察其视觉器官的组织结构。结果表明,高场强的高功率微波辐照可造成家兔眼视网膜组织的即刻损伤,对家兔视觉电生理功能可产生明显的抑制作用,其作用强度与辐照剂量和辐照时间存在一定的量效、时效关系。     

A new method for circular grid analysis in the sheet metal forming test

Computer vision systems are employed to determine the major and minor lengths of deformed elliptic grids while determining a sheet metal's workability. The existing method identifies the ellipse using the least squares analysis. It suffers two drawbacks: assumptions in direct conflict with the observed real-world processes and an undesirable property of orientation dependence. For the remedy, this paper presents a new method that, in addition to achieving the desired property of orientation invariance, discards assumptions that conflict with real-world processes. The proposed method is implemented and tested using simulated and real-world data. Results are reported and compared with those obtained by the existing method.     

太阳能帆板平面度测量系统中光斑图像处理方法研究

太阳能帆板的平面度测量是卫星生产制造过程中的一个关键技术，现有的测量方法存在着精度差，效率低的问题。该文在基于实际工程课题的太阳能帆板平面度测量系统基础上，针对测量系统中光斑图像处理这一关键技术进行了详细的研究。对现有测量方法进行了比较与分析，介绍了测量系统的基本原理、图像采集系统设计过程中采取的主要措施、图像处理的具体方法与步骤和测量系统的基本组成。提出了一种新的快速滤波算法和光斑图像能量中心求取算法，大大提高了光斑图像能量中心的求取精度和实时性，提高了太阳能帆板平面度的测量精度。给出了光斑图像处理结果、静态测量重复性误差以及实际帆板的测量结果与分析，实验结果证明所采用的光斑图像处理方法可以满足对实际帆板测量精度的要求。     

RGB pattern of images allows rapid and efficient prediction of antioxidant potential in Calycophyllum spruceanum barks

The use of fast and low-cost methods to optimize the total phenolic compounds (TPC) extraction has been gaining attention in ethnopharmacological research. Extraction conditions of the bioactive compounds from Calycophyllum spruceanum barks were established through multivariate regression models. In this sense, fractional factorial design (FFD) and central rotational composite design (CCRD) were developed using partial least squares regression (PLSR) combined with the information from the color images and spectrophotometry tools to evaluate the antioxidant activity from C. spruceanum barks. In fact, was possible to optimize the extraction of TPC with AA (ethanol 10% v/v, 1 h extraction time at 75 °C temperature). Besides, the precision and performance of generated models were established for the three response variables (TPC, AA by ABTS and FRAP methods) with R² above 0.98 in the PLSR and residual predictive value (RPD) above 3. Thus, the approaches suggested in this study, with emphasis on the use of image analysis, proved to be potential and promising as simple, fast, non-destructive methods for quantifying TPC and antioxidant activity in C. spruceanum barks.