基于混沌时间序列的变电站设备温度预测

变电站输变线路和设备的温度变化能够反映其老化、负载过高等引起的安全隐患.通过对变电站设备温度数据的非线性分析和预测,实现对设备的有效预警,将避免事故引起的巨大损失.对变电站已测温度数据建立时间序列,利用小数据量法验证变电站设备温度时间序列的混沌特性.研究基于RBF神经网络的混沌时间序列预测并与神经网络预测进行对比,单步预测与多步预测结果均优于神经网络预测.仿真结论证明了基于神经网络的混沌时间序列预测方法的有效性.     

Computational simulation of Belousov–Zhabotinskii oscillating chemical reaction

The suitability of computational simulation of the Belousov–Zhabotinskii oscillating chemical reaction by differential kinetic methodology for resolving nonlinear multi-component system is demonstrated in this work. According to the Field–KÖrÖs–Noyes mechanism and the Oregonator model, the change of the concentrations of HBrO₂, bromide ion and cerium ion are simulated. The results of computational simulation are consistent with experimental results very well. At the same time, the effect of variables and parameters, especially the rate constant on the oscillation curve, are investigated deeply. A simple method of estimating rate constants is obtained through simulation the concentrations of key components of the system, and then comparison the simulation results with the experimental ones. The reasonable rate constant is also proposed.     

FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.     

图像边缘检测在DM642上的应用研究

采用TMS320DM642EVM作为数字图像处理硬件平台,利用CCS开发环境来构建程序并对其进行优化;通过对改进后的Log算子和小波多尺度方法提取到的边缘图像进行数据融合,得到一幅新的边缘图像。实验结果表明,该方法提高了边缘检测的运算速度,满足了实时性要求;并能有效地抑制噪声的影响,保留了图像边缘细节。     

Immunoglobulin 1 (IgG1) Fc-glycosylation profiling of anti-citrullinated peptide antibodies from human serum

In several autoimmune disorders, including rheumatoid arthritis (RA), autoantibodies are thought to be the driving force of pathogenicity. Glycosylation of the Fc-part of human Igs is known to modulate biological activity. Hitherto, glycosylation of human IgG-Fc has been analyzed predominantly at the level of total serum IgG, revealing reduced galactosylation in RA. Given the pathogenic relevance of autoantibodies in RA, we wished, in the present study, to address the question whether distinct Fc-glycosylation features are observable at the level of antigen-specific IgG subpopulations. For this purpose, we have developed a method for the microscale purification and Fc-glycosylation analysis of anti-citrullinated peptide antibodies (ACPA). ACPA represent a group of autoantibodies that occur with unique specificity in RA patients. Their presence is associated with increased inflammatory disease activity and rapid joint destruction. Results indicate that ACPA of the IgG1 subclass vary considerably from total serum IgG1 with respect to Fc-galactosylation, with galactosylation being higher on ACPA than on serum IgG1 for some patients, while other patients show higher galactosylation on serum IgG1 than on ACPA. Using this method, studies can be performed on the biological and clinical relevance of ACPA glycosylation within RA patient cohorts.     

Usability Research in the Writing Lab: Sustaining Discourse and Pedagogy

Redesigning Purdue's Online Writing Lab (OWL) presented the opportunity for collaboration among Writing Center and Professional Writing Program members. While the article briefly describes the OWL redesign process, the argument focuses on collaboration and presents a model for sustainable intraprogram collaboration. Following Hawhee, usability research is defined as “invention-in-the-middle,” which offers a model for understanding research process as part of the infrastructure of new media instruction as described by DeVoss, Cushman, and Grabill. This article offers four stakeholder perspectives on the process of participatory technology design: of writing center administrators, graduate students, technical writing practitioners, and writing program graduate faculty members. The model asserted by this article presents a dynamic understanding of expertise and of fluidity in the roles of participants. Collaborative usability research, seen as invention-in-the-middle, contributes both to long-term sustainability of technological artifacts as well as the discursive interactions among stakeholders whose work supports these artifacts.     

基于人工免疫的网络安全态势评估模型

本文将人工免疫原理应用到网络安全态势评估的研究中.通过对入侵检测模型中有关自体、非自体、抗原的形式化描述,建立了抗体细胞的演化机制、动态免疫记忆、免疫监视等免疫机制.依据人体免疫系统抗体浓度的变化与病原体入侵强度的对应关系,建立了网络安全态势实时定量计算模型.     

基于微波无线能量传输多径效应的多角度超表面能量收集器

文中深入研究了基于微波无线能量传输技术的多径效应及其对多角度超表面能量收集器的性能的影响。首先,概述了微波无线能量传输技术的基本原理与发展现状,随后详细讨论了超表面结构在多角度能量接收中的应用。通过理论分析与仿真实验,文中提出了一种优化的超表面设计方案,以提高能量收集效率。实验结果显示,该方案能显著提高多角度超表面能量收集器在复杂传播环境下的能量收集性能,不仅为超表面能量收集器的设计提供了新的视角,也为未来无线能量传输技术的发展提供了理论参考。     

红芪活性成分免疫调节作用及机制研究进展

免疫功能受损或免疫系统紊乱会导致机体疾病发生,影响人身体健康,基于调节机体免疫治疗疾病已成为临床应用热点。红芪作为甘肃大宗药材,临床应用广泛,具有抗肿瘤、抗炎、免疫调节等多种药理作用。红芪活性成分主要有红芪多糖、黄酮类、萜类、皂苷类等,其中红芪多糖为红芪发挥免疫调节作用主要的物质基础。红芪增强机体免疫力主要体现在增强免疫器官功能,诱导免疫细胞分化、促进免疫因子分泌,提高单核巨噬细胞吞噬功能、调节炎性因子水平以及激活相关通路等方面来达到其抗肿瘤、抗炎作用。本文主要对红芪活性物质免疫调节作用进行阐述,为红芪的进一步临床开发应用提供思路。     

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.