基于重要度排序的配电自动化终端优化布局方法

针对配电自动化终端优化布局问题，提出了一种基于重要度排序的终端优化布局方法。首先以等年值综合费用为目标函数，供电可靠性和投入产出比为双重约束建立了配电自动化终端优化布局模型。然后通过分析配电自动化终端对供电可靠性提升的影响，给出各个节点的“二遥”及“三遥”终端安装重要度定义及计算公式。最后采用枚举法确定最优终端安装数量，基于节点终端安装重要度排序确定终端的最优安装位置。该方法考虑了已布局节点对剩余节点终端安装重要度的影响，能够在降低计算量的同时兼顾布局合理性。运用所提方法RBTS-BUS2系统及扩充模型进行终端优化布局并与智能优化算法的布局结果进行对比，验证了本文方法的有效性及优越性。     

用于电弧检测的电力专用SoC设计

针对传统的电弧电路故障检测结果不准确的问题,设计用于电弧检测的SoC系统,并且在55nm工艺下进行流片验证。采用包含两种结构的模数转换器的片上电压源,设计了锁相环以及复位电路,精度最高可达8.67 bit。验证结果表明,本设计可提高电弧检测的准确性。     

Leucokinin and Associated Neuropeptides Regulate Multiple Aspects of Physiology and Behavior in Drosophila

Leucokinins (LKs) constitute a family of neuropeptides identified in numerous insects and many other invertebrates. LKs act on G-protein-coupled receptors that display only distant relations to other known receptors. In adult Drosophila, 26 neurons/neurosecretory cells of three main types express LK. The four brain interneurons are of two types, and these are implicated in several important functions in the fly’s behavior and physiology, including feeding, sleep–metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. The 22 neurosecretory cells (abdominal LK neurons, ABLKs) of the abdominal neuromeres co-express LK and a diuretic hormone (DH44), and together, these regulate water and ion homeostasis and associated stress as well as food intake. In Drosophila larvae, LK neurons modulate locomotion, escape responses and aspects of ecdysis behavior. A set of lateral neurosecretory cells, ALKs (anterior LK neurons), in the brain express LK in larvae, but inconsistently so in adults. These ALKs co-express three other neuropeptides and regulate water and ion homeostasis, feeding, and drinking, but the specific role of LK is not yet known. This review summarizes Drosophila data on embryonic lineages of LK neurons, functional roles of individual LK neuron types, interactions with other peptidergic systems, and orchestrating functions of LK.     

Hepatitis B Virus-Like Particle: Targeted Delivery of Plasmid Expressing Short Hairpin RNA for Silencing the Bcl-2 Gene in Cervical Cancer Cells

Gene therapy research has advanced to clinical trials, but it is hampered by unstable nucleic acids packaged inside carriers and there is a lack of specificity towards targeted sites in the body. This study aims to address gene therapy limitations by encapsidating a plasmid synthesizing a short hairpin RNA (shRNA) that targets the anti-apoptotic Bcl-2 gene using truncated hepatitis B core antigen (tHBcAg) virus-like particle (VLP). A shRNA sequence targeting anti-apoptotic Bcl-2 was synthesized and cloned into the pSilencer 2.0-U6 vector. The recombinant plasmid, namely PshRNA, was encapsidated inside tHBcAg VLP and conjugated with folic acid (FA) to produce FA-tHBcAg-PshRNA VLP. Electron microscopy revealed that the FA-tHBcAg-PshRNA VLP has an icosahedral structure that is similar to the unmodified tHBcAg VLP. Delivery of FA-tHBcAg-PshRNA VLP into HeLa cells overexpressing the folate receptor significantly downregulated the expression of anti-apoptotic Bcl-2 at 48 and 72 h post-transfection. The 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay demonstrated that the cells’ viability was significantly reduced from 89.46% at 24 h to 64.52% and 60.63%, respectively, at 48 and 72 h post-transfection. As a conclusion, tHBcAg VLP can be used as a carrier for a receptor-mediated targeted delivery of a therapeutic plasmid encoding shRNA for gene silencing in cancer cells.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

计及RSC控制的DFIG短路电流直流分量解析表达

现有文献针对计及转子变流器(RSC)控制的双馈感应风电机组(DFIG)定子短路电流解析表达,将定子磁链当作一阶直流衰减分量或忽略功率外环控制。基于DFIG电压、磁链和RSC控制方程,得到定子电流关于定子电压和定子功率的传递函数,提出定子电流的精确解析表达式。基于RSC内、外环PI参数关系,推导直流分量衰减时间常数和角频率关于PI参数的表达式。分析了RSC内外环PI参数对定子电流直流衰减分量的影响。仿真结果验证了解析表达式的准确性,为PI参数选取和保护装置测量、整定提供依据。     

矿用直线振动筛结构故障分析

针对煤矿选煤厂直线振动筛工作过程中出现的结构故障问题,采用有限元仿真与现场测试结合的方法对振动筛出现故障的原因进行了分析研究。介绍了振动筛的主要结构及常见故障,并以某矿选煤厂发生大梁断裂的振动筛为例,对该振动筛进行三维建模和有限元模态分析,分析结果表明:随着模态阶次的增加,振型的变化逐渐由移动转为结构本身的扭曲变形。在该振动筛空载工作状态下,选用本质安全型便携式测振记录仪进行振动测试并处理测试信号,得到该振动筛的工作频率。结合模态分析结果和实际测试结果,判断出空载时频繁运行振动筛是造成其大梁断裂的主要原因。根据故障原因给出维护建议:应当尽量避免振动筛在空载或物料较少的状态下长时间运行;在振动筛投入使用前,应预先了解振动筛的工作状态及可能存在的薄弱环节,尽可能避免结构故障,保证振动筛可靠运行。     

Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.     

基于改进奇异值分解滤波和谱峭度的滚动轴承故障诊断

针对含噪信号的有效奇异值个数难以确定的问题，提出了一种改进的奇异值分解降噪方法--奇异值累积法。该方法通过计算奇异值的实际下降值与奇异值平均下降速度累积量的差值，并取该差值最大值点的位置作为有效奇异值的分界点来确定有效奇异值的个数。在此基础上，提出了一种基于奇异值累积法与快速谱峭度的滚动轴承故障诊断方法。采用奇异值累积法对原信号进行降噪处理，然后利用快速谱峭度确定滤波器中心频率及带宽，通过分析频段包络谱中明显的频率成分来诊断故障。该方法可以有效去除信号中的噪声，使得到的峭度值所反映的故障冲击更接近实际情况。对含内圈、外圈故障的滚动轴承实验数据进行分析，实验结果表明，相比快速谱峭度的故障诊断方法，该方法具有更好的故障识别效果。     

基于并行多通道卷积长短时记忆网络的轴承寿命预测方法

在预测轴承剩余使用寿命时，数据间的时序特性是一个可以利用的重要隐藏信息。为了更好地提取具有时序信息的特征用于预测，提出了一种基于并行多通道卷积长短时记忆网络（PMCCNN-LSTM）的剩余使用寿命预测模型。该模型主要由两部分组成：前端为并行多通道卷积网络（PMCCNN），提取信号特征，挖掘数据的时序特性，并采用逐层训练和微调的方式提升参数的收敛性；后端为长短时记忆（LSTM）网络，基于特征进行剩余使用寿命预测，并采用加权平均的方法对预测结果进行平滑处理。在一个轴承加速寿命实验的公开数据集上使用留一法验证了该模型的准确性，实验结果表明：所提模型的平均误差与最大误差分别比传统的卷积神经网络（CNN）低23.38%和15.84%，比传统的LSTM低24.14%和19.01%，比卷积长短时记忆网络（CNN-LSTM）低30.32%和23.09%。