WiNoC中基于Edge first算法的流量平衡设计

在无线片上网络中,无线节点拥塞以及不同子网和全局网络内的流量平衡情况对整个片上网络的通信效率有着重要的影响,为此提出了基于Edge first算法的全局流量平衡机制（GTB）。首先优化了划分有线无线数据包的机制,减少了无线节点处的拥塞;其次根据无线路由器（WR）的拥塞情况,提出Edge first路由算法平衡子网内的流量;最后在全局网络中提出了全局子网拥塞感知（GSCA）判断机制,使得长距离数据包优先从低拥塞子网通过,平衡了全局网络的流量。实验表明,该方案在可接受的硬件开销、功耗开销下,保证较低的网络延迟和较高的网络吞吐率,并且大幅的提升了网络的流量平衡性能。     

基于DBSCAN-FPN的输电线路螺栓缺销检测方法

螺栓作为输电线路上数量最大的紧固件,其缺陷检测是输电线路巡检工作中的一项重要内容。针对螺栓缺销为小目标,其定位困难、特征难提取的问题,提出一种基于DBSCAN算法与FPN模型相结合的螺栓缺销检测方法。首先,利用FPN模型定位螺栓缺销目标区域,同时基于DBSCAN聚类算法对具有相同形态结构的区域进行聚类;然后,改进FPN模型：基于螺栓先验知识,利用卷积网络实现自底向上的特征提取,采用双线性插值方法将特征的高层语义信息自顶向下地传递到各个层级,通过卷积滤波方法横向加强高层语义特征与高分辨率特征的融合信息,获得更优化的螺栓缺销特征表达;利用改进FPN模型实现螺栓缺销的初步检测;最后,采用DBSCAN聚类算法对初步检测结果进行误检甄别,实现了螺栓缺销的精确检测。实验结果表明,DBSCAN-FPN在自建数据集上的检测精度达到76.23%,检测效果优于FPN、R-FCN和Faster R-CNN。所提方法可以有效提高螺栓缺销检测精度,对输电线路运维有实际意义。     

基于灰色关联分析的电网安全事故关键致因分析

探讨和研究电网安全事故的影响因素及控制已成为预防电网安全事故发生的热点问题。为揭示电网安全事故发生的深层机理,基于事故致因理论,采用质性分析和灰色关联分析的方法,分析中国电网安全事故的关键致因。研究发现,电网安全事故的影响因素主要包括主体因素（认知因素、行为因素和身体因素）、过程因素（组织氛围因素和组织过程因素）、对象因素（设备可靠性低和设备管理松懈）及情境因素（作业环境因素和非作业环境因素）。灰色关联分析表明：电网安全事故的致因因素可以分为红色危险因素（r≥0.8）、橙色警备因素（0.7≤r＜0.8）和黄色警告因素（r＜0.7）,其中,组织氛围因素、认知因素和行为因素是电网安全事故的关键致因因素。在此基础上提出电网安全事故管理的政策建议,以期推动中国电网安全发展进程。     

基于短路阻抗及ΔU-I1轨迹特征联合分析的变压器绕组变形故障在线检测方法

为解决短路阻抗法不能进行故障类型识别及其与ΔU-I1轨迹法均易受设备测量误差干扰的问题,提出了基于短路电抗及ΔU-I1轨迹特征联合分析的绕组变形在线检测方法.介绍了在线短路阻抗法的原理,并根据互感器测量误差的短时不变性提出了减小测量误差的计算方法.介绍了ΔU-I1轨迹法的原理,然后给出基于短路阻抗及ΔU-I1轨迹特征联合分析的变压器绕组变形在线检测步骤和判据.通过建立变压器的仿真模型,对所提方法的有效性及考虑测量误差时的准确性进行了验证.结果表明,所提方法能在考虑测量误差时准确识别变压器的绕组变形故障,具有工频带电监测和故障类型识别的优点,提高了绕组变形故障识别的精度.     

镇安抽水蓄能电站弃渣坝材料大型三轴试验研究

根据镇安抽水蓄能电站弃渣坝地质条件和现场实际工况,通过对不同干密度土料开展土工试验,分析了坝体材料各力学参数特性。试验结果表明：随着围压增大,该坝弃渣料的峰值应力相应增大;在低应力情况下,弃渣料的体积缩小,在围压较高时,体积变化增大;弃渣料先出现剪缩,随后变为剪胀,剪胀随着侧压力的增大出现缩小现象,应力-应变关系曲线呈软化型,最终转变为剪缩型;保持围压条件不变,材料的峰值应力在干密度逐渐增大的情况下出现略微增大,但最大体变随着干密度增大出现较明显的减小情况;保持相同围压状态,邓肯-张模型参数对干密度的敏感性大小依次为Kb、k、m、c、Δφ、Rf、φ0、n。相关研究可为以后抽水蓄能电站的弃渣坝设计和后期防护奠定理论基础。     

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L⁻¹) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.     

TP63 Is Significantly Upregulated in Diabetic Kidney

The role of tumor protein 63 (TP63) in regulating insulin receptor substrate 1 (IRS-1) and other downstream signal proteins in diabetes has not been characterized. RNAs extracted from kidneys of diabetic mice (db/db) were sequenced to identify genes that are involved in kidney complications. RNA sequence analysis showed more than 4- to 6-fold increases in TP63 expression in the diabetic mice’s kidneys, compared to wild-type mice at age 10 and 12 months old. In addition, the kidneys from diabetic mice showed significant increases in TP63 mRNA and protein expression compared to WT mice. Mouse proximal tubular cells exposed to high glucose (HG) for 48 h showed significant decreases in IRS-1 expression and increases in TP63, compared to cells grown in normal glucose (NG). When TP63 was downregulated by siRNA, significant increases in IRS-1 and activation of AMP-activated protein kinase (AMPK (p-AMPK-Th¹⁷²)) occurred under NG and HG conditions. Moreover, activation of AMPK by pretreating the cells with AICAR resulted in significant downregulation of TP63 and increased IRS-1 expression. Ad-cDNA-mediated over-expression of tuberin resulted in significantly decreased TP63 levels and upregulation of IRS-1 expression. Furthermore, TP63 knockdown resulted in increased glucose uptake, whereas IRS-1 knockdown resulted in a decrease in the glucose uptake. Altogether, animal and cell culture data showed a potential role of TP63 as a new candidate gene involved in regulating IRS-1 that may be used as a new therapeutic target to prevent kidney complications in diabetes.     

环境温度对电励磁双凸极发电机输出特性的影响

电励磁双凸极发电机DSEG(doubly salient electro-magnetic generator)是一种高温运行能力强的新型无刷直流发电机,但对其温度特性尚缺乏研究。为给DSEG设计和应用提供理论依据,对环境温度改变时的DSEG发电性能进行研究。采用等效磁路法和有限元法,并结合不同温度下的铁心材料磁导率曲线,分析了温度对电机磁场和绕组电感的影响;研究了不同温度下电机的负载特性;将不同温度下的铁心材料和绕组铜材进行组合,通过有限元建模对比分析,研究了温升导致的电枢绕组电阻变化和铁心磁阻的变化单一变量对电机外特性的影响。最后在电机动态温升实验及静止热实验的基础上,进行温度特性实验,将电机冷态和热态实验结果和仿真结果进行对比分析,验证了理论研究和仿真的正确性。所得结论对DSEG的电磁设计和工程应用具有指导性意义。     

基于改进SSD的变压器套管红外图像油位智能识别方法

为解决现有基于红外图像识别变压器套管油位存在的过于依赖温度信息、人工处理效率低下等问题,文中结合目标检测技术提出了一种基于改进单次检测器(SSD)的套管智能油位识别方法。通过引入SSD目标检测方法,检测红外图像中的套管区域,加入损失函数以改进SSD算法从而提高套管检测准确率,并进一步通过简单线性迭代聚类(SLIC)的应用实现了不依赖红外图像温度信息的油位检测。对比文中提出的基于红外图像的油位识别算法检测结果与人工油位检测结果,表明文中提出的算法不仅在效率上领先于传统的温度检测方式,且其误差较小,仅为0.08%。对比结果验证了所提算法在保证检测精度的情况下可大幅度提高检测效率,有效提升套管故障诊断效率和智能化水平。     

电网故障处置预案文本中的命名实体识别研究

电网故障处置预案是电网故障处置的重要参考,对电网故障处置预案文本中各类电力设备、名称编号等细粒度的关键实体信息进行抽取,是实现计算机学习理解预案内容并进一步支撑故障处置智能化的重要基础。文中提出一种基于深度学习的电网故障处置预案文本命名实体识别技术,首先采用字向量表征预案文本,然后将注意力机制以及双向长短期记忆网络相结合,有所侧重地提取实体词深层字符特征,最后采用条件随机场求解最优序列化的标注。算例表明:文中所提预案文本命名实体识别模型不依赖人工特征,能够自动高效地提取文本特征,准确识别预案文本中细粒度的实体词,满足预案文本中关键实体信息精确定位和识别的要求。