Research on the longitudinal protection of a through-type cophase traction direct power supply system based on the empirical wavelet transform

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform(EWT)for a through-type cophase traction direct power supply system,where both sides of a traction network line exhibit a distinctive boundary structure.This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals,resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line.During internal line faults,the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines.Conversely,for faults external to the line,the energy is lower compared to adjacent lines.EWT is employed to decompose the collected fault current signals,allowing access to the high-frequency transient energy.The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary.Moreover,simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles,distances to faults,and fault transition resistances.

Mechanism of Tea Polyphenols in Alleviating Thermal Damage Based on Network Pharmacology

[Objective]This paper was to investigate the action targets and pathways of tea polyphenols in alleviating heat stress-induced injury by using network pharmacological analysis and an H9C2 cell model.[Method]First,the corresponding targets of tea polyphenols were obtained from the PubChem database.Then,the core targets were screened based on topological parameters.The relevant metabolism pathways of tea polyphenols related to diseases were identified through GO functional annotation and KECG signaling pathway enrichment.Moreover,common targets for thermal injury and targets of tea polyphenols were obtained.Then,GO functional annotation was performed to explore the pathway of tea polyphenols in alleviating heat stress damage.H9C2 cells were cultured at 42 C to construct the heat stress model,and the cells were treated with 10μg/mL tea polyphenols.The key genes were confirmed using RT-PCR technology.[Result]The study yielded 364 targets corresponding to tea polyphenols,including 68 core targets.These targets are related to various biological processes such as involve oxidative stress,cancer,lipopolysaccharide-mediated signaling pathways,antiviral responses,regulation of cellular response to heat,apoptosis,and cellular lipid metabolic metabolism.Tea polyphe nols alleviate thermal damage by targeting BCL2,HSP90AA1,HSPA1A,JUN,MAPK1,NFKB1,NFKBIA,NOS3,and TP53.Moreover,10 mg/L tea polyphenols were found to upregulate the transcription levels of Hsp70,HO-1,NQ-O1,Nrf2,and MAPKI,and the transcription levels of Bax/Bcl2,p38,and JNK were downregulated to alleviate the heat stress-induced injury.[Conclusion]Tea polyphenols may enhance the antioxidant ability of H9C2 cells and inhibit cell apoptosis,thereby reducing heat stress injury.

基于网络药理学和分子对接探讨尿C方治疗IgA肾病作用机制

目的:通过网络药理学探讨尿C方治疗IgA肾病(IgAN)的关键成分和作用机制。方法:通过中药系统药理学数据库与分析平台(TCMSP)、PubChem、Swiss ADEM和SwissTargetPrediction获得尿C方作用靶点;应用Cytoscape 3.9.1软件构建“药物-成分-靶点”相互作用网络;通过GeneCards、OMIM和DisGeNET平台获得IgAN作用靶点;通过Venn 2.1制作韦恩图获得尿C方和IgAN之间的交叉靶点并进行鉴定和可视化,并利用STRING数据库建立蛋白质-蛋白质相互作用网络(PPI);借助DAVID数据库对交集靶点进行GO和KEGG富集分析并运用微生信在线平台对交集靶点进行GO和KEGG富集分析可视化;使用Autodock Vina软件进行分子对接验证,并利用PyMol软件得出对接结果。结果:共筛选得到尿C方有效成分70种及对应1 363个作用靶点,其中确定了10个关键活性成分。IgAN相关靶点1 593个,除重复项后获得823个疾病靶点,交集靶点111个。GO富集分析得到706个BP条目,63个CC条目,123个MF条目。KEGG富集分析得到161条与IgAN显著相关的通路。分子对接结果表明,核心活性成分与核心靶点之间存在较强的结合活性。结论:尿C方治疗IgAN的作用机制可能是通过槲皮素、山奈酚、β-谷甾醇、黄芩素等调控AGE-RAGE信号通路、IL-17信号通路、脂质和动脉粥样硬化等发挥治疗作用。

Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population

BACKGROUND Hepatitis C virus(HCV)infection progresses through various phases,starting with inflammation and ending with hepatocellular carcinoma.There are several invasive and non-invasive methods to diagnose chronic HCV infection.The invasive methods have their benefits but are linked to morbidity and complications.Thus,it is important to analyze the potential of non-invasive methods as an alternative.Shear wave elastography(SWE)is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis.Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards.AIM To analyzed the diagnostic efficacy of non-invasive indices[serum fibronectin,aspartate aminotransferase to platelet ratio index(APRI),alanine aminotransferase ratio(AAR),and fibrosis-4(FIB-4)]in relation to SWE.We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity.METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay.We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis.We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects.RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages(mild,moderate,and severe).This was based on its sensitivity(100%,92.2%,96.2%),specificity(96%,100%,98.6%),Youden’s index(0.960,0.922,0.948),area under receiver operating characteristic curve(0.999,0.993,0.922),and Likelihood test(LR+>10 and LR-<0.1).Additionally,our Bayesian Network analysis revealed that fibronectin(>200),AAR(>1),APRI(>3),and FIB-4(>4)were all strongly associated with patients who had severe fibrosis,with a 100% probability.CONCLUSION We have found a strong correlation between fibronectin and liver fibrosis progression in HCV patients.Additionally,we observed that the severity of liver fibrosis increases with an increase in the non-invasive indices that we investigated.

基于C-RAN架构的高负荷场景无线规划研究与应用

介绍了C-RAN架构的基本原理、特点及演进方向,然后分析了高负荷场景网络所面临的挑战。提出了一种基于C-RAN架构的高负荷场景网络规划解决方案。在C-RAN架构下,通过解耦网络覆盖层与容量层,就近部署容量层发射点来下沉承载业务,并利用Massive MIMO的多流与复用特性优势,提高了高负荷场景的网络容量与网络性能;同时,研究网络业务的潮汐效应,利用C-RAN架构开发载波自动快速调度算法,进一步提高载波资源利用效率与业务承载能力,最终实现用户感知质量的有效提升。

考虑相似日相关信息的基于FCM-UW-ADAGRU的超短期光伏预测方法

提出了一种模糊C均值聚类和不确定性加权自适应门控单元神经网络(FCM-UW-ADAGRU)模型对日前分钟级光伏出力进行预测。首先,基于FCM对历史日天气进行划分,采用历史功率数据的5个统计指标(协调平均值、几何平均值、变异系数、峰度和偏度)作为聚类特征。其次,通过分布识别模块从相同天气类型的相似日样本中识别出不同的数据分布,并通过分布匹配模块从所有相似日数据中挖掘相关信息,以处理未来可能遇到的未知气象信息。最后,基于不确定性加权(UW)平衡预测误差和相关信息误差,提高模型训练精度。与现有方法的比较实验表明该方法具有较高的精度和鲁棒性,验证了模型的有效性。

Scheme of long distance power supply for electrified railway traction network based on traction cable

Purpose–The traction cable is paralleled with the existing traction network of electrified railway through transverse connecting line to form the scheme of long distance power supply for the traction network.This paper aims to study the scheme composition and power supply distance(PSD)of the scheme.Design/methodology/approach–Based on the structure of parallel traction network(referred to as“cable traction network(CTN)”),the power supply modes(PSMs)are divided into cableþdirect PSM and cableþautotransformer(AT)PSM(including Japanese mode,French mode and new mode).Taking cableþJapanese AT PSM as an example,the scheme of long distance power supply for CTN under the PSMs of co-phase and out-of-phase power supply are designed.On the basis of establishing the equivalent circuit model and the chain circuit model of CTN,taking the train working voltage as the constraint condition,and based on the power flow calculation of multiple train loads,the calculation formula and process for determining the PSD of CTN are given.The impedance and PSD of CTN under the cableþAT PSM are simulated and analyzed,and a certain line is taken as an example to compare the scheme design.Findings–Results show that the equivalent impedance of CTN under the cableþAT PSM is smaller,and the PSD is about 2.5 times of that under the AT PSM,which can effectively increase the PSD and the flexibility of external power supply location.Originality/value–The research content can effectively improve the PSD of traction power supply system and has important reference value for the engineering application of the scheme.

Comparison of Three Modes of AT-fed Traction Power Networks

The features of three modes of AT-fed traction power networks (TPNs) have been briefly described in the existing literature, which is not adequate yet. In this paper, these three modes of TPNs are compared mainly on TPN capacity, transformer capacity, rail potential and voltage level, etc., and conclusions are drawn.

Improved fault location method for AT traction power network based on EMU load test

The autotransformer(AT)neutral current ratio method is widely used for fault location in the AT traction power network.With the development of high-speed electrified railways,a large number of data show that the relation between the AT neutral current ratio and the distance from the beginning of the fault AT section to the fault point(Q-L relation)is mostly nonlinear.Therefore,the linear Q-L relation in the traditional fault location method always leads to large errors.To solve this problem,a large number of load-related current data that can be used to describe the Q-L relation are obtained through the load test of the electric multiple unit(EMU).Thus,an improved fault location method based on the back propagation(BP)neural network is proposed in this paper.On this basis,a comparison between the improved method and the traditional method shows that the maximum absolute error and the average absolute error of the improved method are 0.651 km and 0.334 km lower than those of the traditional method,respectively,which demonstrates that the improved method can effectively eliminate the influence of nonlinear factors and greatly improve the accuracy of fault location for the AT traction power network.Finally,combined with a shortcircuit test,the accuracy of the improved method is verified.

Road network extraction in classified SAR images using genetic algorithm

Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.

Evaluation of Traffic Network between Tourist Attractions Based on GIS in Tai’an City

Tai’an City is rich in tourism resources,and there are many A-class tourist attractions.Based on the functions and characteristics of geographic information system(GIS),as well as the spatial distribution characteristics and distribution structure of tourist attractions,the research methods of nearest-neighbor distance,connectivity,accessibility and closeness analysis are used to analyze road traffi c distribution between tourist attractions in Tai’an City.①The R value of the nearest-neighbor point is 0.27,which proves that tourist attractions in Tai’an City are in the state of clustering,and the spatial distance of tourist attractions in clustering area is small.②Theβindex andγindex of connectivity are 1.42 and 0.58,respectively,indicating that the traffi c network connectivity between tourist attractions is low,there is no dense traffi c network,and the optimal combination of tourism resources in tourist attractions is not high.③The accessibility index Ai is 37.68 km,indicating that the accessibility is low and the traffi c between tourist attractions is not perfect.The research results will provide the reference for scientifi c,systematic and objective comprehensive evaluation of existing A-class scenic areas and traffi c conditions in Tai’an City.

基于IHBA优化模糊C均值的三维WSN分簇路由算法

针对分簇路由算法在三维场景下能量消耗大、网络生存周期短的问题,提出了一种基于改进蜜獾算法优化模糊C均值的三维无线传感网分簇路由算法IFCRA。IFCRA分别对网络成簇、簇首选举以及数据传输阶段进行了优化。首先,采用改进蜜獾算法优化模糊C均值算法,解决模糊C均值易陷入局部最优的问题,并综合节点的距离和能量特征划分拓扑结构。其次,结合簇内节点的能量和相对距离构建最优簇首函数,平衡簇首负载。最后,采用自适应传输机制寻找中继节点,综合节点距离和能量构建最优传输路径函数,采用改进蜜獾算法进行寻优,均衡数据传输能耗。仿真结果表明,IFCRA在三维异构场景中分簇合理、能耗均衡且生存周期长。

基于POA-CNN-REGST的电梯钢丝绳滑移量预测方法

电梯曳引轮与钢丝绳之间的滑移量过大会导致重大意外事故的发生,针对这一问题,提出了一种基于鹈鹕优化算法-卷积神经网络-堆叠回归(POA-CNN-REGST)的电梯钢丝绳滑移量预测方法。首先,使用数据产生函数生成了样本,并对样本添加了高斯白噪声,分别使用POA-CNN-REGST、支持向量机(SVM)、相关向量机(RVM)对仿真数据进行了训练和学习;然后,对试验基地采集的电梯滑移量等相关数据进行了归一化处理,并用POA-CNN-REGST进行了电梯钢丝绳滑移量预测;最后,将结果与传统的统计学模型SVM和RVM进行了比较。研究结果表明:在使用相同的训练集和测试集时,在仿真数据分析中,其均方根误差为0.0496;在真实数据分析中,其均方根误差和平均绝对百分比误差低至0.0661和0.0733。无论是仿真数据或是真实数据分析,该模型预测准确度都远高于SVM和RVM,这表明其在电梯钢丝绳滑移量预测方面具有高度可靠性。

基于5G C−RAN技术的数字化矿山全光网研究

建设数字化矿山的首要问题之一是构建一张低时延、大带宽、高可靠的高品质信息网络,WiFi、4G等传统无线通信技术已无法满足矿山数字化转型的新需求。从数字化矿山对通信网络的需求出发,研究了5G技术在井下矿山应用的必要性和部署难点,指出基于集中式无线接入网(C−RAN)的井下5G组网方案可有效降低5G网络在井下的部署要求和难度,但须解决光纤资源消耗大、“哑资源”故障管理难2个问题。提出了基于5G C−RAN技术的数字化矿山全光网系统,从C−RAN接入网、高速全光网、智能管控平台3个层面阐述了系统架构,并研究了半有源光网络架构、低成本波分复用(WDM)高速传输、智能管控平台等关键技术。该系统采用直检WDM技术节省光纤资源,可将光纤使用数量减少91.67%,同时基于半有源架构、调顶操作维护管理(OAM)技术实现对光纤网络的低成本管控和灵活部署,破解井下巷道光纤资源紧张和光纤网络管理难题。实验结果表明:12个不同波长WDM光模块的发送光功率为3.5~5.2 dBm,接收灵敏度为−16.9~−19.0 dBm,链路预算能力可达21 dB以上,满足应用要求;消光比为4.7~5.1 dB,眼图裕量大于17.5%,表明了较高的信号质量;在低温−40℃和高温85℃下,WDM光模块发送光功率、接收灵敏度均存在一定性能劣化,但仍然能够满足10 km传输需求。现场应用结果表明,12个不同波长WDM光模块的发送光功率为3.7~5.6 dBm,接收灵敏度为−17.9~−16.3 dBm,其中最差通道的链路预算能力仍在20.2 dB以上,满足应用要求。

基于多导体回路法的AT供电方式长回路电容计算方法研究

提出了一种基于多导体回路法以接触网和正馈线为传输导体的电气化铁路AT供电方式牵引网长回路电容计算方法。该方法以与电荷唯一对应的回路为基本单元,对复杂多导体传输线系统开展电场描述,实现了AT供电方式牵引网长回路电容的精确计算。

基于FCM-WOA-LSTM的大坝变形预测模型及其应用

随着大坝变形监测资料的持续积累和变形测点数量的不断增多,预测分析全部变形测点往往需耗费大量的时间,容易造成反馈不及时的问题。对此,引入模糊C-均值聚类算法(FCM),根据大坝变形规律的相似程度进行分区,将鲸鱼优化算法(WOA)用于长短期记忆神经网络(LSTM)模型的参数优化,建立基于FCM-WOA-LSTM的大坝变形预测模型,以某混凝土双曲拱坝的实测变形资料作为样本数据进行预测分析,并与LSTM模型和SVM模型的预测结果进行对比。结果表明,FCM-WOA-LSTM模型预测结果的平均绝对误差MMAE、均方误差MMSE、均方根误差RRMSE均为3种模型中最小,且拟合段的3个评价指标值和预测段的3个评价指标值均接近,FCM-WOA-LSTM模型具有更高的预测精度和更好的适用性。

基于直观汉字构形原理的C^(3)-GAN字体生成优化方法

目的为了提升生成对抗网络汉字风格迁移的图像生成质量,实现汉字智能生成在字库产业中的实际应用,提出了一种基于直观汉字构形学的条件生成对抗网络字体生成优化方法(Optimizationof Conditional Fonts Generation with Chinese Character Configuration GANs,C^(3)-GAN)。方法建构了直观汉字构形模组(C^(3)Module),该模组包含了利于条件生成对抗网络进行汉字构形语义特征学习的全特征汉字字符集。C^(3)-GAN在条件生成对抗网络模型下进行字体生成训练,降低了必要训练样本数量,实现对字体生成效果的优化。结果使用C^(3)-GAN生成汉字图像的清晰度更高、字形更准确。在图像相似性定量评估中,使用C^(3)-GAN的实验组相比于其他模型,获得了更高的相似值和更小的误差值。结论使用C^(3)-GAN可以降低必要训练样本数量、提升汉字图像质量。在实际项目中具有一定的应用性和可操作性。

基于circRNA调控网络探讨七氟烷致老年小鼠认知功能障碍的分子机制研究

目的 基于circRNA调控网络探究circ0014063在七氟烷致老年小鼠认知功能障碍中的作用及其相关机制。方法 circRNA-seq检测七氟烷麻醉和未处理老年小鼠海马组织,根据关键差异circRNA绘制ceRNA调控网络,并对靶基因进行KEGG分析。将30只老年C57/BL6小鼠随机分为:对照组、模型组和治疗组,每组10只。模型组和治疗组使用七氟烷麻醉,治疗组尾静脉注射si-circ0014063,对照组和模型组注射si-NC。水迷宫实验比较各组小鼠认知功能差异,qRT-PCR检测各组小鼠circ0014063、miR-744-3p、Apoe基因表达,western blot检测各组小鼠Caspase3、Bcl-2蛋白表达,ELISA检测各组小鼠β淀粉样蛋白(Aβ)、β淀粉样物质前体蛋白(APP)、IL-1 β、TNF-α含量,并通过spearman法分析circ0014063与A β、APP、IL-1 β、TNF-α的相关性。结果 与未处理组比较,七氟烷麻醉组有238个circRNA表达降低,241个circRNA表达升高,其中circ0014063和circ 0032434表达显著升高(P <0.05)。circRNA调控网络预测circ0014063靶向4个miRNA,18个mRNA,circRNA0032434靶向4个miRNA,16个mRNA。KEGG分析circ0014063靶基因参与阿尔茨海默病(AD)、坏死性凋亡等通路。与对照组比较,模型组小鼠逃脱时间增加,穿台次数降低(P<0.05),海马组织circ0014063、Apoe、Caspase3表达升高(P<0.05),miR-744-3p、Bcl-2表达降低(P<0.05),Aβ、APP、IL-1 β、TNF-α含量升高(P<0.05)。与模型组比较,治疗组小鼠逃脱时间减少,穿台次数升高(P<0.05),海马组织circ0014063、Apoe、Caspase3表达降低(P <0.05),miR-744-3p、Bcl-2表达升高(P<0.05),Aβ、APP、IL-1 β、TNF-α含量降低(P<0.05)。circ0014063表达与Aβ、APP、IL-1 β、TNF-α含量呈正相关。结论 七氟烷可导致老年小鼠认知功能损伤,circRNA调控网络在其中发挥重要作用。七氟烷可能通过调控circ0014063/miR-744-3p/Apoe信号轴介导海马组织凋亡,最终诱发老年小鼠认知功能障碍。

基于TCNN-MADLSTM的全并联AT牵引网多元信号融合故障定位

全并联AT牵引供电系统上下行线路并入自耦变压器,致使故障信号多路径传播,且牵引网导线阻抗不连续,传统方法很难实现牵引网故障准确定位。基于AT牵引网结构,推导牵引网上下行导线故障电流幅值与故障距离的非线性关系;基于改进的卷积神经网络(Transformer-based CNN,TCNN)和记忆注意力解耦长短期记忆神经网络(Memory Attended Decoupled LSTM,MADLSTM),通过增加注意力机制和残差连接,增强多导线电流幅值与故障距离的非线性函数关系,从而提高牵引网故障定位的精度;将前述方法与传统的卷积神经网络(CNN)和长短期记忆神经网络(LSTM)进行不同噪声条件下的对比验证。结果表明:基于TCNN+MADLSTM算法进行故障定位时,可自适应构建故障距离与多导线电流幅值的非线性函数关系,以及自适应计算故障距离,无须考虑波速影响;相较于传统的CNN+LSTM算法,TCNN+MADLSTM算法故障定位精度更高,故障区段识别精度可达100%,故障定位精度达72.100 m,均方误差为0.016 km^(2)。

基于FCM-LSTM的光热发电出力短期预测

对光热电站的出力进行短期预测,可以有效应对太阳能随机性和波动性带来的影响,为电网调度做好准备.该文以青海某光热电站为例,首先使用模糊C均值聚类算法对预处理后的实验数据进行分类,然后通过分析不同聚类类型下出力和气象数据中各因子间的关联程度,充分挖掘出数据间的关系,确定不同类型预测模型的输入变量,进而构建出不同类别下的长短期记忆神经网络预测模型.结果表明,与传统长短期记忆神经网络模型、BP神经网络模型、支持向量机模型和随机森林模型的预测结果相比,基于模糊C均值聚类的长短期记忆神经网络预测模型效果良好,大幅减少了预测误差,验证了该预测模型的有效性.