Prediction and Analysis of Society’s Networks

According to the information theory model, I use the qualitative analysis to explain the information flows through the media channel become news. Besides, I make use of a pyramid model to expound the relationship among each media. I combine the information theory model into the prediction process, taking advantage of curve fitting and listing out different ratios of media capacities. Finally I get the small relative error between the fitting result and the reality. Based on the ELM model, I classify the information in two ways on the basis of the four specific factors. By taking the influence degree of public opinion into account, I quantify the factors with different ratios to determine how they could be used in information spreading.

Changes of China’s Status in the Global System and Its Influencing Factors:A Multiple Contact Networks Perspective

Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

基于D-S证据理论的农作物气候品质预测方法研究:以晚熟杂交柑橘春见为例

【目的】基于多源气象数据构建果实品质(糖含量等级)预测模型,为科学评价果实气候品质及深入挖掘农产品气候资源提供科学依据。【方法】以晚熟柑橘春见果实为研究对象,利用多源数据融合技术、人工神经网络(BP神经网络、RBF神经网络和Elman神经网络)和D-S证据理论,包括气象数据质量控制、特征选取、特征级融合、决策级融合4个步骤,构建基于多源气象数据的果实品质(糖含量等级)预测模型。【结果】春见果实品质预测模型采用BP神经网络预测结果总体准确率为87.50%,平均绝对误差(MAE)为0.150,均方根误差(RMSE)为0.447;RBF神经网络预测结果总体准确率为85.00%,MAE为0.175,RMSE为0.474;Elman神经网络预测结果总体准确率为87.50%,MAE为0.150,RMSE为0.447;D-S证据理论决策融合总体预测准确率达95.20%,分别较BP神经网络、RBF神经网络和Elman神经网络提升7.7百分点、10.2百分点和7.7百分点,MAE和RMSE分别为0.040和0.214,均明显降低。【结论】D-S证据理论决策融合后的果实品质预测准确率相比单一神经网络预测更高、误差更小。

基于加权D-S证据理论的旋翼故障诊断

旋翼作为直升机的升力面和操作面,其健康状态对直升机的安全至关重要。旋翼故障诊断技术仍是直升机健康与使用监测系统(Health and usage monitoring system, HUMS)领域的薄弱环节,开发旋翼故障诊断技术具有重要价值。基于信息融合技术,首先分析了旋翼故障的诊断机理,建立了旋翼故障模型,通过流固耦合仿真获取了不同故障下桨叶、轮毂和机身的故障特征信息,生成数据集进行网络训练和验证。然后,利用遗传算法反向传播(Genetic algorithm-backpropagation, GA-BP)优化神经网络诊断3种类型的直升机旋翼故障,即后缘调整片误调、变距拉杆误调和桨叶质量不平衡。3个逐级神经网络分别对旋翼故障类型、故障位置和故障程度进行了诊断识别。最后采用加权的Dempster-Shafer(D-S)证据理论对旋翼故障进行诊断和分析。结果证明基于改进D-S证据理论的旋翼故障诊断方法能够成功应用到旋翼故障诊断中,并具有良好的识别效果。

Long-lasting,reinforced electrical networking in a high-loading Li_(2)S cathode for high-performance lithium–sulfur batteries

Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.

Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Alzheimer′s disease using network pharma⁃cology approach and in vitro experimental validation

OBJECTIVE To explore the new indications and key mechanism of Bazi Bushen capsule(BZBS)by network pharmacology and in vitro experiment.METHODS The potential tar⁃get profiles of the components of BZBS were pre⁃dicted.Subsequently,new indications for BZBS were predicted by disease ontology(DO)enrich⁃ment analysis and initially validated by GO and KEGG pathway enrichment analysis.Further⁃more,the therapeutic target of BZBS acting on AD signaling pathway were identified by intersec⁃tion analysis.Two Alzheimer′s disease(AD)cell models,BV-2 and SH-SY5Y,were used to pre⁃liminarily verify the anti-AD efficacy and mecha⁃nism of BZBS in vitro.RESULTS In total,1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula,and 1320 BZBS targets with high confidence were predicted.Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treat⁃ment of AD.In vitro experiments showed that BZ⁃BS could significantly reduce the release of TNF-αand IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells.BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells,significantly increased mitochon⁃drial membrane potential,reduced the expres⁃sion of Caspase3 active fragment and PSEN1,and increased the expression of IDE.CONCLU⁃SIONS BZBS formula has a potential use in the treatment of AD,which is achieved through regu⁃lation of ERK1/2,NF-κB signaling pathways,and GSK-3β/β-catenin signaling pathway.Further⁃more,the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action.The study lays a foun⁃dation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.

Wireless Network Security Using Load Balanced Mobile Sink Technique

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Study on the Action Mechanism of Euphorbia peplus in the Treatment of Alzheimer s Disease Based on UPLC-Q-TOF-MS/MS Combined with Network Pharmacology and Molecular Docking

[Objectives]Based on UPLC-Q-TOF-MS/MS,network pharmacology and molecular docking techniques,the mechanism of Euphorbia peplus in the treatment of Alzheimer s disease(AD)was studied.[Methods]The UPLC-Q-TOF-MS/MS technique was used to rapidly analyze the chemical components of E.peplus.Active components and potential targets of E.peplus were retrieved from TCMSP and Swiss Target Prediction database,and AD targets were screened using GeneCards database.The targets of E.peplus in the treatment of AD were obtained.The PPI network was constructed using String platform,and the network topology of Cytoscape software was used to compute and screen key targets,and the GO and KEGG pathway enrichment analysis was carried out in Metascape database to construct the"component-target-pathway-disease"network.Molecular docking was used to predict the binding properties of active ingredients and targets.[Results]The results of UPLC-Q-TOF-MS/MS showed that 83 compounds were identified from E.peplus,including 19 terpenoids,10 phenolic acids and phenols,16 flavonoids,2 phenylpropanoids,4 coumarins,1 alkaloid,1 anthraquinone and 30 other compounds.The results of network pharmacological analysis showed that 82 active ingredients were screened,and 279 common targets were identified for the treatment of AD,among which the key targets were ALB(albumin),GAPDH(glyceraldehyde triphosphate dehydrogenase),TNF(tumor necrosis factor),AKT1(serine/threonine protein kinase 1),and IL6(interleukin-6).KEGG enrichment analysis showed that key signaling pathways include cancer pathways,lipid and atherosclerosis,Alzheimer s disease,insulin resistance,serotonergic synapses,calcium signaling pathway,cAMP signaling pathway and other signaling pathways.Molecular docking results showed that 14-deoxyandrographolide,dehydroandrographolide,licochalcone B,apigenin and naringenin may be the key components of E.peplus in the treatment of AD.[Conclusions]The results suggest that E.peplus can be used to treat Alzheimer s disease through multi-component,multi-target and multi-pathway.

基于DWDM的超100 Gbit/s混合组网分析

【目的】近年来,数据通信量呈爆炸性增长,为了应对高速、高容量数据传输及网络应用场景多样化的需求,超100 Gbit/s密集波分复用(DWDM)混合组网作为一种高效的解决方案逐渐受到关注。文章通过对超100 Gbit/s DWDM混合组网的需求、关键技术及实际案例进行分析,为构建高容量、高效率的通信网络提供了技术支持和指导。【方法】文章首先阐述了网络发展在容量扩展和支持复杂网络设计方面的需求,其次重点介绍了超100 Gbit/s混合组网的关键技术,包括星座图整形、频谱整形和灵活栅格技术等。其中,为支撑混合组网的业务速率设计,提供了一种用于级联掺铒光纤放大器(EDFA)通信系统的光信噪比(OSNR)计算方法,仅利用信道配置信息、发端信号光功率、EDFA的增益及噪声等相关参数,即可计算出整个链路中各个波长的输出OSNR。最后,结合海外某网络案例,根据实际链路OSNR评估情况,合理进行混合速率网络设计,论证了超100 Gbit/s DWDM混合组网在实际工程中的应用效果。【结果】通过应用超100 Gbit/s DWDM混合组网,根据OSNR评估情况灵活配置传输速率和传输带宽,实现了200、600和800 Gbit/s混合速率网络部署,既满足了核心站点的大容量需求,也兼顾了边缘站点的长途大跨度需求,并在3年期间实现了网络的平滑升级和扩容。【结论】实践证明,超100 Gbit/s DWDM组网能够有效提升网络容量、灵活性及频谱资源利用率,同时也为网络的持续演进提供了空间,是推进大容量光传输网络发展的重要手段。

Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.

Exploring the mechanism of Huangjing pill in the treatment of Alzheimer’s disease based on network pharmacology and molecular docking

Objective To reveal the mechanism of Huangjing pill in treating Alzheimer’s disease(AD)based on network pharmacology and molecular docking technology.Methods We obtained the active ingredients and targets of Huangjing pill through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,and supplemented the effective components by consulting literature and predicted targets through the PharmMapper database.We used DrugBank,the GeneCards,the TTD,and the OMIM database to collect targets of AD.The Venn diagram was drawn and the key targets of Huangjing pill in the treatment of AD were obtained by Venny 2.1 platform.The Cytoscape 3.8.1 software was used to construct a network diagram of“drugs-active ingredients-key targets-disease”.The protein-protein interaction(PPI)network diagram was constructed through the STRING 11.5 database.DAVID database was used for Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.AutoDock Vina1.1.2 software was used for molecular docking of the active components and core targets,and PyMOL 1.7.2.1 software was used for visual processing.Results After screening,we obtained 13 active ingredients and 116 targets of Huangjing Pill,1438 related targets for AD,and 75 common targets.566 items by GO enrichment analysis and 149 items related to KEGG pathway enrichment were obtained.Molecular docking results showed that there is a strong affinity between the key active ingredients and the core targets.Conclusion This study revealed that Huangjing pill could treat AD through multiple components,multiple targets and multiple pathways.

Exploring the mechanism of Saposhnikoviae Radix and Chuanxiong Rhizoma treating Parkinson's disease based on network pharmacology and molecular docking

Backgroud:Parkinson’s disease(PD)is a neurodegenerative disorder with an increasing global prevalence.However,the development of drugs for PD treatment has not kept pace with the continuously growing number of patients.Currently,the search for new effective substances from natural drugs is a major research direction.Two Chinese medicinal materials,Saposhnikoviae Radix(Fangfeng)and Chuanxiong Rhizoma(Chuanxiong),are commonly used in the treatment of PD in China.However,the mechanism of their combination is not clear,and further research is needed.Methods:Data were collected from publicly available databases:TCMSP,UnitProt,GeneCards OMIM,PharmGKB,Therapeutic Target Database and DrugBank.Network pharmacology and molecular docking methods was used to analyze the data to discover the possible pharmacological effects of the two drugs in the treatment of PD.Results:Beta-sitosterol,Mandenol and Wallichilide were the active components of Saposhnikoviae Radix and Chuanxiong Rhizoma(FC),and they stably bonded with PD targets,including PTGS2,CASP3,AKT1 and JUN.The target genes of FC were significantly enriched in PD-associated pathways,including calcium signaling and apoptosis pathways.Moreover,the study revealed that the active components of FC may affect cellular structures,such as membrane rafts,membrane microdomains,membrane regions,and postsynaptic membranes,which,in turn,affect a variety of molecular functions and biological processes.Conclusion:The results of this study indicate the direction for clarifying the pharmacodynamic substances of FC,the extraction method of pharmacodynamic substances,as well as the mechanism and efficacy of pharmacodynamic substances.Importantly,this study provides a strategy for developing new therapeutic drugs for PD.

基于BiLSTM-Attention的F_(10.7)指数预测模型与中国自主数据集的应用

F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.

50 Gbit/s前传光模块关键技术研究与应用展望

随着全球5G网络的规模化商用及移动互联网流量的迅猛增长,更为丰富的无线频谱资源得以释放,高速率光模块在无线前传网络中的需求日益凸显。聚焦于50 Gbit/s前传光模块的关键技术,深入剖析了前传网络的两种主要组网方式,分析了前传链路的典型传输距离及链路损耗情况,探讨了10 km场景下粗波分复用(coarse wavelength division multiplex,CWDM)中各波长的最大色散值和最小色散值,并研究了相应的色散补偿策略,通过实验验证了不同多径干扰(multi-path interference,MPI)强度下系统光功率代价的变化,详细分析了多径干扰噪声对系统误码性能的潜在影响,为前传光模块参数的优化提供了重要的理论依据。此外,深入研究了50 Gbit/s前传光模块的光电芯片技术方案,对多种不同类型光模块的参数性能进行了严格的测试,提出了针对50 Gbit/s前传光模块光电接口参数的标准化建议,详细介绍了当前50 Gbit/s前传光模块在国内外的标准现状,并展望了未来应用的发展趋势。

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.

基于TS-DBN的地铁牵引系统可靠性分析

论文提出一种结合连续时间T-S动态故障树和贝叶斯网络(TS-DBN)的可靠性评估方法来模拟实际运营过程中牵引系统的动静态失效行为。首先,构建基于牵引系统单元结构的故障树模型;然后,通过应用T-S动态门的时序规则,给出T-S门的逻辑定义,进而计算子节点后验概率及重要度参数;将敏感节点进行排序,建立系统连续时间状态下的稳定度函数数学模型。该方法能够直观地反映牵引系统结构单元的敏感薄弱环节,可以为后续维修策略优化提供理论参考。

S波段与X波段天气雷达探测参量差异及组网融合处理研究

随着X波段双偏振雷达探测网的增多,与S波段双偏振雷达开展组网观测愈发具有必要性。利用北京地区2021年7月27日的一次强对流天气过程的观测资料,对X波段双偏振雷达组网观测值和S波段双偏振雷达观测值进行一致性定量分析,结果表明:两者观测的反射率因子(Z)一致性好,差分反射率(Z_(DR))差异集中在0 dB值附近,Z≥30 dBz时,差分相移率(K_(DP))差异显著。根据观测值差异特征,比较了探测参量直接融合和距离指数权重融合两种数据融合方式,通过分析偏振参量垂直剖面图发现,融合后可以相互弥补探测盲区,K_(DP)通过距离指数权重融合的方式过渡更加自然。本文融合效果可供不同波段雷达融合组网提供参考。