Verifying hierarchical network nonlocality in general quantum networks

Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.

A NOVEL INTRUSION DETECTION MODE BASED ON UNDERSTANDABLE NEURAL NETWORK TREES

Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.

基于贝叶斯攻击树的电力系统网络物理安全研究

文章提出了一种基于贝叶斯攻击树与继电保护相结合的电力系统通信网络安全性评估策略,即在电力通信网络的信息层级,将贝叶斯网络、安全评估架构以及联合树算法进行融合,对信息层级的电力设备子站节点的安全性概率进行赋值,在物理层级采用模型分析电力设备子站所控制线路的故障时,会触发继电保护机制,并计算出切除故障时所要切除的负载占比。文章将信息层级的安全性概率和物理层级的故障相结合,提出新的安全性评估指标负载消减率,并从中寻找出安全性不佳的节点,以完成安全性评估。算例分析验证了文章所提方法的科学性和有效性,并证明了其可以进行大规模推广应用。

基于PNN神经网络的凿岩台车电液控制系统故障诊断研究

针对凿岩台车电液控制系统故障诊断效率低的问题,该文提出一种结合故障树分析法和概率神经网络(probabilistic neural network,PNN)的故障诊断方法。首先,基于电液控制系统的结构和工作原理构建其故障树模型;然后通过对故障树模型进行定性分析,确定其最小割集和典型故障种类,以选取的典型故障种类的关键参数构建故障征兆矩阵,通过PNN神经网络对该矩阵进行训练和计算,实现对系统典型故障状态的自动识别。实验结果表明,该文方法的平均诊断时间为1.2 s,平均诊断准确率为80%,能够快速准确地定位系统故障,可满足凿岩台车电液控制系统故障诊断的工程实际需求。

基于故障树与贝叶斯网络的动车组制动系统可靠性分析

高速列车制动系统的性能直接关系到行车的安全性。动车组的制动系统结构和原理复杂,故障模式复杂多样,研究分析其故障的模式和特点,有助于提高动车组的运行安全性和降低维修成本。该研究收集CRH某型动车组制动系统在一定运用周期内的典型故障数据,构建故障树,将故障树转化为贝叶斯网络模型,借助Netica软件构建贝叶斯网络计算模型,利用其双向计算功能,计算制动系统的故障率,分析导致制动系统异常的主要故障,为维护策略的制定提供科学依据。

证据实时更新的海上平台采油树系统动态风险评估方法研究

海上平台采油树系统是油气生产的重要设备之一。为研究证据实时更新的采油树系统动态风险评估问题,提出了基于动态贝叶斯网络、物元理论和证据理论的海上平台采油树系统动态风险评估方法。首先,根据采油树结构组成和故障率等,建立动态贝叶斯网络结构模型和参数模型;其次,依据物元理论对各故障模式的失效后果进行量化分析;最后,将新加入的证据转化为风险区间,计算采油树系统的风险区间,并对风险进行预测。以某采油树为例,对构建的模型进行了实证研究。结果表明:失效后果最严重的关键部件是采油树本体;对于采油树系统的各类阀门,最主要的故障模式是开关故障和阀门外漏;随着新证据的增加,风险区间的不确定性减小;采油树系统的套管翼阀、节流阀和地面安全阀风险偏高,需要采取相应的预防措施,以保证采油树系统的安全可靠运行。

基于T-S动态故障树的桁架机器人系统可靠性分析

为了提高桁架机器人系统的动态可靠性,并且在发生故障时提升排查故障的效率,提出一种对桁架机器人系统动态可靠性分析的方法。首先,对桁架机器人系统建立T-S动态故障树;然后,把T-S动态故障树和T-S动态门规则分别转化为离散时间贝叶斯网络(DTBN)与相应网络节点的条件概率表,根据DTBN正反向推理分别计算得到各时间段以及任务时间内系统失效概率和根节点后验概率,并获得各根节点的T-S概率重要度、T-S关键重要度和灵敏度;最后,应用Monte Carlo仿真法进行验证,结果显示相对误差为0.29%,证明所提方法可行。给出当系统发生故障时应优先排查的零部件,并确定了系统薄弱环节,得出应优先提高可靠度的零部件,为桁架机器人系统的动态可靠性分析提供了理论依据。

基于动态贝叶斯网络的液压系统可靠性研究

液压系统组成结构复杂且维修难度高,因此进行液压系统的可靠性评估对企业生产安全有着重要作用。结合故障树和动态贝叶斯网络建立评估模型对液压系统进行可靠性分析。利用动态贝叶斯网络的推理功能得出液压系统可靠度和可用度随时间推移的变化规律,并实现液压系统故障诊断,识别关键故障因素源。液压系统的可靠度会随时间推移而逐渐降低,但定期的维修可以使液压系统的可用度在较长一段时间内维持在很高水平。实验结果为液压系统的日常维护和修理策略提供了借鉴。

Providing Delay-Guaranteed Multicast Transmission Services for an SDN-Based Publish/Subscribe System

Providing end-to-end delay guarantees in traditional Internet is a complex task due to the distributed nature of TCP/IP protocols. Software Defined Networking(SDN) gives a new dimension to improve QoS(Quality of Service) as it can benefit from its flexibility, programmability and centralized view. In this paper, we provide delay-guaranteed data transmission service instead of "best efforts" service for a topic-based publish/subscribe system by means of exploring these specific features of SDN. We attribute this routing problem in such conditions to Delay-Constraint Lowest Cost Steiner Tree(DCLCST) problem. To solve it, we compute the shortest delay paths from source node to every subscribe node and the shortest cost paths from every subscribe node to any other node using dijkstra algorithm. Then we construct a delay-constraint least cost steiner tree for per-topic based on these paths as multicast tree. We also present experimental results to demonstrate the effectiveness of the algorithms and methods we proposed.

对建筑学专业知识体系变革与研究性课程体系建设的思考

跨学科专业课程建设是培养创新人才的关键,也是当前我国专业教育改革的重点和难点。围绕当前对新工科课程体系建设的普遍认知,本文通过深入分析当代信息化背景下知识传播和专业知识体系构成特征,指出面对专业知识由“树状”结构向网络化结构转变的趋势,应以问题为导向构建跨学科研究型专业课程体系,从而实现以学生为中心、以创新能力培养为主体的教学目标。以建筑学专业课程改革为例,从高年级通识课程体系建设、前沿性课题策划、师生共建的研究性专业课程等方面,提出了以创新能力为核心的网络化结构专业课程体系构建设想。为适应未来专业教育变革发展,需根据学科研究特点与教师的研究、实践特长,设置专门化培养方向,构建本硕贯通的相应研究性课程体系。通过多学科、复合型的建筑学专业“双师型”教学团队建设,引入高水平企业教师,从根本上解决建筑学专业教育与行业发展需求间脱节的问题,以保障建筑学专业教育体系对未来知识更新与行业快速发展变化的有效适应。

Design and implementation of an expert system for remote fault diagnosis in ship lift

In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

Task-Specific Feature Selection and Detection Algorithms for IoT-Based Networks

As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of applying Machine Learning-based cybersecurity in IoT devices is feature selection as most IoT devices are resource-constrained. This paper studies two feature selection algorithms: Information Gain and PSO-based, to select a minimum number of attack features, and Decision Tree and SVM are utilized for performance comparison. The consistent use of the same metrics in feature selection and detection algorithms substantially enhances the classification accuracy compared to the non-consistent use in feature selection by Information Gain (entropy) and Tree detection algorithm by classification. Furthermore, the Tree with consistent feature selection is comparable to the ensemble that provides excellent performance at the cost of computation complexity.

基于3D可视化的虚拟传输机房故障排查学习训练系统研究

光纤通信传送网是整个通信网系的基础传输平台,当前网络自动优化闭环自治的整体水平不高,对网络故障的快速感知和精准定位还不够智能,在网络规模不断扩大的同时,需要大量的人力资源去处理各种故障。因此,这就需要网络运维人员具备熟练的故障排查与处理能力,而目前关于故障排查学习训练的手段比较单一,且时效性不足,针对这种情况,该文提出一种基于3D可视化的虚拟传输机房故障排查学习训练系统设计构想,供大家借鉴。

活塞式氢气压缩机排气温度超限故障诊断方法

针对活塞式氢气压缩机各级排气温度超限的故障难以用传统故障的二态性准确描述,以及目前未见能对该故障实施自诊断的故障诊断系统等问题,以DW-(5.6-13)/(0.2-0.5)-20型无油润滑变频活塞式氢气压缩机为研究对象,对相关零部件进行了重要度分析,并设计了故障自诊断系统。首先,对该故障搭建了模糊高木-关野(T-S)故障树模型,并利用基于贝叶斯网络(BN)的T-S故障树分析法对底事件进行了重要度分析,以确定薄弱环节和故障零部件诊断顺序;然后,利用模糊数表征了零部件的故障状态,对排气温度超限故障发生概率进行了预测;最后,设计了基于规则的故障诊断专家系统,并对实例进行了故障原因的推理排查。研究结果表明:基于规则的故障诊断专家系统在对现有故障数据进行分析后,其诊断结果与事实相符,说明该系统能够准确地诊断出故障原因,并进行自诊断;同时,基于BN的T-S故障树分析法,使该系统可对顶事件故障发生概率进行预测,为活塞式氢气压缩机故障诊断提供了一个可行方案。

基于动态故障树联合动态Bayes网络的汽轮机跳机电磁阀配电系统可靠性研究

跳机电磁阀配电系统可靠性关系到机组安全运行。针对跳机电磁阀配电系统中因电源切换易造成跳机电磁阀误动问题,分析了误动及遮断系统失效原理,从取消电源切换装置和改变供电拓扑结构两方面考虑,提出两种配电系统改进方案,并采用动态故障树蒙特卡洛联合动态Bayes网络对跳机电磁阀配电系统进行可靠性研究。仿真结果表明,所提的改进方案不仅解决了电源切换引起电磁阀误动问题,提高了供电稳定性,而且系统具有较高的可用率。

基于改进决策树的高校网络安全态势感知系统设计

为解决传统系统应用效果不佳的问题,提出基于改进决策树的高校网络安全态势感知系统。硬件方面,利用网络流量采集器采集高校网络流量信息;软件方面,利用插值算法对网络流量信息进行插值处理,根据网络流量基尼纯度系数确定网络流量属性,建立用于态势感知的决策树,根据网络安全威胁等级划分原则确定等级。实验结果表明,该系统的正确率在90%以上,响应时间在120 ms以内,在高校网络安全态势感知方面具有广阔的应用前景。

人工智能算法在数字孪生系统中数据挖掘与数据分析的研究与应用

为解决数字孪生系统数据利用率低下的问题,结合5G、边缘计算、网络切片等新兴网络技术,提高系统前后端模块的数据采集和实时传输功能.使用YOLOv5s目标检测算法和CutMix数据增强法,对数据进行深度挖掘,将非结构化数据进行特征提取,标记为结构化数据;最终通过XGBoot算法进行多轮线性回归测试,优化系统的决策树,提高系统数据的整体利用率.

船舶压载水系统可靠性分析

压载水系统能过保证船舶在航行过程中船体稳定,是邮轮重要的组成部分。由于压载水系统结构复杂,系统可靠性难以分析。为了分析压载水系统的可靠性变化,考虑系统在连续时间下的状态变化,基于连续贝叶斯网络,建立了一种新的船舶压载水系统可靠性分析方法。首先基于压载水系统的故障机理,提出动态故障树模型。然后利用单位阶跃函数和冲激函数,将动态故障树模型转化为连续时间的贝叶斯网络,对系统可靠性进行分析,最后,利用算例对船舶压载水系统进行仿真,得到船舶压载水系统的可靠性以及剩余寿命随时间变化的曲线。

Uncovering hidden nodes and hidden links in complex dynamic networks

Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging task.In this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay interactions.By calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.

基于贝叶斯网络的地铁车辆空调系统可靠性分析

通过对地铁车辆空调系统可靠性分析确定系统薄弱环节,是确保设备安全可靠运行的关键技术之一。针对地铁车辆空调系统结构复杂、可靠性分析困难,提出一种基于贝叶斯网络的系统可靠性分析方法。通过对系统结构分析,结合现场故障数据,确定系统关键部件故障特征,构建基于贝叶斯网络的故障树可靠性推理模型,实现系统的可靠性分析。结果表明,地铁车辆空调系统发生故障的失效概率为0.025 5,其中电子膨胀阀为系统故障的最主要原因,通风机电机烧坏为系统的薄弱环节。