中期冲突探测算法的研究与设计

综合考虑飞机实际飞行中存在多个航路转弯点及空中交通管制辅助系统需满足实时准确性的要求,设计了一种改进型的中期冲突探测算法.以雷达获取的飞机的当前位置和速度矢量作为初始状态,再结合航路飞行意图信息和转弯后的航迹误差积累方法,计算整合后的各航路分段冲突发生的概率来预测冲突发生的可能性.仿真实验结果表明,该算法能有效地用于中期冲突探测.     

适用于多航路的概率型中期冲突探测方法

通过分析飞机飞行过程中的航迹误差,提出了估计飞行冲突可能性大小的概率型中期冲突探测的思想。针对我国民航航路由多个航路段组成的特点,提出了改进的中期冲突探测算法。该方法参考飞行过程中的最小间距点,找出两飞机在整个预测时间内的5个极小间距点作为航迹误差的累积过程,从而更精确地描述了两机相遇几何中的联合航迹误差。最后实例仿真计算表明,该算法完全能满足空中交通管制对算法实时性的要求,可以应用于实际的空中交通管制系统中。     

基于交互式方法的飞行器短期冲突检测模型

为了提高航空器的短期冲突检测的准确性和可靠性,对传统算法与模型进行了研究,在此基础上设计了具有机动检测的交互式UKF算法,提出了短期冲突检测模型。该模型对飞行器的航迹进行了分析,然后对飞行器的冲突域检测、冲突脱离、机动检测和冲突确认进行了讨论,并根据该模型设计了冲突检测算法。仿真结果表明,该算法能够精确、实时的检测飞行器的航迹,克服了传统算法的冲突盲警告和轨迹不变问题,提高了冲突预警的准确性,为管制员提供了可靠的保障。     

飞行计划冲突预探测算法研究

对飞行计划冲突预探测进行了彻底的研究，给出了相应的预探测算法。在对各种冲突类型进行研究的同时，针对航迹交叉时横向间隔的冲突探测——预探测中最困难的问题，给出了相关的探测模型，结合严密的数学理论推导，得出相关的求解公式，全面地解决了该问题。当冲突存在时，该算法确定地给出冲突时间段和冲突的航路段。实践表明，该算法是非常有效的。     

空中交通管制中改进型冲突探测算法研究与应用

航路区飞行中的冲突探测由于误差的存在,决定了只能采用基于概率的冲突探测算法。文章对基于布朗运动的二维冲突概率算法进行了仿真分析,得出各种因素对冲突概率的影响效果。在此基础上对算法进行了三维扩展,给出了实际应用的流程。该改进型算法已经实际应用于空中交通管制指挥监测系统(ATCCMS)中,运行结果表明改进型算法是满足有效性和实时性要求的。     

非授权局域网拓扑探测系统的设计与实现

局域网拓扑探测作为网络安全研究的一项重要内容，目前已经很难适应复杂网络环境的需要，存在很多问题亟待解决。在对局域网网络层和物理拓扑探测算法研究的基础上，本文设计并实现了非授权局域网拓扑探测系统Network Probe。该系统以网络层和物理拓扑探测算法为核心，结合了SNMP协议和多种探测方法。测试表明，Network Probe在准准确性、完整性和探测效率等方面能够满足大规模异构局域网拓扑探测的要求。     

一种基于ADS-B信息的冲突告警算法

为了解决飞行流量持续快速增长导致的飞行安全问题,给出一种基于ADS-B信息的冲突告警算法。该算法的目的在于发现终端区或机场附近可能存在的飞行冲突,为飞行员采取避让措施提供足够的时间。算法主要由2个模型构成：第1个模型是飞行冲突区域划分模型,该模型包含保护空域(PAZ)和冲突空域(CAZ)2个区域;第2个模型是飞行冲突探测模型,该模型首先对冲突目标初选,然后对有可能发生飞行冲突的飞机从竖直和水平2个方向进行冲突探测并根据探测结果发出相应的告警信息。经大量的Monte-Carlo仿真实验表明该算法性能良好,能及时有效地发现飞行冲突,对确保终端区或者机场附近的安全有着重要意义。     

一种高效的船舶活动热点海域探测算法

针对传统数据处理工具处理海洋船舶位置大数据效率低、空间特征不敏感的问题,引入Spatial Hadoop空间大数据处理平台,提出一种基于Spatial Hadoop的船舶活动热点海域探测算法。该算法结合空间索引和MapReduce分布式处理框架实现了基于区域统计的热点海域探测方法。从理论上分析,该方法能够高效地探测船舶活动热点海域。利用真实AIS数据进行实验,并从不同海域和算法运行效率两个方面对实验结果进行了对比分析,结果表明该算法具有有效的探测结果和较高的处理效率,验证了理论分析的正确性。     

一种基于二分搜索的网络拓扑探测方法*

网络拓扑测量是人们对网络进行管理的基础,对网络仿真与建模、网络协议设计、网络算法优化等有重要意义,网络拓扑测量的关键在于其采用的方法。通过分析制约传统的Traceroute方法和DoubleTree算法性能的关键因素,基于二分搜索的思想,提出了一种网络拓扑探测方法BisearchTrace。实验结果表明,该方法有效地降低了网络负载,加快了拓扑探测的速度,提高了网络拓扑测量的效率,具有良好的实用性。     

基于粒子群优化算法的滤波器优化

给出一种基于粒子群优化算法（PSO）的模拟滤波器优化设计方法。传统的模拟滤波器的精度与效率均较差,引入PSO算法可对滤波器参数进行寻优。将滤波器的设计问题转化为滤波器参数的优化问题,然后利用粒子群优化算法对整个参数空间进行高效搜索以获得最优解;通过变异、重新随机化及采用自适应的惯性权重,提高了算法的搜索效率及收敛性。实例计算表明了算法在该类问题中的有效性和可行性。     

Fast conflict detection using probability flow

Probabilistic conflict detection has recently attracted much attention from researchers due to its importance in safe automated transport systems; however, current methods struggle to accurately calculate in real-time the probability of conflict for complex vehicle maneuvers in cluttered environments. We present a formulation for the general probabilistic conflict detection problem using the flow rate of conflict probability at the boundary of a conflict zone. For Gaussian distributed vehicle states, we then derive a tight upper bound for the probability of conflict over a time period, which can be calculated in real-time using adaptive numerical integration techniques. We present two examples to illustrate the performance of this method: the first example shows that this method is very efficient for simple environments and the second example shows that this method can calculate the conflict probability upper bound in real-time even for complex vehicle maneuvers in cluttered environments.     

授权中异常冲突的一种解决方法

冲突检测和解决是访问控制授权中的重要问题.对这些问题的探索我们通过研究方法比较进行.首先研究基于逻辑程序的一种授权系统规范,然后通过一个保健部门的典型问题来分析基于传统优先和组织结构的两种冲突解决方案,最后提出基于逻辑程序LPOD(带有序析取的逻辑程序)的针对高水平授权规范中异常冲突的一种解决方法.     

An intrusion detection and alert correlation approach based on revising probabilistic classifiers using expert knowledge

Bayesian networks are important knowledge representation tools for handling uncertain pieces of information. The success of these models is strongly related to their capacity to represent and handle dependence relations. Some forms of Bayesian networks have been successfully applied in many classification tasks. In particular, naive Bayes classifiers have been used for intrusion detection and alerts correlation. This paper analyses the advantage of adding expert knowledge to probabilistic classifiers in the context of intrusion detection and alerts correlation. As examples of probabilistic classifiers, we will consider the well-known Naive Bayes, Tree Augmented Naïve Bayes (TAN), Hidden Naive Bayes (HNB) and decision tree classifiers. Our approach can be applied for any classifier where the outcome is a probability distribution over a set of classes (or decisions). In particular, we study how additional expert knowledge such as “it is expected that 80 % of traffic will be normal” can be integrated in classification tasks. Our aim is to revise probabilistic classifiers’ outputs in order to fit expert knowledge. Experimental results show that our approach improves existing results on different benchmarks from intrusion detection and alert correlation areas.     

Web robot detection: A probabilistic reasoning approach

In this paper, we introduce a probabilistic modeling approach for addressing the problem of Web robot detection from Web-server access logs. More specifically, we construct a Bayesian network that classifies automatically access log sessions as being crawler- or human-induced, by combining various pieces of evidence proven to characterize crawler and human behavior. Our approach uses an adaptive-threshold technique to extract Web sessions from access logs. Then, we apply machine learning techniques to determine the parameters of the probabilistic model. The resulting classification is based on the maximum posterior probability of all classes given the available evidence. We apply our method to real Web-server logs and obtain results that demonstrate the robustness and effectiveness of probabilistic reasoning for crawler detection.     

基于非一致性数据库的概率查询策略与算法

完整性约束是保证关系型数据库中数据确定性的重要条件,现实中存在大量不确定、不满足完整约束条件,但仍具有使用价值。结合概率数据库理论,提出了一种新的针对非一致性数据库的查询策略,利用并、交、差、选择、投影、连接等约束方法,对非一致性数据进行修复,四元组概率计算方法和概率查询重写技术弥补了非一致性数据库查询的不足,减少了数据冲突的发生机率。     

A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach     

Multi-objective probabilistic optimum monitoring planning considering fatigue damage detection,maintenance, reliability,service life and cost

Effective and efficient service life management is essential for a deteriorating structure to ensure its structural safety and extend its service life. The difficulties encountered in the service life management are due to the uncertainties associated with detecting and identifying structural damages, and assessing and predicting the structural performance. To reduce these uncertainties, continuous long-term structural health monitoring (SHM) can be employed. However, a rational and practical SHM planning is required to simultaneously maximize the accuracy, efficiency, and cost-effectiveness in service life management. This paper proposes a probabilistic optimum SHM planning based on five objectives to be simultaneously optimized: minimizing the expected damage detection delay, minimizing the expected maintenance delay, maximizing the damage detection time-based reliability index, maximizing the expected service life extension, and minimizing the expected life-cycle cost. The formulations of the five objectives are based on the probabilistic fatigue damage assessment. The monitoring plannings associated with both a single- and a multi-objective probabilistic optimization process (MOPOP) are investigated. For efficient decision making in identifying the essential objectives and selecting a well-balanced solution among the Pareto optimal solutions, the degree of conflict among objectives and objective weights are estimated. The novel approach proposed in this paper accounts for the interdependencies among the five objectives considered and demonstrates the role of the optimum SHM planning in service life management of deteriorating structures. The proposed MOPOP SHM planning is applied to the hull structure of a ship subjected to fatigue.     

一种有效的可视化孤立点发现与预测新途径

孤立点发现是数据挖掘活动的重要组成部分,被广泛应用于电子贸易、信用卡等领域的欺诈检测。由于优良的拓扑结构保持和概率分布保持特性,SOM（Self-Organizing Maps）可作为一种有效的降维工具供分析人员获取隐藏于数据中的分布结构信息。在分析了当前基于距离的孤立点发现的基础上,提出了一种基于SOM的孤立点发现与预测新途径,具有可扩展性、可预测性、交互性、简明性等特征。实验结果表明,基于SOM的孤立点发现与预测是有效的。     

Detection probability criterion on the path for mobile object control problem in conflict environment

A problem to design probabilistic optimality criterion for mobile object in conflict environment is considered. Probability of the event that the object would not be detected during motion along the path is represented as a certain functional of the path. The relationship between detection probability of the object and its traveling time along the path is derived. Optimal traveling time along the path is evaluated.     

ATC系统中短期冲突探测方法的研究

飞行冲突探测是空中交通管制系统中的一个重要课题,本文结合我国空管的有关规定和飞行参数标准,介绍了在实践中探索并应用的一种冲突探测告警处理方法和运算步骤。