共查询到19条相似文献,搜索用时 78 毫秒
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使用可用度分配模型研究 总被引:3,自引:0,他引:3
使用可用性是某些复杂系统战备完好性的重要参数。将复杂系统按组成结构分成若干分系统,重点提出重要程度因素,并构建了重要度相同和不同的两种使用可用度模型,最后给出了混联系统的使用可用度分配模型及其求解方法。 相似文献
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中国是世界上在建核电机组数量最多的国家,未来中国的核电发电量将会逐渐占据更高的发电比例,随着犯罪人员技术手段的升级,提升实物保护PPS(Physical Protection Systems)的防护效能、保障核设施安全运转至关重要.本文从分析防护系统关键节点入手,提出一种基于马尔可夫链与敌手入侵中断评估EASI(Est... 相似文献
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针对目前软件可靠性不易评估的特点,建立一种基于马儿可夫链的软件可靠性评估模型。该模型在软件运行流程图的框架下,利用一定的统计学方法,使用线性代数方法来计算软件运行流程中各个状态的概率,建立软件可靠性评估模型,从而计算软件的可靠性;最后,利用软件设计时的判别准则来判断软件是否符合需求。 相似文献
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为解决网络攻击行为趋向分布化、规模化、复杂化、复杂化的问题,转传统被动防御为主动感知预测,实时掌握网络安全状况,及时发现甚至提前预测网络中的攻击行为,降低网络安全风险,本文提出一种基于基于马尔可夫链的网络安全威胁评估方法。试验结果表明,本文提出的模型方法与其他方法相比,表明基于马尔可夫链改进的网络安全态势感知方法的可行性和有效性。 相似文献
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针对网络攻击场景下一段时间内信息系统面临的安全风险,文中提出一种基于隐马尔可夫模型的风险评估方法,将网络主机的漏洞建模为隐马尔可夫模型中的状态,将可能受到的攻击建模为隐马尔可夫模型中的观察值,求解一段时间内的成功攻击概率;根据攻击成功后产生的代价和成功攻击的概率,得到时间段内总风险度量值。该方法可从整体角度对网络攻击场景下一段时间内的信息安全风险进行量化评估。 相似文献
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为了发现软件的脆弱点,通过动态监测行为,对软件及其模块在一段时间内运行的可信状况进行研究,提出了基于马尔可夫的检查点可信评估模型。模型通过在软件行为轨迹中织入若干检查点来反映软件运行的行为表现,然后对检查点可信程度进行等级划分,通过马尔可夫模型及检查点权重反映检查点可信情况,最后综合每个检查点的可信情况得到软件整体的可信性。实验结果表明该模型能够有效反映软件中各部分可信情况,验证了模型的合理性和有效性。 相似文献
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Qingchen Zhang Zhikui Chen Laurence T. Yang 《International Journal of Communication Systems》2015,28(9):1610-1619
Streaming data analysis is an important part of big data processing. However, streaming data is difficult to be analyzed and processed in real time because of the rapid data arriving speed and huge size of data set in stream model. The paper proposes a nodes scheduling model based on Markov chain prediction for analyzing big streaming data in real time by following three steps: (i) construct data state transition graph using Markov chain to predict the varying trend of big streaming data; (ii) choose appropriate cloud computing nodes to process big streaming data depending on the predicted result of the data state transition graph; and (iii) assign big streaming data to these computing nodes using the load balancing theory, which ensures that all subtasks are accomplished synchronously. Experiments demonstrate that the proposed scheduling algorithm can fast process big streaming data effectively. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Website navigability is acquiring a growing importance in website design and redesign, quality evaluation, and improvement.Existing navigability measures mainly depend on site link structure, so that they only consider the impact of site link structure for navigability and ignore the impact of Web page content. A continuous Markov chain model which depicts the user's surfing behavior can balance these two factors in the evaluation of website navigability, and it needs to estimate the page transition probabilities and user stay time according to user access log. In this way, we can obtain more reliable results for website navigability measure than the existed methods. Experiments show that our method is effective. 相似文献
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Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series. 相似文献
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现有多频带频谱感知方法经常利用宽带频谱的稀疏性来实现检测,当频谱占用率较高时具有较差的性能。针对这一问题,提出了一种基于相邻频带状态的多频带频谱感知方法。首先,通过引入黏性因子,建立了多频带状态和观测值的黏性隐马尔可夫模型。接着,详细分析了黏性隐马尔可夫模型中参数的迭代更新方式。最后,通过估计各频段观测值的后验均值实现了多频带频谱感知。仿真结果表明,不管宽带频谱是否具有稀疏性,所提方法的检测性能都优于传统方法,且在虚警概率为0.1、频带平均占用率为50%、平均信噪比为?12 dB时能达到接近0.99的检测概率,比其他方法的检测概率提升了约30%。另外,所提方法的收敛速度快于已有方法,因此具有更低的计算复杂度。 相似文献
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通过对马尔可夫模型进行深入的分析的基础上对隐马尔科夫模型做了详细的讨论,对马尔科夫模型在语音识别、疾病分析等方面的应用做了介绍,同时针对隐马尔科夫模型在估值问题、解码问题和学习问题等经典问题上的应用做了研究。最后讨论了马尔科夫模型其隐马尔可夫模型的缺陷,并提出相关的改进建议。 相似文献