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Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model. 相似文献
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借鉴自然界生态系统的典型特征,提出机器人生态圈概念。通过使集群机器人进行智能协同与复杂演化,涌现自我维持、自我复制与自我进化等生命特征,实现无人条件下的长期生存、繁衍与进化,并执行特定的任务。针对机器人生态圈典型任务场景的自主任务决策需求,分析不同机器学习任务决策方法的特点,建立机器人生态圈自主任务决策的决策树模型和神经网络模型。分析表明,两种模型的正确率均在80%~90%,且均具有良好的稳定性。这说明,机器人生态圈自主任务决策问题可以通过决策树、神经网络等机器学习方法来很好地加以解决,从而为面向无人化场景的任务应用提供技术支持。 相似文献
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在现代舰载指控系统的设计中软件所占比例日益增加,软件故障对系统可靠性的影响也更突出,需要对软件故障进行分析。根据现代舰载指控系统的特点,采用软件故障树分析方法,以舰载指控系统的软件系统故障为根节点,从上至下找出导致软件系统故障的所有可能因素,分析其因果逻辑关系,最后建立了现代舰载指控系统的软件故障树。软件故障树以图形演绎的方法直观地反映了现代舰载指控系统的软件故障特性,有助于采取有效措施提高系统的可靠性。 相似文献
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一种基于Petri网模型的系统动态安全性分析方法 总被引:1,自引:1,他引:0
针对目前安全分析方法通常只能给出导致系统失效的事件发生序列,而对系统运行过程中的一些时序、动态等因素考虑不足等问题,提出了基于Petri网模型的系统安全性动态分析方法,并结合系统状态可达树,建立了系统动态安全性分析模型,得出导致系统失效的所有可能事件的序列及其发生概率.最后,以实例验证了该方法的可靠性和有效性. 相似文献
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针对典型的舰船机械设备系统,运用故障树分析等现代可靠性理论分析方法,结合系统设备的技术性能分析、非电产品的结构和故障特点以及运行管理实际情况,研究系统设备、部件的故障机理,故障模式和故障影响效应,明确导致设备、部件故障的原因及发生模式。应用FTAS故障树分析软件包对其进行了定性、定量分析,获得了以概率结构事件、最小割集等形式表达的系统设备、部件故障对系统故障的作用模式,并对系统故障概率随时间的变化进行了拟合检验及评价了系统可靠性,为确定在指定远航任务期(90天)内所需的备品备件决策提供依据。 相似文献
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本文深入研究了基于YH-F2系统的算术表达式的并行计算特点,提出了嵌套层次树的概念和相应的计算模式。这种模式充分利用了YH-F2系统的并行计算能力,完满地解决了基于YH-F2的算术表达式的并行编译问题 相似文献
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徐章遂 《军械工程学院学报》1990,(2)
工程设备的结构日趋复杂,使故障诊断的难度不断增大。本文在最大故障信息量诊断的基础上提出了最大故障信息增量判断准则,可从故障特征群中一次找出最可能的故障点,简化了诊断过程,提高了诊断速度。 相似文献
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