针对航空发动机滑油箱油量测量值易受多个参数影响导致滑油消耗率难以计算和预测的问题,提出了一种改进的滑油量数据提取规则和滑油消耗率预测方法。基于密度聚类算法(Density-based spatial clustering of applications with noise,DBS...针对航空发动机滑油箱油量测量值易受多个参数影响导致滑油消耗率难以计算和预测的问题,提出了一种改进的滑油量数据提取规则和滑油消耗率预测方法。基于密度聚类算法(Density-based spatial clustering of applications with noise,DBSCAN)等方法对发动机数据进行了清洗,获取平稳飞行状态下滑油量数据。使用最小二乘法对滑油量进行拟合,得到了滑油消耗率,平均拟合优度达到了0.86。在此基础上,利用多层感知器(Multi-layer perception,MLP)建立了滑油消耗率与飞行状态参数之间的关系,预测结果与实际值的平均绝对百分比误差为1.15%。本文提出的方法能够满足实际工程需求,为评估航空发动机滑油系统的健康状况提供了可靠参考。展开更多
Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classif...Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classify packets and improve visibility.Multi-Layer Perceptron(MLP)neural networks and Convolutional Neural Networks(CNNs)have been used to successfully identify individual packets.However,some datasets create instability in neural network models.Machine learning can also be subject to data injection and misclassification problems.In addition,when attempting to address complex communication network challenges,extremely high classification accuracy is required.Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models.After ensembles tuning,training time can be reduced,and a viable and effective architecture can be obtained.Because of their effectiveness,ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors.In this work,ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%.In addition,ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.展开更多
文摘针对航空发动机滑油箱油量测量值易受多个参数影响导致滑油消耗率难以计算和预测的问题,提出了一种改进的滑油量数据提取规则和滑油消耗率预测方法。基于密度聚类算法(Density-based spatial clustering of applications with noise,DBSCAN)等方法对发动机数据进行了清洗,获取平稳飞行状态下滑油量数据。使用最小二乘法对滑油量进行拟合,得到了滑油消耗率,平均拟合优度达到了0.86。在此基础上,利用多层感知器(Multi-layer perception,MLP)建立了滑油消耗率与飞行状态参数之间的关系,预测结果与实际值的平均绝对百分比误差为1.15%。本文提出的方法能够满足实际工程需求,为评估航空发动机滑油系统的健康状况提供了可靠参考。
文摘Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classify packets and improve visibility.Multi-Layer Perceptron(MLP)neural networks and Convolutional Neural Networks(CNNs)have been used to successfully identify individual packets.However,some datasets create instability in neural network models.Machine learning can also be subject to data injection and misclassification problems.In addition,when attempting to address complex communication network challenges,extremely high classification accuracy is required.Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models.After ensembles tuning,training time can be reduced,and a viable and effective architecture can be obtained.Because of their effectiveness,ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors.In this work,ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%.In addition,ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.
