网络化系统易受到DoS攻击(Denial of Service Attacks)和FDI攻击(False Data Injection Attacks)等多重攻击,破坏了传感器量测数据的完整性和有效性,降低了滤波器的准确性,甚至使得滤波器发散。此外,系统建模误差、数据传输过程的多径...网络化系统易受到DoS攻击(Denial of Service Attacks)和FDI攻击(False Data Injection Attacks)等多重攻击,破坏了传感器量测数据的完整性和有效性,降低了滤波器的准确性,甚至使得滤波器发散。此外,系统建模误差、数据传输过程的多径传播等因素,使得过程噪声和量测噪声不再服从标准的高斯分布,呈现厚尾特性,降低了滤波器的估计性能。本文针对一类噪声服从厚尾分布的网络化非线性系统状态估计问题,考虑到系统面临的DoS攻击和FDI攻击等多重攻击导致滤波器估计性能差等问题,提出了基于学生t分布的抗攻击扩展卡尔曼滤波器。首先,借助学生t分布来近似厚尾噪声的概率分布,引入两个二元随机变量分别表征DoS攻击和FDI攻击,使用最小化估计误差协方差上界的方法,计算最优的滤波器增益矩阵,设计了多重攻击下的学生t扩展卡尔曼滤波器;其次,考虑到数据传输带宽有限,数据传输过程中面临的信道堵塞、信息丢失等问题,引入了事件触发机制,提高通信信道利用率,给出了事件触发机制下的学生t扩展卡尔曼滤波;最后,仿真验证了所提方法的有效性。展开更多
Epilepsy is a common brain disorder that about 1% of world's population suffers from this disorder. EEG signal is summation of brain electrical activities and has a lot of information about brain states and also u...Epilepsy is a common brain disorder that about 1% of world's population suffers from this disorder. EEG signal is summation of brain electrical activities and has a lot of information about brain states and also used in several epilepsy detection methods. In this study, a wavelet-approximate entropy method is ap-plied for epilepsy detection from EEG signal. First wavelet analysis is applied for decomposing the EEG signal to delta, theta, alpha, beta and gamma sub- ands. Then approximate entropy that is a chaotic measure and can be used in estimation complexity of time series applied to EEG and its sub-bands. We used this method for separating 5 group EEG signals (healthy with opened eye, healthy with closed eye, interictal in none focal zone, interictal in focal zone and seizure onset signals). For evaluating separation ability of this method we used t-student statistical analysis. For all pair of groups we have 99.99% separation probability in at least 2 bands of these 6 bands (EEG and its 5 sub-bands). In comparing some groups we have over 99.98% for EEG and all its sub-bands.展开更多
文摘网络化系统易受到DoS攻击(Denial of Service Attacks)和FDI攻击(False Data Injection Attacks)等多重攻击,破坏了传感器量测数据的完整性和有效性,降低了滤波器的准确性,甚至使得滤波器发散。此外,系统建模误差、数据传输过程的多径传播等因素,使得过程噪声和量测噪声不再服从标准的高斯分布,呈现厚尾特性,降低了滤波器的估计性能。本文针对一类噪声服从厚尾分布的网络化非线性系统状态估计问题,考虑到系统面临的DoS攻击和FDI攻击等多重攻击导致滤波器估计性能差等问题,提出了基于学生t分布的抗攻击扩展卡尔曼滤波器。首先,借助学生t分布来近似厚尾噪声的概率分布,引入两个二元随机变量分别表征DoS攻击和FDI攻击,使用最小化估计误差协方差上界的方法,计算最优的滤波器增益矩阵,设计了多重攻击下的学生t扩展卡尔曼滤波器;其次,考虑到数据传输带宽有限,数据传输过程中面临的信道堵塞、信息丢失等问题,引入了事件触发机制,提高通信信道利用率,给出了事件触发机制下的学生t扩展卡尔曼滤波;最后,仿真验证了所提方法的有效性。
文摘Epilepsy is a common brain disorder that about 1% of world's population suffers from this disorder. EEG signal is summation of brain electrical activities and has a lot of information about brain states and also used in several epilepsy detection methods. In this study, a wavelet-approximate entropy method is ap-plied for epilepsy detection from EEG signal. First wavelet analysis is applied for decomposing the EEG signal to delta, theta, alpha, beta and gamma sub- ands. Then approximate entropy that is a chaotic measure and can be used in estimation complexity of time series applied to EEG and its sub-bands. We used this method for separating 5 group EEG signals (healthy with opened eye, healthy with closed eye, interictal in none focal zone, interictal in focal zone and seizure onset signals). For evaluating separation ability of this method we used t-student statistical analysis. For all pair of groups we have 99.99% separation probability in at least 2 bands of these 6 bands (EEG and its 5 sub-bands). In comparing some groups we have over 99.98% for EEG and all its sub-bands.
