共查询到19条相似文献,搜索用时 543 毫秒
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分析了电力远动通道监控系统的现状,为解决其不足之处提出了一种电力远动通道监控的新方案.采用了一种基于模糊模式识别的综合评判远动通道通信质量的方法来实现对远动通道的监控,并详细论述了方案中模糊模式识别模型的建立,同时给出了系统的实现方案.在实际应用中表明,该系统能够有效地解决目前监控系统存在的不足,为电力远动通道监控开辟了新途径. 相似文献
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针对航空机载设备可靠性增长试验数据不规则的特点,在AMSAA模型的基础上提出了识别异常点的AMSAA模型与处理截断数据的AMSAA模型,并给出了模型拟合优度检验方法。经实例验证表明,识别异常点的AMSAA模型可以在给定置信度下识别出异常点并排除异常点对瞬时MTBF(平均故障间隔时间)极大似然估计值的干扰;截断数据的AMSAA模型能够利用被截取的部分数据,得到准确的瞬时极大似然估计值。 相似文献
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针对常规抗差自适应滤波算法在PPP/INS组合导航应用中存在难以准确识别和分离观测粗差及运动异常对定位结果影响的问题,基于分类因子自适应滤波原理,提出了一种抗差自适应分步滤波算法。该算法首先执行第一步滤波,对状态模型异常信息进行隔离,仅对观测粗差进行诊断和抗差处理;然后在第一步滤波的基础上,执行第二步滤波,对状态模型异常进行诊断和自适应处理。算法分析表明,抗差自适应分步滤波算法可以准确地识别和分离观测粗差和运动异常扰动。实验结果表明,抗差自适应分步滤波算法能够进一步增强滤波算法抵抗观测粗差和运动异常扰动对滤波结果的影响,提高PPP/INS组合导航系统定位结果的稳定性和可靠性。 相似文献
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建立了双转子-支撑系统的有限元模型,模拟了质量不平衡故障和局部轴弯曲故障下的振动信号.根据有限元模型,提出了基于模型的双转子-支撑系统故障识别方法,依次通过单一故障遍历、双故障遍历和三故障遍历方法,实现了故障快速准确识别.仿真结果表明:该方法能够准确识别单一故障和多故障,同时确定故障发生的位置、严重程度和相位情况,优化了故障识别过程,理想情况下减少了98.9%的计算量,加快了故障识别的速度.此外,比较了添加了不同信噪比噪音信号的诊断结果,诊断结果相对误差控制在1%左右,表明该方法具有良好的抗噪声干扰的能力. 相似文献
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为通过声发射技术识别铝合金蜂窝板超高速撞击(HVI)的损伤状态,提出一种基于神经网络的损伤模式识别方法。通过超高速撞击实验获取声发射信号,结合精确源定位技术、时频分析技术、小波分析技术及模态声发射技术,提出了10个与损伤相关的特征参数,通过非参数检验分析其与损伤的关系,设计了一种基于贝叶斯正则化BP神经网络的超高速撞击损伤模式识别方法。建立最优网络模型,通过不同参数组合识别能力分析,优选出2种特征参数组合,通过非同源样本对其损伤模式识别能力进行验证。结果表明:传播距离与损伤模式无关,却是识别损伤模式的重要参数;125~250kHz频域的自动加窗小波能量比会降低损伤模式的识别能力;采用贝叶斯正则化的BP神经网络可以较好地识别蜂窝板超高速撞击损伤模式,参数组合为传播距离、上升时间、持续时间、截止频率、4个自动加窗小波能量比及小波能量熵,共9个参数,对任意选取非同源样本识别错分率仅为9.38%。 相似文献
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Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatially Smooth (WMFA-SS) for extracting invariant features in aircraft rec- ognition is proposed. According to the Graph Embedding (GE) framework, Heat Kernel function is firstly introduced to characterize the interclass separability when choosing the weights of penalty graph. Furthermore, Laplacian penalty is applied to constraining the coefficients to be spatially smooth in this algorithm. Laplacian penalty is able to incorporate the prior information that neigh- boring pixels are correlated. Besides, using a Laplacian penalty can also avoid the singularity of Laplacian matrix of intrinsic graph. Once compact representations of the images are obtained, it can be considered as invariant features and then be performed in classification to recognize different patterns of aircraft. Real aircraft recognition experiments show the superiority of our proposed WMFA-SS in comparison to other GE algorithms and the current aircraft recognition algorithm; the accuracy rate of our proposed method is 90.00% for dataset BH-AIR1.0 and 99.25% for dataset BH-AIR2.0. 相似文献
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失谐叶栅的受迫振动响应特性分析 总被引:1,自引:0,他引:1
采用基于计算流体力学(CFD)方法的降阶气动力模型并耦合结构运动方程,实现了存在外激励载荷时失谐叶栅受迫振动响应的快速分析。针对典型的跨声速叶栅,通过求解其位移响应幅值较系统地研究了失谐方式、失谐强度和叶片质量比对失谐叶栅受迫振动响应幅值的影响。研究表明文中刚度失谐形式可以改善叶栅振动的稳定性,同时导致系统受迫振动响应局部化程度的增加,并且受迫响应的最大振幅放大因子随失谐强度增加或者质量比降低存在先增大后减小的一个峰值,不同失谐形式则对这个峰值的大小有着明显的影响。由于该方法可高效地分析失谐叶栅受迫振动各参数对模态局部化的影响,在工程上有一定的应用价值。 相似文献
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《中国航空学报》2023,36(3):316-334
The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system. 相似文献
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提出了一种基于LSTM (Long Short Time Memory)模型的飞行历史数据挖掘模型的构建方法,此模型可以将飞行数据中有价值的目标数据自动提取出来。首先,通过滑动窗口法获得待检测数据;然后,将预先做好的训练样本数据输入到所构造的LSTM模型中进行训练,得到数据挖掘模型;最后,将待检测数据导入到训练好的LSTM模型中进行模式识别,将目标数据片段挖掘出来。结果表明,基于LSTM模型的飞行数据挖掘模型构建方法通用化程度高,可用于挖掘不同类型的目标数据,且识别率高,具有很高的工程应用价值。 相似文献
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Ljubisa Stankovic Igor Djurovic Thayananthan Thayaparan 《IEEE transactions on aerospace and electronic systems》2006,42(4):1496-1506
Micro-Doppler (m-D) effect is caused by moving parts of the radar target. It can cover rigid parts of a target and degrade the inverse synthetic aperture radar (ISAR) image. Separation of the patterns caused by stationary parts of the target from those caused by moving (rotating or vibrating) parts is the topic of this paper. Two techniques for separation of the rigid part from the rotating parts have been proposed. The first technique is based on time-frequency (TF) representation with sliding window and order statistics techniques. The first step in this technique is recognition of rigid parts in the range/cross-range plane. In the second step, reviewed TF representation and order statistics setup are employed to obtain signals caused by moving parts. The second technique can be applied in the case of very emphatic m-D effect. In the first step the rotating parts are recognized, based on the inverse Radon transform (RT). After masking these patterns, a radar image with the rigid body reflection can be obtained. The proposed methods are illustrated by examples 相似文献
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基于几何模式识别的发动机传感器故障诊断 总被引:4,自引:0,他引:4
提出一种基于几何模式识别技术的发动机传感器故障诊断方法,以解决传感器缓慢漂移故障和由于安装制造差异和性能蜕化等造成的模型不匹配难以区分的问题。传感器测量值输入到自适应模型中,产生一组部件性能修正因子,作为故障模式来对传感器故障进行诊断,每种故障或性能蜕化都对应惟一的模式,采用几何模式识别技术隔离出传感器故障。以某型涡扇发动机为对象进行的仿真结果表明,该方法能诊断出传感器小漂移故障,并能对部件状态进行监控。 相似文献