人体染色体计算机自动分析

本文简要介绍了染色体图像的输入和预处理技术,建立了染色体的特征空间以及特征空间之间的距离,从而实现了最小距离法自动识别人体染色体。对染色体的其他识别方法文中也作了简要的讨论,最后给出了满意的实验结果,在临床中可用于优生诊断。     

小推力最优轨迹协态估计的高效机器学习方法

针对变比冲小推力轨迹间接优化中的协态变量初值猜测问题,提出了一种基于机器学习的协态变量初值高精度高效估计方法。首先,基于标称最优轨迹延拓,建立了状态量边值高扰动上限情形下的数据集生成方法,并分析了扰动上限对求解效率的影响。然后,构建了基于位置速度、轨道根数和改进春分点轨道根数多形式状态量组合输入的人工神经网络(ANN)映射关系,分析并优化了神经网络结构。将提出的方法应用于深空探测小推力转移场景,仿真结果表明该方法相对于标称轨迹直接扰动的数据集生成方法及单一形式状态量输入的人工神经网络映射方法,均有效地提升了求解收敛率,能够高效高精度地估计协态变量初值,实现轨迹快速优化。     

基于视觉识别的电解加工多孔表面评定方法研究

针对电解加工多孔表面特点,提出表面形貌评价指标,获取多孔表面图像并对其进行预处理,提取腐蚀率、不均匀度、灰度等特征参数,揭示各特征参数与电解加工多孔表面形貌的相关性,并通过实例证明该方法适用于电解加工多孔表面的测量。     

基于聚偏二氟乙烯(PVDF)压电薄膜的密封舱壁穿孔损伤识别技术

日益恶化的空间碎片环境将严重威胁空间站及航天员的在轨安全。文章基于聚偏二氟乙烯（polyvinylidene fluoride,PVDF）压电薄膜设计了一种密封舱壁穿孔损伤识别技术方案,用于识别空间碎片超高速撞击对航天器密封舱壁造成的损伤模式,可为航天员合理选择应急措施提供依据。首先,发射超高速弹丸穿透铝合金靶板以模拟密封舱壁被击穿损伤的情况,形成的碎片云撞击PVDF压电薄膜探头,利用信号采集设备获取高速撞击引起的去极化效应信号,识别该信号的频率特征;其次,对PVDF压电薄膜探头及其支撑结构进行敲击试验,模拟在轨运行时因碰撞产生的干扰信号,掌握此类干扰信号的频率特征。试验结果表明,当系统采样频率为20 MHz时：1）所获得的探头去极化效应信号具有极为陡峭的上升沿,且上升沿的时长为亚μs量级;2）去极化效应信号主要由1 MHz以下的信号组成,但也包含少量的3～10 MHz高频成分;3）敲击探头及其支撑结构所产生的干扰信号频率在20 kHz以下。可根据频率差异进行两种信号的识别。     

An unsupervised pattern recognition methodology based on factor analysis and a genetic-DBSCAN algorithm to infer operational conditions from strain measurements in structural applications

Structural Health Monitoring (SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for operational condition clustering in a structure sample using the well known Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The methodology was validated using a data set from an experiment with 32 Fiber Bragg Gratings bonded to an aluminum beam placed in cantilever and submitted to cyclic bending loads under 13 different operational conditions (pitch angles). Further, the computational cost and precision of the machine learning pipeline called FA + GA-DBSCAN (which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a genetic algorithm for the automatic selection of initial parameters of DBSCAN) was measured. The obtained results have shown a good performance, detecting 12 of 13 operational conditions, with an overall precision over 90%.     

基于模糊理论的复合材料超声检测缺陷类型识别

本文以复合材料常见缺陷分层、孔隙、疏松的超声波检测缺陷信号为研究对象,选取缺陷波的时域及频域特征,考虑到各特征量之间的不可避免地存在耦合关系,而且对缺陷区分的重要性、敏感度也有很大的差别,采用加权多面体隶属度概念构造隶属函数,依据最大隶属原则进行模式识别,从而区分复合材料缺陷类型。     

基于模糊粗集的航空发动机特征参数提取算法

提取发动机性能状态的特征参数,是提高发动机故障识别正确率和可靠性的必要条件.针对实际航空发动机故障参数所具有的模糊和连续性特点,提出了一种基于模糊粗糙集的特征参数提取算法,并应用到某型航空发动机故障识别.研究结果表明:属性约简的核则为导致发动机故障的特征参数,以此特征参数进行故障诊断,可保证较高的诊断精度;同时,该算法的抗干扰性提高了整个系统故障识别的正确率.该算法可用于航空发动机故障分类、故障诊断以及状态监控.      

基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。     

An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size....     

Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments....