基于QPSO算法的支持向量机参数优化研究

应用具有量子行为的粒子群优化算法,对支持向量（SVM）进行参数优化研究．根据支持向量机的分类准确率和泛化能力之间的关系,应用QPSO算法选取比较优秀的参数模型,比较参数模型的各项性能,选取最适合实际需要的参数模型．仿真表明,QPSO算法的SVM模型与PSO算法相比在分类准确率和泛化能力上均获得更好的效果,经QPSO优化后的SVM整体性能明显提高．     

基于状态变换的数字电路冗余确认研究

以状态跳变图为基础,深入分析了冗余变换与非法变换的特征,提出结构冗余和功能冗余的概念,并讨论了可测故障、不可测故障和冗余之间的联系.最后结合验证和测试生成,提出状态冗余的隐含遍历确认策略.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

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.     

机床颤振中“双频颤振”的研究

文中首次提出了机床颤振中“双频颤振”的论点,并对其产生机理进行了理论探讨且得实验验证。作者认为:对机床颤振中“双频颤振”进行研究不仅具有很重要的实用价值,而且将导致对现行的机床颤振理论的某些修改。     

果蝇算法优化的相关向量机在电子装备中的应用

针对电子装备渐变故障预测问题,提出一种基于果蝇算法优化相关向量机的故障预测方法。该方法将原始时间序列数据进行相空间重构处理,并基于折交叉验证和果蝇算法优化相关向量机模型的核函数参数,从而建立故障预测模型,并以某型雷达发射机速调管监测数据对模型性能进行了验证。实验结果表明,相比已有方法,该方法在全局优化、收敛速度、预测精度以及预测可靠性方面都具有一定优势。     

先进上面级数字逻辑姿态控制律设计

建立了先进上面级姿态动力学模型和推力器配置方案,根据快速大角速度精确姿态机动的任务要求,设计了数字逻辑姿态控制律.考虑姿态角和姿态角速度的相互关系和测置误差的存在,将姿态相平面划分为多个控制区域,以节省燃料和避免测量误差的影响.在实际上面级参数下进行姿态机动控制仿真,采用数字逻辑姿态控制律能在16s内实现先进上面级俯仰角60°的大角度姿态机动,并能很好地保证姿态指向精度和姿态稳定度,控制效果也优于脉冲宽度调制.     

几类基本学习认知模型

本文给出了几个类型的学习认知模型,并加以讨论     

基于小波系数相关性的图像隐写分析

提出了一种新的针对空域图像隐写的盲检测方法。利用互信息分析秘密信息嵌入对图像小波系数在尺度方向和空间方向相关性的影响,使用马尔可夫模型挖掘小波系数层内和层间相关性,提取转移概率矩阵作为特征,以支持向量机（SVM）作为分类器。针对LSB匹配和随机调制隐写算法。实验表明,本方法能有效检测到未经JPEG压缩过的含密图像。     

基于小波矩和支持向量机的装甲车辆识别研究

分析了应用小波矩特征进行地面复杂背景下装甲车辆识别的理论依据,实地采集了某型坦克和某型步兵战车的灰度图像,提取其小波矩特征,采用支持向量机进行分类识别,进行了性能测试实验。结果表明：归一化后的图像的小波矩特征具有良好的不变性;小波矩特征对噪声和局部遮挡有较强的适应性,识别率比较稳定;支持向量机方法具有良好的分类识别能力。     

基于机器学习的固体火箭发动机无损检测

传统固体火箭发动机无损检测图像判读工作存在人工识别效率低、图像数据分散及数据利用率低等问题。本文借助机器学习算法与计算机视觉技术,利用大量发动机无损检测图像数据开展无损检测图像数据预处理、边缘检测以及数据模型训练和应用等技术研究,探索快速、准确获得发动机无损检测图像数据特征的方法,深入挖掘固体发动机无损检测数据的内在联系,找到潜在规律。本研究不仅为固体发动机无损检测图像判读提供了一种准确、高效的手段,同时,能够为发动机无损检测图像识别、测量、判读和发动机相关故障模式分析与故障诊断提供数据和决策支持,也能够为未来机器学习在固体发动机无损检测图像判读领域的深入应用提供实践探索和理论研究方面的参考。