基于盲分离技术的水下多源环形阵定位方法研究

根据水下自动探测的工程实际技术发展需求,本文基于盲分离技术研究了环形阵对水下目标辐射噪声信号进行定位的方法,利用水声信号统计特性,通过构建针对水中目标的非线性函数和学习因子等,对自然梯度算法进行了改进,实船信号的盲分离实验结果表明,该方法改进了分离效果。在此基础上,建立了基于线性瞬时混合情况下的定位模型,完成了对六个水中目标的定位研究,取得了令人满意的结果。     

利用等效源方法计算轴对称场源分布的场

本文介绍的方法，是直接从场源分布出发，把电 介质的极化源及磁介质的磁化源等效为自由源的分布， 并巧妙利用场的叠加原理计算具有轴对称性的场源分 布的场．从而避免了繁杂的数学计算，突出了物理概 念．只要具备电磁学和数学分析的知识，就能解决很 多类似的问题．     

基于多源信息和深度学习的多作物叶面积指数预测模型研究

叶面积指数(LAI)是评价作物长势的重要参数,快速、准确、低成本地获取作物LAI对于指导作物田间管理有重要的意义。为了低成本获取多种作物的LAI,基于多源信息和深度学习构建了通用的LAI预测模型。在大豆、小麦、花生、玉米四种作物的六个生长时期进行了大田实验,以获取用于建模的多源信息。使用航拍无人机获取作物低空可见光图像、红边图像和近红外图像等多光谱图像信息,此外还采集相关的一维数据信息,包括无人机飞行姿态、拍摄高度、作物生长状态和环境光照。借助深度学习出色的图像和数据处理能力建立基于复杂输入信息的LAI预测模型,考虑到一维数据也要参与模型的训练过程,在设计模型时,采用了组合型网络架构。在卷积神经网络(CNN)算法提取图像深度特征的基础上加入了LightGBM算法用于结合图像特征和一维数据实现作物LAI的最终预测。CNN模型部分使用了VGG19, ResNet50, Inception V3和DenseNet201四种常见的结构。为了更好地说明CNN模型提取图像特征的能力,分析了不同图像输入下四种模型的作物分类情况。结果表明,以可见光、红边和近红外图像为输入时,四种模型的分类准确度均相较...     

基于线性子链法的压水堆裂变产物源项一体化计算方法

核电厂设计基准源项计算可为核电厂安全评审提供依据,同时也是辐射屏蔽计算的基础。基于压水堆堆芯、一回路和气载源项的研究基础,类比衰变常数引入了迁移常数和核反应常数的概念,进而总结了一体化计算上述源项中裂变产物源项的源项方程。针对源项方程变系数、大型、稀疏和刚性的特点,在时间离散近似的基础上,基于线性子链算法编写程序求解了上述方程。通过与典型压水堆工程文件对比,证明了程序的正确性和必要性。     

基于线性子链法的压水堆裂变产物源项一体化计算方法

核电厂设计基准源项计算可为核电厂安全评审提供依据,同时也是辐射屏蔽计算的基础。基于压水堆堆芯、一回路和气载源项的研究基础,类比衰变常数引入了迁移常数和核反应常数的概念,进而总结了一体化计算上述源项中裂变产物源项的源项方程。针对源项方程变系数、大型、稀疏和刚性的特点,在时间离散近似的基础上,基于线性子链算法编写程序求解了上述方程。通过与典型压水堆工程文件对比,证明了程序的正确性和必要性。     

基于多源信息加权融合的研制阶段测试性评估方法

装备在研制阶段通常缺乏有效的试验和使用数据,其测试性水平往往难以评估,针对上述问题,提出基于多源信息加权融合的研制阶段测试性评估方法;方法综合考虑了装备研制阶段可利用的仿真信息、专家经验信息以及类似装备的历史信息,构建了以3类信息为基础的装备测试性指标值密度分布函数,同时依据可信度原则确定了3类信息的融合权重;在此基础上,利用证据折扣组合方法评估装备当前的测试性水平;最后,通过案例应用证明了该方法的有效性。     

Burton-Miller改进型边界方程的多频计算方法

提出了一种采用Burton-Miller改进型边界积分方程进行多频计算的方法。将Burton-Miller方程中的高奇异积分转化为弱奇异积分形式,获得Burton-Miller改进型边界积分方程;将方程中格林函数进行Taylor级数展开,并把波数从方程中分离出来,从而使随波数变化的计算矩阵表示为波数的矩阵级数形式。数值分析表明,本方法不仅保证了解在全波数范围内的唯一性,并且计算频率点数较多时可以节约大量时间,提高计算效率。     

多源光谱特征组合的COD光学检测方法研究

水样的化学需氧量大小直接决定水质的污染程度,传统的检测方法都是源于氧化还原反应,对水样会造成二次污染。为此,提出一种基于多源光谱特征组合的水质化学需氧量光学检测方法,以不同地点实际水样为被测对象,分别采集其紫外和近红外光谱曲线,进行预处理后,通过非负矩阵分解算法进行光谱数据的特征提取、数据特征归一化,然后将组合特征输入训练集样本,通过粒子群最小二乘支持向量机算法对验证集水样的化学需氧量进行定量预测。讨论了非负矩阵分解算法中基光谱数目对预测模型的影响。实验结果显示,紫外光谱的最佳基光谱数目为5,近红外光谱的最佳基光谱数目为2;预测模型的验证集平方相关系数为0.999 8,预测均方根误差为3.26 mg·L-1;分别与不同特征提取方法(主成分分析, 独立成分分析)、不同光谱法(紫外光谱法, 近红外光谱法)以及不同的组合方式(数据直接组合, 先组合数据再提取特征)加以比较,表明非负矩阵分解算法更适合光谱数据的特征提取,粒子群最小二乘支持向量机算法作为实际水样的定量模型校正方法可以得到良好的预测精度。     

A Compressed Sensing Measurement Matrix Construction Method Based on TDMA for Wireless Sensor Networks

Compressed sensing theory has been widely used for data aggregation in WSNs due to its capability of containing much information but with light load of transmission. However, there still exist some issues yet to be solved. For instance, the measurement matrix is complex to construct, and it is difficult to implement in hardware and not suitable for WSNs with limited node energy. To solve this problem, a random measurement matrix construction method based on Time Division Multiple Access (TDMA) is proposed based on the sparse random measurement matrix combined with the data transmission method of the TDMA of nodes in the cluster. The reconstruction performance of the number of non-zero elements per column in this matrix construction method for different signals was compared and analyzed through extensive experiments. It is demonstrated that the proposed matrix can not only accurately reconstruct the original signal, but also reduce the construction complexity from O(MN) to O(d2N) (d≪M), on the premise of achieving the same reconstruction effect as that of the sparse random measurement matrix. Moreover, the matrix construction method is further optimized by utilizing the correlation theory of nested matrices. A TDMA-based semi-random and semi-deterministic measurement matrix construction method is also proposed, which significantly reduces the construction complexity of the measurement matrix from O(d2N) to O(dN), and improves the construction efficiency of the measurement matrix. The findings in this work allow more flexible and efficient compressed sensing for data aggregation in WSNs.     

基于多核点共享树的多源光组播路由方法

通过网络编码方法优化多核点选择和组播信息传输,本文提出一种基于多核点共享树和网络编码的光组播路由构造和波长分配方法、减少波长资源消耗和提高网络的负载平衡性能.首先,删除产生源点迂回回路的网络编码备选核点集合,采用启发式矩阵运算方法确定多源共享树的网络编码核点,实现多源共享树以最少的核点覆盖最多的源节点;然后,为减少波长信道消耗数目,在确定的核点到目的节点间加入网络编码方法传输信息;最后,讨论了多核点共享树的波长分配方法和目的节点成功解码的边分离路径方法.仿真结果表明:与单核共享树、基于网络编码的单核共享树相比,基于网络编码的多核点共享树组播路由方法需求最少的波长数目和获得最好的网络负载平衡性能.     

Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.     

基于邻域粗糙集和并行神经网络的故障诊断

针对目前使用神经网络诊断故障时出现的输入向量选择困难、网络结构复杂、对并发故障诊断效果不好等问题,提出了基于邻域粗糙集和并行神经网络的故障诊断方法。先利用邻域粗糙集对初始征兆进行约简,留下有价值的征兆作为神经网络的输入向量,然后针对每种故障类型设计一个神经网络。用多个训练好的神经网络来并行诊断故障,综合每个神经网络的结果给出最终的诊断结论。用转子实验台的实验数据对这种故障诊断方法进行验证,结果显示该方法能优化神经网络结构,且神经网络具有训练速度快、诊断正确率高的特点。     

基于改进高斯混合模型的运动目标检测与跟踪算法

针对传统高斯模型存在的不足,为了搞高运动目标跟踪精度,提出一种基于改进高斯混合模型的目标检测与跟踪算法。首先提取目标特征建立目标分类器,并将目标从前景是标记出来;然后通过多目标跟踪将目标为多种运动模式;最后采用高斯混合模型对跟踪与分类的结果进行融合,获得最终目标的位置。结果表明,本文方法不仅提高了目标检测与跟踪精度,而且可高斯模型以较好的满足目标跟踪的实时性要求。     

Mg-Si基热电材料量子化学计算

对于掺杂合适的元素Sb,Te,Ag,Cu使得Mg-Si基热电材料的热电性能大幅度提高的实验事实,欲从理论和计算上寻求支持,试图从原子、分子的层次上对此现象作出解释,因此建立了简化的Mg₂Si量子化学计算模型,采用密度泛函离散变分X_α量子化学计算法,计算了物质内部的结构信息,如共价键级和态密度等.计算结果表明,掺杂以后,晶体的共价键级被削弱,态密度图中的禁带宽度明显变窄,这与实验测试的结果是一致的.     

Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure

Deep neural networks may achieve excellent performance in many research fields. However, many deep neural network models are over-parameterized. The computation of weight matrices often consumes a lot of time, which requires plenty of computing resources. In order to solve these problems, a novel block-based division method and a special coarse-grained block pruning strategy are proposed in this paper to simplify and compress the fully connected structure, and the pruned weight matrices with a blocky structure are then stored in the format of Block Sparse Row (BSR) to accelerate the calculation of the weight matrices. First, the weight matrices are divided into square sub-blocks based on spatial aggregation. Second, a coarse-grained block pruning procedure is utilized to scale down the model parameters. Finally, the BSR storage format, which is much more friendly to block sparse matrix storage and computation, is employed to store these pruned dense weight blocks to speed up the calculation. In the following experiments on MNIST and Fashion-MNIST datasets, the trend of accuracies with different pruning granularities and different sparsity is explored in order to analyze our method. The experimental results show that our coarse-grained block pruning method can compress the network and can reduce the computational cost without greatly degrading the classification accuracy. The experiment on the CIFAR-10 dataset shows that our block pruning strategy can combine well with the convolutional networks.     

Self-Organized Criticality Analysis of Earthquake Model Based on Heterogeneous Networks

The original Olami-Feder-Christensen (OFC) model, which displays a robust power-law behavior, is a quasistatic two-dimensional version of the Burridge--Knopoff spring-block model of earthquakes. In this paper, we introduce a modified OFC model based on heterogeneous network, improving the redistribution rule of the original model. It can be seen as a generalization of the original OFC model. We numerically investigate the influence of theparameters θ and β, which respectively control the intensity of the evolutivemechanism of the topological growth and the inner selection dynamicsin our networks, and find that there are two distinct phases in theparameter space (θ, β). Meanwhile, we study the influence of the control parameter a either. Increasing a, the earthquake behavior of the model transfers from local to global.     

基于神经网络的钞票真假识别研究

利用神经网络与光电检测的技术研制了钞票真假识别系统.介绍了系统的结构组成、工作原理、软件系统、神经网络的优化设计、实验及测试结果.经实践验证,其识别结果稳定可靠,可应用于金融智能防伪点钞机与ATM机中.     

基于多层神经网络的非线性图像分割

提出了一种用多层神经网络对图像进行非线性分割的方法。讨论了所用多层神经网络的学习速度的改进与训练样本的选择方法。实验表明，该多层神经网络系统可用于实时图像分割，并能获得很好的结果。