一种基于三维激光点云数据的单木树冠投影面积和树冠体积自动提取算法

树冠投影面积和树冠体积是研究单木生物量估测、三维绿量测算等的重要测量因子。针对树冠结构复杂、形态各异,树冠因子难以精确测量等问题,为实现单木树冠投影面积和树冠体积的自动提取和精准测量,以三维激光扫描获取的树木点云数据为数据源,运用平面散乱点集凸包算法——Graham扫描算法和不规则体切片分割累加算法,以VC++6.0和Matlab 7.0混合编程,实现树冠投影面积和树冠体积的自动提取。以实验区选取具有代表性的22株不同树种的样木为研究对象,并与传统人工测量方法进行了对比分析。试验结果表明,树冠投影面积人工测量(A₄)结果与点云数据的自动计算(A_V)结果相关性较好,r=0.964(p<0.01),A₄高于A_V的平均值为25.5%。近似规则几何体方法树冠体积测量(V_C)结果与基于点云数据的自动计算(V_VC)结果之间呈现较强的相关性,r=0.960(p<0.001),V_C低于V_VC的平均值为8.03%。该方法基于高精度单木点云数据,实现了树冠结构的高精度快速重构、单木树冠投影面积和树冠体积的无损自动提取,可以为单木树冠结构的研究提供参考,在精准林业领域具有推广应用价值。     

基于模糊神经网络的自适应PID控制器

提出了一种应用于自适应PID控制器的神经网络与模糊控制相结合的算法,该算法可以有效地解决普通PID控制器依赖于对象的数学模型的缺点,可实现控制系统的在线自适应调整,可满足实时控制的要求。仿真结果表明,基于模糊神经网络整定的PID控制器具有较好的自学习和自适应性,具有较快的响应速度。     

连续级联喇曼光纤激光器耦合波方程的修正

从最基本的耦合波方程出发,考虑光波正反向传输的情况下,推导出了一个全面的、准确的关于连续级联喇曼光纤激光器的理论模型,描述了各级Stokes光波功率沿光纤长度的变化.指出了相关一些文献中存在的不足,并且给出了多级斯托克斯光波相互作用的有效作用面积的表达式.此模型有助于喇曼光纤激光器的设计.     

In-situ EXAFS study on the thermal decomposition of TiH_2

Thermal decomposition behaviors of TiH_2 powder under a flowing helium atmosphere and in a low vacuum condition have been studied using an in situ EXAFS technique.By an EXAFS analysis containing the multiple scattering paths including H atoms,the changes of the hydrogen stoichiometric ratio and the phase transformation sequence are obtained.The results demonstrate that the initial decomposition temperature is dependent on experimental conditions,which occurs,respectively,at about 300 and 400℃ in a low vacuum condition and under a flowing helium atmosphere.During the decomposition process of TiH_2 in a low vacuum condition,the sample experiences a phase change process:δ(TiH_2)→δ(TiH_x)→δ(TiH_1)+β(TiH_x)→δ(TiH_x)+β(TiH_x)+α(Ti)→β(TiH_x)+α(Ti)→α(Ti)+β(Ti).This study offers a way to detect the structural information of hydrogen.A detailed discussion about the decomposition process of TiH_2 is given in this paper.     

基于直推式学习的网络故障诊断算法

网络故障的及时诊断能够保证日常工作、学习和生活能够正常进行。传统的基于监督式学习的诊断方法依赖于大量具有鉴别意义的样本,这在实际情况中通常难以得到满足。针对上述问题,本文提出了一种基于直推式学习的诊断算法。针对大规模的网络管理的特征数据,本算法利用主成分分析对特征进行降维,并利用新的度量下的特征数据来构建拉普拉斯矩阵;该矩阵能够很好的描述带检测样本和训练样本之间的关系。在此基础上,本文设计了基于直推式学习的目标函数,并利用拉格朗日乘子法完成了优化。实验部分证明了本算法能够在有限数目的带标签的样本的前提下获得精确的分类结果,能够显著提高网络故障诊断的检测率。     

Detecting community structure using label propagation with consensus weight in complex network

Community detection is a fundamental work to analyse the structural and functional properties of complex networks.The label propagation algorithm(LPA) is a near linear time algorithm to find a good community structure. Despite various ubsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the asic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these onsensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the dge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number f partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps,y computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter o adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an pproach named the label propagation algorithm with consensus weight(LPAcw), and the experimental results showed that he LPAcw could enhance considerably both the stability and the accuracy of community partitions.     

The criteria for selecting a method for unfolding neutron spectra based on the information entropy theory

To further expand the application of an artificial neural network in the field of neutron spectrometry, the criteria for choosing between an artificial neural network and the maximum entropy method for the purpose of unfolding neutron spectra was presented. The counts of the Bonner spheres for IAEA neutron spectra were used as a database, and the artificial neural network and the maximum entropy method were used to unfold neutron spectra; the mean squares of the spectra were defined as the differences between the desired and unfolded spectra. After the information entropy of each spectrum was calculated using information entropy theory, the relationship between the mean squares of the spectra and the information entropy was acquired. Useful information from the information entropy guided the selection of unfolding methods. Due to the importance of the information entropy, the method for predicting the information entropy using the Bonner spheres' counts was established. The criteria based on the information entropy theory can be used to choose between the artificial neural network and the maximum entropy method unfolding methods. The application of an artificial neural network to unfold neutron spectra was expanded.     

Passive neutron area monitor with pairs of TLDs as neutron detector

A passive neutron area monitor has been designed using Monte Carlo methods; the monitor is a polyethylene cylinder with pairs of thermoluminescent dosimeters (TLD600 and TLD700) as thermal neutron detector. The monitor was calibrated with a bare and a thermalzed ²⁴¹AmBe neutron sources and its performance was evaluated measuring the ambient dose equivalent due to photoneutrons produced by a 15 MV linear accelerator for radiotherapy and the neutrons in the output of a TRIGA Mark III radial beam port.     

New collective modes of interaction nature in inhomogeneous Ising networks

We propose a vertex formulation of the Ising model with inhomogeneous external field on multiconnected networks possessing a superbond structure. The related technique based on gauge degrees of freedom enables us to recognize new collective modes of interaction nature, which provide an exact solution of the inverse profile problem and an explicit form of a local free-energy functional on an extended magnetization-mode space. Application is made to a square strip.