Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

Using neural networks for the derivation of Runge-Kutta-Nyström pairs for integration of orbits

In this paper we present Runge-Kutta-Nyström (RKN) pairs of orders 4(3) and 6(4). We choose a test orbit from the Kepler problem to integrate for a specific tolerance. Then we train the free parameters of the above RKN4(3) and RKN6(4) families to perform optimally. For that we form a neural network approach and minimize its objective function using a differential evolution optimization technique. Finally we observe that the produced pairs outperform standard pairs from the literature for Pleiades orbits and Kepler problem over a wide range of eccentricities and tolerances.     

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.     

建筑室内空间定位技术研究

为解决某市行政中心楼因建筑体量巨大、室内结构复杂而导致办事人员不易寻找目的地的问题,本文研究了室内空间定位搜寻方案,探讨了室内无卫星信号的定位方式。借助无线网络信号定位分析了无线网络信号传输中涉及的测距、信号滤波、空间定位等核心技术问题,并进行了试验验证,收到了良好的效果。     

关于带有协作/竞争机制时变积分器网络一致性的收敛速率

本文探讨了时变无向网络达到状态一致过程中的收敛速率问题.首先基于一般的时变无向网络展开研究,证明了其收敛速率随耦合权重的增加而提高.同时,给出了收敛速率的明确下界,并指出这个下界是耦合权重的线性函数.基于这些结论,本文研究了带符号网络的收敛速率问题,发现只要带有正权值的边具有（δ,T）连通性,就可以增大这些边的权重而保持其余边（带有负权值）不变,使一致性以指数的速率达到.本文的证明过程主要基于完全一致可观这一概念.另外,本文也得到了这样的结论：一致性误差值可以从系统一段时间内的相对状态值中重建.这对于分布式网络中滤波器的状态估计有一定的借鉴意义.     

面向下一代灵活光网络的数字相干光传输技术

为了适应不同颗粒度的业务需求,最大化波分复用传输系统的频谱效率,光网络正逐渐朝着更灵活、高效、节能的趋势发展.基于带宽可变收发机（BVT）的灵活光网络传输技术因而得到了广泛的关注与研究.本文概述了国内外支持灵活光网络的数字相干光传输技术的最新研究进展,包括发射端支持传输速率可变的自适应编码、调制技术以及接收端相应的调制码型识别技术,最后对灵活光网络传输的未来研究方向进行了展望.     

基于马氏切换的时滞脉冲随机Cohen-Grossberg神经网络模型的均方指数稳定性分析

通过向量Lyapunov函数,给随机CGNNs以均方估计,研究基于马氏切换的脉冲时滞随机Cohen-Grossberg神经网络模型的均方指数稳定性,并利用数值例子对结论加以证明.     

矩阵半张量积方法在逻辑网络和相关系统中的应用综述

本文介绍了近期关于逻辑网络及其应用的一些最近的结果,包括逻辑网络的背景、矩阵半张量积（STP）的新矩阵乘积的理论、逻辑网络的一些基本研究以及一些当前的重要工作.特别介绍了逻辑网络的一些基本问题,比如可控性、可观性、稳定性、镇定、同步、最优控制以及扰动解耦等.由于在处理逻辑网络问题方面具有很大的潜力和优势,STP方法被海内外很多学者关注并将其用于相关问题的研究.目前,一些新的领域被广泛研究,包括牵引控制、函数扰动、系统解耦、输出追踪、符号动力学等.本文主要对基于STP方法研究逻辑网络以及STP方法的其他相关应用进行了综述.     

Improving neural network performance on the classification of complex geographic datasets

Neural Networks are now established computational tools used for search minimisation and data classification. They offer some highly desirable features for landuse classification problems since they are able to take in a variety of data types, recorded on different statistical scales, and combine them. As such, neural networks should offer advantages of increased accuracy. However, a barrier to their general acceptance and use by all but `experts' is the difficulty of configuring the network initially.  This paper describes the architectural problems of applying neural networks to landcover classification exercises in geography and details some of the latest developments from an ongoing research project aimed at overcoming these problems. A comprehensive strategy for the configuration of neural networks is presented, whereby the network is automatically constructed by a process involving initial analysis of the training data. By careful study of the functioning of each part of the network it is possible to select the architecture and initial weights on the node connections so the constructed network is `right first time'. Further adaptations are described to control network behaviour, to optimise functioning from the perspective of landcover classification. The entire configuration process is encapsulated by a single application which may be treated by the user as a `black box', allowing the network to the applied in much the same way as a maximum likelihood classifier, with no further effort being required of the user.