一个网络并行计算新平台

编写网络并行计算程序对一般人来说是艰难的,用户任务的分解、分配以及在子任务间的交互等问题都需要具有高超的技巧。从改善用户并行程序设计环境出发,给出了在网络并行计算的一种新平台。说明了该系统平台的结构实现,组成该平台的任务描述器、任务的调度和任务控制器。而在网络并行计算的一种新架构中,用户只需提交数据和对它们的操作,而复杂问题让系统处理,这无疑是网络并行计算方法发展的有益尝试。     

Delay learning and polychronization for reservoir computing

We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich [Polychronization: computation with spikes, Neural Comput. 18(2) (2006) 245–282], on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network.     

Evaluation of reservoir sedimentation using data driven techniques

The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets.     

航空遥感数据同化算法集成计算与可视化

遥感数据同化技术在动力模型框架内,使用数据同化算法对动力模型输出的定量(物理、化学量)数据与观测数据进行一致性处理与结果误差分析。将多源遥感数据同化到动力模型预测与参数估计中,可帮助改善地表、大气和海洋变化的分析和预测精度。以国家发改委"十二五"建设的国家航空遥感系统项目为依托,针对航空遥感系统10种传感器设计开发数据同化系统。因无法找到适用于该系统的3DVAR和EnKF算法程序,必须自主开发核心算法程序。介绍了研究开发的航空遥感数据同化算法集成计算与可视化系统及其核心算法的关键技术流程。实验结果证实,该系统可以有效地对航空遥感数据进行同化。     

Evolutionary neural networks and DNA computing algorithms for dual-axis motion control

A new method is proposed to deal with the dual-axis control of a multi-variables system with two induction motors. Investigation of resolving the cross-coupling problem of dual-axis platform is addressed by a neural net-based decoupling compensator and a sufficient condition ensuring closed-loop stability is derived. An evolutionary algorithm processing the universal seeking capability is proposed for finding the optimal connecting weights of the neural decoupling compensator and the gains of PID controllers. Extensive numerical studies verify the performance and applicability of the proposed design under a variety of operating conditions.     

自愿计算网络中新的距离预测模型

描述了自愿计算环境下主-从模式的并行应用的特点及自愿计算网络的拓扑结构,提出了一种该环境下的网络距离预测模型.该模型处于应用级别上、采用被动监控的方式获取距离预测信息,该网络距离预测模型可以减少测量开销并使预测精度满足实际应用所需.分析了距离预测模型的协议实现与代价评估,最后通过构建自愿计算网络环境,运行并行程序,对该模型进行了测试与性能分析,实验结果验证了该模型的高效性与负载均衡性.     

Distributed cooperative spectrum sensing in cognitive radio for ad hoc networks

Cognitive radio refers to an intelligent radio with the capability of sensing the radio environment and dynamically reconfiguring the operating parameters. Recent research has focused on using cognitive radios in ad hoc environments. Spectrum sensing is the most important aspect of successful cognitive radio ad hoc network deployment to overcome spectrum scarcity. Multiple cognitive radio users can cooperate to sense the primary user and improve sensing performance. Cognitive radio ad hoc networks are dynamic in nature and have no central point for data fusion. In this paper, gradient-based fully distributed cooperative spectrum sensing in cognitive radio is proposed for ad hoc networks. The licensed band used for TV transmission is considered the primary user. The gradient field changes with the energy sensed by cognitive radios, and the gradient is calculated based on the components, which include energy sensed by secondary users and received from neighbors. The proposed scheme was evaluated from the perspective of reliable sensing, convergence time, and energy consumption. Simulation results demonstrated the effectiveness of the proposed scheme.     

Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks

Tropical forest condition has important implications for biodiversity, climate change and human needs. Structural features of forests can serve as useful indicators of forest condition and have the potential to be assessed with remotely sensed imagery, which can provide quantitative information on forest ecosystems at high temporal and spatial resolutions. Herein, we investigate the utility of remote sensing for assessing, predicting and mapping two important forest structural features, stem density and basal area, in tropical, littoral forests in southeastern Madagascar. We analysed the relationships of basal area and stem density measurements to the Normalised Difference Vegetation Index (NDVI) and radiance measurements in bands 3, 4, 5 and 7 from the Landsat Enhanced Thematic Mapper Plus (ETM+). Strong relationships were identified among all of the individual bands and field based measurements of basal area (p<0.01) while there were weak and insignificant relationships among spectral response and stem density measurements. NDVI was not significantly correlated with basal area but was strongly and significantly correlated with stem density (r=−0.69, p<0.01) when using a subset of the data, which represented extreme values. We used an artificial neural network (ANN) to predict basal area from radiance values in bands 3, 4, 5 and 7 and to produce a predictive map of basal area for the entire forest landscape. The ANNs produced strong and significant relationships between predicted and actual measures of basal area using a jackknife method (r=0.79, p<0.01) and when using a larger data set (r=0.82, p<0.01). The map of predicted basal area produced by the ANN was assessed in relation to a pre-existing map of forest condition derived from a semi-quantitative field assessment. The predictive map of basal area provided finer detail on stand structural heterogeneity, captured known climatic influences on forest structure and displayed trends of basal area associated with degree of human accessibility. These findings demonstrate the utility of ANNs for integrating satellite data from the Landsat ETM+ spectral bands 3, 4, 5 and 7 with limited field survey data to assess patterns in basal area at the landscape scale.     

A variational approach to intensity approximation for remote sensing images using dynamic neural networks

Abstract: In remote sensing image processing, image approximation, or obtaining a high‐resolution image from a corresponding low‐resolution image, is an ill‐posed inverse problem. In this paper, the regularization method is used to convert the image approximation problem into a solvable variational problem. In regularization, the constraints on smoothness and discontinuity are considered, and the original ill‐posed problem is thereby converted to a well‐posed optimization problem. In order to solve the variational problem, a Hopfield‐type dynamic neural network is developed. This neural network possesses two states that describe the discrepancy between a pixel and adjacent pixels, the intensity evolution of a pixel and two kinds of corresponding weights. Based on the experiment in this study with a Landsat TM image free of added noise and a noisy image, the proposed approach provides better results than other methods. The comparison shows the feasibility of the proposed approach.     

面向数据密集应用的云计算服务架构

提出了一种具有云计算特点的可伸缩的服务器架构,通过采用模块化的方式有效地分割服务功能,能以对用户透明的方式满足三维网络应用的各种存储和带宽的需要。将该方案在一个三维虚拟现实项目中进行测试,结果表明,该方案有较好的伸缩性和效率。     

无线传感器网络中基于压缩感知的分簇数据收集算法

针对无线传感器网络(WSNs)能量有限、通信链路不可靠的特点,提出一种基于稀疏分块对角矩阵进行压缩感知的分簇(SBDMC)数据收集算法.该算法以稀疏分块对角矩阵作为观测矩阵以减少参与收集节点数目;采用分布式分簇路由实现数据的分布式收集;通过分析能耗模型得到最优簇头数目以减少网络能耗.在此基础上,给出一种有效的分簇路由数据收集算法.仿真分析表明:提出的算法较之已有算法可以减少通信能耗、延长网络寿命,同时均衡能耗负载.     

基于LabVIEW的无线分布式传感网络控制系统的研究

为了满足无线分布式传感网络中仪器间的无线通信和实验数据的统一管理和控制,以LabVIEW为开发工具,采用PC机多线程技术和虚拟仪器技术,构建了基于LabVIEW的无线分布式传感网络的控制系统,阐述了控制系统的硬件组成和软件设计方法,分析了在LabVIEW平台下,数据显示、分析、存储和通信等方面的特点。     

基于感知网格的无线传感器网络动态采样策略

在分析无线传感器网络时空相关性模型的基础上,提出一种基于感知网格的无线传感器网络动态采样策略.将监测区域划分为多个感知网格,感知网格内只有簇头节点保持活跃状态,当出现异常数据后再激活感知网格内其他节点来获得更详细的信息.该策略通过减少无线传感器节点之间相同的或相近的采样数据上传来降低冗余信息的传输.仿真结果表明:该策略显著提高了无线传感器网络能量效率.     

Detection time analysis for the multiple-user cooperative spectrum sensing scheme in cognitive radio networks

In this paper, a novel multiple-user cooperative spectrum sensing (MCSS) scheme is proposed to achieve the spatial diversity gains for cognitive radio (CR) networks, where only the best relay, selected among all the candidate relays in accordance with the quality of channel conditions, is utilized to cooperatively detect the primary user. Closed-form expressions of detection time for the traditional non-cooperative and the proposed MCSS schemes are derived over Rayleigh fading channels. In addition, numeric...     

IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications

Reputation systems are fundamental for assessing the quality of user contributions in participatory sensing. However, naively associating reputation scores to contributions allows adversaries to establish links between multiple contributions and thus de-anonymize users. We present the IncogniSense framework as a panacea to these privacy threats. IncogniSense utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. Simulations are used to analyze various reputation cloaking schemes that address the inherent trade-off between anonymity protection and loss in reputation. Our threat analysis confirms the robustness of IncogniSense and a prototype demonstrates that associated overheads are minimal.     

应用压缩感知的无线传感器网络数据处理综述

研究了压缩感知在无线传感器网络数据处理方面的应用。介绍了压缩感知技术和无线传感器网络的发展及研究现状,并从数据融合、信号采集、信号路由传输以及信号重构4个方面,对近年来基于压缩感知的无线传感器网络数据处理研究进行了详尽的分析,提出数据安全的重要性。总结并展望了压缩感知技术未来的研究方向。     

RBF神经网络在储层表征中的应用研究

研究利用RBF神经网络技术进行石油储层表征中有关储层参数的计算与岩性的识别;建立了储层参数(渗透率)预测模型与岩性识别模型,并利用该两个模型对未知样本进行预测,预测结果与实际测量结果相比具有较好的一致性,其渗透率预测精度与收敛速度较BP神经网络模型有了很大的提高;应用表明,RBF神经网络在储层表征问题中有着广阔的应用前景。     

基于压缩感知的链型无线传感器网络节能数据收集

无线传感器网络(WSNs)中节点受体积、功率、成本等限制而导致了节点能量、生命周期有限的问题.提出一种基于压缩感知算法的无线传感器网络节能优化方法,并结合无线传感器网络中的链型拓扑网络模型,给出基于压缩感知理论的节能网络数据传输模型.通过理论分析比较表明压缩感知方法在节能方面的优越性,然后在得出的网络能耗模型的基础上进行仿真.仿真结果表明:压缩感知方法有效减少了网络能耗.     

Models, methods and middleware for grid-enabled multiphysics oil reservoir management

Meeting the demands for energy entails a better understanding and characterization of the fundamental processes of reservoirs and of how human made objects affect these systems. The need to perform extensive reservoir studies for either uncertainty assessment or optimal exploitation plans brings up demands of computing power and data management in a more pervasive way. This work focuses on high performance numerical methods, tools and grid-enabled middleware systems for scalable and data-driven computations for multiphysics simulation and decision-making processes in integrated multiphase flow applications. The proposed suite of tools and systems consists of (1) a scalable reservoir simulator, (2) novel stochastic optimization algorithms, (3) decentralized autonomic grid middleware tools, and (4) middleware systems for large-scale data storage, querying, and retrieval. The aforementioned components offer enormous potential for performing data-driven studies and efficient execution of complex, large-scale reservoir models in a collaborative environment.