A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Sinusoidal disturbance rejection in chaotic planar oscillators

The main goal of this paper is to design a compensator able to restore the nominal behavior of a planar system, which is rendered chaotic by an unmeasurable sinusoidal disturbance input. To reach such a goal, some instruments, taken from algebraic geometry, are used to estimate the unmeasurable disturbance from the time derivatives of the output of the system and of the control input. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive actuator failure compensation design for unknown chaotic multi‐input systems

In this paper, an adaptive control approach is designed for compensating the faults in the actuators of chaotic systems and maintaining the acceptable system stability. We propose a state‐feedback model reference adaptive control scheme for unknown chaotic multi‐input systems. Only the dimensions of the chaotic systems are required to be known. Based on Lyapunov stability theory, new adaptive control laws are synthesized to accommodate actuator failures and system nonlinearities. An illustrative example is studied. The simulation results show the effectiveness of the design method. Copyright © 2014 John Wiley & Sons, Ltd.     

Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Non-chaotic and chaotic propagation of stationary and dynamic images through MVKS turbulence

Anisoplanatic electromagnetic (EM) propagation across a turbulent atmosphere has been recently examined for an unmodulated carrier propagating over an image-bearing transparency through optical lensing, and for the embedded information inside a carrier recovered using heterodyning and digital demodulation. Carrier modulation yielded better recovery than simple lens-based imaging. A possible mitigation strategy is proposed whereby the image information is encrypted on an RF chaotic carrier, thereafter secondarily embedded onto an optical carrier. Results based on the modified von Karman (MVKS) and the Hufnagel-Valley (H-V) models showed that the signal/image recovery under turbulence is improved compared with non-chaotic propagation. The case of time-varying/dynamic images is also taken up; it is demonstrated via cross-correlation products that turbulence is mitigated by the use of chaotic carrier encryption. Overall, transmission via chaos offers mitigation against distortions due to turbulence along with the security feature inherent via the chaos keys which prevent signal recovery without key-matching.     

基于有限状态的全周期序列混沌映射的研究

在有限长度的数字计算机中，混沌迭代序列是一个有限周期序列，其相关性能远远低于理想序列。基于skew tent映射，提出一种离散化的有限状态混沌映射，它允许精确的数值计算，没有舍入误差，通过选择合适的参数，映射能产生全周期长度的序列。仿真实验表明，归一化的序列平均周期对计算机精度不敏感，生成的二进制序列具有良好的相关性能。     

海杂波中基于混沌预测的目标检测方法改进

基于记忆库非线性预测的NP-CFAR方法是目前混沌海杂波背景中目标检测的一种典型而先进的方法.考虑到海杂波功率与特征的时变不稳定性,本文提出运用旋转超盒分类取代这一方法中的NP-CFAR进行目标检测,并探讨了运用盒维数特征提取进行预处理以节省运算开销的问题.仿真实验验证了本文所提改进方法的有效性.     

基于一种特定混沌映射的跳频序列

使用一种简单的分段线性映射构造混沌跳频序列。用于跳频通信中取代传统的伪随机序列（PN序列），分析了此混沌序列的周期性。平衡性，汉明相关特性和线性间隔，最后给出了混沌序列的神经网络实现。仿真和实验结果表明，此序列具有较好的统计特性。应用于跳频多址通信中，可获得较好的抗干扰。抗截获能力。     

基于混沌映射的鲁棒性图像水印算法

本文提出一种基于混沌映射的强鲁棒性数字图像水印算法,首先用一混沌动态系统对水印信息进行预处理,而后将其扩频调制在图像空间域,并且使水印嵌入强度自适应于图像;通过预置已知模板和嵌入多块水印可实现水印检测位置的几何校准.实验结果表明,采用该算法实现的水印对常见的图像处理和几何变换具有很好的鲁棒性.