Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.     

Optimization of media processing workflows with adaptive operator behaviors

In this paper, we present the ARIA media processing workflow architecture that processes, filters, and fuses sensory inputs and actuates responses in real-time. The components of the architecture are programmable and adaptable; i.e. the delay, size, and quality/precision characteristics of the individual operators can be controlled via a number of parameters. Each data object processed by qStream components is subject to transformations based on the parameter values. For instance, the quality of an output data object and the corresponding processing delay and resource usage depend on the values assigned to parameters of the operators in the object flow path. In Candan, Peng, Ryu, Chatha, Mayer (Efficient stream routing in quality- and resource-adaptive flow architectures. In: Workshop on multimedia information systems, 2004), we introduced a class of flow optimization problems that promote creation and delivery of small delay or small resource-usage objects to the actuators in single-sensor, single-actuator workflows. In this paper, we extend our attention to multi-sensor media processing workflow scenarios. The algorithms we present take into account the implicit dependencies between various system parameters, such as resource consumption and object sizes. We experimentally show the effectiveness and efficiency of the algorithms.

安阳市地下水应急供水水源评估研究

以安阳市平原区为例,根据水均衡原理,利用当年汛期末浅层地下水位埋深和汛期降雨量以及汛期前地下水补给量等信息,建立安阳市平原区基于数值模拟模型的枯季地下水可开采量（可供水量）实时预报模型。研究结果可为区域地下水水源评估提供参考。     

算子值鞅变换的凸Φ不等式及其应用(英文)

建立了算子值鞅变换的凸Φ不等式,并且通过算子值鞅变换进一步研究了极大算子和均方算子的性质,讨论了鞅在其中取值的Banach空间的几何性质.     

The geometry of flex tangents to a cubic curve and its parameterizations

We show how the study of the geometry of the nine flex tangents to a cubic produces pseudo-parameterizations, including the ones given by Icart, Kammerer, Lercier, Renault and Farashahi, and infinitely many new ones.     

3种湍流模型模拟水下重力流的比较研究(英文)

运用最新文献记载的3种用于数值模拟水下重力流的湍流模型模拟同一组新的水下重力流的实验。实验数据不仅包括传统的各垂向剖面的速度、浓度,也含有对应剖面的湍流动能。结果表明:在所比较的3湍流模型当中,k-ε模型的模拟性能最好,而MY和k-l模型的模拟均在重力流的上部混合区域呈现过量的扩散。进一步分析表明,MY模型和k-l模型所采用的依据墙面距离定义的宏观长度尺度L尽管可适用于水下重力流底部的壁面边界层,但不适用于重力流上部的自由剪切层,因为重力流上部自由剪切层的湍流长度尺度和依据壁面距离定义的宏观长度尺寸完全不同。     

社上水库大坝加固处理效果评价

通过分析水库加固处理前后大坝测压管水位和渗漏量的变化,评价大坝加固处理的质量和效果,表明社上水库大坝加固处理措施合理,且效果良好,质量较高,为大坝加固处理效果评价探索了一种简单、有效的方法.     

Minimum variance prediction and control for adaptive optics

Adaptive Optics (AO) systems enable to compensate the adverse effects of atmospheric turbulence on ground-based telescopes’ images in real time, using a deformable mirror (DM) inserted in the telescope’s optical path, and measurements provided by a wavefront sensor (WFS). This paper revisits minimum-variance (MV) control design for astronomical AO systems in a state-space framework. It presents a survey of the modeling and control issues arising in this multi-variable disturbance rejection problem. In a linear time-invariant framework, and under some mild assumptions, the optimal solution to MV control for AO systems is shown to be a discrete-time LQG controller. This result holds for a DM with instantaneous response, and for a fairly general class of DM’s dynamics. The state-space approach is extended to Wide-field Adaptive Optics (WfAO) configurations involving several DMs and/or WFSs. Integral-action control used in existing AO systems is compared with the LQG controller. Experimental WfAO results obtained on a laboratory test bench are presented, showing significant improvement in performance. Finally, open issues and perspectives of applicative and/or theoretical interests are discussed.     

RBC-OLSR: Reputation-based clustering OLSR protocol for wireless ad hoc networks

We consider the problem of assuring the trustworthiness (i.e. reliability and robustness) and prolonging the lifetime of wireless ad hoc networks, using the OLSR routing protocol, in the presence of selfish nodes. Assuring the trustworthiness of these networks can be achieved by selecting the most trusted paths, while prolonging the lifetime can be achieved by (1) reducing the number of relay nodes (MPR) propagating the topology control (TC) messages and (2) considering the residual energy levels of these relay nodes in the selection process. In this paper, we propose a novel clustering algorithm and a relay node selection algorithm based on the residual energy level and connectivity index of the nodes. This hybrid model is referred to as H-OLSR. The OLSR messages are adapted to handle the cluster heads election and the MPR nodes selection algorithms. These algorithms are designed to cope with selfish nodes that are getting benefits from others without cooperating with them. Hence, we propose an incentive compatible mechanism that motivates nodes to behave truthfully during the selection and election processes. Incentive retributions increase the reputation of the nodes. Since network services are granted according to nodes’ accumulated reputation, the nodes should cooperate. Finally, based on nodes’ reputation, the most trusted forwarding paths are determined. This reputation-based hybrid model is referred to as RH-OLSR. Simulation results show that the novel H-OLSR model based on energy and connectivity can efficiently prolong the network lifetime, while the RH-OLSR model improves the trustworthiness of the network through the selection of the most trusted paths based on nodes’ reputations. These are the two different processes used to define the reputation-based clustering OLSR (RBC-OLSR) routing protocol.