Natural and human forcing in recent geomorphic change; case studies in the Rio de la Plata basin

An analysis of geomorphic system's response to change in human and natural drivers in some areas within the Río de la Plata basin is presented. The aim is to determine whether an acceleration of geomorphic processes has taken place in recent years and, if so, to what extent it is due to natural (climate) or human (land-use) drivers. Study areas of different size, socio-economic and geomorphic conditions have been selected: the Río de la Plata estuary and three sub-basins within its watershed. Sediment cores were extracted and dated (²¹⁰Pb) to determine sedimentation rates since the end of the 19th century. Rates were compared with time series on rainfall as well as human drivers such as population, GDP, livestock load, crop area, energy consumption or cement consumption, all of them related to human capacity to disturb land surface. Data on river discharge were also gathered. Results obtained indicate that sedimentation rates during the last century have remained essentially constant in a remote Andean basin, whereas they show important increases in the other two, particularly one located by the São Paulo metropolitan area. Rates in the estuary are somewhere in between. It appears that there is an intensification of denudation/sedimentation processes within the basin.Rainfall remained stable or varied very slightly during the period analysed and does not seem to explain increases of sedimentation rates observed. Human drivers, particularly those more directly related to capacity to disturb land surface (GDP, energy or cement consumption) show variations that suggest human forcing is a more likely explanation for the observed change in geomorphic processes. It appears that a marked increase in denudation, of a “technological” nature, is taking place in this basin and leading to an acceleration of sediment supply. This is coherent with similar increases observed in other regions.     

Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC)

In order to optimize the proton exchange membrane fuel cell (PEMFC) model parameters, a novel approach based on seeker optimization algorithm (SOA) is proposed. The SOA is based on the concept of simulating human searching behaviors, where the choice of search direction is based on the empirical gradient by evaluating the response to the position changes and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, after evaluated on benchmark function optimization, the SOA is applied to optimal modelling of the PEMFC by using a fuel cell test system in Fuel Cell Application Centre (FAC) at the Temasek Polytechnic, and compared with several state-of-the-art versions of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The simulation results show that the proposed approach is superior to other compared algorithms, and the PEMFC model with optimized parameters by SOA fitted experimental data well. Hence, SOA is an effective and reliable technique for optimizing the parameters of PEMFC model, and can be helpful for system analysis, optimization design and real-time control of the PEMFCs.     

An innovative technique to evaluate network equivalent for voltage stability assessment in a widespread sub-grid system

The equivalent two-bus network models currently available are obtained by lumping all the series impedances and shunt admittances of transmission lines within a series equivalent impedance, to assess voltage stability of multi-bus power system. This paper reports the development of an equivalent pi-network model using a new methodology considering series and shunt parts of line loss separately obtained from the operational parameters of optimal power flow solution of the original multi-bus power network, which can be applied to assess the overall voltage stability status of the system accurately by developing the concept of a generalized global voltage stability margin (GVSM). Simulation results for a typical longitudinal power supply (LPS) system and a robust practical (Indian Eastern Grid) system establish that the pi-equivalent model obtained by the proposed method is highly promising for assessing voltage stability of any power system at any operating point in global scenario in a better way as compared to available series equivalent model. Continuation power flow (CPF) method has also been adopted here to verify the potential of the proposed method for voltage stability assessment. In the proposed equivalent network the generators have been modeled more accurately considering optimal operating criteria.     

ATTITUDE RATE ESTIMATION BY GPS DOPPLER SIGNAL PROCESSING

A method is presented for near-Earth spacecraft or aviation vehicle‘s attiude rate cstimation by using relative Doppler frequency shift of the Global Positioning System(GPS)carrier.It comprises two GPS receiving antennas,a signal processing circuit and an algorithm.The whole system is relatvely simple,the cost and weight,as well power consumption,are very low.     

一种新的GPS接收机C/A码跟踪环鉴别器算法

该文分析了GPS C/A码相位估计误差与载波频率估计误差对相关函数的影响,并推导出了相关公式。从线性,对信号幅度与载波频率估计误差的敏感性和计算量等角度分析了现有的码鉴别器算法的优缺点。基于上面的分析与研究,提出了一种新的C/A码鉴别器算法。通过理论分析与仿真实验,证明该算法有良好的线性特性,能有效抑制对信号幅度与载波频率估计误差的敏感性,且计算量低,跟踪精度高,抗多经性能好,在性能上优于已有的算法。     

Global attention retinex network for low light image enhancement

Most low-light image enhancement methods only adjust the brightness, contrast and noise reduction of low-light images, making it difficult to recover the lost information in darker areas of the image, and even cause color distortion and blurring. To solve the above problems, a global attention-based Retinex network (GARN) for low-light image enhancement is proposed in this paper. We propose a novel global attention module which computes multiple dimensional information in the channel attention module to help facilitate inference learning. Then the global attention module is embedded into different layers of the network to extract richer shallow texture features and deep semantic features. This means that the rich features are more conducive to learning the mapping relationship between low-light images to normal-light images, so that the detail recovery of dark regions is enhanced in low-light images. We also collected a low/normal light image dataset with multiple scenes, in which the images paired as training set can succeed to be applied to low-light image enhancement under different lighting conditions. Experimental results on publicly available datasets show that our method has better effectiveness and generality than the state-of-the-art methods in terms of evaluations metrics such as PSNR, SSIM, NIQE, Entropy.     

基于混合图的总体布线调整

本文提出了一种基于混合图的总体布线调整方法。混合图是对表示布局的有向图进行一系列精确的顶点分解而产生的。用得到的图模型来表示总体布线信息,从而可以在总体布局优化的同时估计布线对芯片面积的影响,并对总体布线进行调整。由于总体布线是自动更新的,所以布局同时随着布线的调整和模块的移动而改进。本文还提出了多种瓶颈的概念和瓶颈间走线随模块旋转而变化的规律。最后给出的实验结果表明,这种算法在减小芯片面积上获得比较好的结果。     

具有轴突信号传输延迟的双向联想记忆神经网络

双向联想记忆模型是两层异联想网络,本文重点讨论了具有轴突信号传输延迟的双向联想记忆神经网络平衡点存在的条件以及全局稳定性.并用具体例子验证了结论的正确性。本文所讨论的方法具有普遍性,所得到的结果对于网络的设计和应用具有重要的指导意义。     

Automatic, robust global motion estimation using clustering

Global motion estimation (GME) is a vital part of many video compression and computer vision applications. However, the large moving foreground objects that are present in many video scenes make the task of GME more challenging. In this paper, we propose an automatic, efficient, and robust approach for GME that addresses the issue of large foreground objects. The proposed GME algorithm is based on two key ideas: a new clustering technique, to automate the initial segmentation of background and foreground blocks, and a modified Lorentzian estimator, to reduce the impact of any remaining foreground blocks on the GME process. We also apply an up-sampling technique to the estimated motion parameters to remove any errors caused by under-sampling during the warping process. These ideas provide a significant improvement in performance when combined into a common framework. Simulation results and analyses demonstrate the improved performance of our proposed algorithm over other state-of-the-art methods.     

一种快速自适应全局运动估计算法的研究

该文提出一种快速自适应全局运动估计算法。该方法从分析块匹配运动矢量统计特性出发,通过 引入一个可预设置参数,计算当前帧与参考帧之间的基本平移矢量,并自适应地采取混合全局运动、局部运 动估计的算法处理不同特征的源数据.仿真结果表明,与传统方法相比,该文算法复杂性低、处理延时小、 更利于硬件实现,在PSNR变化不大的情况下,编码效率有较大的改善.