奥林奇县水库二维溃坝洪水流量分析

二维溃坝模型已用来预测奥林奇县水库下游冲积扇的淹没范围。这个模型是以浅水长波的连续方程和动量方程的扩散形式为基础的。而控制方程是用显式数值方法求解的。与一维模型比较,二维模型可以预测更大的淹没范围。     

大地电磁测深数据显示中视电阻率的定义

本文对大地电磁测深数据显示中视电阻率重新进行了定义。该定义以频率归一化阻抗函数为基础。我们从理论上分析了现有定义和本文提出的定义,并用一维地层模型曲线对两者做了比较。用本文的定义计算视电阻率曲线能更好地近似地层的实际电阻率。     

结构应用的CVD钨和钨—铼合金

研究了ＣＶＤ钨和钨－铼合金的显微组织，估价了它们作为高温材料的适用性。我们发现反应室的分压影响了沉积纯钨和钨－铼合金的显微组织和表面粗糙度。沉积纯钨和钨－铼合金的晶粒度都在０．５ｕｍ以下。实验证明，细晶粒钨的硬度（Ｈｖ〉１０００，室温（Ｒ．Ｔ）、弯曲强度（Ｒ．Ｔ￣１１７５Ｋ，δ＝７００￣１２３０ＭＰａ）和线性热膨胀系数（５７５￣１１７５Ｋ，β＝４．５￣４．９×１０＾－６Ｋ＾－１）比柱状结构钨大。     

Design of internal model control based fractional order PID controller

This article presents a design of the internal model control(IMC)based single degree of freedom(SDF) fractional order(FO)PID controller with a desired bandwidth specification for a class of fractional order system(FOS). The drawbacks of the SDF FO-IMC are eliminated with the help of the two-degree of freedom(TDF)FO PID controller. The robust stability and robust performance of the designed controller are analyzed using an example.     

On growth and dispersal of populations

In his unpublished MA thesis of 1921, the young Hotelling invented an ingenious model of population growth and diffusion. The contribution remained widely unknown until Waldo Tobler and Alan Wilson edited it as an article in 1978. Not even then did it trigger off any outburst of contributions. Meanwhile, in 1951, Skellam invented exactly the same model for non-human populations. Unlike in economics, it was a great success in ecology. In a recent contribution Skellam is named Father of Ecological Diffusion and more than a thousand articles on the subject are listed. There were many attempts at solving the equation, but none were quite successful. The Hotelling and Skellam models both assumed a given saturation population that nature could support. In 1985 the present author suggested that an explicit production function be introduced in the Hotelling model, as man produces his own means of subsistence. Moreover it was proposed that diffusion be related to spatial differences in per capita productivity, rather than to those in population density. The present contribution is a rejoinder. It is shown that the stationary solutions to the original Hotelling model are periodic and dip into negative populations, whereas this is avoided by the modified model suggested. It can thus be used to show how agglomerative structures may evolve.     

Training for an agricultural discrimination task

Skeleton weed, centaurea juncea, is a declared weed in Western Australia because it competes with grain crops for nutrients and moisture. When it is found during harvesting, mechanised teams search and eradicate it. In an earlier report of field trials (Hartley et al, 1989) it was reported that search teams' detection rate was poor and since search teams had usually never seen skeleton weeds, visual discrimination learning was to be expected and observed during searches. The present study investigated the nature of this discrimination learning in a laboratory by developing a training programme of colour photographic slides of weeds in stubble. Subjects receiving specific training with feedback on their performance compared with those receiving pseudo-training showed a significant improvement in detections. Subsequently the benefit of the programme was validated in a field trial.     

Visual reasoning about social networks using centrality sensitivity

In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.     

Adaptive synthesis of distance fields

We address the computational resource requirements of 3D example-based synthesis with an adaptive synthesis technique that uses a tree-based synthesis map. A signed-distance field (SDF) is determined for the 3D exemplars, and then new models can be synthesized as SDFs by neighborhood matching. Unlike voxel synthesis approach, our input is posed in the real domain to preserve maximum detail. In comparison to straightforward extensions to the existing volume texture synthesis approach, we made several improvements in terms of memory requirements, computation times, and synthesis quality. The inherent parallelism in this method makes it suitable for a multicore CPU. Results show that computation times and memory requirements are very much reduced, and large synthesized scenes exhibit fine details which mimic the exemplars.     

Least squares approach in measurement-dependent filtering for selective medical images

An image-processing method called measurement-dependent filtering has been introduced to improve the SNR (signal-to-noise ratio) of selective images produced by various medical imaging systems. The basic algorithm involves the combination of the low-frequency information of the selective image with the high-frequency information of a nonselective image. A spatially variant control function modulates the amount of high frequency to be added at each point. A least-mean-square (LMS) control function formed from two basis images, namely the high-passed versions of the nonselective image (M(b)) and the selective image (S(b)), is introduced. The original algorithm is now viewed as a two-stage filtering method, including the low-pass filtering noise reduction and least squares filtering for the edge restoration. An appropriate linear transformation is used to convert the original basis images M(b) and S(b) into a new pair with orthogonal noise. This allows the implementation of the LMS and control function with practically obtainable a priori knowledge.