Effect of high-level content organizers on hypertext learning

This study investigates the cognitive abilities involved in hypertext learning and design approaches that can help users. We examined the effects of two types of high-level content organizers - a graphic spatial map and an alphabetical list - on readers’ memory for hypertext structure. In the control condition, a simple “home” page with no navigational aid was offered. Subjects were asked to read the hypertext with the purpose of learning the content, but in the post test phase they also had to recall the layout of nodes and links. Memory for links and page places varied as a function of condition. When a spatial map was available participants reconstructed more accurate formal structure then in the two other conditions. Participants’ memory about page places was the least accurate in the list condition. Results also indicate that participants use the content organizer when it is available in order to orientate during learning from hypertext documents.Our results prove that a content organizer showing the formal structure can facilitate the spatial mapping process. However, an organizer exposing a different structure than the real one would generate a conflict.     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

Similarity-based image organization and browsing using multi-resolution self-organizing map

An algorithm is presented in this paper to facilitate the exploration of large image collections based on visual similarities. Starting with an unordered and unannotated set of images, the algorithm first extracts the salient details into feature vectors using both color and gradient information. The feature vectors are then used to train a self-organizing map which maps high-dimensional feature vectors onto a 2D canvas so that images with similar feature vectors are grouped together. When users browse the image collection, an image collage is generated that selects and displays the most pertinent set of images based on which portion of the 2D canvas is currently in view. Flowing from an overview to details is a seamless operation controlled simply by pan and zoom, with representative images selected in a consistent and predictable way. To make organizing larger image collections practical in interactive time, the organization algorithm is designed to run in parallel on graphics processing units. Overall this paper presents an end-to-end solution that facilitates the surfing of image collections in a fresh way.     

用VB开发Duffing方程混沌动力学系统仿真软件

基于Lisual Basic6.0编制了杜芬（Duffing)方程混沌特性分析的仿真软件,利用该软件不仅可方便地显示该系统在相空间上的轨迹线图,而且可绘制时程曲线图、幅频图和庞加菜（Poincare)截面图等。软件操作简便,运行可靠。     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

Optimal design of butterfly-shaped linear ultrasonic motor using finite element method and response surface methodology

A new method for optimizing a butterfly-shaped linear ultrasonic motor was proposed to maximize its mechanical output. The finite element analysis technology and response surface methodology were combined together to realize the optimal design of the butterfly-shaped linear ultrasonic motor. First, the operation principle of the motor was introduced. Second, the finite element parameterized model of the stator of the motor was built using ANSYS parametric design language and some structure parameters of the stator were selected as design variables. Third, the sample points were selected in design variable space using latin hypercube Design. Through modal analysis and harmonic response analysis of the stator based on these sample points, the target responses were obtained. These sample points and response values were combined together to build a response surface model. Finally, the simplex method was used to find the optimal solution. The experimental results showed that many aspects of the design requirements of the butterfly-shaped linear ultrasonic motor have been fulfilled. The prototype motor fabricated based on the optimal design result exhibited considerably high dynamic performance, such as no-load speed of 873 mm/s, maximal thrust of 27.5 N, maximal efficiency of 43%, and thrust-weight ratio of 45.8.     

三角代数上的Jordan三重初等映射的可加性

主要研究了三角代数上的Jordan三重初等映射的可加性,给出了一个保证Jor-dan三重初等映射满足可加性的充分条件。     

Dynamics of polymer electrolyte fuel cells undergoing load changes

Numerical simulations are carried out for a single-channel polymer electrolyte fuel cell (PEFC) undergoing a step increase in current density. The objective is to elucidate profound interactions between the cell voltage response and water transport dynamics occurring in a low-humidity PEFC where the membrane hydration and hence resistance hinges upon the product water. Detailed results are presented to show that a step increase in the current density leads to anode dryout due to electroosmotic drag, while it takes several seconds for water back-diffusion and anode humidified gas to re-wet the anode side of the polymer membrane. The water redistribution process is controlled by water production, membrane hydration, electroosmotic drag, and water diffusion in the membrane. The anode dryout results in a substantial drop in cell voltage and hence temporary power loss. Under extreme situations such as dry anode feed, large step increase in the current density, and/or lower temperatures, the cell voltage may even reverse, resulting in not only power loss but also cell degradation. Finally, the dynamics of current distribution after a step change in gas humidification is numerically examined.     

Some spectral response characteristics of ZnTe thin films

Zinc telluride thin films have been grown at room temperature and higher temperature substrates by thermal evaporation technique in a vacuum of 10^-6 torr. A main peak in the photocurrent is observed at 781 nm (1.58 eV) with two lower amplitude peaks on the lower wavelength side and one on higher wavelength side. The evaluated thermal activation energy is found to correspond well with the main spectral peak. From these studies it can be inferred that temperatures up to 453 K is still in the extrinsic conductivity region of the studied ZnTe thin films. The paper was presented at the 6th Asian Thermophysical Properties Conference (6th ATPC), held at Gauhati University, during 8–11 October 2001.