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.     

VPN构建的一种设计思想

Internet的发展对安全问题的要求越来越高，网络安全日益受到重视。文章以虚拟专用网为主要研究对象，结合软件工程的一些开发思想，对虚拟专用网的构建做出了理论上的设计。文章分析了虚拟专用网目前所涉及的主要几大模型，具体构建了一种可行的VPN设计方案，同时结合软件工程，对VPN构建给出自行设计的一种模块结构图，并对各个模块结构给出了具体解释，今后的工作在于将模块具体细化运用到实践中。     

神经网络控制器用于永磁电机的伺服驱动

提出用一种遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍ，简称ＧＡ）训练的多层神经网络结构（ＮＮ），并把它用于交流直线永磁同步电动机（简称ＬＰＭＳＭ）的辩识与控制，使其运行在伺服驱动环境．ＮＮ可随时辩识被控系统的动态待性，而不需要ＬＰＭＳＭ的精确的数学模型．仿真结果令人满意     

Modeling users through an expert system and a neural network

With the number of Internet and Web users increasing rapidly, electronic service providers are competing to satisfy and better serve customers looking for information or channels of advertisement. A wide variety of browses, specialized sites, custom made software, etc. are being offered on a regular basis. However, the user has to filter through a large number of files before finding what he/she is really looking for. This paper presents a user modeling expert system, SIGMA, based on neural networks for encapsulating Internet and Web users' habits and preferences. SIGMA is an artificial intelligence application designed to answer an Internet client needs and preferences. It analyses the user supplied demographic data and the monitored transactions then generate a tailored profile that is ultimately used to filter what information is being passed on to him/her in an effort to reduce and hopefully eliminate the time and energy expended in sifting through raw and often unwanted data.     

Colour classification by neural networks in graphic arts

This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected. Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.     

A Scalable Interconnection Network Architecture for Petaflops Computing

Extrapolating technology advances in the near future, a computer architecture capable of petaflops performance will likely be based on a collection of processing nodes interconnected by a high-performance network. One possible organization would consist of thousands of inexpensive, low-power symmetric multiprocessor (SMP) nodes. Each node will inject data into the interconnection network at a very large rate and consequently, the interconnect scheme is one of the most crucial design issues affecting system performance. This paper describes the 2D simultaneous optical multiprocessor exchange bus (2D SOME-Bus) which has the potential to become the basis of a high-end computer architecture capable of petaflops performance. It consists of N horizontal, N vertical 1D SOME-Bus networks, and N ² nodes. Each node is connected to one horizontal and one vertical 1D SOME-Bus. Each of N nodes connected to one 1D SOME-Bus has a dedicated broadcast channel and an input channel interface based on an array of N receivers monitoring all N channels and allowing multiple simultaneous broadcasts. In the 2D SOME-Bus, messages being broadcast on one Bus can be broadcast in a cut-through manner on one or more Buses in the other dimension. This paper describes the optoelectronic devices and technology which make the 2D SOME-Bus possible, and the network interface organization. It also presents simulation results which compare the performance of the 2D SOME-Bus, the 1D SOME-Bus, the crossbar and the torus under the message-passing paradigm.     

Characterization of indoor wireless channel at 5 GHz U-NII bands

The paper reports on the experiments undertaken at the University of Wollongong to characterise fading profiles and delay parameters of an indoor wireless channels at 5 GHz U-NII bands. The measurements were undertaken at different locations around the campus with results recorded for a post-processing to calculate the Rician K-factor, the level crossing rate and the average fade duration as well as mean excess delay, rms delay spread, and the coherence bandwidth of the channel. The presented measurement results can be useful in developing a Markov chain based model of the transport channel for IEEE802.11a or HYPRLAN-2 networks. The results also indicate scenarios where the coherence bandwidth of the channel is smaller than the width of the sub-carrier OFDM channels in either of the mentioned systems.     

A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model. This revised version was published online in June 2006 with corrections to the Cover Date.     

The evidence for neural information processing with precise spike-times: A survey

This paper surveys recent findings in neuroscience regarding the behavioral relevancy of the precise timing with which real spiking neurons emit spikes. The literature suggests that in almost any system where the processing-speed of a neural (sub)-system is required to be high, the timing of single spikes can be very precise and reliable. Additionally, new, more refined methods are finding precisely timed spikes where previously none where found. This line of evidence thus provides additional motivation for researching the computational properties of networks of artificial spiking neurons that compute with more precisely timed spikes. This revised version was published online in June 2006 with corrections to the Cover Date.     

网络化测控设备间的时间同步

为使广域网络范围内的测控设备同步工作，必须准确的计算出网络数据传输的时延。依据排队论，本文提出并证明了准确的网络节点模型和数据延时分布统计模型，以此为基础计算出数据报在互联网上传输的精确时延并给出时延修正方法。实验数据论证了模型的正确性。最后提出了广域自动测试系统内设备的同步解决方案，时间同步精度在10～50μs范围内。