Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method

Contaminant intrusion in a water distribution network is a complex but a commonly observed phenomenon, which depends on three elements – a pathway, a driving force and a contamination source. However, the data on these elements are generally incomplete, non-specific and uncertain. In an earlier work, Sadiq, Kleiner, and Rajani (2006) have successfully applied traditional Dempster–Shafer theory (DST) to estimate the “risk” of contaminant intrusion in a water distribution network based on limited uncertain information. However, the method used for generating basic probability assignment (BPA) was not very flexible, and did not handle and process uncertain information effectively. In this paper, a more pragmatic method is proposed that utilizes “soft” computing flexibility to generate BPAs from uncertain information. This paper compares these two methods through numerical examples, and demonstrates the efficiency and effectiveness of modified method.     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

利用反馈的车牌字符分割算法

字符分割是车牌识别系统的重要步骤。提出一种利用反馈的车牌字符分割算法。首先,对车牌图像进行预处理;其次,根据车牌图像的垂直投影进行字符粗分割;最后,通过两级反馈进行字符精分割。第一级反馈是字符个数与字符宽度反馈,第二级反馈是字符识别反馈。实验结果表明,算法对光照不均、对比度较小、倾斜、污迹、字符粘连和断裂等严重退化的车牌图像具有很好的字符分割性能。     

Experimental investigation and thermodynamic assessment of the Cu-Sn-Ti ternary system

The Cu-Sn-Ti ternary system has been studied via experiments and thermodynamic modelling. In the experimental section, the composition of the alloys was selected based on the preliminary calculations and available literature data. Metallography, scanning electron microscopy and electron probe microanalysis were employed to analyse alloy samples prepared by arc-melting after annealing at 800 °C for 760 h. Solid phase relations at 800 °C were established. In contrast to earlier reports, the CuSn₃Ti₅ phase was interpreted as a binary intermetallic compound (Sn₃Ti₅) with extended Cu solubility. In the modelling section, three binary sub-systems were critically evaluated and updated according to the new experimental data and theoretical calculations reported in literature. According to their crystal structures and homogeneity ranges, appropriate sublattice models were proposed for SnTi₃,SnTi₂,Sn₃Ti₅ and Sn₅Ti₆. A set of self-consistent thermodynamic parameters for the Cu-Sn-Ti ternary system was obtained by considering the present experimental results and reported experimental information. The calculated results compare with the available experimental data to validate the present thermodynamic assessment.     

Weight determination in the cross-efficiency evaluation

A neutral data envelopment analysis (DEA) model for cross-efficiency evaluation was recently proposed by Wang and Chin [Wang and Chin (2010b). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666–3675], which maximinimizes the relative efficiency of each output and effectively reduces the number of zero weights of outputs. Since a large number of zero weights may still exist among inputs, this paper proposes a simultaneously input- and output-oriented weight determination DEA model for the cross-efficiency evaluation. The new DEA model proves to reduce the number of zero weights for both inputs and outputs very significantly, as illustrated by numerical examples. The weights determined by the new DEA model are neutral, neither aggressive nor benevolent.     

Model order reduction methods for coupled systems in the time domain using Laguerre polynomials

In this paper, based on Laguerre polynomials, we present new methods for model reduction of coupled systems in the time domain. By appropriately selected projection matrices, a reduced order system is produced to retain the topology structure of the original system. Meanwhile, it preserves a desired number of Laguerre coefficients of the system’s output, thereby providing good approximation accuracy. We also study the two-sided projection method in the time domain, as well as the stability of reduced order systems. Two numerical examples are used to illustrate the efficiency of the proposed methods.     

Almost sure limit theorems for the maxima of some strongly dependent Gaussian sequences

We proved some almost sure limit theorems for standard strongly dependent Gaussian sequences in nonstationary cases under some mild conditions.     

Fuzzy image fusion based on modified Self-Generating Neural Network

A new fusion algorithm for multi-sensor images based on Self-Generating Neural Network (SGNN) and fuzzy logic is proposed in this paper. This study is an extension of the work described in Qin and Bao (2005). First, the order and frequency modifications for the current McKusick and Langley (M–L) optimization are proposed; next, by combining optimization and pruning together, the Pruning-And-One-Optimization-Composite (PAOOC) processing method is raised; and finally, a modified fuzzy fusion scheme using improved SGNN is put forward. Experimental results demonstrate that the posed fuzzy fusion scheme outperforms region-based fusion using wavelet multi-resolution (MR) segmentation, and region-based fusion using tree-structure wavelet MR segmentation, both in visual effect and objective evaluation criteria. In the meantime, simulations also show the effectiveness of our modifications for the current optimization and pruning methods, visually and objectively.