Characterization of conducting polymer coated synthetic fabrics for heat generation

Heat generation in fabrics coated with the conductive polymer polypyrrole was investigated. The PET fabrics were coated by chemical synthesis using four different oxidizing agent–dopant combinations. The samples from the four different dopant systems all show an increase in temperature when a fixed voltage is applied to the fabric. The antraquinone-2-sulfonic acid (AQSA) sodium salt doped polypyrrole coating was the most effective in heat generation whereas the sodium perchlorate dopant system was the least effective. The power density per unit area achieved in polypyrrole coated polyester–Lycra® fabric with 0.027 mol/l of AQSA acting as dopant was 430 W/m². The power density per unit area achieved for the sodium perchlorate system, using the same synthesis conditions, was 55 W/m².

A flow control methodology for the rapid generation of vehicle traffic models

This paper presents a conveyor-based methodology to model complex vehicle flows common to factory and distribution warehouse facilities. The AGV and human path modelling techniques available in many commercial discrete event simulation packages require extensive knowledge and time to implement even the simplest flow control rules for multiple vehicle interaction. Although discrete event simulation is accepted as an effective tool to model vehicle delivery movements, human paths and delivery schedules for modern assembly lines, the time to generate accurate models is a significant limitation of existing simulation-based optimisation methodologies. The flow control method has been successfully implemented using two commercial simulation packages. It provides a realistic visual representation, as well as accurate statistical results, and reduces the model development process cost.

The effects of testing procedure on critical fall height determination for third-generation synthetic turf

Test procedures and their accuracy in determining critical fall height (CFH) on sporting grounds are paramount to player safety. The procedure currently adopted for synthetic turf in Australian football [1] consists of four consecutive drops at various drop heights at three test locations on the sample. The quantity and packing of the infill in third-generation turf and the pooling effect of the rubber particles with consecutive drops suggests that the current standard protocol may need re-assessment. Therefore, the purpose of this pilot study was to investigate whether current methods of testing for CFH are appropriate for third-generation synthetic turf or whether an alternative or adapted method needs to be developed. CFH was measured, using a HISUN Uniaxe-II Impact Tester, on 12 combinations of synthetic turf samples (four different products with three shock pad options). Three conditions were investigated on each sample; the existing protocol; an alternative 12 single-drop protocol and four single drops from the CFH determined from the existing protocol. A significant difference was found for both absolute and percentage difference between the existing and 12 single-drop protocol, with p = 0.001 and t = 4.33 and p < 0.001 and t = 6.03, respectively. There was also a significant difference between the CFH reached with and without a shock pad for both the existing protocol and the 12 single-drop protocol. The results of this pilot study demonstrate that differences do occur with alterations to the existing protocol and highlight the need for a more detailed characterisation of testing methods on third-generation synthetic turf and the response of surfaces to them.

A simulation model for video traffic performance via ATM over TCP/IP

Although TCP has emerged as the standard in data communication, the introduction of ATM technology has raised numerous problems regarding the effectiveness of using TCP over A TM networks, especially when video traffic performance is considered. This paper presents a simulation model for transmission performance of video traffic via ATM over TCP/IP. The interactivity between TCP/IP and ATM, generation of MPEG traffic and evaluation of traffic performance are implemented in the model. The design and implementation details of the model are carefully described. The experiments conducted using the model and experimental results are briefly introduced, revealing the capability of our model in simulating network events and in evaluating potential solutions to performance issues.

Synthetic evaluation method for bridge priority ranking

In order to maintain the transportation operation, proper monitoring systems should be established on road structures, especially bridges. Since these systems need enormous investments, only a part of bridges should be equipped. Thus, the priorities of the bridges should be ranked. In this paper, a method based on two-level synthetic evaluation is proposed. First, the importance of each bridge is analyzed through the economic analysis. Six factors are considered for the bridges in a network, including construction cost, service duration, length, location importance coefficient, traffic volume, and reconstruction time. Second, the safety condition of the bridge is evaluated by using improved entropy method (IEM) which combines subjective weight with objective entropy weight. Five indices are incorporated in this step, i.e., design and construction condition, technical condition, level of overloading, hazard of wind and earthquake and environmental factors. Finally, the priorities of all the bridge in one network can be ranked and classified through a judge matrix. To demonstrate the effectiveness of the proposed method, a main highway including 16 bridges is taken as an illustrative example. The results show that the bridges can be ranked and classified quickly by using the proposed method.

Network traffic clustering using random forest proximities

The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for the task, which performs clustering based on Random Forest (RF) proximities instead of Euclidean distances. The approach consists of two steps. In the first step, we derive a proximity measure for each pair of data points by performing a RF classification on the original data and a set of synthetic data. In the next step, we perform a K-Medoids clustering to partition the data points into K groups based on the proximity matrix. Evaluations have been conducted on real-world Internet traffic traces and the experimental results indicate that the proposed approach is more accurate than the previous methods.