Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

Initial steps in optimal planning of a distribution system

This paper reports the initial steps of research on planning of rural networks for MV and LV. In this paper, two different cases are studied. In the first case, 100 loads are distributed uniformly on a 100 km transmission line in a distribution network and in the second case, the load structure become closer to the rural situation. In case 2, 21 loads are located in a distribution system so that their distance is increasing, distance between load 1 and 2 is 3 km, between 2 and 3 is 6 km, etc). These two models to some extent represent the distribution system in urban and rural areas, respectively. The objective function for the design of the optimal system consists of three main parts: cost of transformers, and MV and LV conductors. The bus voltage is expressed as a constraint and should be maintained within a standard level, rising or falling by no more than 5%.

Model Based Estimating System for Civil Concrete Structures

An earlier CRC-CI project on ‘automatic estimating’ (AE) has shown the key benefit of model-based design methodologies in building design and construction to be the provision of timely quantitative cost evaluations. Furthermore, using AE during design improves design options, and results in improved design turn-around times, better design quality and/or lower costs. However, AEs for civil engineering structures do not exist; and research partners in the CRC-CI expressed interest in exploring the development of such a process. This document reports on these investigations. The central objective of the study was to evaluate the benefits and costs of developing an AE for concrete civil engineering works. By studying existing documents and through interviews with design engineers, contractors and estimators, we have established that current civil engineering practices (mainly roads/bridges) do not use model-based planning/design. Drawings are executed in 2D and only completed at the end of lengthy planning/design project management lifecycle stages. We have also determined that estimating plays two important, but different roles. The first is part of project management (which we have called macro level estimating). Estimating in this domain sets project budgets, controls quality delivery and contains costs. The second role is estimating during planning/design (micro level estimating). The difference between the two roles is that the former is performed at the end of various lifecycle stages, whereas the latter is performed at any suitable time during planning/design.

Final Report : learning system for life prediction of infrastructure

The project has further developed two programs for the industry partners related to service life prediction and salt deposition. The program for Queensland Department of Main Roads which predicts salt deposition on different bridge structures at any point in Queensland has been further refined by looking at more variables. It was found that the height of the bridge significantly affects the salt deposition levels only when very close to the coast. However the effect of natural cleaning of salt by rainfall was incorporated into the program. The user interface allows selection of a location in Queensland, followed by a bridge component. The program then predicts the annual salt deposition rate and rates the likely severity of the environment. The service life prediction program for the Queensland Department of Public Works has been expanded to include 10 common building components, in a variety of environments. Data mining procedures have been used to develop the program and increase the usefulness of the application. A Query Based Learning System (QBLS) has been developed which is based on a data-centric model with extensions to provide support for user interaction. The program is based on number of sources of information about the service life of building components. These include the Delphi survey, the CSIRO Holistic model and a school survey. During the project, the Holistic model was modified for each building component and databases generated for the locations of all Queensland schools. Experiments were carried out to verify and provide parameters for the modelling. These included instrumentation of a downpipe, measurements on pH and chloride levels in leaf litter, EIS measurements and chromate leaching from Colorbond materials and dose tests to measure corrosion rates of new materials. A further database was also generated for inclusion in the program through a large school survey. Over 30 schools in a range of environments from tropical coastal to temperate inland were visited and the condition of the building components rated on a scale of 0-5. The data was analysed and used to calculate an average service life for each component/material combination in the environments, where sufficient examples were available.

Paramedic knowledge of infectious disease aetiology and transmission in an Australian Emergency Medical System

Introduction: Paramedics and other emergency health workers are exposed to infectious disease particularly when undertaking exposure-prone procedures as a component of their everyday practice. This study examined paramedic knowledge of infectious disease aetiology and transmission in the pre-hospital care environment.--------- Methods: A mail survey of paramedics from an Australian ambulance service (n=2274) was conducted.--------- Results: With a response rate of 55.3% (1258/2274), the study demonstrated that paramedic knowledge of infectious disease aetiology and modes of transmission was poor. Of the 25 infectious diseases included in the survey, only three aetiological agents were correctly identified by at least 80% of respondents. The most accurate responses for aetiology of individual infectious diseases were for HIV/AIDS (91.4%), influenza (87.4%), and hepatitis B (85.7%). Poorest results were observed for pertussis, infectious mononucleosis, leprosy, dengue fever, Japanese B encephalitis and vancomycin resistant enterococcus (VRE), all with less than half the sample providing a correct response. Modes of transmission of significant infectious diseases were also assessed. Most accurate responses were found for HIV/AIDS (85.8%), salmonella (81.9%) and influenza (80.1%). Poorest results were observed for infectious mononucleosis, diphtheria, shigella, Japanese B encephalitis, vancomycin resistant enterococcus, meningococcal meningitis, rubella and infectious mononucleosis, with less than a third of the sample providing a correct response.--------- Conclusions: Results suggest that knowledge of aetiology and transmission of infectious disease is generally poor amongst paramedics. A comprehensive in-service education infection control programs for paramedics with emphasis on infectious disease aetiology and transmission is recommended.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

HVAC system size – getting it right

There is evidence that many heating, ventilating & air conditioning (HVAC) systems, installed in larger buildings, have more capacity than is ever required to keep the occupants comfortable. This paper explores the reasons why this can occur, by examining a typical brief/design/documentation process. Over-sized HVAC systems cost more to install and operate and may not be able to control thermal comfort as well as a “right-sized” system. These impacts are evaluated, where data exists. Finally, some suggestions are developed to minimise both the extent of, and the negative impacts of, HVAC system over-sizing, for example: • Challenge “rules of thumb” and/or brief requirements which may be out of date. • Conduct an accurate load estimate, using AIRAH design data, specific to project location, and then resist the temptation to apply “safety factors • Use a load estimation program that accounts for thermal storage and diversification of peak loads for each zone and air handling system. • Select chiller sizes and staged or variable speed pumps and fans to ensure good part load performance. • Allow for unknown future tenancies by designing flexibility into the system, not by over-sizing. For example, generous sizing of distribution pipework and ductwork will allow available capacity to be redistributed. • Provide an auxiliary tenant condenser water loop to handle high load areas. • Consider using an Integrated Design Process, build an integrated load and energy use simulation model and test different operational scenarios • Use comprehensive Life Cycle Cost analysis for selection of the most optimal design solutions. This paper is an interim report on the findings of CRC-CI project 2002-051-B, Right-Sizing HVAC Systems, which is due for completion in January 2006.

Low achieving mathematics students' attitudinal and achievement changes as a result of using an integrated learning system

This paper reports on a study to measure the effectiveness of an integrated learning system (ILS) in improving mathematics achievement for low achieving Year 5 to 9 students. The study found that statistically significant gains on the integrated learning system were not supported by scores on standardised mathematics achievement tests. It also found that although student attitudes to computers decreased (significantly for some items), the students still liked the integrated learning system and felt that it had helped them to learn.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.