Biologically inspired SLAM using Wi-Fi

Wi-Fi is a commonly available source of localization information in urban environments but is challenging to integrate into conventional mapping architectures. Current state of the art probabilistic Wi-Fi SLAM algorithms are limited by spatial resolution and an inability to remove the accumulation of rotational error, inherent limitations of the Wi-Fi architecture. In this paper we leverage the low quality sensory requirements and coarse metric properties of RatSLAM to localize using Wi-Fi fingerprints. To further improve performance, we present a novel sensor fusion technique that integrates camera and Wi-Fi to improve localization specificity, and use compass sensor data to remove orientation drift. We evaluate the algorithms in diverse real world indoor and outdoor environments, including an office floor, university campus and a visually aliased circular building loop. The algorithms produce topologically correct maps that are superior to those produced using only a single sensor modality.

Dynamic agent composition for large-scale agent-based models

PURPOSE: This paper describes dynamic agent composition, used to support the development of flexible and extensible large-scale agent-based models (ABMs). This approach was motivated by a need to extend and modify, with ease, an ABM with an underlying networked structure as more information becomes available. Flexibility was also sought after so that simulations are set up with ease, without the need to program. METHODS: The dynamic agent composition approach consists in having agents, whose implementation has been broken into atomic units, come together at runtime to form the complex system representation on which simulations are run. These components capture information at a fine level of detail and provide a vast range of combinations and options for a modeller to create ABMs. RESULTS: A description of the dynamic agent composition is given in this paper, as well as details about its implementation within MODAM (MODular Agent-based Model), a software framework which is applied to the planning of the electricity distribution network. Illustrations of the implementation of the dynamic agent composition are consequently given for that domain throughout the paper. It is however expected that this approach will be beneficial to other problem domains, especially those with a networked structure, such as water or gas networks. CONCLUSIONS: Dynamic agent composition has many advantages over the way agent-based models are traditionally built for the users, the developers, as well as for agent-based modelling as a scientific approach. Developers can extend the model without the need to access or modify previously written code; they can develop groups of entities independently and add them to those already defined to extend the model. Users can mix-and-match already implemented components to form large-scales ABMs, allowing them to quickly setup simulations and easily compare scenarios without the need to program. The dynamic agent composition provides a natural simulation space over which ABMs of networked structures are represented, facilitating their implementation; and verification and validation of models is facilitated by quickly setting up alternative simulations.

Impact of technology uptake on an Australian electricity distribution network

This paper presents simulation results for future electricity grids using an agent-based model developed with MODAM (MODular Agent-based Model). MODAM is introduced and its use demonstrated through four simulations based on a scenario that expects a rise of on-site renewable generators and electric vehicles (EV) usage. The simulations were run over many years, for two areas in Townsville, Australia, capturing variability in space of the technology uptake, and for two charging methods for EV, capturing people's behaviours and their impact on the time of the peak load. Impact analyses of these technologies were performed over the areas, down to the distribution transformer level, where greater variability of their contribution to the assets peak load was observed. The MODAM models can be used for different purposes such as impact of renewables on grid sizing, or on greenhouse gas emissions. The insights gained from using MODAM for technology assessment are discussed.

Prevalence of refractive errors among schoolchildren in Shiraz, Iran

PURPOSE To determine the prevalence of refractive errors in Shiraz schoolchildren by age and gender. METHODS For this cross-sectional study, random cluster sampling was carried out from students of the 2008-2009 academic year. After the initial interview, ophthalmic examinations including tests of visual acuity, non-cycloplegic and cycloplegic refraction and binocular vision were performed. Myopia was defined as a spherical equivalent < or =-0.50 dioptre (D), hyperopia as > or =+2.0 D, and astigmatism as a cylinder refraction > or =0.75 D. All values for school grade and gender were directly standardized based on the total student population in the 2008-2009 school year. RESULTS A total of 2130 students were sampled, of which 1872 participated in the study (response rate = 87.88%). The prevalence of uncorrected, best-corrected, presenting and spectacle corrected visual acuity of 6/12 or worse in the better eye was 6.46%, 0%, 1.49% and 0.9%, respectively. The prevalence rates of myopia, hyperopia and astigmatism were 4.35% (95% confidence interval [CI]: 2.89-5.82%), 5.04% (95% CI: 3.50-6.58%), and 11.27% (95% CI: 9.81-12.74%), respectively. Anisometropia was detected in 2.58% of schoolchildren. The prevalence of hyperopia significantly decreased with age (P = 0.021). CONCLUSIONS Compared with other reported rates, the prevalence of myopia in the schoolchildren of Shiraz is similar to that in most places excluding East Asian countries, and that of hyperopia is in the mid range.

Prevalence and risk factors of refractive errors among schoolchildren in Shiraz, Iran

Purpose: To determine the prevalence and risk factors of refractive errors among schoolchildren in Shiraz, Iran. Methods: In a cross-sectional study, using random cluster sampling, 3065 Shiraz schoolchildren were selected in this study. The participants totaled 2683; 1872 elementary and middle school and 811 high school students. For the primary and middle schoolchildren, cycloplegic refraction and for the high school students, non-cycloplegic autorefraction was measured. Myopia, defined as spherical equivalent (SE) refraction -0.50 diopter (D) or worse, hyperopia as SE +2.00D and +0.50D or more for cycloplegic and noncycloplegic refractions respectively, and astigmatism as cylinder -0.75D or worse. Results: The prevalence of refractive errors in elementary and middle school students was: myopia 4.35 % (95% confidence interval (CI), 2.89 -5.81), hyperopia 5.04 % (95%CI, 3.49 -6.58) and astigmatism 11.79 % (95%CI, 10.21 -13.38). For high school students, these rates were 22.4 % (95%CI, 18.44 -26.36), 10.52 % (95%CI, 6.75 -14.29) and 20.99% (95%CI, 16.55 -25.44), respectively.The prevalence of myopia increased with age in primary and middle school students (OR=1.15, 95% CI, 0.98 to1.33, p=0.073). Conclusions: The result of this study indicated a relatively low prevalence of refractive errors among schoolchildren in Shiraz according to the protocol by "Refractive Error Study in Children" (RESC) in other investigations.

Compositional agent-based models for electricity distribution networks

This thesis presents a novel approach to building large-scale agent-based models of networked physical systems using a compositional approach to provide extensibility and flexibility in building the models and simulations. A software framework (MODAM - MODular Agent-based Model) was implemented for this purpose, and validated through simulations. These simulations allow assessment of the impact of technological change on the electricity distribution network looking at the trajectories of electricity consumption at key locations over many years.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Assessing vulnerability at the urban interface

House loss during unplanned bushfires is a complex phenomenon where design, configuration, material and siting, can significantly influence the loss. In collaboration with the Bushfire Cooperative Research Centre the CSIRO has developed a tool to assess the vulnerability of a specific house at the urban interface. The tool is based on a spatial profiling of urban assets including their design, material, surrounding objects and their relationship amongst one another. The analysis incorporates both probabilistic and deterministic parameters, and is based on the impact of radiant heat, flame and embers on the surrounding elements and the structure itself. It provides a breakdown of the attributes and design parameters that contribute to the vulnerability level. This paper describes the tool which allows the user to explore the vulnerability of a house to varying levels of bushfire attacks. The tool is aimed at government agencies interested in building design, town planning and community education for bushfire risk mitigation.