Effects of free flow state highway traffic parameters on the performance of amplify and forward cooperative multi-hop vehicular networks

In this paper we analyse the effects of highway traffic flow parameters like vehicle arrival rate and density on the performance of Amplify and Forward (AF) cooperative vehicular networks along a multi-lane highway under free flow state. We derive analytical expressions for connectivity performance and verify them with Monte-Carlo simulations. When AF cooperative relaying is employed together with Maximum Ratio Combining (MRC) at the receivers the average route error rate shows 10-20 fold improvement compared to direct communication. A 4-8 fold increase in maximum number of traversable hops can also be observed at different vehicle densities when AF cooperative communication is used to strengthen communication routes. However the theorical upper bound of maximum number of hops promises higher performance gains.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Resources for improving journalists' understanding of the economic and financial parameters of Australia's agricultural and commodity producers

The research addresses how an understanding of the fundamentals of economics will better inform general journalists who report on issues or events affecting rural and regional Australia. The research draws on practice-based experience of the author, formal economics studies, interviews with news editors from Australian television news organisations, and interviews from leading economists. A guidebook has also been written to help journalists apply economic theories to their reporting. The guidebook enables reporters to think strategically and consider the 'big picture' when they inform society about policies, commodity trade, the environment, or any issues involving rural and regional Australia.

Ratchetting of railhead in the vicinity of the gap of the insulated rail joints

Insulated Rail Joints (IRJs) are designed to electrically isolate two rails in rail tracks to control the signalling system for safer train operations. Unfortunately the gapped section of the IRJs is structurally weak and often fails prematurely especially in heavy haul tracks, which adversely affects service reliability and efficiency. The IRJs suffer from a number of failure modes; the railhead ratchetting at the gap is, however, regarded as the root cause and attended to in this thesis. Ratchetting increases with the increase in wheel loads; in the absence of a life prediction model, effective management of the IRJs for increased wagon wheel loads has become very challenging. Therefore, the main aim of this thesis is to determine method to predict IRJs' service life. The distinct discontinuity of the railhead at the gap makes the Hertzian theory and the rolling contact shakedown map, commonly used in the continuously welded rails, not applicable to examine the metal ratchetting of the IRJs. Finite Element (FE) technique is, therefore, used to explore the railhead metal ratchetting characteristics in this thesis, the boundary conditions of which has been determined from a full scale study of the IRJ specimens under rolling contact of the loaded wheels. A special purpose test set up containing full-scale wagon wheel was used to apply rolling wheel loads on the railhead edges of the test specimens. The state of the rail end face strains was determined using a non-contact digital imaging technique and used for calibrating the FE model. The basic material parameters for this FE model were obtained through independent uniaxial, monotonic tensile tests on specimens cut from the head hardened virgin rails. The monotonic tensile test data have been used to establish a cyclic load simulation model of the railhead steel specimen; the simulated cyclic load test has provided the necessary data for the three decomposed kinematic hardening plastic strain accumulation model of Chaboche. A performance based service life prediction algorithm for the IRJs was established using the plastic strain accumulation obtained from the Chaboche model. The predicted service lives of IRJs using this algorithm have agreed well with the published data. The finite element model has been used to carry out a sensitivity study on the effects of wheel diameter to the railhead metal plasticity. This study revealed that the depth of the plastic zone at the railhead edges is independent of the wheel diameter; however, large wheel diameter is shown to increase the IRJs' service life.

Shoes that restrict metatarsophalangeal dorsiflexion cause proximal joint compensations

To describe barefoot, shod and in-shoe kinematics during stance phase of walking gait in a normal arched adult population. An equal sample of males and females (n = 24) was recruited. In order to quantify the effect of footwear independent of technical design features, an ASICS shoe (Onitsuka Tiger-Mexico 66, Japan) was used in this study. Markers were applied to three conditions; barefoot, shod, and in-shoe. The calibration markers were used to define static pose. The order of testing was randomised. Participants completed five trials in each condition. Kinematic data were captured using a 12 camera VICON MX40 motion capture system at 100 Hz and processed in Visual3D. A previously developed model was used to describe joint angles [1]. A univariate two-way ANOVA was used to identify any differences between the pairs of conditions. Post-hoc Sheffé tests were used to further interrogate the data for differences. At peak hallux dorsiflexion (Figure 1), during propulsion, the metatarsophalangeal joint (MPTJ) was significantly more dorsiflexed in the barefoot condition compared to the shod condition (p = 0.004). At the same gait event, the tibiocalcaneal joint (TCJ) was significantly more plantarflexed than both the shod and in-shoe conditions (p < 0.001), and the tarsometatarsal joint (TMTJ) was significantly less dorsiflexed in the barefoot condition compared to the shod and in-shoe conditions (p < 0.001). The findings of the current study demonstrate that footwear has significant effects on sagittal plane MPTJ joint dorsiflexion at peak hallux dorsiflexion, which results in compensations at proximal foot joints.

The essential parameters of a resource-based carrying capacity assessment model : an Australian case study

Carrying capacity assessments model a population’s potential self-sufficiency. A crucial first step in the development of such modelling is to examine the basic resource-based parameters defining the population’s production and consumption habits. These parameters include basic human needs such as food, water, shelter and energy together with climatic, environmental and behavioural characteristics. Each of these parameters imparts land-usage requirements in different ways and varied degrees so their incorporation into carrying capacity modelling also differs. Given that the availability and values of production parameters may differ between locations, no two carrying capacity models are likely to be exactly alike. However, the essential parameters themselves can remain consistent so one example, the Carrying Capacity Dashboard, is offered as a case study to highlight one way in which these parameters are utilised. While examples exist of findings made from carrying capacity assessment modelling, to date, guidelines for replication of such studies in other regions and scales have largely been overlooked. This paper addresses such shortcomings by describing a process for the inclusion and calibration of the most important resource-based parameters in a way that could be repeated elsewhere.

Identification and estimation of equivalent area parameters using synchronised phasor measurements

A new approach is proposed for obtaining a non-linear area-based equivalent model of power systems to express the inter-area oscillations using synchronised phasor measurements. The generators that remain coherent for inter-area disturbances over a wide range of operating conditions define the areas, and the reduced model is obtained by representing each area by an equivalent machine. The parameters of the reduced system are identified by processing the obtained measurements, and a non-linear Kalman estimator is then designed for the estimation of equivalent area angles and frequencies. The simulation of the approach on a two-area system shows substantial reduction of non-inter-area modes in the estimated angles. The proposed methods are also applied to a ten-machine system to illustrate the feasibility of the approach on larger and meshed networks.

An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle

An important aspect of robotic path planning for is ensuring that the vehicle is in the best location to collect the data necessary for the problem at hand. Given that features of interest are dynamic and move with oceanic currents, vehicle speed is an important factor in any planning exercises to ensure vehicles are at the right place at the right time. Here, we examine different Gaussian process models to find a suitable predictive kinematic model that enable the speed of an underactuated, autonomous surface vehicle to be accurately predicted given a set of input environmental parameters.

Student focus and prioritization of design parameters in first-year engineering design projects

BACKGROUND Research on engineering design is a core area of concern within engineering education and a fundamental understanding of how engineering students approach and undertake design is necessary in order to develop effective design models and pedagogies. Understanding the factors related to design experiences in education and how they affect student practice can help educators as well as designers to leverage these factors as part of the design process. PURPOSE This study investigated the design practices of first-year engineering students’ and their experiences with a first-year engineering course design project. The research questions that guided the investigation were: 1. From a student perspective, what design parameters or criteria are most important? 2. How does this perspective impact subsequent student design practice throughout the design process? DESIGN/METHOD The authors employed qualitative multi-case study methods (Miles & Huberman, 1994) in order to the answer the research questions. Participant teams were observed and video recorded during team design meetings in which they researched the background for the design problem, brainstormed and sketched possible solutions, as well as built prototypes and final models of their design solutions as part of a course design project. Analysis focused on explanation building (Yin, 2009) and utilized within-case and cross-case analysis (Miles & Huberman, 1994). RESULTS We found that students focused disproportionally on the functional parameter, i.e. the physical implementation of their solution, and the possible/applicable parameter, i.e. a possible and applicable solution that benefited the user, in comparison to other given parameters such as safety and innovativeness. In addition, we found that individual teams focused on the functional and possible/ applicable parameters in early design phases such as brainstorming/ ideation and sketching. When prompted to discuss these non-salient parameters (from the student perspective) in the final design report, student design teams often used a post-hoc justification to support how the final designs fit the parameters that they did not initially consider. CONCLUSIONS This study suggests is that student design teams become fixated on (and consequently prioritize) certain parameters they interpret as important because they feel these parameters were described more explicitly in terms how they were met and assessed. Students fail to consider other parameters, perceived to be less directly assessable, unless prompted to do so. Failure to consider other parameters in the early design phases subsequently affects their approach in design phases as well. Case studies examining students’ study strategies within three Australian Universities illustrate similarities with some student approaches to design.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Distribution harmonic state estimation based on a modified PSO considering parameters uncertainty

This paper presents a new algorithm based on a Modified Particle Swarm Optimization (MPSO) to estimate the harmonic state variables in a distribution networks. The proposed algorithm performs the estimation for both amplitude and phase of each injection harmonic currents by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as the uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WTs). The main features of the proposed MPSO algorithm are usage of a primary and secondary PSO loop and applying the mutation function. The simulation results on 34-bus IEEE radial and a 70-bus realistic radial test networks are presented. The results demonstrate that the speed and the accuracy of the proposed Distribution Harmonic State Estimation (DHSE) algorithm are very excellent compared to the algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO, and Honey Bees Mating Optimization (HBMO).

Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

GNSS ambiguity resolution with controllable failure rate for long baseline network RTK

Many large-scale GNSS CORS networks have been deployed around the world to support various commercial and scientific applications. To make use of these networks for real-time kinematic positioning services, one of the major challenges is the ambiguity resolution (AR) over long inter-station baselines in the presence of considerable atmosphere biases. Usually, the widelane ambiguities are fixed first, followed by the procedure of determination of the narrowlane ambiguity integers based on the ionosphere-free model in which the widelane integers are introduced as known quantities. This paper seeks to improve the AR performance over long baseline through efficient procedures for improved float solutions and ambiguity fixing. The contribution is threefold: (1) instead of using the ionosphere-free measurements, the absolute and/or relative ionospheric constraints are introduced in the ionosphere-constrained model to enhance the model strength, thus resulting in the better float solutions; (2) the realistic widelane ambiguity precision is estimated by capturing the multipath effects due to the observation complexity, leading to improvement of reliability of widelane AR; (3) for the narrowlane AR, the partial AR for a subset of ambiguities selected according to the successively increased elevation is applied. For fixing the scalar ambiguity, an error probability controllable rounding method is proposed. The established ionosphere-constrained model can be efficiently solved based on the sequential Kalman filter. It can be either reduced to some special models simply by adjusting the variances of ionospheric constraints, or extended with more parameters and constraints. The presented methodology is tested over seven baselines of around 100 km from USA CORS network. The results show that the new widelane AR scheme can obtain the 99.4 % successful fixing rate with 0.6 % failure rate; while the new rounding method of narrowlane AR can obtain the fix rate of 89 % with failure rate of 0.8 %. In summary, the AR reliability can be efficiently improved with rigorous controllable probability of incorrectly fixed ambiguities.

The influence of Monte Carlo source parameters on detector design and dose perturbation in small field dosimetry

To obtain accurate Monte Carlo simulations of small radiation fields, it is important model the initial source parameters (electron energy and spot size) accurately. However recent studies have shown that small field dosimetry correction factors are insensitive to these parameters. The aim of this work is to extend this concept to test if these parameters affect dose perturbations in general, which is important for detector design and calculating perturbation correction factors. The EGSnrc C++ user code cavity was used for all simulations. Varying amounts of air between 0 and 2 mm were deliberately introduced upstream to a diode and the dose perturbation caused by the air was quantified. These simulations were then repeated using a range of initial electron energies (5.5 to 7.0 MeV) and electron spot sizes (0.7 to 2.2 FWHM). The resultant dose perturbations were large. For example 2 mm of air caused a dose reduction of up to 31% when simulated with a 6 mm field size. However these values did not vary by more than 2 % when simulated across the full range of source parameters tested. If a detector is modified by the introduction of air, one can be confident that the response of the detector will be the same across all similar linear accelerators and the Monte Carlo modelling of each machine is not required.