Droop control of converter interfaced micro sources in rural distributed generation

This paper proposes new droop control methods for load sharing in a rural area with distributed generation. Highly resistive lines, typical of rural low voltage networks, always create a big challenge for conventional droop control. To overcome the conflict between higher feedback gain for better power sharing and system stability in angle droop, two control methods have been proposed. The first method considers no communication among the distributed generators (DGs) and regulates the converter output voltage and angle ensuring proper sharing of load in a system having strong coupling between real and reactive power due to high line resistance. The second method, based on a smattering of communication, modifies the reference output volt-age angle of the DGs depending on the active and reactive power flow in the lines connected to point of common coupling (PCC). It is shown that with the second proposed control method, an economical and minimum communication system can achieve significant improvement in load sharing. The difference in error margin between proposed control schemes and a more costly high bandwidth communication system is small and the later may not be justified considering the increase in cost. The proposed control shows stable operation of the system for a range of operating conditions while ensuring satisfactory load sharing.

Analysis of phasor measurement method in tracking the power frequency of distorted signals

This paper presents an analysis of phasor measurement method for tracking the fundamental power frequency to show if it has the performance necessary to cope with the requirements of power system protection and control. In this regard, several computer simulations presenting the conditions of a typical power system signal especially those highly distorted by harmonics, noise and offset, are provided to evaluate the response of the Phasor Measurement (PM) technique. A new method, which can shorten the delay of estimation, has also been proposed for the PM method to work for signals free of even-order harmonics.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Development of a method to measure upper eyelid pressure on the cornea

Purpose: The cornea is known to be susceptible to forces exerted by eyelids. There have been previous attempts to quantify eyelid pressure but the reliability of the results is unclear. The purpose of this study was to develop a technique using piezoresistive pressure sensors to measure upper eyelid pressure on the cornea. Methods: The technique was based on the use of thin (0.18 mm) tactile piezoresistive pressure sensors, which generate a signal related to the applied pressure. A range of factors that influence the response of this pressure sensor were investigated along with the optimal method of placing the sensor in the eye. Results: Curvature of the pressure sensor was found to impart force, so the sensor needed to remain flat during measurements. A large rigid contact lens was designed to have a flat region to which the sensor was attached. To stabilise the contact lens during measurement, an apparatus was designed to hold and position the sensor and contact lens combination on the eye. A calibration system was designed to apply even pressure to the sensor when attached to the contact lens, so the raw digital output could be converted to actual pressure units. Conclusions: Several novel procedures were developed to use tactile sensors to measure eyelid pressure. The quantification of eyelid pressure has a number of applications including eyelid reconstructive surgery and the design of soft and rigid contact lenses.

Sources of coding discrepancies in injury morbidity data: Implications for injury surveillance

Objective: To examine the sources of coding discrepancy for injury morbidity data and explore the implications of these sources for injury surveillance.-------- Method: An on-site medical record review and recoding study was conducted for 4373 injury-related hospital admissions across Australia. Codes from the original dataset were compared to the recoded data to explore the reliability of coded data aand sources of discrepancy.---------- Results: The most common reason for differences in coding overall was assigning the case to a different external cause category with 8.5% assigned to a different category. Differences in the specificity of codes assigned within a category accounted for 7.8% of coder difference. Differences in intent assignment accounted for 3.7% of the differences in code assignment.---------- Conclusions: In the situation where 8 percent of cases are misclassified by major category, the setting of injury targets on the basis of extent of burden is a somewhat blunt instrument Monitoring the effect of prevention programs aimed at reducing risk factors is not possible in datasets with this level of misclassification error in injury cause subcategories. Future research is needed to build the evidence base around the quality and utility of the ICD classification system and application of use of this for injury surveillance in the hospital environment.

Using honeypots to analyse anomalous Internet activities

Monitoring Internet traffic is critical in order to acquire a good understanding of threats to computer and network security and in designing efficient computer security systems. Researchers and network administrators have applied several approaches to monitoring traffic for malicious content. These techniques include monitoring network components, aggregating IDS alerts, and monitoring unused IP address spaces. Another method for monitoring and analyzing malicious traffic, which has been widely tried and accepted, is the use of honeypots. Honeypots are very valuable security resources for gathering artefacts associated with a variety of Internet attack activities. As honeypots run no production services, any contact with them is considered potentially malicious or suspicious by definition. This unique characteristic of the honeypot reduces the amount of collected traffic and makes it a more valuable source of information than other existing techniques. Currently, there is insufficient research in the honeypot data analysis field. To date, most of the work on honeypots has been devoted to the design of new honeypots or optimizing the current ones. Approaches for analyzing data collected from honeypots, especially low-interaction honeypots, are presently immature, while analysis techniques are manual and focus mainly on identifying existing attacks. This research addresses the need for developing more advanced techniques for analyzing Internet traffic data collected from low-interaction honeypots. We believe that characterizing honeypot traffic will improve the security of networks and, if the honeypot data is handled in time, give early signs of new vulnerabilities or breakouts of new automated malicious codes, such as worms. The outcomes of this research include: • Identification of repeated use of attack tools and attack processes through grouping activities that exhibit similar packet inter-arrival time distributions using the cliquing algorithm; • Application of principal component analysis to detect the structure of attackers’ activities present in low-interaction honeypots and to visualize attackers’ behaviors; • Detection of new attacks in low-interaction honeypot traffic through the use of the principal component’s residual space and the square prediction error statistic; • Real-time detection of new attacks using recursive principal component analysis; • A proof of concept implementation for honeypot traffic analysis and real time monitoring.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

The effect of prolonged reading on visual functions and reading performance in students with low vision

This study is the first to investigate the effect of prolonged reading on reading performance and visual functions in students with low vision. The study focuses on one of the most common modes of achieving adequate magnification for reading by students with low vision, their close reading distance (proximal or relative distance magnification). Close reading distances impose high demands on near visual functions, such as accommodation and convergence. Previous research on accommodation in children with low vision shows that their accommodative responses are reduced compared to normal vision. In addition, there is an increased lag of accommodation for higher stimulus levels as may occur at close reading distance. Reduced accommodative responses in low vision and higher lag of accommodation at close reading distances together could impact on reading performance of students with low vision especially during prolonged reading tasks. The presence of convergence anomalies could further affect reading performance. Therefore, the aims of the present study were 1) To investigate the effect of prolonged reading on reading performance in students with low vision 2) To investigate the effect of prolonged reading on visual functions in students with low vision. This study was conducted as cross-sectional research on 42 students with low vision and a comparison group of 20 students with normal vision, aged 7 to 20 years. The students with low vision had vision impairments arising from a range of causes and represented a typical group of students with low vision, with no significant developmental delays, attending school in Brisbane, Australia. All participants underwent a battery of clinical tests before and after a prolonged reading task. An initial reading-specific history and pre-task measurements that included Bailey-Lovie distance and near visual acuities, Pelli-Robson contrast sensitivity, ocular deviations, sensory fusion, ocular motility, near point of accommodation (pull-away method), accuracy of accommodation (Monocular Estimation Method (MEM)) retinoscopy and Near Point of Convergence (NPC) (push-up method) were recorded for all participants. Reading performance measures were Maximum Oral Reading Rates (MORR), Near Text Visual Acuity (NTVA) and acuity reserves using Bailey-Lovie text charts. Symptoms of visual fatigue were assessed using the Convergence Insufficiency Symptom Survey (CISS) for all participants. Pre-task measurements of reading performance and accuracy of accommodation and NPC were compared with post-task measurements, to test for any effects of prolonged reading. The prolonged reading task involved reading a storybook silently for at least 30 minutes. The task was controlled for print size, contrast, difficulty level and content of the reading material. Silent Reading Rate (SRR) was recorded every 2 minutes during prolonged reading. Symptom scores and visual fatigue scores were also obtained for all participants. A visual fatigue analogue scale (VAS) was used to assess visual fatigue during the task, once at the beginning, once at the middle and once at the end of the task. In addition to the subjective assessments of visual fatigue, tonic accommodation was monitored using a photorefractor (PlusoptiX CR03™) every 6 minutes during the task, as an objective assessment of visual fatigue. Reading measures were done at the habitual reading distance of students with low vision and at 25 cms for students with normal vision. The initial history showed that the students with low vision read for significantly shorter periods at home compared to the students with normal vision. The working distances of participants with low vision ranged from 3-25 cms and half of them were not using any optical devices for magnification. Nearly half of the participants with low vision were able to resolve 8-point print (1M) at 25 cms. Half of the participants in the low vision group had ocular deviations and suppression at near. Reading rates were significantly reduced in students with low vision compared to those of students with normal vision. In addition, there were a significantly larger number of participants in the low vision group who could not sustain the 30-minute task compared to the normal vision group. However, there were no significant changes in reading rates during or following prolonged reading in either the low vision or normal vision groups. Individual changes in reading rates were independent of their baseline reading rates, indicating that the changes in reading rates during prolonged reading cannot be predicted from a typical clinical assessment of reading using brief reading tasks. Contrary to previous reports the silent reading rates of the students with low vision were significantly lower than their oral reading rates, although oral and silent reading was assessed using different methods. Although the visual acuity, contrast sensitivity, near point of convergence and accuracy of accommodation were significantly poorer for the low vision group compared to those of the normal vision group, there were no significant changes in any of these visual functions following prolonged reading in either group. Interestingly, a few students with low vision (n =10) were found to be reading at a distance closer than their near point of accommodation. This suggests a decreased sensitivity to blur. Further evaluation revealed that the equivalent intrinsic refractive errors (an estimate of the spherical dioptirc defocus which would be expected to yield a patient’s visual acuity in normal subjects) were significantly larger for the low vision group compared to those of the normal vision group. As expected, accommodative responses were significantly reduced for the low vision group compared to the expected norms, which is consistent with their close reading distances, reduced visual acuity and contrast sensitivity. For those in the low vision group who had an accommodative error exceeding their equivalent intrinsic refractive errors, a significant decrease in MORR was found following prolonged reading. The silent reading rates however were not significantly affected by accommodative errors in the present study. Suppression also had a significant impact on the changes in reading rates during prolonged reading. The participants who did not have suppression at near showed significant decreases in silent reading rates during and following prolonged reading. This impact of binocular vision at near on prolonged reading was possibly due to the high demands on convergence. The significant predictors of MORR in the low vision group were age, NTVA, reading interest and reading comprehension, accounting for 61.7% of the variances in MORR. SRR was not significantly influenced by any factors, except for the duration of the reading task sustained; participants with higher reading rates were able to sustain a longer reading duration. In students with normal vision, age was the only predictor of MORR. Participants with low vision also reported significantly greater visual fatigue compared to the normal vision group. Measures of tonic accommodation however were little influenced by visual fatigue in the present study. Visual fatigue analogue scores were found to be significantly associated with reading rates in students with low vision and normal vision. However, the patterns of association between visual fatigue and reading rates were different for SRR and MORR. The participants with low vision with higher symptom scores had lower SRRs and participants with higher visual fatigue had lower MORRs. As hypothesized, visual functions such as accuracy of accommodation and convergence did have an impact on prolonged reading in students with low vision, for students whose accommodative errors were greater than their equivalent intrinsic refractive errors, and for those who did not suppress one eye. Those students with low vision who have accommodative errors higher than their equivalent intrinsic refractive errors might significantly benefit from reading glasses. Similarly, considering prisms or occlusion for those without suppression might reduce the convergence demands in these students while using their close reading distances. The impact of these prescriptions on reading rates, reading interest and visual fatigue is an area of promising future research. Most importantly, it is evident from the present study that a combination of factors such as accommodative errors, near point of convergence and suppression should be considered when prescribing reading devices for students with low vision. Considering these factors would also assist rehabilitation specialists in identifying those students who are likely to experience difficulty in prolonged reading, which is otherwise not reflected during typical clinical reading assessments.

Sequential decision fusion for controlled detection errors

Information fusion in biometrics has received considerable attention. The architecture proposed here is based on the sequential integration of multi-instance and multi-sample fusion schemes. This method is analytically shown to improve the performance and allow a controlled trade-off between false alarms and false rejects when the classifier decisions are statistically independent. Equations developed for detection error rates are experimentally evaluated by considering the proposed architecture for text dependent speaker verification using HMM based digit dependent speaker models. The tuning of parameters, n classifiers and m attempts/samples, is investigated and the resultant detection error trade-off performance is evaluated on individual digits. Results show that performance improvement can be achieved even for weaker classifiers (FRR-19.6%, FAR-16.7%). The architectures investigated apply to speaker verification from spoken digit strings such as credit card numbers in telephone or VOIP or internet based applications.

Semi-analytic method of contact modelling

An algorithm to improve the accuracy and stability of rigid-body contact force calculation is presented. The algorithm uses a combination of analytic solutions and numerical methods to solve a spring-damper differential equation typical of a contact model. The solution method employs the recently proposed patch method, which especially suits the spring-damper differential equations. The resulting semi-analytic solution reduces the stiffness of the differential equations, while performing faster than conventional alternatives.

Visual localisation in outdoor industrial building environments

This paper presents a vision-based method of vehicle localisation that has been developed and tested on a large forklift type robotic vehicle which operates in a mainly outdoor industrial setting. The localiser uses a sparse 3D edgemap of the environment and a particle filter to estimate the pose of the vehicle. The vehicle operates in dynamic and non-uniform outdoor lighting conditions, an issue that is addressed by using knowledge of the scene to intelligently adjust the camera exposure and hence improve the quality of the information in the image. Results from the industrial vehicle are shown and compared to another laser-based localiser which acts as a ground truth. An improved likelihood metric, using peredge calculation, is presented and has shown to be 40% more accurate in estimating rotation. Visual localization results from the vehicle driving an arbitrary 1.5km path during a bright sunny period show an average position error of 0.44m and rotation error of 0.62deg.

Incorporating uncertainty in environmental models informed by imagery

In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given the increased data size with models informed by imagery. Measurement error models are another approach to incorporating uncertainty when data is informed by imagery. These models for measurement uncertainty, considered in a Bayesian conditional independence framework, are applied to ecological data generated from imagery. The models are shown to be appropriate and useful in certain situations. Measurement uncertainty is also considered in the context of change detection when two images are not co-registered. An approach for detecting change in two successive images is proposed that is not affected by registration. The procedure uses the Kolmogorov-Smirnov test on homogeneous segments of an image to detect change, with the homogeneous segments determined using a Bayesian mixture model of pixel values. Using the mixture model to segment an image also allows for uncertainty in the composition of an image. This thesis concludes by comparing several different Bayesian image segmentation approaches that allow for uncertainty regarding the allocation of pixels to different ground components. Each segmentation approach is applied to a data set of chlorophyll values and shown to have different benefits and drawbacks depending on the aims of the analysis.

Improved correlation-based modal strain energy method for global damage detection of truss bridge structures

The previous investigations have shown that the modal strain energy correlation method, MSEC, could successfully identify the damage of truss bridge structures. However, it has to incorporate the sensitivity matrix to estimate damage and is not reliable in certain damage detection cases. This paper presents an improved MSEC method where the prediction of modal strain energy change vector is differently obtained by running the eigensolutions on-line in optimisation iterations. The particular trail damage treatment group maximising the fitness function close to unity is identified as the detected damage location. This improvement is then compared with the original MSEC method along with other typical correlation-based methods on the finite element model of a simple truss bridge. The contributions to damage detection accuracy of each considered mode is also weighed and discussed. The iterative searching process is operated by using genetic algorithm. The results demonstrate that the improved MSEC method suffices the demand in detecting the damage of truss bridge structures, even when noised measurement is considered.

A modified quality control method for manufacturing process in mask industry

Masks are widely used in different industries, for example, traditional metal industry, hospitals or semiconductor industry. Quality is a critical issue in mask industry as it is related to public health and safety. Traditional quality practices for manufacturing process have some limitations in implementing them in mask industries. This paper aims to investigate the suitability of Six Sigma quality control method for the manufacturing process in the mask industry to provide high quality products, enhancing the process capacity, reducing the defects and the returned goods arising in a selected mask manufacturing company. This paper suggests that modifications necessary in Six Sigma method for effective implementation in mask industry.