Smart Grid Test Bed Design and Implementation

Given there is currently a migration trend from traditional electrical supervisory control and data acquisition (SCADA) systems towards a smart grid based approach to critical infrastructure management. This project provides an evaluation of existing and proposed implementations for both traditional electrical SCADA and smart grid based architectures, and proposals a set of reference requirements which test bed implementations should implement. A high-level design for smart grid test beds is proposed and initial implementation performed, based on the proposed design, using open source and freely available software tools. The project examines the move towards smart grid based critical infrastructure management and illustrates the increased security requirements. The implemented test bed provides a basic framework for testing network requirements in a smart grid environment, as well as a platform for further research and development. Particularly to develop, implement and test network security related disturbances such as intrusion detection and network forensics. The project undertaken proposes and develops an architecture of the emulation of some smart grid functionality. The Common Open Research Emulator (CORE) platform was used to emulate the communication network of the smart grid. Specifically CORE was used to virtualise and emulate the TCP/IP networking stack. This is intended to be used for further evaluation and analysis, for example the analysis of application protocol messages, etc. As a proof of concept, software libraries were designed, developed and documented to enable and support the design and development of further smart grid emulated components, such as reclosers, switches, smart meters, etc. As part of the testing and evaluation a Modbus based smart meter emulator was developed to provide basic functionality of a smart meter. Further code was developed to send Modbus request messages to the emulated smart meter and receive Modbus responses from it. Although the functionality of the emulated components were limited, it does provide a starting point for further research and development. The design is extensible to enable the design and implementation of additional SCADA protocols. The project also defines an evaluation criteria for the evaluation of the implemented test bed, and experiments are designed to evaluate the test bed according to the defined criteria. The results of the experiments are collated and presented, and conclusions drawn from the results to facilitate discussion on the test bed implementation. The discussion undertaken also present possible future work.

Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

The synthesis of novel nitroxides and their application as small-molecule antioxidants and profluorescent probes for oxidative stress

The detection and potential treatment of oxidative stress in biological systems has been explored using isoindoline-based nitroxide radicals. A novel tetraethyl-fluorescein nitroxide was synthesised for its use as a profluorescent probe for redox processes in biological systems. This tetraethyl system, as well as a tetramethyl-fluorescein nitroxide, were shown to be sensitive and selective probes for superoxide in vitro. The redox environment of cellular systems was also explored using the tetramethylfluorescein species based on its reduction to the hydroxylamine. Flow cytometry was employed to assess the extent of nitroxide reduction, reflecting the overall cellular redox environment. Treatment of normal fibroblasts with rotenone and 2-deoxyglucose resulted in an oxidising cellular environment as shown by the lack of reduction of the fluorescein-nitroxide system. Assessment of the tetraethyl-fluorescein nitroxide system in the same way demonstrated its enhanced resistance to reduction and offers the potential to detect and image biologically relevant reactive oxygen species directly. Importantly, these profluorescent nitroxide compounds were shown to be more effective than the more widely used and commercially available probes for reactive oxygen species such as 2’,7’-dichlorodihydrofluorescein diacetate. Fluorescence imaging of the tetramethyl-fluorescein nitroxide and a number of other rhodamine-nitroxide derivatives was undertaken, revealing the differential cellular localisation of these systems and thus their potential for the detection of redox changes in specific cellular compartments. As well as developing novel methods for the detection of oxidative stress, a number of novel isoindoline nitroxides were synthesised for their potential application as small-molecule antioxidants. These compounds incorporated known pharmacophores into the isoindoline-nitroxide structure in an attempt to increase their efficacy in biological systems. A primary and a secondary amine nitroxide were synthesised which incorporated the phenethylamine backbone of the sympathomimetic amine class of drugs. Initial assessment of the novel primary amine derivative indicated a protective effect comparable to that of 5-carboxy-1,1,3,3- tetramethylisoindolin-2-yloxyl. Methoxy-substituted nitroxides were also synthesised as potential antioxidants for their structural similarity to some amphetamine type stimulants. A copper-catalysed methodology provided access to both the mono- and di-substituted methoxy-nitroxides. Deprotection of the ethers in these compounds using boron tribromide successfully produced a phenolnitroxide, however the catechol moiety in the disubstituted derivative appeared to undergo reaction with the nitroxide to produce quinone-like degradation products. A novel fluoran-nitroxide was also synthesised from the methoxy-substituted nitroxide, providing a pH-sensitive spin probe. An amino-acid precursor containing a nitroxide moiety was also synthesised for its application as a dual-action antioxidant. N-Acetyl protection of the nitroxide radical was necessary prior to the Erlenmeyer reaction with N-acetyl glycine. Hydrolysis and reduction of the azlactone intermediate produced a novel amino acid precursor with significant potential as an effective antioxidant.

Fixed-wing attitude estimation using temporal tracking of the horizon and optical flow

In this paper, a method has been developed for estimating pitch angle, roll angle and aircraft body rates based on horizon detection and temporal tracking using a forward-looking camera, without assistance from other sensors. Using an image processing front-end, we select several lines in an image that may or may not correspond to the true horizon. The optical flow at each candidate line is calculated, which may be used to measure the body rates of the aircraft. Using an Extended Kalman Filter (EKF), the aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and the location of the horizon. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To test the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42◦ and 0.71◦ respectively when compared with a truth attitude source. The Cessna flight resulted in pitch and roll error standard deviations of 1.79◦ and 1.75◦ respectively. The benefits of selecting and tracking the horizon using a motion model and optical flow rather than naively relying on the image processing front-end is also demonstrated.

Development and validation of a pregnancy symptoms inventory

BACKGROUND. Physical symptoms are common in pregnancy and are predominantly associated with normal physiological changes. These symptoms have a social and economic cost, leading to absenteeism from work and additional medical interventions. There is currently no simple method for identifying common pregnancy related problems in the antenatal period. A validated tool, for use by pregnancy care providers would be useful. AIM: The aim of the project was to develop and validate a Pregnancy Symptoms Inventory for use by healthcare professionals (HCPs). METHODS: A list of symptoms was generated via expert consultation with midwives and obstetrician gynaecologists. Focus groups were conducted with pregnant women in their first, second or third trimester. The inventory was then tested for face validity and piloted for readability and comprehension. For test-re-test reliability, it was administered to the same women 2 to 3 days apart. Finally, outpatient midwives trialled the inventory for 1 month and rated its usefulness on a 10cm visual analogue scale (VAS). The number of referrals to other health care professionals was recorded during this month. RESULTS: Expert consultation and focus group discussions led to the generation of a 41-item inventory. Following face validity and readability testing, several items were modified. Individual item test re-test reliability was between .51 to 1 with the majority (34 items) scoring .0.70. During the testing phase, 211 surveys were collected in the 1 month trial. Tiredness (45.5%), poor sleep (27.5%) back pain (19.5%) and nausea (12.6%) were experienced often. Among the women surveyed, 16.2% claimed to sometimes or often be incontinent. Referrals to the incontinence nurse increased > 8 fold during the study period. The median rating by midwives of the ‘usefulness’ of the inventory was 8.4 (range 0.9 to 10). CONCLUSIONS: The Pregnancy Symptoms Inventory (PSI) was well accepted by women in the 1 month trial and may be a useful tool for pregnancy care providers and aids clinicians in early detection and subsequent treatment of symptoms. It shows promise for use in the research community for assessing the impact of lifestyle intervention in pregnancy.

Analysis of micro-structural changes and measurement of their parameters of a food material

Food microstructure represents the way their elements arrangement and their interaction. Researchers in this field benefit from identifying new methods of examination of the microstructure and analysing the images. Experiments were undertaken to study micro-structural changes of food material during drying. Micro-structural images were obtained for potato samples of cubical shape at different moisture contents during drying using scanning electron microscopy. Physical parameters such as cell wall perimeter, and area were calculated using an image identification algorithm, based on edge detection and morphological operators. The algorithm was developed using Matlab.

Design and development of automatic visual inspection system for PCB manufacturing

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. This paper proposes two inspection modules for an automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localisation and segmentation. The “back-end” inspection involves the classification of solder joints using the Log Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. The Log Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. This system could contribute to the development of automated non-contact, non-destructive and low cost solder joint quality inspection systems.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

A circuit-breaker restrike diagnostic algorithm using ATP and wavelet transforms

Circuit breaker restrikes are unwanted occurrence, which can ultimately lead to breaker. Before 2008, there was little evidence in the literature of monitoring techniques based on restrike measurement and interpretation produced during switching of capacitor banks and shunt reactor banks. In 2008 a non-intrusive radiometric restrike measurement method, as well a restrike hardware detection algorithm was developed. The limitations of the radiometric measurement method are a band limited frequency response as well as limitations in amplitude determination. Current detection methods and algorithms required the use of wide bandwidth current transformers and voltage dividers. A novel non-intrusive restrike diagnostic algorithm using ATP (Alternative Transient Program) and wavelet transforms is proposed. Wavelet transforms are the most common use in signal processing, which is divided into two tests, i.e. restrike detection and energy level based on deteriorated waveforms in different types of restrike. A ‘db5’ wavelet was selected in the tests as it gave a 97% correct diagnostic rate evaluated using a database of diagnostic signatures. This was also tested using restrike waveforms simulated under different network parameters which gave a 92% correct diagnostic responses. The diagnostic technique and methodology developed in this research can be applied to any power monitoring system with slight modification for restrike detection.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Fault diagnostics and supervised testing : How fault diagnostic tools can be proactive?

The topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Social influences on Chinese drivers : cultural context and traffic law enforcement

Reducing road crashes and associated trauma is a critical focus as the Decade of Action for Road Safety commences. China is one of many rapidly-motorizing nations to experience recent increases in private-vehicle ownership and an associated escalation in novice drivers. Unfortunately, however, China also experiences a high rate of death and injury from road crashes. Several key pieces of legislation have been introduced in recent decades in China to deal with these changes. While managing the legal aspects of road use is important, social influences on driver behaviour may offer additional avenues for promoting safe driving, particularly in a culture where such factors carry high importance. To date, there is limited research on the role of social influence factors on driver behaviour in China, yet we know that Chinese society is strongly based on social rules, customs, and relationships. There is reason to assume therefore, that road use and driving-related issues may also be strongly influenced by social relationships. One previous study that has investigated such issues highlighted the need to consider culturally-specific issues such as interpersonal networks and social hierarchy when examining driver behaviour in China (Xie & Parker, 2002). Those authors suggested that there are some concepts relating to Chinese driving culture that may not necessarily have been identified from research conducted in western contexts and that research conducted in China must be considered in light of such concepts. The current paper reports qualitative research conducted with Beijing drivers to investigate such social influence factors. Findings indicated that family members, friends, and driving instructors appear influential on driver behaviour and that some novice drivers seek additional assistance after obtaining their licence. The finding relating to the influence of driving instructors was not surprising, given the substantial number of new drivers in China. In Beijing, driving instruction is conducted off-road in purpose-specific driving facilities rather than on the road network. Once licensed, it is common for new drivers to have little or no experience driving in complex traffic situations. This learning situation is unlikely to provide all the skills necessary to successfully negotiate crowded city streets and assess the related risk associated with such driving. Therefore, it was not surprising to find that one reported strategy to assist new drivers was to employ the services of an ‘accompanying driver’ to provide ongoing driving instruction once licensed. In more highly motorised countries supervised practice is part of a graduated licensing system where it is compulsory for new drivers to be supervised by a more experienced driver for a requisite period of time before progressing to solo driving. However, as this system is not in place in China, it appears that some drivers seek out and pay for additional support once they commence on-road driving. Additionally, strategies to avoid detection and penalties for inappropriate road use were discussed, many of which involve the use of a third person. These findings indicate potential barriers to implementing effective traffic enforcement and highlight the importance of understanding culturally-specific social factors relating to driver behaviour.

Resilience of a corrupt police network : the first and second jokes in Queensland

Resilient organised crime groups survive and prosper despite law enforcement activity, criminal competition and market forces. Corrupt police networks, like any other crime network, must contain resiliency characteristics if they are to continue operation and avoid being closed down through detection and arrest of their members. This paper examines the resilience of a large corrupt police network, namely The Joke which operated in the Australian state of Queensland for a number of decades. The paper uses social network analysis tools to determine the resilient characteristics of the network. This paper also assumes that these characteristics will be different to those of mainstream organised crime groups because the police network operates within an established policing agency rather than as an independent entity hiding within the broader community.