Academic literature on the topic 'Fields of Research – 290000 Engineering and Technology – 290900 Electrical and Electronic Engineering'

Electrified fencing is commonly used throughout the world to control animals with smaller and cheaper fence constructions than would otherwise be necessary with non-electrified wires. Typical installations have a long wire or wires starting from an electric fence energiser and then surrounding fields in various complex configurations. Faults on electric fences can be difficult to locate, with the average fence using tens of kilometres of wire. Basic fault-finding tools allow an operator to read the peak fence voltage, requiring the user to decide whether a fault is present and to randomly search for the source of the problem. The focus of this thesis is to develop a device that reduces the time to locate faults on a fence by providing more information about the location and nature of a fault, and will point in the direction of the fault.

The deployment of third generation (3G) cellular systems is resulting in a transition from cellular systems that predominantly carry constant-bit-rate (CBR) voice traffic to multi-service packet based systems that predominantly carry variable-bit-rate (VBR) traffic. With 3G DS-CDMA cellular systems there is a direct relationship between user traffic and propagation dependent performance as additional traffic causes increased system interference. This thesis investigates the impact of VBR traffic on the propagation dependent performance of DS-CDMA cellular systems that utilise frame-by-frame dynamic resource allocation on the radio channel. A DS-CDMA cellular system model is developed and the downlink performance of both outdoor macro-cellular and indoor pico-cellular systems is evaluated with a variety of traffic types. Both traffic scheduling performance and propagation dependent performance are evaluated as the two are inter-linked. Scenarios are identified where propagation dependent performance is sensitive to the statistical properties of the user traffic streams and it is shown that a significant performance difference potentially exists between different traffic types when the number of users per cell is low. When a significant performance difference does exist, burstier more variable traffic generally results in superior propagation dependent performance. The base transceiver station (BTS) transmitter power mean and variance provides a good indication of the level of propagation dependent performance regardless of the specific traffic type. Traffic scheduling policies that deliberately reduce the variability of user traffic streams are considered and in terms of propagation dependent performance these are shown to have a minimal impact on the performance difference between different traffic types. The implications of VBR traffic on DS-CDMA cellular system design are outlined and it is shown that VBR traffic can be approximated as CBR traffic in many scenarios and this is a convenient approximation as it simplifies system design and detailed traffic models do not need to be developed.

Considerable ongoing risk of condenser in-leakage exists at Otahuhu B (OTB) Power Station. The condenser cooling water used at OTB station is corrosive brackish water with exceedingly high sodium and chloride concentrations. Significant signs of corrosion inside the condenser have been found recently. In the event of condenser in-leakage, the salt contaminants in the cooling water will directly enter the Heat Recovery Steam Generator (HRSG) with the potential for significant and costly damage resulting in a long plant outage. A dynamic mathematical model was developed in the thesis to analyse the consequences of condenser in-leakage at OTB station. The analysis results show that the tolerance of the condenser to any leakage of cooling water is almost zero. Because the existing condensate monitoring system is not designed to detect contamination in this time frame, a new fast response system is required to detect condenser in-leakage immediately. A new dedicated fast response condensate monitoring system has been engineered and installed at OTB station as a part of the project scope. The new system dramatically reduces the response time to condenser in-leakage events. Critical instruments utilise multiple redundancy schemes to enhance the availability and reliability of the system. In addition, action level voting, timing, and alarming has been automated to assist operators in making correct decisions. The new condensate monitoring system is presently fully functional. The project has successfully achieved the objective of controlling the risk of condenser in-leakage events and minimising damage and negative effects on the plant.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms.
Whole document restricted, but available by request, use the feedback form to request access.

The proliferation of robots in industry and every day human life is gaining momentum. After the initial few decades of employment of robots in the industry, especially the automotive assembly plants, robots are now entering the home and offices. From being pick-and-place manipulators, robots are slowly being transformed in shape and form to be more anthropomorphic. The wheeled robots are however here to stay for the foreseeable future until such time as artificial muscles, and efficient means to control them, are well developed. The next phase of development of robots will be for the service industry. Robots will cooperate with each other to accomplish collaborative tasks to aid human life. They will also collaborate with human beings to assist them in doing tasks such as lifting loads and moving objects. At the same time, with the advancement of hardware, robots are becoming very fast and are capable of being programmed with more intelligence. Coupled with this is the availability of sophisticated sensors with which the robots can perceive the real world around them. Combinations of these factors have created many challenging areas of research. Several factors affect the performance of robots in a dynamic collaborative environment. The research presented in this thesis has identified the major contributing factors, namely fast vision processing, behaviour programming, predictive movement and interception control, and precise motion control, that collectively have influence on the performance of robots which are engaged in a collaborative effort to accomplish a task. Several novel techniques have been proposed in this thesis to enhance the collective performance of collaborating robots. In many systems, vision is used as one of the sensory inputs for the robot’s perception of the environment. This thesis describes a new colour space and the use of discrete look-up-tables (LUT) for very fast and robust colour segmentation and real-time identification of objects in the robot’s work space. A distributed camera system and a stereo vision using a single camera are reported. Advanced filtering has been applied to the vision data for predictive identification of the position and orientation of moving robots and targets, and for anticipatory interception control. Collaborative tasks are generally complex and robots need to be capable of exhibiting sophisticated behaviours. This thesis has detailed the use of State Transition Based Control (STBC) methodology to build a hierarchy of complex behaviour. Behaviour of robots in a robot soccer game and features such as role selection and obstacle avoidance have been built using STBC. A novel methodology for advanced control of fast robots is detailed. The algorithm uses a combination of Triangular Targeting Algorithm (TTA) and Proximity Positioning Algorithm (PPA) to position a robot behind an object aligned with a target. Various forms of velocity profiling have been proposed and validated with substantial test results. The thesis ends by looking at future scenarios where robots and human beings will coexist and work together to do many collaborative tasks. Anthropomorphic robots will be more prevalent in future and teleoperation will gain momentum. Throughout the thesis, the engineering applicability of proposed algorithms and architectures have been emphasised by testing on real robots.

Rising from the “excess demand” modern societies and economies place on limited road resources, congestion causes increased vehicle emissions, decreases national efficiency, and wastes time (Downs, 2004). In order to minimise congestion’s impacts, traffic management systems gather traffic data and use it to implement efficient management algorithms (Downs, 2004). This dissertation’s purpose has been the development of a distributable vehicle presence detection sensor, which will wirelessly provide vehicle presence information in real time. To address the sensor’s wireless power requirements, the feasibility of self-powering the device via harvested energy has been investigated. Piezoelectric, electrostatic, and electromagnetic energy harvesting devices’ principles of operation and underlying theory has been investigated in detail and an overview presented alongside a literature review of previous vibration energy harvesting research. An electromagnetic energy harvesting device was designed, which consists of: a nylon reinforced rubber bladder, hydraulic piston, neodymium magnets, and wire-wound coil housing. Preliminary testing demonstrated a harvested energy between 100mJ and 205mJ per axle. This amount is able to be transferred to a 100O load when driven over at speeds between 10km/h and 50km/h. Combined with an embedded circuit, the energy harvester facilitated the development of a passive sensor, which is able to wirelessly transmit a vehicle’s presence signal to a host computer. The vehicle detected event is displayed via a graphical user interface. Energy harvesting’s ability to power the embedded circuit’s wireless transmission, demonstrated the feasibility of developing systems capable of harvesting energy from their environment and using it to power discrete electronic components. The ability to wirelessly transmit a vehicle’s presence facilitates the development of distributable traffic monitoring systems, allowing for remote traffic monitoring and management.

Continual advancements in technology have led to the development of reliable, efficient and economical farm management systems, many of which utilize electric fences for effective control of farm animals. An electric fence system constitutes a conducting fence structure that is energized by a high voltage signal generated from an electric fence energizer. Modern electric fence energizers employ a pulsed power supply together with an appropriate high voltage charging scheme to generate high voltage pulses that energize the fence structure. The high voltage pulse delivers a non-lethal electric shock to an animal that comes into contact with the fence, and the consequent psychological impact on the animal is such that it is less likely to come into contact with the fence again. The complexity associated with modelling electric fence systems has hindered the development of proper mathematical tools that aid their design and optimization, and as a consequence, electric fence systems are currently designed using empirical rules together with a trial and error design approach. This Thesis therefore aims to fulfil this need by presenting new technologies and mathematical tools that can be used to design both intelligent and optimized electric fence systems. It presents a comprehensive study on electric fencing systems, which includes a detailed mathematical analysis on pulse propagation properties of electric fence networks and the development of high performance fence energizers that incorporates new pulses power supply technologies and high voltage charging schemes. With regard to the pulsed power technologies, two novel topologies with the ability to adapt their output pulse shape according to the fence conditions are proposed. The performance of these technologies is analyzed mathematically, and verified experimentally. In comparison to the existing fence energizer technology, energizers that are based on the proposed pulsed power supply designs are superior in performance. Furthermore, a novel Buck-Boost pushpull parallel-resonant converter technique, which is suitable for charging high voltage storage capacitors in an energizer, is also presented. The proposed technique allows for the push-pull parallel-resonant converter to operate with a frequency dependent variable voltage gain over a wide load range while maintaining zero voltage switching (ZVS). The operation of the converter is analyzed mathematically and verified experimentally to validate the proposed technique. In order to gain an insight into the propagation characteristics of electric fence networks, the Thesis presents a comprehensive mathematical model. The model uses the propagation properties of fence networks with frequency dependent distributed line parameters to obtain analytical solutions for the propagation function in the frequency-domain. As these analytical solutions are complex in nature, they are solved numerically to obtain time-domain solutions, the accuracy of which are verified through experiments and simulations. The mathematical tools and new technologies proposed in the thesis can be used to design electric fence systems that are more efficient and effective than the existing systems. In addition, the tools proposed are also expected to aid the design of electric fence based communication channels for intelligent farm management systems.

Whole document restricted, see Access Instructions file below for details of how to access the print copy.
Future telecommunication networks will employ digital transmission techniques. Such networks will provide a number of benefits including the ability to integrate voice and non-voice messages. The transmission channel of this network can be represented by a cascaded channel composed of a number of elementary channels connected in series. Therefore the modelling of such a channel is of particular interest. The influence of noise and other impairments in the cascaded binary channel cause errors which may be represented by a binary signal called the error sequence. Consequently, an important step in digital channel modelling is estimation of parameters and distribution functions which characterise the statistical properties of error sequences in the channel. Thus, the development of efficient methods for this estimation is a problem of long term interest which should be properly solved. This thesis presents methods and techniques for parameter (primarily the probability of error) and distribution function (primarily the error gap complementary distribution function) estimation using the error sequences obtained by measurement or simulation in elementary or cascaded channels. Theoretical analysis and testing confirm that it is possible to control the accuracy and reliability of estimation. Two principal and practical methods for the probability of error estimation are developed: the modified Monte Carlo method (MMC); and the method based on Chebyshev inequality (MCI). In contrast to the traditional Monte Carlo method based on classical statistics, the methods developed in this thesis aim to specify the sample size required to achieve the desired accuracy. The methods developed are based on the dependence of the sample size on the estimated value of a parameter being estimated. Hence the sample size is a random variable and the confidence limits factor (which specifies the width of confidence interval in respect to the estimated value) is a constant. Based on these methods, this thesis proposes and demonstrates two techniques for parameter estimation. The traditional Monte Carlo method has been primarily used for the probability of error estimation in channels without memory. In this thesis the capabilities of this method are extended to the case of estimating the probability of error in channels with memory and cascaded channels. However, even with this extension, this method is not practical due to its complexity and limitations on the qualification and quantification of the accuracy and reliability of estimation. Also, the extended method is unable to satisfactorily estimate the probability of error in cascaded channels with memory; nor could it improve the speed of the estimation process. Two methods and two techniques for distribution function estimation are developed in this thesis. They are demonstrated by estimating the error gap complementary functions of simulated data. For this purpose, simulators of binary channels with and without memory have been developed. The methods and techniques are characterised by their simplicity in application; ability to quantify the accuracy and reliability; time efficiency; and real time capability. The wider application of the methods and techniques developed in this thesis are demonstrated on three examples: a distribution function estimation using data obtained by indoor wideband radio propagation measurement; BER characteristics measurement; and measurement of the residual probability of error in transmission systems using error correcting codes. From the results obtained in the thesis some recommendations for future work in the field of digital channel modelling and simulation are discussed.

This thesis describes a novel approach for adaptive optimal control and demonstrates its application to a variety of systems, including motion control learning for legged robots. The new controller, called “FOX”, uses a modified form of Albus’s CMAC neural network. It is trained to generate control signals that minimize a system’s performance error. A theoretical consideration of the adaptive control problem is used to show that FOX must assign each CMAC weight an “eligibility” value which controls how that weight is updated. FOX thus implements a kind of reinforcement learning which makes it functionally similar to the cerebellum (a part of the brain that modulates movement). A highly efficient implementation is described which makes FOX suitable for on-line control. FOX requires a small amount of dynamical information about the system being controlled: the system’s impulse response is used to choose the rules that update the eligibility values. A FOX-based controller design methodology is developed, and FOX is tested on four control problems: controlling a simulated linear system, controlling a model gantry crane, balancing an inverted pendulum on a cart, and making a wheeled robot follow a path. In each case FOX is effective: it associates sensor values with (and anticipates) the correct control actions, it compensates for system nonlinearities, and it provides robust control as long as the training is comprehensive enough. FOX is also applied to the control of a simulated hopping monoped, and a walking biped. FOX learns parameters that fine tune the movements of pre-programmed controllers, in a manner analogous to the cerebellar modulation of spinal cord reflexes in human movement. The robots are successfully taught how to move with a steady gait along flat ground, in any direction, and how to climb and descend slopes.

Far from being simply a necessary appliance to extend our day, artificial light has a great influence on human behaviour and wellbeing, perception of the surroundings and comfort. The energy needed for lighting is also a significant impact on our natural resources. For these two broad reasons lighting systems that improve the human visual and perceptual experience and reduce energy use are of widespread value. This work covers research into the application of LED technology as the next generation of mainstream lighting. It looks at the reasons why this technology is set to become the dominant way in which we light our lives, and the technical hurdles that are slowing this shift in lighting. It also presents the development, testing and prototyping of such an LED lighting product for use in the architectural market. This niche application is where LED lighting is currently most suited, due to the compactness, colour adjustability and lower colour rendering required. Establishing the technology here will help to gain consumer appreciation and acceptance of this beneficial and useful new paradigm in lighting. The design incorporates a shape that is pleasing to the eye with a simple oval profile. It was designed to be subtle and compact, blending into the ceiling as cleanly as possible. Practical testing on the finished prototype showed it to produce a wide range of colours and colour temperatures, while maintaining a safe LED temperature. The simplicity also makes the unit competitive in terms of cost.