Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

The public management issues of access to electronic gaming machines (EGMs) : a case study of policy in Victoria, its effects and requirements for evaluation

Wynne and Schaffer (2003) have highlighted both the strong growth of gambling activity in recent years, and the revenue streams this has generated for governments and communities. Gambling activities and the revenues derived from them have, unsurprisingly, therefore also been seen as a way in which to increase economic development in deprived areas (Jinkner-Lloyd, 1996). Consequently, according to Brown et al (2003), gambling is now a large taxation revenue earner for many western governments, at both federal and state levels, worldwide (for example UK, USA, Australia). In size and importance, the Australian gambling industry in particular has grown significantly over the last three decades, experiencing a fourfold increase in real gambling turnover. There are, however, also concerns expressed about gambling and Electronic Gaming in particular, as illustrated in economic, social and ethical terms in Oddo (1997). There are also spatial aspects to understanding these issues. Marshall’s (1998) study, for example, highlights that benefits from gambling are more likely to accrue at the macro as opposed to the local level, because of centralised tax gathering and spending of tax revenues, whilst localities may suffer from displacement of activities with higher multipliers than the institutions with EGMs that replace them. This also highlights a regional context of costs, where benefits accrue to the centre, but the costs accrue to the regions and localities, as simultaneously resources leave those communities through both the gambling activities themselves (in the form of revenue for the EGM owners), and the government (through taxes).

Hypercapitalism:Political economy, electric identity, and authorial alienation

Hypercapitalism, with its "knowledge economy", is the form of capitalism under which thought itself is produced, commodified, and exchanged within the globally integrated system of communication technologies. As such, hypercapitalism may be seen as not so much a revolution, but rather an evolution: the progressively thorough, inexorable totalisation of social relations by Capital. The study on which this paper is based synthesises the sociological perspectives of Marx (1970, 1844/1975, 1846/1972, 1976, 1978, 1981) and Adorno (1951/1974, 1991; Horkheimer & Adorno, 1944/1998), and the Critical Discourse perspectives of Fairclough (1989, 1992) and Lemke (1995) to argue that alienated thought and language are the fundamental, irreducible commodity-forms of Cybersociety’s knowledge economy.

Do androids dream of electric chimera?

This paper describes the use of the Chimera Architecture as the basis for a generative rhythmic improvisation system that is intended for use in ensemble contexts. This interactive soft- ware system learns in real time based on an audio input from live performers. The paper describes the components of the Chimera Architecture including a novel analysis engine that uses prediction to robustly assess the rhythmic salience of the input stream. Analytical results are stored in a hierarchical structure that includes multiple scenarios which allow ab- stracted and alternate interpretations of the current metrical context. The system draws upon this Chimera Architecture when generating a musical response. The generated rhythms are intended to have a particular ambiguity in relation to the music performance by other members of the ensemble. Ambi- guity is controlled through alternate interpretations of the Chimera. We describe an implementation of the Chimera Ar- chitecture that focuses on rhythmic material, and present and discuss initial experimental results of the software system playing along with recordings of a live performance.

Temperature and electric field dependent mobility in poly„3-hexylthiophene diodes

Current-voltage (I-V) curves of Poly(3-hexyl-thiophene) (P3HT) diodes have been collected to investigate the polymer hole-dominated charge transport. At room temperature and at low electric fields the I-V characteristic is purely Ohmic whereas at medium-high electric fields, experimental data shows that the hole transport is Trap Dominated - Space Charge Limited Current (TD-SCLC). In this regime, it is possible to extract the I-V characteristic of the P3HT/Al junction showing the ideal Schottky diode behaviour over five orders of magnitude. At high-applied electric fields, holes’ transport is found to be in the trap free SCLC regime. We have measured and modelled in this regime the holes’ mobility to evaluate its dependence from the electric field applied and the temperature of the device.

Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

An optimising compiler for generated tiny virtual machines

VMSCRIPT is a scripting language designed to allow small programs to be compiled for a range of generated tiny virtual machines, suitable for sensor network devices. The VMSCRIPT compiler is an optimising compiler designed to allow quick re-targeting, based on a template, code rewriting model. A compiler backend can be specified at the same time as a virtual machine, with the compiler reading the specification and using it as a code generator.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia.