Fuzzy logic power system stabilizer in multimachine stability studies

Power system stabilizers (PSS) work well at the particular network configuration and steady state conditions for which they were designed. Once conditions change, their performance degrades. This can be overcome by an intelligent nonlinear PSS based on fuzzy logic. Such a fuzzy logic power system stabilizer (FLPSS) is developed, using speed and power deviation as inputs, and provides an auxiliary signal for the excitation system of a synchronous motor in a multimachine power system environment. The FLPSS's effect on the system damping is then compared with a conventional power system stabilizer's (CPSS) effect on the system. The results demonstrate an improved system performance with the FLPSS and also that the FLPSS is robust

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

Design optimisation of ironless motors based on magnet selection

This paper considers the design of a radial flux permanent magnet iron less core brushless DC motor for use in an electric wheel drive with an integrated epicyclic gear reduction. The motor has been designed for a continuous output torque of 30 Nm and peak rating of 60 Nm with a maximum operating speed of 7000 RPM. In the design of brushless DC motors with a toothed iron stator the peak air-gap magnetic flux density is typically chosen to be close to that of the remanence value of the magnets used. This paper demonstrates that for an ironless motor the optimal peak air-gap flux density is closer to the maximum energy product of the magnets used. The use of a radial flux topology allows for high frequency operation and can be shown to give high specific power output while maintaining a relatively low magnet mass. Two-dimensional finite element analysis is used to predict the air-gap flux density. The motor design is based around commonly available NdFeB bar magnet size

Condensed phase combustion travelling waves with sequential exothermic or endothermic reactions

The one-dimensional propagation of a combustion wave through a premixed solid fuel for two-stage kinetics is studied. We re-examine the analysis of a single reaction travelling-wave and extend it to the case of two-stage reactions. We derive an expression for the travelling wave speed in the limit of large activation energy for both reactions. The analysis shows that when both reactions are exothermic, the wave structure is similar to the single reaction case. However, when the second reaction is endothermic, the wave structure can be significantly different from single reaction case. In particular, as might be expected, a travelling wave does not necessarily exist in this case. We establish conditions in the limiting large activation energy limit for the non-existence, and for monotonicity of the temperature profile in the travelling wave.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.

Total theory (vertical) and total theory (horizontal)

The works depicted two ostensibly plaster figures 'cocooned' in protective overalls. The pose of both figures had a sense of instability, balancing improbably due to internal weights. This teetering, arching quality, combined with the empty sleeves of the overalls, made reference to the Rodin's Balzac and its aura of heroic subjectivity. As the Tyvek suits depicted in the works are a common part of my studio paraphernalia, these works sought to draw a line between these two opposing aspects of the subjectivity of the artist - the transcendent and the quotidian. The works were shown as part of ‘The Day the Machine Started’ for Dianne Tanzer Gallery + Projects at the 2010 Melbourne Art Fair. The works received citations in The Age and The Australian newspapers.

A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design

Here we present a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model which is essentially a function of importance sampling weights. Other methods for this task such as quadrature, often used in design, suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from neurological diseases such as Motor Neuron disease.

Score-level multibiometric fusion based on Dempster-Shafer theory incorporating uncertainty factors

While existing multi-biometic Dempster-Shafer the- ory fusion approaches have demonstrated promising perfor- mance, they do not model the uncertainty appropriately, sug- gesting that further improvement can be achieved. This research seeks to develop a unified framework for multimodal biometric fusion to take advantage of the uncertainty concept of Dempster- Shafer theory, improving the performance of multi-biometric authentication systems. Modeling uncertainty as a function of uncertainty factors affecting the recognition performance of the biometric systems helps to address the uncertainty of the data and the confidence of the fusion outcome. A weighted combination of quality measures and classifiers performance (Equal Error Rate) are proposed to encode the uncertainty concept to improve the fusion. We also found that quality measures contribute unequally to the recognition performance, thus selecting only significant factors and fusing them with a Dempster-Shafer approach to generate an overall quality score play an important role in the success of uncertainty modeling. The proposed approach achieved a competitive performance (approximate 1% EER) in comparison with other Dempster-Shafer based approaches and other conventional fusion approaches.

Haemodialysis work environment contributors to job satisfaction and stress: A sequential mixed methods study

Background: Haemodialysis nurses form long term relationships with patients in a technologically complex work environment. Previous studies have highlighted that haemodialysis nurses face stressors related to the nature of their work and also their work environments leading to reported high levels of burnout. Using Kanters (1997) Structural Empowerment Theory as a guiding framework, the aim of this study was to explore the factors contributing to satisfaction with the work environment, job satisfaction, job stress and burnout in haemodialysis nurses. Methods: Using a sequential mixed-methods design, the first phase involved an on-line survey comprising demographic and work characteristics, Brisbane Practice Environment Measure (B-PEM), Index of Work Satisfaction(IWS), Nursing Stress Scale (NSS) and the Maslach Burnout Inventory (MBI). The second phase involved conducting eight semi-structured interviews with data thematically analyzed. Results: From the 417 nurses surveyed the majority were female (90.9 %), aged over 41 years of age (74.3 %), and 47.4 % had worked in haemodialysis for more than 10 years. Overall the work environment was perceived positively and there was a moderate level of job satisfaction. However levels of stress and emotional exhaustion (burnout) were high. Two themes, ability to care and feeling successful as a nurse, provided clarity to the level of job satisfaction found in phase 1. While two further themes, patients as quasi-family and intense working teams, explained why working as a haemodialysis nurse was both satisfying and stressful. Conclusions: Nurse managers can use these results to identify issues being experienced by haemodialysis nurses working in the unit they are supervising.

An extension of the continual reassessment method using decision theory

The primary goal of a phase I trial is to find the maximally tolerated dose (MTD) of a treatment. The MTD is usually defined in terms of a tolerable probability, q*, of toxicity. Our objective is to find the highest dose with toxicity risk that does not exceed q*, a criterion that is often desired in designing phase I trials. This criterion differs from that of finding the dose with toxicity risk closest to q*, that is used in methods such as the continual reassessment method. We use the theory of decision processes to find optimal sequential designs that maximize the expected number of patients within the trial allocated to the highest dose with toxicity not exceeding q*, among the doses under consideration. The proposed method is very general in the sense that criteria other than the one considered here can be optimized and that optimal dose assignment can be defined in terms of patients within or outside the trial. It includes as an important special case the continual reassessment method. Numerical study indicates the strategy compares favourably with other phase I designs.

Sequential allocation in clinical trials

Suppose two treatments with binary responses are available for patients with some disease and that each patient will receive one of the two treatments. In this paper we consider the interests of patients both within and outside a trial using a Bayesian bandit approach and conclude that equal allocation is not appropriate for either group of patients. It is suggested that Gittins indices should be used (using an approach called dynamic discounting by choosing the discount rate based on the number of future patients in the trial) if the disease is rare, and the least failures rule if the disease is common. Some analytical and simulation results are provided.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.