Voltage oscillations in power distribution networks in the presence of DFIGs and induction motor loads

This paper investigates the oscillatory behavior of power distribution systems in the presence of distributed generation. The analysis is carried out over a distribution test system with two doubly fed induction type wind generators and different types of induction motor loads. The system is linearized by the perturbation method. Eigenvalues are calculated to see the modal interaction within the system. The study indicates that interactions between closely placed converter controllers and induction motor loads significantly influence the damping of the oscillatory modes of the system. The critical modes have a frequency of oscillation between the electromechanical and subsynchronous oscillations of power systems. Time-domain simulations are carried out to verify the validity of the modal analysis and to provide a physical feel for the types of oscillations that occur in distribution systems. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

Space vector modulation based fast speed response field-orientation control of induction motor drive with adaptive neural integrator

This paper presents a novel fast speed response control strategy for the poly-phase induction motor drive system based on flux angle. The control scheme is derived in rotor field coordinates and employs the estimation of the rotor flux and its position. An adaptive notch filter is proposed to eliminate the dc component of the integration of signals used for the rotor flux estimation. To improve the performance of the rotor flux estimator, derivative term of the back emf is incorporated in the system. The voltage components in the synchronous reference frame are generated in the controllers which are transformed to stationary reference frame for driving the motor. Space vector modulation technique is used here. Simulation of the drive system was carried out and the results were compared with those obtained for a system that produces the above mentioned voltage components using the conventional PI controller. It is observed that the proposed control methodology provides faster response than the conventional PI controller incorporated system.

Genetic algorithm based PI controller tuning for induction motor drive with ANN flux estimator

This paper presents a Genetic Algorithm (GA) based fast speed response controller for poly-phase induction motor drive. Here the proportional and integral gains of PI controller are optimized by GA to achieve quick speed response. An adaptive Recurrent Neural Network (RNN) with Real Time Recurrent Learning (RTRL) algorithm is proposed to estimate rotor flux. An online tuning scheme to update the weight of RNN is presented to overcome stator resistance variation problem. This tuning scheme requires torque estimator to calculate the torque error. Space vector modulation (SVM) technique is used to produce the motor input voltage. Simulation tests have been performed to study the dynamic performances of the drive system for both the classical PI and the genetic algorithm based PI controllers.

Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation

This paper presents a comparative study of three algorithms for learning artificial neural network. As neural estimator, back-propagation (BP) algorithm, uncorrelated real time recurrent learning (URTRL) algorithm and correlated real time recurrent learning (CRTRL) algorithm are used in the present work to learn the artificial neural network (ANN). The approach proposed here is based on the flux estimation of high performance induction motor drives. Simulation of the drive system was carried out to study the performance of the motor drive. It is observed that the proposed CRTRL algorithm based methodology provides better performance than the BP and URTRL algorithm based technique. The proposed method can be used for accurate measurement of the rotor flux.

A possible anatomical and biomechanical explanation of the 10% rule used in the clinical assessment of prehensile hand movements and handed dominance

Abstract
A current doctrine in the dynamometric approach to determine lateralization of hand function states that in 10% of cases, the non-dominant hand will be stronger than the dominant hand. In this study, a novel MRI based modelling approach was applied to the first dorsal introsseus muscle (FDI), to determine whether the 10% rule may be applied to the FDI and may be partially explained by the arrangement of the anatomical components of the FDI.

Methods
Initially the force generated by the thumb segment during an isometric pushing task in the horizontal plane was measured from 25 strongly right-handed young males. Nine of these participants then had structural magnetic resonance imaging (sMRI) of the thumb and index osseous compartment. A modelling technique was developed to extract the muscle data and quantify the muscle line of action onto to the first metacarpal bone segment in order to quantify the muscle force at the point of momentary rotation – equilibrium.

Results
Eight of 25 subjects exhibited stronger force from the left hand. Six out of nine subjects from the MRI possessed significantly greater angles of attachment of the index osseous compartment on the left (non-dominant) hand. These six subjects also generated greater maximal isometric forces from the FDI of the left side. There was a significantly greater muscle volume for the right FDI muscle as compared to the left as measured from the reconstructed MRI slice data.

Conclusions
The calculated force produced by the muscle is related to the angle of attachment of the muscle to bone in the index osseous compartment. The MRI findings indicate that the 10% rule may be anatomically and biomechanically explained.

An analytical study on causal induction

Automatic causal discovery is a challenge research with extraordinary significance in sceintific research and in many real world problems where recovery of causes and effects and their causality relationship is an essential task. This paper firstly introduces the causality and perspectives of causal discovery. Then it provides an anlaysis on the three major approaches that are proposed in the last decades for the automatic discovery of casual models from given data. Afterwards it presents a analysis on the capability and applicability of the different proposed approaches followed by a conclusion on the potentials and the future research. © 2013 IEEE.

The principle of legality as clear statement rule: significance and problems

In Australia, the common law principle of legality has hardened into a strong clear statement rule that is applied when legislation engages common law rights and freedoms. It has transformed a loose collection of rebuttable interpretive presumptions into a quasi-constitutional common law bill of rights. However, these developments are not without controversy or issue. The analysis undertaken in this article suggests that the principle of legality as clear statement rule -- as mandated by the High Court in Coco v The Queen -- can only work legitimately if Parliament has clear and prior notice of the rights and freedoms that it operates to protect. But it is problematic if what a common law right, such as freedom of speech, requires or guarantees in any given legislative context is unclear and contested, and so must be judicially divined at the point of application. In these cases, the principle operates to enforce a (post-legislative) judicial approximation of what best protects and promotes an abstract legal value or principle. It amounts to the illegitimate judicial remaking of prior legislative decisions on rights. This undercuts the normative justifications for the principle of legality as it obscures from Parliament the common law (rights) backdrop against which its legislation is enacted and interpreted.

Conservation gone to the dogs : when canids rule the beach in small coastal reserves

On most developed coastlines, dunes backing ocean beaches constitute an urbanised landscape mosaic containing remnant pockets of small conservation areas. Urbanised beaches are also prime sites for domestic dogs, known to be environmentally harmful in many other settings. It is unknown, however, whether small, protected parcels of dune are adequate for biological conservation and whether dogs compromise their functional conservation objectives. Here we examine, for two small (2 km ocean boundary) reserves in Eastern Australia abutting an urban area, whether such small reserves can continue to function as effective conservation instruments on ocean beaches, using scavenger community composition and efficiency to assess ecosystem function. Two non-native species of canids—domestic dogs (Canis lupus familiaris) and red foxes (Vulpes vulpes)—were ubiquitous and numerous inside conservation areas, to the point of having become the most abundant vertebrate scavengers at the beach-dune interface, outcompeting native scavengers for wave-cast carrion. Dogs and foxes have effectively supplanted raptors, normally abundant on non-urban beaches in the region, and other avian scavengers, as the principal consumers of animal carcasses both inside the declared reserves and at the urban beach. Whilst the ecological threats posed by foxes are widely and intensively addressed in Australia in the form of fox-control programs, dog controls are less common and stringent. Our data emphasize, however, that managing domestic dogs may be required to the same extent in order to maintain key forms and functions in coastal reserves situated close to urban areas.

Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.

The National Safety and Quality Health Service Standards requirements for orientation and induction within Australian healthcare: a review of the literature

Introduction: A workplace orientation program is a core requirement of the National Safety and Quality Health Service (NSQHS) Standards in Australia. This is particularly important within healthcare as patient safety and the patient experience are at risk if the healthcare workforce is not supported with an effective orientation and induction program. Aim: This study aimed to review the literature and map the requirements of the NSQHS Standards in relation to orientation and induction. Method: This study utilised online databases to search for literature pertaining to orientation and induction within healthcare. Inclusion criteria included relevance to research questions, and originating in a country with a comparative health system to Australia. Results: The search identified a total of 202 articles of potential relevance with 42 articles meeting the inclusion criteria. Articles were ranked according to hierarchy of evidence criteria for both qualitative and quantitative studies. The importance of using orientation to detail safety and quality roles, the organisations' risk management system, governance structure, operational processes and procedures was highlighted. Patient-centred care, antimicrobial stewardship, clinical handover and mechanisms for escalation of care and emergency assistance should also be covered within the orientation process. Conclusion: There is a dearth of studies in relation to orientation and induction in the healthcare literature. Orientation content is now clearly prescribed, what is lacking within healthcare is a standardised framework. Concept mapping, educational theory and adult learning methods have been shown to enhance workforce problem solving and engagement with orientation, however further research is needed to enhance practice

Validation and development of a clinical prediction rule in clinically suspected community-acquired pneumonia

To develop a mathematical model to predict the probability of having community-acquired pneumonia and to evaluate an already developed prediction rule that has not been validated in a clinical scenario.