Optimal fault tolerant Jacobian matrix generators for redundant manipulators

The design of locally optimal fault-tolerant manipulators has been previously addressed via adding constraints on the bases of a desired null space to the design constraints of the manipulators. Then by algebraic or numeric solution of the design equations, the optimal Jacobian matrix is obtained. In this study, an optimal fault-tolerant Jacobian matrix generator is introduced from geometric properties instead of the null space properties. The proposed generator provides equally fault-tolerant Jacobian matrices in R3 that are optimally fault tolerant for one or two locked joint failures. It is shown that the proposed optimal Jacobian matrices are directly obtained via regular pyramids. The geometric approach and zonotopes are used as a novel tool for determining relative manipulability in the context of fault-tolerant robotics and for bringing geometric insight into the design of optimal fault-tolerant manipulators.

Is psychotherapy a vector for isolation and anomie?

A key traditional question the client learns in the conventional psychotherapies is ‘Am I getting what I want?’. But can this question incite a mindset that does not align with the ‘give and take’ essence of sustainable everyday relations? Is it possible that the psychotherapies—if these practices can be bundled together—might teach clients to become more self-centred and relationally illiterate? MARK FURLONG suggests that well-intentioned practitioners can inadvertently de-empathise, ignore or even disrupt their clients’ intimate networks. Findings from his research support the proposition that the action of the mainstream therapies tends to undermine the service users’ prospects for sustainable personal relationships. Exceptions were found in the specialist settings of paediatric and aged care, and in narrative and family therapy practice.

Optimal fault-tolerant robotic manipulators

This thesis addresses “Optimal Fault-Tolerant Robotic Manipulators” for locked-joint failures and consists of three components. It begins by investigating the regions of workspace where the manipulator can operate with high reliability. It then continues with an efficient deployment of kinematic redundancies for fault-tolerant operation. Finally, it presents a novel method for design of optimal fault-tolerant manipulators.

Well-conditioned configurations of fault-tolerant manipulators

Fault-tolerant motion of redundant manipulators can be obtained by joint velocity reconfiguration. For fault-tolerant manipulators, it is beneficial to determine the configurations that can tolerate the locked-joint failures with a minimum relative joint velocity jump, because the manipulator can rapidly reconfigure itself to tolerate the fault. This paper uses the properties of the condition numbers to introduce those optimal configurations for serial manipulators. The relationship between the manipulator's locked-joint failures and the condition number of the Jacobian matrix is indicated by using a matrix perturbation methodology. Then, it is observed that the condition number provides an upper bound of the required relative joint velocity change for recovering the faults which leads to define the optimal fault-tolerant configuration from the minimization of the condition number. The optimization problem to obtain the minimum condition number is converted to three standard Eigen value optimization problems. A solution is for selected optimization problem is presented. Finally, in order to obtain the optimal fault-tolerant configuration, the proposed method is applied to a 4-DoF planar manipulator.

Isolation and characterisation of polymorphic microsatellite loci for Noisy Miners Manorina melanocephala, with successful cross-amplification in Bell Miners M. melanophrys

We characterized 15 polymorphic microsatellite loci identified from a Noisy Miner (Manorina melanocephala) blood sample using 454 whole genome shotgun sequencing. Levels of polymorphism were assessed using 15 Noisy Miners. The average number of alleles per locus was 5.1. These loci were then cross-amplified to assess their suitability in a single population of Bell Miners (M. melanophrys). Given the landscape level impact that these species are having on the health of vegetation and biodiversity of a range of vertebrates throughout much of south-eastern Australia, these primers will help identify colony dispersal patterns and thus aid in modeling predictions of miner presence and tenure length in threatened ecosystems.

Isolation and characterization of 12 polymorphic tetranucleotide microsatellite loci in the apostlebird (Struthidea cinerea)

The apostlebird (Struthidea cinerea) is an Australian endemic passerine belonging to the Corcoracidae family. The species is highly gregarious throughout the year and the name of the species refers to the apparent prevalence of social groups of around 12 birds. The species is becoming a model system for the study of sociality in vertebrates, which will require the analysis of relatedness, paternity and maternity. We characterize 12 microsatellite loci tested for polymorphism on 25 individuals from a population in western New South Wales, Australia. The number of alleles ranged from 4 to 9 per locus. Expected heterozygosities ranged from 0.69 to 0.88. This microsatellite panel will facilitate future studies that will advance our understanding of dispersal processes, inbreeding avoidance and reproductive skew in social animals.

A hybrid intelligent system and its application to fault detection and diagnosis

This paper proposes a hybrid system that integrates the SOM (Self Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.

A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors

In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.