Bond graph toolbox for handling complex variable

Bond graph is an apt modelling tool for any system working across multiple energy domains. Power electronics system modelling is usually the study of the interplay of energy in the domains of electrical, mechanical, magnetic and thermal. The usefulness of bond graph modelling in power electronic field has been realised by researchers. Consequently in the last couple of decades, there has been a steadily increasing effort in developing simulation tools for bond graph modelling that are specially suited for power electronic study. For modelling rotating magnetic fields in electromagnetic machine models, a support for vector variables is essential. Unfortunately, all bond graph simulation tools presently provide support only for scalar variables. We propose an approach to provide complex variable and vector support to bond graph such that it will enable modelling of polyphase electromagnetic and spatial vector systems. We also introduced a rotary gyrator element and use it along with the switched junction for developing the complex/vector variable's toolbox. This approach is implemented by developing a complex S-function tool box in Simulink inside a MATLAB environment This choice has been made so as to synthesise the speed of S-function, the user friendliness of Simulink and the popularity of MATLAB.

Auditory - Visual Matching in Learning Disabilities : Intervention Studies from Finland and Sweden

The present thesis discusses relevant issues in education: 1) learning disabilities including the role of comorbidity in LDs, and 2) the use of research-based interventions. This thesis consists of a series of four studies (three articles), which deepens the knowledge of the field of special education. Intervention studies (N=242) aimed to examine whether training using a nonverbal auditory-visual matching computer program had a remedial effect in different learning disabilities, such as developmental dyslexia, Attention Deficit Disorder (ADD) and Specific Language Impairment (SLI). These studies were conducted in both Finland and Sweden. The intervention’s non-verbal character made an international perspective possible. The results of the intervention studies confirmed, that the auditory-visual matching computer program, called Audilex had positive intervention effects. In Study I of children with developmental dyslexia there were also improvements in reading skills, specifically in reading nonsense words and reading speed. These improvements in tasks, which are thought to rely on phonological processing, suggest that such reading difficulties in dyslexia may stem in part from more basic perceptual difficulties, including those required to manage the visual and auditory components of the decoding task. In Study II the intervention had a positive effect on children with dyslexia; older students with dyslexia and surprisingly, students with ADD also benefited from this intervention. In conclusion, the role of comorbidity was apparent. An intervention effect was evident also in students’ school behavior. Study III showed that children with SLI experience difficulties very similar to those of children with dyslexia in auditory-visual matching. Children with language-based learning disabilities, such as dyslexia and SLI benefited from the auditory-visual matching intervention. Also comorbidity was evident among these children; in addition to formal diagnoses, comorbidity was explored with an assessment inventory, which was developed for this thesis. Interestingly, an overview of the data of this thesis shows positive intervention effects in all studies despite learning disability, language, gender or age. These findings have been described by a concept inter-modal transpose. Self-evidently these issues need further studies. In learning disabilities the aim in the future will also be to identify individuals at risk rather than by deficit; this aim can be achieved by using research-based interventions, intensified support in general education and inclusive special education. Keywords: learning disabilities, developmental dyslexia, attention deficit disorder, specific language impairment, language-based learning disabilities, comorbidity, auditory-visual matching, research-based interventions, inter-modal transpose

Improving decisions for invasive species management: Reformulation and extensions of the Panetta-Lawes eradication graph

Aim: Effective decisions for managing invasive species depend on feedback about the progress of eradication efforts. Panetta & Lawes. developed the eradograph, an intuitive graphical tool that summarizes the temporal trajectories of delimitation and extirpation to support decision-making. We correct and extend the tool, which was affected by incompatibilities in the units used to measure these features that made the axes impossible to interpret biologically. Location: Victoria, New South Wales and Queensland, Australia. Methods: Panetta and Lawes' approach represented delimitation with estimates of the changes in the area known to be infested and extirpation with changes in the mean time since the last detection. We retain the original structure but propose different metrics that improve biological interpretability. We illustrate the methods with a hypothetical example and real examples of invasion and treatment of branched broomrape (Orobanche ramosa L.) and the guava rust complex (Puccinia psidii (Winter 1884)) in Australia. Results: These examples illustrate the potential of the tool to guide decisions about the effectiveness of search and control activities. Main conclusions: The eradograph is a graphical data summary tool that provides insight into the progress of eradication. Our correction and extension of the tool make it easier to interpret and provide managers with better decision support. © 2013 John Wiley & Sons Ltd.

Knowledge discovery from tree databases using balanced optimal search

This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.

Species-site matching in mixed species plantations of native trees in tropical Australia

Mixed species plantations using native trees are increasingly being considered for sustainable timber production. Successful application of mixed species forestry systems requires knowledge of the potential spatial interaction between species in order to minimise the chance of dominance and suppression and to maximise wood production. Here, we examined species performances across 52 experimental plots of tree mixtures established on cleared rainforest land to analyse relationships between the growth of component species and climate and soil conditions. We derived site index (SI) equations for ten priority species to evaluate performance and site preferences. Variation in SI of focus species demonstrated that there are strong species-specific responses to climate and soil variables. The best predictor of tree growth for rainforest species Elaeocarpus grandis and Flindersia brayleyana was soil type, as trees grew significantly better on well-draining than on poorly drained soil profiles. Both E. grandis and Eucalyptus pellita showed strong growth response to variation in mean rain days per month. Our study generates understanding of the relative performance of species in mixed species plantations in the Wet Tropics of Australia and improves our ability to predict species growth compatibilities at potential planting sites within the region. Given appropriate species selections and plantation design, mixed plantations of high-value native timber species are capable of sustaining relatively high productivity at a range of sites up to age 10 years, and may offer a feasible approach for large-scale reforestation.

Patterns of entrepreneurial career development: An optimal matching analysis approach

Longitudinal studies of entrepreneurial career development are rare, and current knowledge of self-employment patterns and their relationships with individual difference characteristics is limited. In this study, the authors analyzed employment data from a subsample of 514 participants from the German Socio-Economic Panel study (1984–2008). Results of an optimal matching analysis indicated that a continuous self-employment pattern could be distinguished from four alternative employment patterns (change from employment to self-employment, full-time employees, part-time employees, and farmers). Results of a multinomial logistic regression analysis showed that certain socio-demographic characteristics (i.e., age and gender) and personality characteristics (i.e., conscientiousness and risk-taking propensity) were related to the likelihood of following a continuous self-employment pattern compared to the other employment patterns. Implications for future research on entrepreneurial career development are discussed.

Flow-graph techniques for epicyclic gear train analysis

Flow-graph techniques are applied in this article for the analysis of an epicyclic gear train. A gear system based on this is designed and constructed for use in Numerical Control Systems.

Efficient Content-based Routing, Mobility-aware Topologies, and Temporal Subspace Matching

Event-based systems are seen as good candidates for supporting distributed applications in dynamic and ubiquitous environments because they support decoupled and asynchronous many-to-many information dissemination. Event systems are widely used, because asynchronous messaging provides a flexible alternative to RPC (Remote Procedure Call). They are typically implemented using an overlay network of routers. A content-based router forwards event messages based on filters that are installed by subscribers and other routers. The filters are organized into a routing table in order to forward incoming events to proper subscribers and neighbouring routers. This thesis addresses the optimization of content-based routing tables organized using the covering relation and presents novel data structures and configurations for improving local and distributed operation. Data structures are needed for organizing filters into a routing table that supports efficient matching and runtime operation. We present novel results on dynamic filter merging and the integration of filter merging with content-based routing tables. In addition, the thesis examines the cost of client mobility using different protocols and routing topologies. We also present a new matching technique called temporal subspace matching. The technique combines two new features. The first feature, temporal operation, supports notifications, or content profiles, that persist in time. The second feature, subspace matching, allows more expressive semantics, because notifications may contain intervals and be defined as subspaces of the content space. We also present an application of temporal subspace matching pertaining to metadata-based continuous collection and object tracking.

Acoustic emission source location and damage detection in a metallic structure using a graph-theory-based geodesic pproach

A geodesic-based approach using Lamb waves is proposed to locate the acoustic emission (AE) source and damage in an isotropic metallic structure. In the case of the AE (passive) technique, the elastic waves take the shortest path from the source to the sensor array distributed in the structure. The geodesics are computed on the meshed surface of the structure using graph theory based on Dijkstra's algorithm. By propagating the waves in reverse virtually from these sensors along the geodesic path and by locating the first intersection point of these waves, one can get the AE source location. The same approach is extended for detection of damage in a structure. The wave response matrix of the given sensor configuration for the healthy and the damaged structure is obtained experimentally. The healthy and damage response matrix is compared and their difference gives the information about the reflection of waves from the damage. These waves are backpropagated from the sensors and the above method is used to locate the damage by finding the point where intersection of geodesics occurs. In this work, the geodesic approach is shown to be suitable to obtain a practicable source location solution in a more general set-up on any arbitrary surface containing finite discontinuities. Experiments were conducted on aluminum specimens of simple and complex geometry to validate this new method.

Popular Mixed Matchings

We study the problem of matching applicants to jobs under one-sided preferences: that is, each applicant ranks a non-empty subset of jobs under an order of preference, possibly involving ties. A matching M is said to be rnore popular than T if the applicants that prefer M to T outnumber those that prefer T to M. A matching is said to be popular if there is no matching more popular than it. Equivalently, a matching M is popular if phi(M,T) >= phi(T, M) for all matchings T, where phi(X, Y) is the number of applicants that prefer X to Y. Previously studied solution concepts based oil the popularity criterion are either not guaranteed to exist for every instance (e.g., popular matchings) or are NP-hard to compute (e.g., least unpopular matchings). This paper addresses this issue by considering mixed matchings. A mixed matching is simply a probability distributions over matchings in the input graph. The function phi that compares two matchings generalizes in a natural manner to mixed matchings by taking expectation. A mixed matching P is popular if phi(P,Q) >= phi(Q,P) for all mixed matchings Q. We show that popular mixed matchings always exist. and we design polynomial time algorithms for finding them. Then we study their efficiency and give tight bounds on the price of anarchy and price of stability of the popular matching problem.

Using a glow graph to represent data flow and dependency in event logs

The idea of extracting knowledge in process mining is a descendant of data mining. Both mining disciplines emphasise data flow and relations among elements in the data. Unfortunately, challenges have been encountered when working with the data flow and relations. One of the challenges is that the representation of the data flow between a pair of elements or tasks is insufficiently simplified and formulated, as it considers only a one-to-one data flow relation. In this paper, we discuss how the effectiveness of knowledge representation can be extended in both disciplines. To this end, we introduce a new representation of the data flow and dependency formulation using a flow graph. The flow graph solves the issue of the insufficiency of presenting other relation types, such as many-to-one and one-to-many relations. As an experiment, a new evaluation framework is applied to the Teleclaim process in order to show how this method can provide us with more precise results when compared with other representations.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.