title: Synthesis of least restrictive controllable supervisors for extended finite-state machines with variable abstraction

authors: Malik, Robi

subjects: Science & technology

abstract: This paper presents an algorithm that combines modular synthesis for extended finite-state machines (EFSM) with abstraction of variables by symbolic manipulation, in order to compute least restrictive controllable supervisors. Given a modular EFSM system consisting of several components, the proposed algorithm synthesises a separate supervisor for each specification component. To synthesise each supervisor, the algorithm iteratively selects components (plants and variables) from a synchronous composition until a least restrictive controllable solution is obtained. This improves on previous results of the authors where abstraction is only performed by the selection of components and not variables. The paper explains the theory of EFSM synthesis and abstraction and its algorithms. An example of a flexible manufacturing system illustrates how the proposed algorithm works to compute a modular supervisor.

text: Synthesis of least restrictive controllable supervisors for extended finite-state machines with variable abstraction This paper presents an algorithm that combines modular synthesis for extended finite-state machines (EFSM) with abstraction of variables by symbolic manipulation, in order to compute least restrictive controllable supervisors. Given a modular EFSM system consisting of several components, the proposed algorithm synthesises a separate supervisor for each specification component. To synthesise each supervisor, the algorithm iteratively selects components (plants and variables) from a synchronous composition until a least restrictive controllable solution is obtained. This improves on previous results of the authors where abstraction is only performed by the selection of components and not variables. The paper explains the theory of EFSM synthesis and abstraction and its algorithms. An example of a flexible manufacturing system illustrates how the proposed algorithm works to compute a modular supervisor.

year: 2020

title: Comparing classical criteria for selecting intra-class correlated features in Multimix

authors: Hunt, Lynette Anne

subjects: Science & Technology

abstract: The mixture approach to clustering requires the user to specify both the number of components to be fitted to the model and the form of the component distributions. In the Multimix class of models, the user also has to decide on the correlation structure to be introduced into the model. The behaviour of some commonly used model selection criteria is investigated when using the finite mixture model to cluster data containing mixed categorical and continuous attributes. The performance of these criteria in selecting both the number of components in the model and the form of the correlation structure amongst the attributes when fitting the Multimix class of models is illustrated using simulated data and a real medical data set. It is found that criteria based on the integrated classification likelihood have the best performance in detecting the number of clusters to be fitted to the model and in selecting the form of the component distributions. The performance of the Bayesian information criterion in detecting the correct model depends on the partitioning structure among the attributes while the Akaike information criterion and classification likelihood criterion perform in a less satisfactory way.

text: Comparing classical criteria for selecting intra-class correlated features in Multimix The mixture approach to clustering requires the user to specify both the number of components to be fitted to the model and the form of the component distributions. In the Multimix class of models, the user also has to decide on the correlation structure to be introduced into the model. The behaviour of some commonly used model selection criteria is investigated when using the finite mixture model to cluster data containing mixed categorical and continuous attributes. The performance of these criteria in selecting both the number of components in the model and the form of the correlation structure amongst the attributes when fitting the Multimix class of models is illustrated using simulated data and a real medical data set. It is found that criteria based on the integrated classification likelihood have the best performance in detecting the number of clusters to be fitted to the model and in selecting the form of the component distributions. The performance of the Bayesian information criterion in detecting the correct model depends on the partitioning structure among the attributes while the Akaike information criterion and classification likelihood criterion perform in a less satisfactory way.

year: 2016

title: Sixty years of adult learning in Aotearoa New Zealand: Looking back to the 1960s and beyond the 2020s

authors: Amundsen, Diana Leigh

abstract: This article offers a historical analysis of the past sixty years of adult learning in Aotearoa New Zealand and critically appraises events which have shaped today’s context. Drawing on a substantial body of research by key adult educators, researchers and scholars, the review assesses historical, socio-cultural, and political factors that influenced adult learning policies and practices. First, a brief discussion is given of traditional Māori education, colonisation, bi-culturalism and multiculturalism for relevance to the Aotearoa New Zealand context. Next, a historical analysis is offered in a decade-by-decade review. Looking back over the past sixty years, this historical analysis exposes key influences which have shaped adult learning in Aotearoa, and discusses trends emerging as significant future directions going into and beyond the 2020s.

text: Sixty years of adult learning in Aotearoa New Zealand: Looking back to the 1960s and beyond the 2020s This article offers a historical analysis of the past sixty years of adult learning in Aotearoa New Zealand and critically appraises events which have shaped today’s context. Drawing on a substantial body of research by key adult educators, researchers and scholars, the review assesses historical, socio-cultural, and political factors that influenced adult learning policies and practices. First, a brief discussion is given of traditional Māori education, colonisation, bi-culturalism and multiculturalism for relevance to the Aotearoa New Zealand context. Next, a historical analysis is offered in a decade-by-decade review. Looking back over the past sixty years, this historical analysis exposes key influences which have shaped adult learning in Aotearoa, and discusses trends emerging as significant future directions going into and beyond the 2020s.

year: 2020