Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

A bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Text mining for screening efficiency? Testing within a Cochrane public health review

Background The requirement for dual screening of titles and abstracts to select papers to examine in full text can create a huge workload, not least when the topic is complex and a broad search strategy is required, resulting in a large number of results. An automated system to reduce this burden, while still assuring high accuracy, has the potential to provide huge efficiency savings within the review process. Objectives To undertake a direct comparison of manual screening with a semi‐automated process (priority screening) using a machine classifier. The research is being carried out as part of the current update of a population‐level public health review. Methods Authors have hand selected studies for the review update, in duplicate, using the standard Cochrane Handbook methodology. A retrospective analysis, simulating a quasi‐‘active learning’ process (whereby a classifier is repeatedly trained based on ‘manually’ labelled data) will be completed, using different starting parameters. Tests will be carried out to see how far different training sets, and the size of the training set, affect the classification performance; i.e. what percentage of papers would need to be manually screened to locate 100% of those papers included as a result of the traditional manual method. Results From a search retrieval set of 9555 papers, authors excluded 9494 papers at title/abstract and 52 at full text, leaving 9 papers for inclusion in the review update. The ability of the machine classifier to reduce the percentage of papers that need to be manually screened to identify all the included studies, under different training conditions, will be reported. Conclusions The findings of this study will be presented along with an estimate of any efficiency gains for the author team if the screening process can be semi‐automated using text mining methodology, along with a discussion of the implications for text mining in screening papers within complex health reviews.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Inflammation, hepatic enzymes and resistance training in individuals with metabolic risk factors

AIMS: Increases in inflammatory markers, hepatic enzymes and physical inactivity are associated with the development of the metabolic syndrome (MetS). We examined whether inflammatory markers and hepatic enzymes are correlated with traditional risk factors for MetS and studied the effects of resistance training (RT) on these emerging risk factors in individuals with a high number of metabolic risk factors (HiMF, 2.9 +/- 0.8) and those with a low number of metabolic risk factors (LoMF, 0.5 +/- 0.5). METHODS: Twenty-eight men and 27 women aged 50.8 +/- 6.5 years (mean +/- sd) participated in the study. Participants were randomized to four groups, HiMF training (HiMFT), HiMF control (HiMFC), LoMF training (LoMFT) and LoMF control (LoMFC). Before and after 10 weeks of RT [3 days/week, seven exercises, three sets with intensity gradually increased from 40-50% of one repetition maximum (1RM) to 75-85% of 1RM], blood samples were obtained for the measurement of pro-inflammatory cytokines, C-reactive protein (CRP), gamma-glutamyltransferase (GGT) and alanine aminotransferase (ALT). RESULTS: At baseline, HiMF had higher interleukin-6 (33.9%), CRP (57.1%), GGT (45.2%) and ALT (40.6%) levels, compared with LoMF (all P < 0.05). CRP, GGT and ALT correlated with the number of risk factors (r = 0.48, 0.51 and 0.57, respectively, all P < 0.01) and with other anthropometric and clinical measures (r range from 0.26 to 0.60, P < 0.05). RT did not significantly alter inflammatory markers or hepatic enzymes (all P > 0.05). CONCLUSIONS: HiMF was associated with increased inflammatory markers and hepatic enzyme concentrations. RT did not reduce inflammatory markers and hepatic enzymes in individuals with HiMF.

ICT in vocational education and training: a view of information and communication technology in vocational education in New Zealand

This report looks at opportunities in relation to what is either already available or starting to take off in Information and Communication Technology (ICT). ICT focuses on the entire system of information, communication, processes and knowledge within an organisation. It focuses on how technology can be implemented to serve the information and communication needs of people and organisations. An ICT system involves a combination of work practices, information, people and a range of technologies and applications organised to make the business or organisation fully functional and efficient, and to accomplish goals in an organisation. Our focus is on vocational, workbased education in New Zealand. It is not about eLearning, although we briefly touch on the topic. We provide a background on vocational education in New Zealand, cover what we consider to be key trends impacting workbased, vocational education and training (VET), and offer practical suggestions for leveraging better value from ICT initiatives across the main activities of an Industry Training Organisation (ITO). We use a learning value chain approach to demonstrate the main functions ITOs engage in and also use this approach as the basis for developing and prioritising an ICT strategy. Much of what we consider in this report is applicable to the wider tertiary education sector as it relates to life-long learning. We consider ICT as an enabler that: a) connects education businesses (all types including tertiary education institutions) to learners, their career decisions and their learning, and as well, b) enables those same businesses to run more efficiently. We suggest that these two sets of activities are considered as interconnected parts of the same education or training business ICT strategy.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Emergency pre-hospital disaster response training. What's the state of play?

Study/Objective This study examines the current state of disaster response education for Australian paramedics from a national and international perspective and identifies both potential gaps in content and challenges to the sustainability of knowledge acquired through occasional training. Background As demands for domestic and international disaster response increase, experience in the field has begun to challenge traditional assumptions that response to mass casualty events requires little specialist training. The need for a “streamlined process of safe medical team deployment into disaster regions”1 is generally accepted and, in Australia, the emergence of national humanitarian aid training has begun to respond to this gap. However, calls for a national framework for disaster health education2 haven’t received much traction. Methods A critical analysis of the peer reviewed and grey literature on the core components/competencies and training methods required to prepare Australian paramedics to contribute to effective health disaster response has been conducted. Research from the past 10 years has been examined along with federal and state policy with regard to paramedic disaster education. Results The literature shows that education and training for disaster response is variable and that an evidence based study specifically designed to outline sets of core competencies for Australian health care professionals has never been undertaken. While such competencies in disaster response have been developed for the American paradigm it is suggested that disaster response within the Australian context is somewhat different to that of the US, and therefore a gap in the current knowledge base exists. Conclusion Further research is needed to develop core competencies specific to Australian paramedics in order to standardise teaching in the area of health disaster management. Until this occurs the task of evaluating or creating disaster curricula that adequately prepares and maintains paramedics for an effective all hazards disaster response is seen as largely unattainable.

Trainee-therapists are not all equal: Examination of therapeutic efficiency, effectiveness and early client dropout after 12 months of clinical training

Objective Contemporary research demonstrates the feasibility of assessing therapeutic performance of trainee-therapists through the use of objective measures of client treatment outcome. Further, significant variation between individual therapists based on their client treatment outcomes has been demonstrated. This study sets out to determine whether a reliable composite measure of therapeutic efficiency, effectiveness and early dropout can be developed and used to objectively compare trainee-therapists against each other. Design and methods Treatment outcomes of 611 clients receiving treatment from 58 trainee-therapists enrolled in a professional training programme were tracked with the OQ-45.2 over a 6-year period to assess therapeutic efficiency, therapeutic effectiveness and early client dropout. Results Significant variation between trainee-therapists was observed for each index. Findings of a moderately strong correlation between therapeutic efficiency and effectiveness enabled the ranking of trainee-therapists based upon a composite measure of these indexes. A non-significant correlation was found between early client dropout and measures of therapeutic effectiveness and efficiency. Conclusions The findings stress the importance of utilizing objective measures to track the treatment outcomes. Despite all trainee-therapists being enrolled in the same training programme, significant variation between trainee-therapists' therapeutic efficiency and effectiveness was found to exist. Practitioner points Developing of potential benchmarking tools that enable trainee-therapists, supervisors and educational institutions to quickly assess therapeutic performance can become part of a holistic assessment of a trainee-therapist's clinical development. Despite an inherent optimistic belief that therapists do not cause harm, there appears to be a small and significant proportion of trainee-therapists who consistently evidence little therapeutic change. Considerable variability in trainee-therapists' therapeutic efficiency and effectiveness can exist in the one training programme. Early client dropout may not be associated with therapists' therapeutic effectiveness and efficiency.

A Best Practice Process for Fleet Safety Training

This paper outlines a process for fleet safety training based on research and management development programmes undertaken at the University of Huddersfield in the UK (www.hud.ac.uk/sas/trans/transnews.htm) and CARRS-Q in Australia (www.carrsq.qut.edu.au/staff/Murray.jsp) over the past 10 years.