Short-term wind power forecast based on cluster analysis and artificial neural networks

[EN]In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids...

Creating Educational Immersive Environments Using a Computer Game Engine: Case Studies and Design Theory

This paper aims to crystallize recent research performed at the University of Worcester to investigate the feasibility of using the commercial game engine ‘Unreal Tournament 2004’ (UT2004) to produce ‘Educational Immersive Environments’ (EIEs) suitable for education and training. Our research has been supported by the UK Higher Education Academy. We discuss both practical and theoretical aspects of EIEs. The practical aspects include the production of EIEs to support high school physics education, the education of architects, and the learning of literacy by primary school children. This research is based on the development of our novel instructional medium, ‘UnrealPowerPoint’. Our fundamental guiding principles are that, first, pedagogy must inform technology, and second, that both teachers and pupils should be empowered to produce educational materials. Our work is informed by current educational theories such as constructivism, experiential learning and socio-cultural approaches as well as elements of instructional design and game principles.

Vision-based tracking for sports performance analysis

With the world of professional sports shifting towards employing better sport analytics, the demand for vision-based performance analysis is growing increasingly in recent years. In addition, the nature of many sports does not allow the use of any kind of sensors or other wearable markers attached to players for monitoring their performances during competitions. This provides a potential application of systematic observations such as tracking information of the players to help coaches to develop their visual skills and perceptual awareness needed to make decisions about team strategy or training plans. My PhD project is part of a bigger ongoing project between sport scientists and computer scientists involving also industry partners and sports organisations. The overall idea is to investigate the contribution technology can make to the analysis of sports performance on the example of team sports such as rugby, football or hockey. A particular focus is on vision-based tracking, so that information about the location and dynamics of the players can be gained without any additional sensors on the players. To start with, prior approaches on visual tracking are extensively reviewed and analysed. In this thesis, methods to deal with the difficulties in visual tracking to handle the target appearance changes caused by intrinsic (e.g. pose variation) and extrinsic factors, such as occlusion, are proposed. This analysis highlights the importance of the proposed visual tracking algorithms, which reflect these challenges and suggest robust and accurate frameworks to estimate the target state in a complex tracking scenario such as a sports scene, thereby facilitating the tracking process. Next, a framework for continuously tracking multiple targets is proposed. Compared to single target tracking, multi-target tracking such as tracking the players on a sports field, poses additional difficulties, namely data association, which needs to be addressed. Here, the aim is to locate all targets of interest, inferring their trajectories and deciding which observation corresponds to which target trajectory is. In this thesis, an efficient framework is proposed to handle this particular problem, especially in sport scenes, where the players of the same team tend to look similar and exhibit complex interactions and unpredictable movements resulting in matching ambiguity between the players. The presented approach is also evaluated on different sports datasets and shows promising results. Finally, information from the proposed tracking system is utilised as the basic input for further higher level performance analysis such as tactics and team formations, which can help coaches to design a better training plan. Due to the continuous nature of many team sports (e.g. soccer, hockey), it is not straightforward to infer the high-level team behaviours, such as players’ interaction. The proposed framework relies on two distinct levels of performance analysis: low-level performance analysis, such as identifying players positions on the play field, as well as a high-level analysis, where the aim is to estimate the density of player locations or detecting their possible interaction group. The related experiments show the proposed approach can effectively explore this high-level information, which has many potential applications.

Effect of haptic guidance and error amplification robotic training interventions on the immediate improvement of timing among individuals that had a stroke

Abstract : Many individuals that had a stroke have motor impairments such as timing deficits that hinder their ability to complete daily activities like getting dressed. Robotic rehabilitation is an increasingly popular therapeutic avenue in order to improve motor recovery among this population. Yet, most studies have focused on improving the spatial aspect of movement (e.g. reaching), and not the temporal one (e.g. timing). Hence, the main aim of this study was to compare two types of robotic rehabilitation on the immediate improvement of timing accuracy: haptic guidance (HG), which consists of guiding the person to make the correct movement, and thus decreasing his or her movement errors, and error amplification (EA), which consists of increasing the person’s movement errors. The secondary objective consisted of exploring whether the side of the stroke lesion had an effect on timing accuracy following HG and EA training. Thirty-four persons that had a stroke (average age 67 ± 7 years) participated in a single training session of a timing-based task (simulated pinball-like task), where they had to activate a robot at the correct moment to successfully hit targets that were presented a random on a computer screen. Participants were randomly divided into two groups, receiving either HG or EA. During the same session, a baseline phase and a retention phase were given before and after each training, and these phases were compared in order to evaluate and compare the immediate impact of HG and EA on movement timing accuracy. The results showed that HG helped improve the immediate timing accuracy (p=0.03), but not EA (p=0.45). After comparing both trainings, HG was revealed to be superior to EA at improving timing (p=0.04). Furthermore, a significant correlation was found between the side of stroke lesion and the change in timing accuracy following EA (r[subscript pb]=0.7, p=0.001), but not HG (r[subscript pb]=0.18, p=0.24). In other words, a deterioration in timing accuracy was found for participants with a lesion in the left hemisphere that had trained with EA. On the other hand, for the participants having a right-sided stroke lesion, an improvement in timing accuracy was noted following EA. In sum, it seems that HG helps improve the immediate timing accuracy for individuals that had a stroke. Still, the side of the stroke lesion seems to play a part in the participants’ response to training. This remains to be further explored, in addition to the impact of providing more training sessions in order to assess any long-term benefits of HG or EA.

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences.

Background: The Medical Education Partnership Initiative, has helped to mitigate the digital divide in Africa. The aim of the study was to assess the level of access, attitude, and training concerning meaningful use of electronic resources and EBM among medical students at an African medical school. Methods: The study involved medical students at the University of Zimbabwe College of Health Sciences, Harare. The needs assessment tool consisted of a 21-question, paper-based, voluntary and anonymous survey. Results: A total of 61/67 (91%), responded to the survey. 60% of the medical students were ‘third-year medical students’. Among medical students, 85% of responders had access to digital medical resources, but 54% still preferred printed medical textbooks. Although 25% of responders had received training in EBM, but only 7% found it adequate. 98% of the participants did not receive formal training in journal club presentation or analytical reading of medical literature, but 77 % of them showed interest in learning these skills. Conclusion: Lack of training in EBM, journal club presentation and analytical reading skills have limited the impact of upgraded technology in enhancing the level of knowledge. This impact can be boosted by developing a curriculum with skills necessary in using EBM.

Agent based simulation as a novel decision support tool for retail managers

Intelligent agents offer a new and exciting way of understanding the world of work. We apply agent-based simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Our research so far has led us to conduct case study work with a top ten UK retailer. Based on our case study experience and data we are developing a simulator that can be used to investigate the impact of management practices (e.g. training, empowerment, teamwork) on customer satisfaction and retail productivity.

Effects of an in-service training program using the routines-based interview

The focus of this study is an in-service training program rooted in routines-based early intervention and designed to improve the quality of goals and objectives on individualized plans. Participants were local intervention team members and other professionals who worked closely with each team. This training program involved a small number of trainees per group, providing multiple learning experiences across time and various opportunities for self-assessment and monitoring. We investigated (a) the perceptions of the participants about the strengths and weaknesses of the training program, (b) medium-term outcomes of the training with a comparison group, (c) and variables associated with the quality of goals and objectives. This study involved training more than 200 professionals, and results support the effectiveness of the program in improving the quality of goals and objectives, showing the importance of the routines-based interview in producing that improvement.

Councellors' Experience During Training and Home Based HIV Councelling and Testing in Zomba District, Malawi

Introduction In 2007, St Luke’s Mission Hospital initiated a district-wide Door to Door HIV counselling and testing (HCT) programme in Zomba district. The intent of the programme was to provide quality HCT services to people in their homes and effectively those found to be HIV positive referred to appropriate services. Methodology This was a cross sectional study using a questionnaire consecutively administered to a sample of 105 counsellors who had resided in the community for a period of over one year. The questionnaire sought to establish, knowledge gained, experiences and recommendations on how the programme has been implemented and assist running of similar future programmes. Data analysis was done manually using both qualitative and quantitative methodologies. Results We report that nearly 23% of the counsellors thought that during their training as a door to door HTC councelor they had benefited in learning to working with communities; an aspect they found to be highly applicable in discharge of their duties. The major setbacks during the training were lack daily allowances, less amount of time spent on understanding child councelling and the manual used was diffucult to follow. Over 32% of the councellors were satisfied with the participation of their clients during pre-test counselling sessions, however, the major challenge they had was the misconception that they were blood suckers, a view reported by nearly 17% of the counsellors. Close to 72% reported not to have met any problems during post-test counselling compared to 24% who reported to have found challenges. Conclusion The study has revealed that there is a need to re-look child children counselling especially in training door to door HCT counsellors. It has also revealed the prevalent allowance culture despite the benefits of training. The common challenges were refusal of test Results and failure to understand discordance. Misconceptions may still exist in the community regarding anything dealing with removing blood. There is still need for more information regarding discordance especially among couples in the community.

A home-based treadmill training reduced epicardial and abdominal fat in postmenopausal women with metabolic syndrome

Introduction: The current study was designed to determine the effect of home-based treadmill training on epicardial and abdominal adipose tissue in postmenopausal women with metabolic syndrome (MS). A secondary objective was to identify significant correlations between imaging and conventional anthropometric parameters. Material and methods: Sixty postmenopausal women with MS volunteered for the current trial. Thirty were randomly assigned to perform a supervised home-based 16-week treadmill training program, 3 sessions/week, consisting of a warm-up, 30-40 min treadmill exercise (increasing 5-minutes each 4-weeks) at a work intensity of 60-75% of peak heart rate (increasing 5% each 4-weeks) and cooling-down. Epicardial fat thickness (EFT) was assessed by echocardiography. Abdominal fat mass in the lumbar regions L1-L4 and L4-L5 was determined by dual X-ray absorptiometry. Results: Epicardial fat thickness and abdominal fat percentages were significantly improved after the completion of the training program. Another striking feature of the current study was the moderate correlation that was found between EFT and waist circumference (WC). Conclusion: Home-based treadmill training reduced epicardial and abdominal fat in postmenopausal women with MS. A secondary finding was that a moderate correlation was found between EFT and WC. While current investigations are promising, future studies are still required to consolidate this approach in clinical application.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

An Evaluative Model to Assess the Organizational Efficiency in Training Corporations

In an organisation any optimization process of its issues faces increasing challenges and requires new approaches to the organizational phenomenon. Indeed, in this work it is addressed the problematic of efficiency dynamics through intangible variables that may support a different view of the corporations. It focuses on the challenges that information management and the incorporation of context brings to competitiveness. Thus, in this work it is presented the analysis and development of an intelligent decision support system in terms of a formal agenda built on a Logic Programming based methodology to problem solving, complemented with an attitude to computing grounded on Artificial Neural Networks. The proposed model is in itself fairly precise, with an overall accuracy, sensitivity and specificity with values higher than 90 %. The proposed solution is indeed unique, catering for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in a quantitative or qualitative arrangement.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.