The Assessment of Basic Learning Abilities (ABLA) is an empirically validated assessment tool for assessing the learning ability of persons with intellectual disabilities and children with autism. During the administration of an ABLA, an examiner attempts to teach an examinee to perform six individual tasks, called levels, using standardized prompting and reinforcement procedures until either a pass or fail criterion is met on each task. The majority of studies investigating the ABLA have been conducted with adults with intellectual disabilities. Research has demonstrated that the six levels of the ABLA are hierarchical in terms of difficulty, and that pass/fail performance on the levels is highly predictive of the ease or difficulty with which examinees will learn a variety of training tasks (Vause, Yu, & Martin, 2007). The present study examined the predictive validity of the ABLA with 9 children with autism, assessed at ABLA levels 2 and 3. A parent of each child was asked to predict the child’s pass-fail learning performance on 20 criterion tasks. In addition, according to the child’s ABLA performance, I predicted that each child would pass the criterion tasks that corresponded to his/her previously passed ABLA levels, and would fail the criterion tasks that were corresponded to his/her previously failed ABLA levels. I then attempted to individually teach each criterion task to each child, using standardized prompting and reinforcement procedures, until each child met either the pass criterion or the fail criterion of the ABLA. Ninety-two percent of the predictions based on the children’s ABLA performance were confirmed, and the ABLA was significantly more accurate than the parents for predicting the children’s performance on the criterion tasks.

No description available.

Introduction.  Despite being thought of as common and having serious consequences, falls have not been extensively studied in Parkinson’s disease (PD).  Prior to this study commencing there were no published large-scale prospective studies looking at the risk factors for falls in PD.  This study aimed to accurately establish the incidence of falls in PD and investigate predictive risk factors for fallers from baseline data in all patients known to a district general hospital PD service.  In addition cardiovascular investigation, autonomic function and osteoporosis in PD were assessed. Methods.  Baseline data was gathered on a cohort of 109 patients with idiopathic PD and the number of falls prospectively ascertained over the following year.  The multidisciplinary baseline assessment included historical data, disease specific rating scales, physiotherapy assessment, tests of visual, cardiovascular and autonomic function and bone densitometry. Results.  Falls occurred in 68.3% of the subjects. Previous falls, disease duration and loss of armswing were independent predictors of falls and recurrent falls.  There were also statistically significant associations between disease severity, balance impairment, depression cognitive impairment and falling.  Males were more likely to suffer from recurrent falls.  Cardiovascular disorders, autonomic dysfunction and osteoporosis were also highly prevalent but not associated with falls. Conclusions.    Falls are a common problem in PD.  Some of the risk factors are potentially modifiable.  Although there are intrinsic factors inherent to PD that can cause falls, patients with PD that fall should be thoroughly assessed to look more closely at the reason for falling in those individuals.  Potential primary prevention of falls should be considered in all patients with PD.  In the future, multi-centre intervention studies will be necessary to further investigate potential methods of decreasing falls and their effects in PD.

The Assessment of Basic Learning Abilities (ABLA) is an empirically validated assessment tool for assessing the learning ability of persons with intellectual disabilities and children with autism. During the administration of an ABLA, an examiner attempts to teach an examinee to perform six individual tasks, called levels, using standardized prompting and reinforcement procedures until either a pass or fail criterion is met on each task. The majority of studies investigating the ABLA have been conducted with adults with intellectual disabilities. Research has demonstrated that the six levels of the ABLA are hierarchical in terms of difficulty, and that pass/fail performance on the levels is highly predictive of the ease or difficulty with which examinees will learn a variety of training tasks (Vause, Yu, & Martin, 2007). The present study examined the predictive validity of the ABLA with 9 children with autism, assessed at ABLA levels 2 and 3. A parent of each child was asked to predict the child’s pass-fail learning performance on 20 criterion tasks. In addition, according to the child’s ABLA performance, I predicted that each child would pass the criterion tasks that corresponded to his/her previously passed ABLA levels, and would fail the criterion tasks that were corresponded to his/her previously failed ABLA levels. I then attempted to individually teach each criterion task to each child, using standardized prompting and reinforcement procedures, until each child met either the pass criterion or the fail criterion of the ABLA. Ninety-two percent of the predictions based on the children’s ABLA performance were confirmed, and the ABLA was significantly more accurate than the parents for predicting the children’s performance on the criterion tasks.

No description available.

Aircraft are dynamic systems that naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems are becoming increasingly important as the performance and complexity of the controlled systems is constantly increasing. It is especially important in the design of control systems for fighter aircraft. These require maximum control performance in order to have the upper hand in a dogfight or when they have to outmaneuver an enemy missile. Therefore pilots often maneuver the aircraft very close to the limit of what it is capable of, and an automatic system (called flight envelope protection system) against violating the restrictions is a necessity. In other application areas, nonlinear optimal control methods have been successfully used to solve this but in the aeronautical industry, these methods have not yet been established. One of the more popular methods that are well suited to handle constraints is Model Predictive Control (MPC) and it is used extensively in areas such as the process industry and the refinery industry. Model predictive control means in practice that the control system iteratively solves an advanced optimization problem based on a prediction of the aircraft's future movements in order to calculate the optimal control signal. The aircraft's operating limitations will then be constraints in the optimization problem. In this thesis, we explore model predictive control and derive two fast, low complexity algorithms, one for guaranteed stability and feasibility of nonlinear systems and one for reference tracking for linear systems. In reference tracking model predictive control for linear systems we build on the dual mode formulation of MPC and our goal is to make minimal changes to this framework, in order to develop a reference tracking algorithm with guaranteed stability and low complexity suitable for implementation in real time safety critical systems. To reduce the computational burden of nonlinear model predictive control several methods to approximate the nonlinear constraints have been proposed in the literature, many working in an ad hoc fashion, resulting in conservatism, or worse, inability to guarantee recursive feasibility. Also several methods work in an iterative manner which can be quit time consuming making them inappropriate for fast real time applications. In this thesis we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefits of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence. / <p>The series name "<em>Linköping studies in science and technology. Licentiate Thesis</em>" is incorrect. The correct series name is "<em>Linköping studies in science and technology. Thesis</em>".</p>

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Includes bibliographical references (leaves 110-111). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Includes bibliographical references (leaves 110-111). Also available on the Internet.

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 40-41). Also available on the World Wide Web.

À l’heure actuelle, les stratégies de gestion de l’énergie pour les bâtiments sontprincipalement basées sur une concaténation de règles logiques. Bien que cette approcheoffre des avantages certains, particulièrement lors de sa mise en oeuvre sur des automatesprogrammables, elle peine à traiter la diversité de situations complexes quipeuvent être rencontrées (prix de l’énergie variable, limitations de puissance, capacitéde stockage d’énergie, bâtiments de grandes dimension).Cette thèse porte sur le développement et l’évaluation d’une commande prédictivepour la gestion de l’énergie dans le bâtiment ainsi que l’étude de l’embarcabilité del’algorithme de contrôle sur une cible temps-réel (Roombox - Schneider-Electric).La commande prédictive est basée sur l’utilisation d’un modèle du bâtiment ainsique des prévisions météorologiques et d’occupation afin de déterminer la séquencede commande optimale à mettre en oeuvre sur un horizon de prédiction glissant.Seul le premier élément de cette séquence est en réalité appliqué au bâtiment. Cetteséquence de commande optimale est obtenue par la résolution en ligne d’un problèmed’optimisation. La capacité de la commande prédictive à gérer des systèmes multivariablescontraints ainsi que des objectifs économiques, la rend particulièrementadaptée à la problématique de la gestion de l’énergie dans le bâtiment.Cette thèse propose l’élaboration d’un schéma de commande distribué pour contrôlerles conditions climatiques dans chaque zone du bâtiment. L’objectif est de contrôlersimultanément: la température intérieure, le taux de CO2 ainsi que le niveaud’éclairement dans chaque zone en agissant sur les équipements présents (CVC, éclairage,volets roulants). Par ailleurs, le cas des bâtiments multi-sources (par exemple:réseau électrique + production locale solaire), dans lequel chaque source d’énergie estcaractérisée par son propre prix et une limitation de puissance, est pris en compte.Dans ce contexte, les décisions relatives à chaque zone ne peuvent plus être effectuéesde façon indépendante. Pour résoudre ce problème, un mécanisme de coordinationbasé sur une décomposition du problème d’optimisation centralisé est proposé. Cettethèse CIFRE 1 a été préparée au sein du laboratoire Gipsa-lab en partenariat avecSchneider-Electric dans le cadre du programme HOMES (www.homesprogramme.com). / Currently, energy management strategies for buildings are mostly based on a concatenationof logical rules. Despite the fact that such rule based strategy can be easilyimplemented, it suffers from some limitations particularly when dealing with complexsituations. This thesis is concerned with the development and assessment ofModel Predictive Control (MPC) algorithms for energy management in buildings. Inthis work, a study of implementability of the control algorithm on a real-time hardwaretarget is conducted beside yearly simulations showing a substantial energy savingpotential. The thesis explores also the ability of MPC to deal with the diversity ofcomplex situations that could be encountered (varying energy price, power limitations,local storage capability, large scale buildings).MPC is based on the use of a model of the building as well as weather forecasts andoccupany predictions in order to find the optimal control sequence to be implementedin the future. Only the first element of the sequence is actually applied to the building.The best control sequence is found by solving, at each decision instant, an on lineoptimization problem. MPC’s ability to handle constrained multivariable systems aswell as economic objectives makes this paradigm particularly well suited for the issueof energy management in buildings.This thesis proposes the design of a distributed predictive control scheme to controlthe indoor conditions in each zone of the building. The goal is to control thefollowing simultaneously in each zone of the building: indoor temperature, indoorCO2 level and indoor illuminance by acting on all the actuators of the zone (HVAC,lighting, shading). Moreover, the case of multi-source buildings is also explored, (e.g.power from grid + local solar production), in which each power source is characterizedby its own dynamic tariff and upper limit. In this context, zone decisions can nolonger be performed independently. To tackle this issue, a coordination mechanismis proposed. A particular attention is paid to computational effectiveness of the proposedalgorithms. This CIFRE2 Ph.D. thesis was prepared within the Gipsa-lab laboratoryin partnership with Schneider-Electric in the scope of the HOMES program(www.homesprogramme.com).