Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

Desarrollo de un estado de la cuestión acerca de las interacciones entre el control de gestión y el aprendizaje organizacional.

Las organizaciones en la actualidad deben encontrar diferentes maneras de sobrevivir en un tiempo de rápida transformación. Uno de los mecanismos usados por las empresas para adaptarse a los cambios organizacionales son los sistemas de control de gestión, que a su vez permiten a las organizaciones hacer un seguimiento a sus procesos, para que la adaptabilidad sea efectiva. Otra variable importante para la adaptación es el aprendizaje organizacional siendo el proceso mediante el cual las organizaciones se adaptan a los cambios del entorno, tanto interno como externo de la compañía. Dado lo anterior, este proyecto se basa en la extracción de documentación soporte valido, que permita explorar las interacciones entre estos dos campos, los sistemas de control de gestión y el aprendizaje organizacional, además, analizar el impacto de estas interacciones en la perdurabilidad organizacional. ​

Adaptive recursive control of a soil testing load frame

Recursive Learning Control (RLC) has the potential to significantly reduce the tracking error in many repetitive trajectory applications. This paper presents an application of RLC to a soil testing load frame where non-adaptive techniques struggle with the highly nonlinear nature of soil. The main purpose of the controller is to apply a sinusoidal force reference trajectory on a soil sample with a high degree of accuracy and repeatability. The controller uses a feedforward control structure, recursive least squares adaptation algorithm and RLC to compensate for periodic errors. Tracking error is reduced and stability is maintained across various soil sample responses.

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.

Correlations between Vocal Input and Visual Response Apparently Enhance Presence in a Virtual Environment

This work investigates novel alternative means of interaction in a virtual environment (VE).We analyze whether humans can remap established body functions to learn to interact with digital information in an environment that is cross-sensory by nature and uses vocal utterances in order to influence (abstract) virtual objects. We thus establish a correlation among learning, control of the interface, and the perceived sense of presence in the VE. The application enables intuitive interaction by mapping actions (the prosodic aspects of the human voice) to a certain response (i.e., visualization). A series of single-user and multiuser studies shows that users can gain control of the intuitive interface and learn to adapt to new and previously unseen tasks in VEs. Despite the abstract nature of the presented environment, presence scores were generally very high.

Correlations between vocal input and visual response apparently enhance presence in a virtual environment

This work investigates novel alternative means of interaction in a virtual environment (VE).We analyze whether humans can remap established body functions to learn to interact with digital information in an environment that is cross-sensory by nature and uses vocal utterances in order to influence (abstract) virtual objects. We thus establish a correlation among learning, control of the interface, and the perceived sense of presence in the VE. The application enables intuitive interaction by mapping actions (the prosodic aspects of the human voice) to a certain response (i.e., visualization). A series of single-user and multiuser studies shows that users can gain control of the intuitive interface and learn to adapt to new and previously unseen tasks in VEs. Despite the abstract nature of the presented environment, presence scores were generally very high.

Väsymisen huomiointi laitteen ohjauksessa

Työssä selvitettiin väsymisen huomioivan ja minimoivan laitteen ohjausmenetelmiä. Väsymisilmiön huomioiva älykäs laite monitoroi itsenäisesti mm. väsymissäröjen kasvua ja muuttaa toimintaansa sen mukaisesti. Reagoinnin hyötyinä saavutetaan väsyttävästi kuormitetulle laitteelle mm. pidempi käyttöikä ja riskin hallinta, jossa laite tietää, miten sitä voidaan käyttää ennen vauriota ja sen jälkeen. Kunnossapitoon liittyen ennustetaan jäljellä olevaa käyttöikää, jolloin voidaan suunnitella huolto. Tutkimuksessa käsiteltiin mm. laitteiden ohjauksen tarvitsemia mittausmenetelmiä, mittaustiedon käsittelyä, vaurion luokittelua ja vauriota minimoivan ohjauksen rakennetta. Lisäksi käsiteltiin lyhyesti vaurion luokittelussa sekä ohjausreaktioiden ratkaisemisessa tarvittavia oppivia menetelmiä. Väsymistä minimoivan laitteen ohjauksen perusedellytys on laitteen kokemien rasitusten ja/tai suorituksen mittaaminen. Mittaustulosten perusteella määritetään vaurioitumista kuvaavat suureet. Ohjauksen vaurioon reagoivassa osassa määritetään tieto vaurioitumisen kriittisyydestä ja tämän perusteella tarvittava ohjauksen optimaalinen muutos sekä optimaalinen ohjaussignaali tai muu korjaava toimenpide. Ohjaus optimoidaan vaurioitumisnopeus minimoiden ja suorituskyky maksimoiden. Näiden välille etsitään sopiva tasapaino, jossa suorituskyvyn häviö on pieni mahdollisimman suurella vaurioitumisen pienenemisellä. Tämän jälkeen mittauksien avulla saadaan tieto korjatun ohjauksen vaikutuksesta vauriosuureisiin.

eLearning im Sportstudium

In der gesamten Hochschullandschaft begleiten eLearning-Szenarien organisatorische Erneuerungsprozesse und stellen damit ein vielversprechendes Instrument zur Unterstützung und Verbesserung der klassischen Präsenzlehre dar. Davon ausgehend wurde von 2010 bis 2011 das Kasseler Sportspiel-Modell um die integrative Vermittlung der Einkontakt-Rückschlagspiele erweitert (Heyer, Albert, Scheid & Blömeke-Rumpf, 2011) und in einen modularisierten eLearning-Content, bestehend aus insgesamt 4 Modulen (17 Lernkurse, 171 Kursseiten, 73 Grafiken, 73 Videos, 38 Lernkontrollfragen), eingebunden. Dieser Content wurde im Rahmen einer Evaluationsstudie in Blended Learning Seminaren, welche die didaktischen Vorteile von Online- und Präsenzphasen zu einer Seminarform vereinen (Treumann, Ganguin & Arens, 2012), vergleichend zur klassischen Präsenzlehre im Sportstudium betrachtet. Die Studie gliedert sich in insgesamt drei Phasen: 1.) Pilotstudie am IfSS in Kassel (WS 2011/12; N=17, Lehramt), 2.) Hauptuntersuchung I am IfSS in Kassel (SS 2012; N=67, Lehramt) und 3.) Hauptuntersuchung II am IfS in Frankfurt a. M. (WS 2012/13; N=112, BA). Mittels varianzanalytischer Untersuchungsverfahren erfasst die Studie auf drei unterschiedlichen Qualitätsebenen folgende Aspekte der Lehr-Lernforschung: 1.) Ebene der Inputqualität: Bewertung der Seminarform (BS), 2.) Ebene der Prozessqualität: Motivation (SELLMO-ST), Lernstrategien (LIST) und computerbezogene Einstellung (FIDEC), 3.) Ebene der Outcomequalität: Lernleistung (Abschlusstest und Transferaufgabe). In der vergleichenden Betrachtung der beiden Hauptuntersuchungen erfolgt eine Gegenüberstellung von je einem Präsenzseminar zu zwei unterschiedlichen Varianten von Blended Learning Seminaren (BL-1, BL-2). Während der Online-Phasen bearbeiten die Sportstudierenden in BL-1 die Module in Lerngruppen. Die Teilnehmer in BL-2 führen in diesen Phasen zusätzlich persönliche Lerntagebücher. Dies soll zu einer vergleichsweise intensiveren Auseinandersetzung mit den Inhalten der Lernkurse sowie dem eigenen Lernprozess auf kognitiver und metakognitiver Ebene anregen (Hübner, Nückles & Renkl, 2007) und folglich zu besseren Ergebnissen auf den drei Qualitätsebenen führen. Die Ergebnisse der beiden Hauptuntersuchungen zeigen in der direkten, standortbezogenen Gegenüberstellung aller drei Seminarformen überwiegend keine statistisch signifikanten Unterschiede. Der erwartete positive Effekt durch die Einführung des Lerntagebuchs bleibt ebenfalls aus. Im standortübergreifenden Vergleich der Blended-Learning-Seminare ist bemerkenswert, dass die Probanden aus Frankfurt gegenüber ihrer Seminarform eine tendenziell kritischere Haltung einnehmen, was möglicherweise mit den vorherrschenden, unterschiedlichen Studiengängen – Lehramt und BA – korrespondiert. Zusammenfassend lässt sich somit für den untersuchten Bereich der Rückschlagspielvermittlung festhalten, dass Blended-Learning-Seminare eine qualitativ gleichwertige Alternative zur klassischen Präsenzlehre im Sportstudium darstellen.

Human factors criteria for vehicle controls and displays. Appendix C. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Effect of bandwidth knowledge of results on the learning of a grip force control task

An experiment was conducted to investigate the persistence of the effect of ""bandwidth knowledge of results (KR)"" manipulated during the learning phase of performing a manual force-control task. The experiment consisted of two phases, an acquisition phase with the goal of maintaining 60% maximum force in 30 trials, and a second phase with the objective of maintaining 40% of maximum force in 20 further trials. There were four bandwidths of KR: when performance error exceeded 5, 10, or 15% of the target, and a control group (0% bandwidth). Analysis showed that 5, 10, and 15% bandwidth led to better performance than 0% bandwidth KR at the beginning of the second phase and persisted during the extended trials.

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.