Learning an Approximate Model Predictive Controller with Guarantees

A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding's Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.Comment: 6 pages, 3 figures, to appear in IEEE Control Systems Letter

Rapid (Mobile) E-Learning-Content-Development. Konzeption u. Entwicklung d. Rapid-E-Learning-Content-Development-Tools "FLOG" (Flash Learning Objects Generator) z. Erstellung u. Verwaltung v. wiederverwendbaren Lernobjekten f. mobile Endgeräte u. PCs

Leistungsfähige Mobiltelefone und Smartphones, aber auch mobile Mediaplayer oder PDAs in Verbindung mit einem Internetzugang und preiswerten mobilen Datentarifen eröffnen immer mehr neue Möglichkeiten für den zeit- und ortsunabhängigen Wissenserwerb. Insbesondere Jugendliche sind in der Regel gut mit derartigen Geräten ausgestattet und im Umgang mit ihnen vertraut. Die Entwicklung von Lerninhalten für das Mobile-Learning (M-Learning) ist jedoch vor allem aufgrund der großen Heterogenität der Endgeräte hinsichtlich Hard- und Softwareausstattung in der Regel aufwändig, teuer und setzt ein umfangreiches Spezialwissen voraus. Im "traditionellen" E-Learning wird versucht, mithilfe des Rapid-E-Learning bzw. Rapid-E-Learning-Content-Development-Konzepts Entwicklungszeiten für Inhalte zu verkürzen, um damit schneller auf aktuelle Entwicklungen reagieren und Entwicklungskosten senken zu können. Das Thema der vorliegenden Arbeit ist die Entwicklung und Konzeption des Rapid-E-Learning-Content-Development-Tools "FLOG", mit dem auf Adobe Flash basierende, wiederverwendbare Lernobjekte (RLOs) für mobile Endgeräte und PCs hergestellt werden können. Schwerpunkt ist damit die Erweiterung des Rapid-E-Learning-Content-Development-Konzepts auf den Bereich des M-Learning unter Berücksichtigung der Mehrfachnutzung von Inhalten mit verschiedenen Endgerätetypen. Als Format für die Entwicklung der Lerninhalte dient die für eine Vielzahl von Mobiltelefonen und mobilen Mediaplayern verfügbare Adobe® Flash® bzw. Adobe® Flash® Lite™ Technologie von Adobe Systems. Die Inhalte werden in einem speziell für dieses Projekt programmierten, webbasierten Content-Development-Tool erstellt und verwaltet

Challenges for nanotechnology

The term "Nanotechnology" describes a large field of scientific and technical activities dealing with objects and technical components with small dimensions. Typically, bodies that are in-at least-two dimensions smaller than 0.1 [my]m are regarded as "nanobjects". By this definition, a lot of advanced materials, as well as the advanced electronic devices, are objects of nanotechnology. In addition, many aspects of molecular biotechnology as well as macromolecular and supermolecular chemistry and nanoparticle techniques are summarized under "nanotechnology". Despite this size-oriented definition, nanotechnology is dealing with physics and chemistry as well as with the realization of technical functions in the area between very small bodies and single particles and molecules. This includes the shift from classical physics into the quantum world of small molecules and low numbers or single elementary particles. Besides the already established fields of nanotechnology, there is a big expectation about technical progress and solution to essential economic, medical, and ecological problems by means of nanotechnology. Nanotechnology can only meet these expectations if fundamental progress behind the recent state of the art can be achieved. Therefore, very important challenges for nanotechnology are discussed here

Vaccination, immunity and breakthrough: quantitative effects in individual immune responses illustrated by a simple kinetic model

Featured Application The goal of this paper is to direct the discussion about individual risks and immunity breakthrough of infectious diseases, such as COVID-19, on quantitative effects concerning background and non-specific immunity, stimulation of antibody production and antibody decay, illustrated by a simple kinetic model. Abstract The personal risks of infection, as well as the conditions for achieving herd immunity, are strongly dependent on an individual’s response to the infective agents on the one hand, and the individual’s reactions to vaccination on the other hand. The main goal of this work is to illustrate the importance of quantitative individual effects for disease risk in a simple way. The applied model was able to illustrate the quantitative effects, in the cases of different individual reactions, after exposition to viruses or bacteria and vaccines. The model was based on simple kinetic equations for stimulation of antibody production using different concentrations of the infective agent, vaccine and antibodies. It gave a qualitative explanation for the individual differences in breakthrough risks and different requirements concerning a second, third or further vaccinations, reconsidering different efficiencies of the stimulation of an immune reaction

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201

Hierarchical assemblies of polymer particles through tailored interfaces and controllable interfacial interactions

Hierarchical assembly architectures of functional polymer particles are promising because of their physicochemical and surface properties for multi-labeling and sensing to catalysis and biomedical applications. While polymer nanoparticles' interior is mainly made up of the cross-linked network, their surface can be tailored with soft, flexible, and responsive molecules and macromolecules as potential support for the controlled particulate assemblies. Molecular surfactants and polyelectrolytes as interfacial agents improve the stability of the nanoparticles whereas swellable and soft shell-like cross-linked polymeric layer at the interface can significantly enhance the uptake of guest nano-constituents during assemblies. Besides, layer-by-layer surface-functionalization holds the ability to provide a high variability in assembly architectures of different interfacial properties. Considering these aspects, various assembly architectures of polymer nanoparticles of tunable size, shapes, morphology, and tailored interfaces together with controllable interfacial interactions are constructed here. The microfluidic-mediated platform has been used for the synthesis of constituents polymer nanoparticles of various structural and interfacial properties, and their assemblies are conducted in batch or flow conditions. The assemblies presented in this progress report is divided into three main categories: cross-linked polymeric network's fusion-based self-assembly, electrostatic-driven assemblies, and assembly formed by encapsulating smaller nanoparticles into larger microparticles