Surface Electromyography (sEMG) has applications in prosthetics, diagnostics and neuromuscular rehabilitation. Self-adhesive Ag/AgCl are the electrodes preferentially used to capture sEMG in short-term studies, however their long-term application is limited. In this study we designed and evaluated a fully integrated smart textile band with electrical connecting tracks knitted with intarsia techniques and knitted textile electrodes. Real-time myoelectric pattern recognition for motor volition and signal-to-noise ratio (SNR) were used to compare its sensing performance versus the conventional Ag-AgCl electrodes. After a comprehending measurement and performance comparison of the sEMG recordings, no significant differences were found between the textile and the Ag-AgCl electrodes in SNR and prediction accuracy obtained from pattern recognition classifiers.

A small wind power plant connected to the grid has been modeled in Modelica/Dymola and controlled using external controllers written in C++. The small wind power plant consists of three wind power units, with a nominal power of 3kW, and one grid connection interconnected with an internal DC-grid. All the controls needed for controlling and optimizing the operation of the individual parts in the plant were developed and implemented. Apart from this a managing control for the entire plant were developed and implemented. The control was implemented using an external static library interconnected with Dymola. the External Object approach for implementing objects in Modelica was also tested. The optimization algorithms developed for the wind turbine was done in a way so that no measurements of the wind speed are needed. The controls were developed so that they can achieve a number of different tasks such as Reactive Power Compensation and Island Control. Models were implemented in Modelica using Dymola as tool. In order to model the power electronics involved in the system the Electric Power library (EPL) has been utilized. Models for the wind turbine were developed and tested. The models were in the end tested and evaluated by running a number of different simulations. The Different test cases consists of optimizing the power output, controlling the power output to a desired level and island operation, that is to power up a small grid on its own.

In this paper, we describe how we generate Functional Mock-up Units (FMUs) for the automation block language Bloqqi. This allows Bloqqi control programs to be tested with simulations of the physical processes they control. The physical process can be specified in any tool that supports the Functional Mockup-Interface (FMI) standard. For example, we have successfully run Bloqqi programs together with Modelica models exported as FMUs. Bloqqi programs execute at discrete times, and we describe how this is handled in the implementation of the DoStep function, specified in the standard.The Functional Mock-up Interface (FMI) is a toolindependent standard with support for both model exchange and co-simulation of dynamic models (Blochwitz et al., 2012). Version 1.0 of the standard was released in 2010, followed by version 2.0 in 2014. Using the FMI standard, models can be shared across all the 100+ tools that are currently supporting the standard. FMI uses a combination of XML-files and compiled C-code to create a Functional Mock-up Unit (FMU). An FMU is a zipfile with two major parts: a model description in XMLformat, and a number of compiled binaries. Each FMU