A distributed generation (DG) system with a photovoltaic (PV) source supported by energy storage devices and feeding dc-and ac-loads in islanded-mode operation, is considered and analyzed. As all the DG parts are interfaced through power electronic dc/dc or dc/ac converters, a control strategy is introduced which is applied directly on each individual duty-ratio converter input. The aim of the control design is to drive the PV-array energy production at the maximum power and to ensure instantaneous power balance in the limits of the storage capacity. In this scheme, critical quality demands are fulfilled, such as operation with constant ac-and dc-voltages at the load sides, independently from the power consumed. The particular controllers are implemented by applying the standard local cascaded structure with the inner-loops being fast nonlinear proportional-integral current-mode controllers. To avoid adverse impacts on the system performance, caused by contradictory actions between the individual controllers, the complete accurate DG model is considered as an isolated microgrid with the fast inner-loop controllers incorporated. Adopting a common modular inner-loop nonlinear controller form, a rigorous novel stability analysis is developed by constructing the appropriate Lyapunov function in a new sequential manner. Finally, the stability and convergence to the equilibrium are verified by simulation and experimental results.Index Terms-Lyapunov stability, microgrid control, nonlinear dynamic system, stability analysis of distributed generation systems.

The proliferation of heterogeneous computing platforms presents the parallel computing community with new challenges. One such challenge entails evaluating the efficacy of such parallel architectures and identifying the architectural innovations that ultimately benefit applications. To address this challenge, we need benchmarks that capture the execution patterns (i.e., dwarfs or motifs) of applications, both present and future, in order to guide future hardware design. Furthermore, we desire a common programming model for the benchmarks that facilitates code portability across a wide variety of different processors (e.g., CPU, APU, GPU, FPGA, DSP) and computing environments (e.g., embedded, mobile, desktop, server).As such, we present the latest release of OpenDwarfs, a benchmark suite that currently realizes the Berkeley dwarfs in OpenCL, a vendor-agnostic and openstandard computing language for parallel computing. Using OpenDwarfs, we characterize a diverse set of modern fixed and reconfigurable parallel platforms: multicore CPUs, discrete and integrated GPUs, Intel Xeon Phi co-processor, as well as a FPGA. We describe the computation and communication patterns exposed by a representative set of dwarfs, obtain relevant profiling data and execution information, and draw conclusions that highlight the complex interplay between dwarfs' patterns and the underlying hardware architecture of modern parallel platforms.

Background: Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes.

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