Energieinformatik

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and highly dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.     

Adapting application execution in CMPs using helper threads

In parallel to the changes in both the architecture domain–the move toward chip multiprocessors (CMPs)–and the application domain–the move toward increasingly data-intensive workloads–issues such as performance, energy efficiency and CPU availability are becoming increasingly critical. The CPU availability can change dynamically due to several reasons such as thermal overload, increase in transient errors, or operating system scheduling. An important question in this context is how to adapt, in a CMP, the execution of a given application to CPU availability change at runtime. Our paper studies this problem, targeting the energy-delay product (EDP) as the main metric to optimize. We first discuss that, in adapting the application execution to the varying CPU availability, one needs to consider the number of CPUs to use, the number of application threads to accommodate and the voltage/frequency levels to employ (if the CMP has this capability). We then propose to use helper threads to adapt the application execution to CPU availability change in general with the goal of minimizing the EDP. The helper thread runs parallel to the application execution threads and tries to determine the ideal number of CPUs, threads and voltage/frequency levels to employ at any given point in execution. We illustrate this idea using four applications (Fast Fourier Transform, MultiGrid, LU decomposition and Conjugate Gradient) under different execution scenarios. The results collected through our experiments are very promising and indicate that significant EDP reductions are possible using helper threads. For example, we achieved up to 66.3%, 83.3%, 91.2%, and 94.2% savings in EDP when adjusting all the parameters properly in applications FFT, MG, LU, and CG, respectively. We also discuss how our approach can be extended to address multi-programmed workloads.     

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

Nanostructure array of coupled RTDs as cellular neural networks

The processing capabilities of a proposed nanoelectronic device are investigated. The device is considered as a global dynamical system with local circuit model components. The system equations and the corresponding network model are presented. The characteristics of this network model are compared with the cellular neural networks. Certain characteristics of the network are analysed theoretically and demonstrated with circuit‐system level simulations. As a novel property, it is shown that the single layer nanodevice network structure is a basic reaction–diffusion system and it is capable of autowave propagation. Furthermore, the same network structure exhibits several image processing capabilities like image smoothing, edge enhancement, and horizontal or vertical line detection using simple arrangements of the device parameters. Copyright ©2003 John Wiley & Sons, Ltd.     

Dynamic Modeling and Hydrodynamic Performance of Biomimetic Underwater Robot Locomotion

We developed a dynamic model of a Nitinol artificial muscle activated biomimetic robot. The robot was reverse engineered from the American lobster and built in the Biomimetic Underwater Robot Program at Northeastern University. It is intended for autonomous remote-sensing operations in shallow waters. An experimentally based Nitinol artificial muscle model was integrated into the robot dynamic model. The hydrodynamic characteristics of the robot were determined experimentally. The muscle control signals were generated by utilizing a readily available biomimetic control architecture. The effects of the timing parameters were investigated. Simulations indicate that the developed robot is able to locomote with high stability. It can walk against constant currents and surge.     

Combating discrimination using Bayesian networks

Discrimination in decision making is prohibited on many attributes (religion, gender, etc…), but often present in historical decisions. Use of such discriminatory historical decision making as training data can perpetuate discrimination, even if the protected attributes are not directly present in the data. This work focuses on discovering discrimination in instances and preventing discrimination in classification. First, we propose a discrimination discovery method based on modeling the probability distribution of a class using Bayesian networks. This measures the effect of a protected attribute (e.g., gender) in a subset of the dataset using the estimated probability distribution (via a Bayesian network). Second, we propose a classification method that corrects for the discovered discrimination without using protected attributes in the decision process. We evaluate the discrimination discovery and discrimination prevention approaches on two different datasets. The empirical results show that a substantial amount of discrimination identified in instances is prevented in future decisions.     

UV curable lens production using molecular weight controlled PEEK based acrylic oligomer (Ac-PEEK)

We produced UV curable lenses with properties blocking short wave UV light. In the UV-curable formulations, we used an oligomer (Ac-PEEK) with another urethan oligomer (Mw = 2000). Radically active, molecular weight controlled Ac-PEEK was obtained by reacting 2-hydroxyl ethyl methacrylate with molecular- weight- controlled and isocyanate terminated PEEK (Mn = 4500). We characterized all synthesized monomer, oligomer and optical materials with UV/Vis spectrophotometer with interferogram, elemental analyser, mass spectrophotometer, proton nuclear magnetic resonance, Fourier transform infrared spectroscopy, thermal gravimetric analyzer, differential scanning calorimeter, scanning electron microscopy and gas chromatography. Results suggested that newly synthesized oligomer with the structure of PEEK absorbs short wave UV-light. Ageing tests [ISO 11979-5, Ophthalmic implants—intraocular lenses (IOL)—Part 5: Biocompatibility] performed on the IOL materials were successful. High contact angle of the obtained lenses suggests that all lenses were hydrophobic and SEM results revealed that lenses are morphologically homogeneous. Based on all positive properties just mentioned, we safely conclude that the lenses produced in this study are very promising for IOL production.     

Domain Wall Motion Across Various Grain Boundaries in Ferroelectric Thin Films

Domain wall movement at and near engineered 10°, 15°, and 24° tilt and 10° and 30° twist grain boundaries was measured by band excitation piezoresponse force microscopy for Pb(Zr,Ti)O₃ films with Zr/Ti ratio of 45/55 and 52/48. A minimum in nonlinear response was observed at the grain boundary for the highest angle twist and tilt grain boundaries, while a maximum in nonlinear response was observed at the 10° tilt grain boundaries. The observed nonlinear response was correlated with the domain configurations imaged in cross section by transmission electron microscopy.     

Bioactive and Physicochemical Properties of Persimmon as Affected by Drying Methods

In this study, the effects of three different drying methods (freeze drying, oven drying, and vacuum oven drying) on bioactive (total phenolics, total flavonoids, condensed tannin and total hydrolyzable tannin contents, antiradical activity, and antidiabetic activity) and some physicochemical (dry matter, ash, water activity (a _w ), color, protein, hydroxymethylfurfural, glucose and fructose content) properties of persimmon fruit were investigated. Simplex lattice mixture design methodology was applied to determine the best solvent mixture for the extraction of phenolics from the samples. It was found that the mixture of acetone:water at the ratio of 50:50 % (v/v) was the best solvent mixture for the extraction. The persimmon powder sample obtained from freeze drying showed significantly (p <0.05) higher bioactivity values than oven- and vacuum-oven-dried samples. Antiradical activity changed significantly depending on the drying method employed and it was superior in freeze-dried samples than that of the other drying methods.