Development of a Force and Airspeed Data Acquisition System for the Embry-Riddle Aeronautical University 30x40 in. Subsonic Wind Tunnel

The very first objective of performing most experiments is collecting numerical data. Values can be read from instruments or gauges by eye and recorded by hand. However, in certain experiments the amount of data collected may become so large that a human alone can not observe and record the data fast enough. This is where a data acquisition system comes into play. A data acquisition system is a network of devices that collects data from instruments and outputs it to hard copies or storage devices. A data acquisition system can also control instruments to take the data at the exact moment and rate at which the user wishes. The system described in this thesis is capable of reading values from various instruments and reducing those data to yield aerodynamic loads, coefficients, temperatures and air velocities. Prior to 2000, a program written in BASIC was used to control the data taking processes. This thesis will emphasize on the new data acquisition program written in the Lab VIEW environment in 2000

Evolutionary Neural Network Modeling for Energy Prediction of Cloud Data Centers

Accurate forecasts of data center energy consumptions can help eliminate risks caused by underprovisioning or waste caused by over-provisioning. However, due to nonlinearity and complexity, energy prediction remains a challenge. An added layer of complexity further comes from dynamically changing workloads. There is a lack of physical principle based clear-box models, and existing black-box based methods such neural networks are restrictive. In this paper, we develop an evolutionary neural network as a structurally optimal black-box model to forecast the energy consumption of a dynamic cloud data center. In particular, the approach to evolving an optimal network is developed from several novel mechanisms of a genetic algorithm, such as a structurally-inclusive matrix encoding and species parallelism that help maintain an overall increasing fitness to overcome slow convergence whilst preventing premature dominance. The model is trained using part of the data obtained from a set of MapReduce jobs on a 120-core Hadoop cluster and is then validated against unseen data. The results, both in terms of prediction speed and accuracy, suggest that this evolutionary neural network approach to cloud data center forecast is highly promising

Changes in the bilateral pulse transit time difference with a moving arm

BACKGROUND: Changes of pulse transit time (PTT) induced by arm position were studied for unilateral arm. However, consistency of the PTT changes was not validated for both arm sides. OBJECTIVE: We aimed to quantify the PTT changes between horizontal and non-horizontal positions from right arm and left arm in order to explore the consistency of both arms. METHODS: Twenty-four normal subjects aged between 21 and 50 (14 male and 10 female) years were enrolled. Left and right radial artery pulses were synchronously recorded from 24 healthy subjects with one arm (left or right) at five angles (90∘, 45∘, 0∘, -45∘ and -90∘) and the other arm at the horizontal level (0∘) for reference. RESULTS: The overall mean PTT changes at the five angles (from 90∘ to -90∘) in the left arm (right as reference) were 16.1, 12.3, -0.5, -2.5 and -2.6 ms, respectively, and in the right arm (left as reference) were 18.0, 12.6, 1.6, -1.6 and -2.0 ms, respectively. CONCLUSIONS: Obvious differences were not found in the PTT changes between the two arms (left arm moving or right arm moving) under each of the five different positions (all P> 0.05)

Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing

The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising

Hydrostatic bath synthesis of conductive polypyrrole/reduced graphene oxide aerogel as compression sensor

A conductive and elastic polypyrrole/reduced graphene oxide aerogel (PGA) was synthesized through a hydrostatic bath method followed by freeze-drying. Through this method, the self-agglomeration and oxidative polymerization of rGO and polypyrrole occurred synergistically in a controlled environment, which resulted in a 3D conductive aerogel matrix. The optical spectroscopy, including FT-IR and XPS, showed the distinguished vibration band of polypyrrole and π-π interaction, which evidenced the successful polymerization of the pyrrole monomer through the synergistic assembly process. The presence of flexible rGO nanosheets as an aerogel backbone provided a strong mechanical support and deposition sites for polypyrrole nanoparticles, which contributed to the overall elasticity. Furthermore, the polypyrrole nanoparticles not only addressed the stacking issue of rGO but further enhanced the reactive surface area by eight times of magnitude compared to pure graphene aerogel (GA) produced by the same technique. Molecular modeling estimates adsorption energies for the polypyrrole molecule over the rGO surface and further predict the dominant functional group that involve in the formation of PGA. The as-synthesized PGA provide a significant electrical resistance changes (>80%) before and after compression, which responded exceptionally well upon compression by lighting up LEDs that were arranged in parallel in an electrical circuit

Characterization of cellulolytic bacterial cultures grown in different substrates

Nine aerobic cellulolytic bacterial cultures were obtained from the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Culture (DSMZ) and the American Type Culture Collection (ATCC). The objectives of this study were to characterize the cellulolytic bacteria and to determine the optimum moisture ratio required for solid state fermentation (SSF) of palm kernel cake (PKC). The bacteria cultures were grown on reconstituted nutrient broth, incubated at 30∘C and agitated at 200 rpm. Carboxymethyl cellulase, xylanase, and mannanase activities were determined using different substrates and after SSF of PKC. The SSF was conducted for 4 and 7 days with inoculum size of 10% (v/w) on different PKC concentration-to-moisture ratios: 1 : 0.2, 1 : 0.3, 1 : 0.4, and 1 : 0.5. Results showed that Bacillus amyloliquefaciens 1067 DSMZ, Bacillus megaterium 9885 ATCC, Paenibacillus curdlanolyticus 10248 DSMZ, and Paenibacillus polymyxa 842 ATCC produced higher enzyme activities as compared to other bacterial cultures grown on different substrates. The cultures mentioned above also produced higher enzyme activities when they were incubated under SSF using PKC as a substrate in different PKC-to-moisture ratios after 4 days of incubation, indicating that these cellulolytic bacteria can be used to degrade and improve the nutrient quality of PKC

Cyclic performance of viscoelastic dielectric elastomers with solid hydrogel electrodes

Hydrogels containing electrolyte can work as ionic conductors to actuate dielectric elastomer (DE) artificial muscles. Based on a popular design of a circular actuator, we study theoretically and experimentally the cyclic performance of acrylic DE actuators with solid hydrogel electrodes. The viscoelasticity of solid electrodes constrains the maximum strain that is attainable for one cycle of triangular voltage, and it also diminishes the accumulated increment of the maximum strain after many cycles of loadings.Engineering and Applied Science