910 resultados para ROS (Robot Operating System)


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Thesis (Master's)--University of Washington, 2016-06

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Response of an aerobic upflow sludge blanket (AUSB) reactor system to the changes in operating conditions was investigated by varying two principle operating variables: the oxygenation pressure and the flow recirculation rate. The oxygenation pressure was varied between 0 and 25 psig (relative), while flow recirculation rates were between 1,300 and 600% correspondingly. The AUSB reactor system was able to handle a volumetric loading of as high as 3.8 kg total organic carbon (TOC)/m(3) day, with a removal efficiency of 92%. The rate of TOC removal by AUSB was highest at a pressure of 20 psig and it decreased when the pressure was increased to 25 psig and the flow recirculation rate was reduced to 600%. The TOC removal rate also decreased when the operating pressure was reduced to 0 and 15 psig, with corresponding increase in flow recirculation rates to 1,300 and 1,000%, respectively. Maintenance of a high dissolved oxygen level and a high flow recirculation rate was found to improve the substrate removal capacity of the AUSB system. The AUSB system was extremely effective in retaining the produced biomass despite a high upflow velocity and the overall sludge yield was only 0.24-0.32 g VSS/g TOC removed. However, the effluent TOC was relatively high due to the system's operation at a high organic loading.

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MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

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This paper introduces a method for power system modeling during the earth fault. The possibility of using this method for selection and adjustment of earth fault protection is pointed out. The paper also contains the comparison of results achieved by simulation with the experimental measurements.

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We study the electrical transport of a harmonically bound, single-molecule C-60 shuttle operating in the Coulomb blockade regime, i.e. single electron shuttling. In particular, we examine the dependance of the tunnel current on an ultra-small stationary force exerted on the shuttle. As an example, we consider the force exerted on an endohedral N@C-60 by the magnetic field gradient generated by a nearby nanomagnet. We derive a Hamiltonian for the full shuttle system which includes the metallic contacts, the spatially dependent tunnel couplings to the shuttle, the electronic and motional degrees of freedom of the shuttle itself and a coupling of the shuttle's motion to a phonon bath. We analyse the resulting quantum master equation and find that, due to the exponential dependence of the tunnel probability on the shuttle-contact separation, the current is highly sensitive to very small forces. In particular, we predict that the spin state of the endohedral electrons of N@C-60 in a large magnetic gradient field can be distinguished from the resulting current signals within a few tens of nanoseconds. This effect could prove useful for the detection of the endohedral spin-state of individual paramagnetic molecules such as N@C-60 and P@C-60, or the detection of very small static forces acting on a C-60 shuttle.

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We present a simulator of a hydropower company’s view of its scheme, and its broader market and network context, which has been developed to evaluate advanced displays for control room operations. Although simplified, the simulator captures all the main aspects of scheme operations. The simulator allows controlled studies to be performed that test the effectiveness of current vs advanced display concepts under normal vs unexpected operating conditions that can be scripted into the simulator.

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This paper presents investigations into an indoor 2×2 multiple input multiple output (MIMO) system, whose diversity performance is assessed using a high precision test-bed. In this system, transmitter and receiver are equipped with 180° or 90° 3dB hybrids with their two output ports terminated with co-polar monopole antennas. By feeding a signal to one of the two input ports of the hybrid (while the other input port is matched terminated) different communication channels in a rich-scattering environment can be created. The test-bed allows for the signal strength measurements around the receiver/ transmitter sides for a given feeding configuration of hybrids when the receiver is moved over a circular region in an indoor environment. The signal strengths maps obtained for various modes of this 2×2 MIMO system are foundations for investigating transmit/receive diversity schemes. As the signal strength measurement results are obtained with Bluetooth modules operating in the ISM 2.4 GHz, the results are of importance to many other wireless systems that aim at utilizing MIMO diversity schemes to enhance their performance in this frequency band.

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In this paper the performance of a multiple input multiple output (MIMO) wireless communication system operating in an indoor environment, featuring both line of sight (LOS) and non-line of sight (NLOS) signal propagation, is assessed. In the model the scattering objects are assumed to be uniformly distributed in an area surrounding the transmitting and receiving array antennas. Mutual coupling effects in the arrays are treated in an exact manner. However interactions with scattering objects are taken into account via a single bounce approach. Computer simulations are carried out for the system capacity for varying inter-element spacing in the receiving array for assumed values of LOS/NLOS power fraction and signal to noise ratio (SNR).

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The paper describes a system for measuring radiation efficiency of a small antenna operating alone or in the presence of objects similar to those as in an actual service. The system applies the direct approach to determining the antenna efficiency by measuring the radiated field over the entire sphere surrounding the tested antenna. In order to overcome problems associated with the conventional measuring equipment, the antenna under test is equipped with a miniature built-in VCO signal generator and supported by a low reflectivity dielectric positioner. The positioner is of sufficient size and strength to hold a human head phantom to investigate changes in radiation characteristics when the antenna operates in the presence of a human operator.

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Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.

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The UQ RoboRoos have been developed to participate in the RoboCup robot soccer small size league over several years. RoboCup 2001 saw a focus on the mechanical design of the RoboRoos, with the introduction of an omni-directional drive system and a high power kicker. The change in mechanical design had implications for the rest of the system particularly navigation and multi-robot planning. In addition, the overhead vision system was upgraded to improve reliability and robustness.

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To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

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This paper describes experiments conducted in order to simultaneously tune 15 joints of a humanoid robot. Two Genetic Algorithm (GA) based tuning methods were developed and compared against a hand-tuned solution. The system was tuned in order to minimise tracking error while at the same time achieve smooth joint motion. Joint smoothness is crucial for the accurate calculation of online ZMP estimation, a prerequisite for a closedloop dynamically stable humanoid walking gait. Results in both simulation and on a real robot are presented, demonstrating the superior smoothness performance of the GA based methods.

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This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observableenvironment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.