Assistance networks for dynamic multirobot tasks

In this paper, we consider dynamic multirobot tasks that can be done by any of the robots, but only with the assistance of any other robot. We propose a novel approach based on the concept of ‘assistance networks’ with two complementary aspects, namely assistant finding and network topology update. Each robot, encountering a new task, seeks an assisting robot among its immediate neighbors in the assistance network in a decentralized manner. The network topology is defined based on pairwise stability via payoff functions that consider general task-related guidelines. As such, the number of potential assisting robots can be ensured a priori depending on tasks’ requirements. As robots move around, the topology is updated via pairwise games. If the games are conducted by a network coordinator, each game is shown to result in a pairwise stable network. A series of simulation and experimental results in a variety of different scenarios demonstrate that the robots are able to get assistance or give assistance flexibly.     

Taking value-networks to the cloud services: security services, semantics and service level agreements

Cloud services have become an emerging solution for organizations striving to address today’s need for agility, but little research has addressed transitioning multiple, collaborating organizations to what can be referred to as a “value-network cloud.” We know that organizations adopting cloud services to execute business processes must concomitantly reconfigure their security solutions for their integrated intra- and inter-organizational collaborations. We address the question, “What is needed to make it possible for an entire value-network to take secure, collaborative business process executions to the cloud?” Future value-network cloud solutions will require completely new security approaches that will leverage contracted brokering solutions operating as part of the cloud solution. We view value-network cloud security service provisioning as a bundle decision characterized by a mix of communication patterns relevant to intra- and inter-enterprise collaboration. We propose a cloud service broker model—using semantics and SLA based middleware—to serve as a trusted interface between the enterprise, cloud service providers and other organizations collaborating in a value-network. The approach enables IT governance for value-network cloud services. The architectural requirements adapt design principles for infrastructure management tailored from approaches to how business cartels historically conducted secure business dealings.     

Decentralized network topologies in multirobot systems

This paper studies the problem of forming and updating the network topology in a multirobot system that is simultaneously engaged in a given task. The contribution of this paper is to propose a decentralized model of how the network may evolve based on network-related payoff functions and pairwise games. As such, pairwise games provide a practical and general scheme for contacting other robots and revising the network topology. A network is deemed acceptable by all the robots using pairwise stability and pairwise Nash equilibrium. As an application, we consider networks that are generated with mutual link-based payoff functions and show that – under some assumptions regarding changes in the configuration states – each game is ensured of converging to a pairwise stable network. This approach is then integrated with a common robotic task where the network is critical to successful task completion. The resulting performance is evaluated with respect to a variety of measures including task completion, network density, and the average payoff along with comparative results with all-to-all communication.     

Automatic generation control application with craziness based particle swarm optimization in a thermal power system

In this study, a novel gain scheduling Proportional-plus-Integral (PI) control strategy is suggested for automatic generation control (AGC) of the two area thermal power system with governor dead-band nonlinearity. In this strategy, the control is evaluated as an optimization problem, and two different cost functions with tuned weight coefficients are derived in order to increase the performance of convergence to the global optima. One of the cost functions is derived through the frequency deviations of the control areas and tie-line power changes. On the other hand, the other one includes the rate of changes which can be variable depends on the time in these deviations. These weight coefficients of the cost functions are also optimized as the controller gains have been done. The craziness based particle swarm optimization (CRAZYPSO) algorithm is preferred to optimize the parameters, because of convergence superiority. At the end of the study, the performance of the control system is compared with the performance which is obtained with classical integral of the squared error (ISE) and the integral of time weighted squared error (ITSE) cost functions through transient response analysis method. The results show that the obtained optimal PI-controller improves the dynamic performance of the power system as expected as mentioned in literature.     

Geotechnical measurements at Izmir LRT system tunnels

The geotechnical monitoring played an important role in guiding the tunneling applications during the construction of IZMIR LRTS (Light Rapid Transit System) 1st stage. The purpose of this paper is to review the deformation measurements and results gathered during the construction of LRTS 1st stage tunnels.The New Austrian Tunneling concept (NATM) was used for the construction of Ucyol–Konak conventional tunnels of the system. The EPBM (Earth Pressure Balance Method) concept used in the construction of Konak–Basmane precast segmental tunnels. Both tunneling concepts required monitoring of deformations during construction as well as for a period after its completion.     

Performance evaluation of evolutionary heuristics in dynamic environments

In recent years, there has been a growing interest in applying genetic algorithms to dynamic optimization problems. In this study, we present an extensive performance evaluation and comparison of 13 leading evolutionary algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying a set of problem parameters including shift length, change frequency, correlation value and number of peaks in the landscape. In order to compare solution quality or the efficiency of algorithms, the results are reported in terms of both offline error metric and dissimilarity factor, our novel comparison metric presented in this paper, which is based on signal similarity. Computational effort of each algorithm is reported in terms of average number of fitness evaluations and the average execution time. Our experimental evaluation indicates that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem. Specifically, hybrid methods provide up to 24% improvement with respect to offline error and up to 30% improvement with respect to dissimilarity factor by requiring more computational effort than other methods.     

Performance of Penman-Monteith FAO56 in a Semiarid Highland Environment

Reliable estimates of evapotranspiration are essential for irrigation and water resources planning and management. Although several methods are available for computing reference evapotranspiration (ETo), the provision of complete and accurate climate data is often a problem. Therefore, weighing lysimeter data from a semiarid highland environment were used to evaluate the performance of six commonly used reference evapotranspiration estimation methods with different data requirements (Penman-Monteith-FAO56, Priestley-Taylor, Radiation-FAO24, Hargreaves, Blaney-Criddle, Class A pan). The lysimeter experiments were conducted at Ankara Research Institute of Rural Services in Turkey, during the April–October cropping seasons of the years 2000–2002. The average ETo for the three seasons, computed from the lysimeter data, was 964 mm. The Penman-Monteith-FAO56 method was also evaluated for cases where relative humidity, wind speed, solar radiation, or all three parameters would be missing. This resulted in a total of 10 different methods. The RMS errors (RMSE) and index of agreement for the daily data and the monthly averages as well as the mean absolute error (MAE) for the seasonal totals were computed to compare these methods. The methods were ranked based on the sum of the ranks for all five evaluation criteria. The Penman-Montheith-FAO56 method with the full data set, with replacement of wind speed, and with replacement of relative humidity took the top three spots, with MAEs for the seasonal totals ranging between 40 and 70 mm. The Hargreaves method came in fourth (MAE 54 mm), followed by the Penman-Montheith-FAO56 method with replacement of all three parameters (MAE 57 mm). The RMSE for the monthly average ETo was 0.43 and 0.50?mm?days?1 for the Penman-Monteith-FAO56 without and with replacement of all three parameters and 0.48?mm?days?1 for Hargreaves. Thus, if only temperature data would be available, the much easier to use Hargreaves method would be preferred above the Penman-Montheith-FAO56 equation with replacement of humidity, radiation, and wind speed data, for this semiarid highland environment.     

Evaluating the performance of microbial fuel cells powering electronic devices

A microbial fuel cell (MFC) is capable of powering an electronic device if we store the energy in an external storage device, such as a capacitor, and dispense that energy intermittently in bursts of high-power when needed. Therefore its performance needs to be evaluated using an energy-storing device such as a capacitor which can be charged and discharged rather than other evaluation techniques, such as continuous energy dissipation through a resistor. In this study, we develop a method of testing microbial fuel cell performance based on storing energy in a capacitor. When a capacitor is connected to a MFC it acts like a variable resistor and stores energy from the MFC at a variable rate. In practice the application of this method to testing microbial fuel cells is very challenging and time consuming; therefore we have custom-designed a microbial fuel cell tester (MFCT). The MFCT evaluates the performance of a MFC as a power source. It uses a capacitor as an energy storing device and waits until a desired amount of energy is stored then discharges the capacitor. The entire process is controlled using an analog-to-digital converter (ADC) board controlled by a custom-written computer program. The utility of our method and the MFCT is demonstrated using a laboratory microbial fuel cell (LMFC) and a sediment microbial fuel cell (SMFC). We determine (1) how frequently a MFC can charge a capacitor, (2) which electrode is current-limiting, (3) what capacitor value will allow the maximum harvested energy from a MFC, which is called the “optimum charging capacitor value,” and (4) what capacitor charging potential will harvest the maximum energy from a MFC, which is called the “optimum charging potential.” Using a LMFC we find that (1) the time needed to charge a 3-F capacitor from 0 to 500 mV is 108 min, (2) the optimum charging capacitor value is 3 F, and (3) the optimum charging potential is 300 mV. Using a SMFC we find that (1) the time needed to charge a 3-F capacitor from 0 to 500 mV is 5 min, (2) the optimum charging capacitor value is 3 F, and (3) the optimum charging potential is 500 mV. Our results demonstrate that the developed method and the MFCT can be used to evaluate and optimize energy harvesting when a MFC is used with a capacitor to power wireless sensors monitoring the environment.     

Gamma-range oscillations in backward-masking functions and their putative neural correlates.

Backward-masking functions have been hitherto categorized into two types, commonly named Type A and Type B. The analysis of a model of Retino-Cortical Dynamics produces the prediction that spatially localized stimuli should reveal an oscillatory metacontrast function. The predicted new type of metacontrast masking function was investigated in a psychophysical experiment. The results show oscillatory metacontrast functions with significant power in the gamma range (30–70 Hz). A marked decrease in the oscillations is observed when the spatial extent of the stimuli is increased. The theoretical basis of the study relates the oscillations found in the metacontrast function to gamma-range oscillations observed in scalp and intracerebral recordings. The qualitative agreement between the model and data provides support for this putative relationship. (PsycINFO Database Record (c) 2010 APA, all rights reserved)