The Use of Software Agents for Autonomous Control of a DC Space Power System

In order to enable manned deep-space missions, the spacecraft must be controlled autonomously using on-board algorithms. A control architecture is proposed to enable this autonomous operation for an spacecraft electric power system and then implemented using a highly distributed network of software agents. These agents collaborate and compete with each other in order to implement each of the control functions. A subset of this control architecture is tested against a steadystate power system simulation and found to be able to solve a constrained optimization problem with competing objectives using only local information

A Project Based Approach to Statistics and Data Science

In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents a model that a mathematician asked to teach statistics can follow. This model entails connecting with faculty from numerous departments on campus to develop a list of topics, building a repository of real-world datasets from these faculty, and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines. The end result is students who are well prepared for interdisciplinary research, who are accustomed to coping with the idiosyncrasies of real data, and who have sharpened their technical writing and speaking skills

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work

Surface and lightning sources of nitrogen oxides over the United States: Magnitudes, chemical evolution, and outflow

We use observations from two aircraft during the ICARTT campaign over the eastern United States and North Atlantic during summer 2004, interpreted with a global 3-D model of tropospheric chemistry (GEOS-Chem) to test current understanding of regional sources, chemical evolution, and export of NOx. The boundary layer NOx data provide top-down verification of a 50% decrease in power plant and industry NOx emissions over the eastern United States between 1999 and 2004. Observed NOx concentrations at 8–12 km altitude were 0.55 ± 0.36 ppbv, much larger than in previous U.S. aircraft campaigns (ELCHEM, SUCCESS, SONEX) though consistent with data from the NOXAR program aboard commercial aircraft. We show that regional lightning is the dominant source of this upper tropospheric NOx and increases upper tropospheric ozone by 10 ppbv. Simulating ICARTT upper tropospheric NOx observations with GEOS-Chem requires a factor of 4 increase in modeled NOx yield per flash (to 500 mol/ flash). Observed OH concentrations were a factor of 2 lower than can be explained from current photochemical models, for reasons that are unclear. A NOy-CO correlation analysis of the fraction f of North American NOx emissions vented to the free troposphere as NOy (sum of NOx and its oxidation products) shows observed f = 16 ± 10% and modeled f = 14 ± 9%, consistent with previous studies. Export to the lower free troposphere is mostly HNO3 but at higher altitudes is mostly PAN. The model successfully simulates NOy export efficiency and speciation, supporting previous model estimates of a large U.S. anthropogenic contribution to global tropospheric ozone through PAN export

Phase segregation in NaxCoO2 for large Na contents

We have investigated a set of sodium cobaltates (NaxCoO2) samples with various sodium content (0.67 \le x \le 0.75) using Nuclear Quadrupole Resonance (NQR). The four different stable phases and an intermediate one have been recognized. The NQR spectra of 59Co allowed us to clearly differentiate the pure phase samples which could be easily distinguished from multi-phase samples. Moreover, we have found that keeping samples at room temperature in contact with humid air leads to destruction of the phase purity and loss of sodium content. The high sodium content sample evolves progressively into a mixture of the detected stable phases until it reaches the x=2/3 composition which appears to be the most stable phase in this part of phase diagram.Comment: 5 pages, 4 figure

A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. 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A genetic approach for long term virtual organization distribution

Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520

Handling Correlations Between Covariates and Random Slopes in Multilevel Models

This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between random effects (intercepts and slopes) and included covariates, which we refer to as “cluster-level endogeneity,” lead to bias when using standard random effects (RE) estimators such as (restricted) maximum likelihood. While the problem of correlations between unit-level covariates and random intercepts is well known and can be handled by fixed-effects (FE) estimators, the problem of correlations between unit-level covariates and random slopes is rarely considered. When applied to models with random slopes, the standard FE estimator does not rely on standard cluster-level exogeneity assumptions, but requires an “uncorrelated variance assumption” that the variances of unit-level covariates are uncorrelated with their random slopes. We propose a “per-cluster regression” (PC) estimator that is straightforward to implement in standard software, and we show analytically that it is unbiased for all regression coefficients under cluster-level endogeneity and violation of the uncorrelated variance assumption. The PC, RE, and an augmented FE estimator are applied to a real data set and evaluated in a simulation study that demonstrates that our PC estimator performs well in practice

The Secret to Successful User Communities: An Analysis of Computer Associates’ User Groups

This paper provides the first large scale study that examines the impact of both individual- and group-specific factors on the benefits users obtain from their user communities. By empirically analysing 924 survey responses from individuals in 161 Computer Associates' user groups, this paper aims to identify the determinants of successful user communities. To measure success, the amount of time individual members save through having access to their user networks is used. As firms can significantly profit from successful user communities, this study proposes four key implications of the empirical results for the management of user communities

Investigation of the Spin Density Wave in NaxCoO2

Magnetic susceptibility, transport and heat capacity measurements of single crystal NaxCoO2 (x=0.71) are reported. A transition to a spin density wave (SDW) state at Tmag = 22 K is observable in all measurements, except chi(ac) data in which a cusp is observed at 4 K and attributed to a low temperature glassy phase. M(H) loops are hysteretic below 15 K. Both the SDW transition and low temperature hysteresis are only visible along the c-axis. The system also exhibits a substantial (~40%) positive magnetoresistance below this temperature. Calculations of the electronic heat capacity gamma above and below Tmag and the size of the jump in C indicate that the onset of the SDW brings about the opening of gap and the removal of part of the Fermi surface. Reduced in-plane electron-electron scattering counteracts the loss of carriers below the transition and as a result we see a net reduction in resistivity below Tmag. Sodium ordering transitions at higher temperatures are observable as peaks in the heat capacity with a corresponding increase in resistivity.Comment: 14 pages, 6 figure