Selecting a multi-agent system development tool for industrial applications: A case study of self-serving aircraft assets

Industrialists have few example processes they can benchmark against in order to choose a multi-agent development kit. In this paper we present a review of commercial and academic agent tools with the aim of selecting one for developing an intelligent, self-serving asset architecture. In doing so, we map and enhance relevant assessment criteria found in literature. After a preliminary review of 20 multiagent platforms, we examine in further detail those of JADE, JACK and Cougaar. Our findings indicate that Cougaar is well suited for our requirements, showing excellent support for criteria such as scalability, persistence, mobility and lightweightness. © 2010 IEEE.

PRELIMINARY STUDY ON MITOCHONDRIAL CYTOCHROME b DNA SEQUENCES AND PHYLOGENY OF FORMALIN FIXED SISORID FISHES(Teleostei: Siluriformes)

从９种科鱼类的福尔马林标本中获得了３３３ｂｐ的细胞色素ｂ基因片段的序列。这９个种分别代表科鱼类的８个属。３３３ｂｐ的ＤＮＡ序列经ＭＵＳＴ软件排序后，有１０１个变异位点，其中有３９个信息位点。序列在成对物种间的距离为８～４８。平均遗传距离为２４％～１４４％。简约分析产生了最大简约系统树，其步长是１６２（ＣＩ＝０７３５，ＲＩ＝０４９４）。在该系统树上，Ｂａｇａｒｉｕｓ是最原始的属，并与所有其他的物种形成姊妹群。其余８个属形成一个单系类群并分为二个姊妹群。尽管在形态上具有１３个离征，但在分子系统树上

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Regression on fixed-rank positive semidefinite matrices: A Riemannian approach

The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks. © 2011 Gilles Meyer, Silvere Bonnabel and Rodolphe Sepulchre.

Riemannian metric and geometric mean for positive semidefinite matrices of fixed rank

This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive cone and the associated natural metric. The resulting Riemannian space has strong geometrical properties: it is geodesically complete, and the metric is invariant with respect to all transformations that preserve angles (orthogonal transformations, scalings, and pseudoinversion). A meaningful approximation of the associated Riemannian distance is proposed, that can be efficiently numerically computed via a simple algorithm based on SVD. The induced mean preserves the rank, possesses the most desirable characteristics of a geometric mean, and is easy to compute. © 2009 Society for Industrial and Applied Mathematics.

A fixed-grid b-spline finite element technique for fluid-structure interaction

We present a fixed-grid finite element technique for fluid-structure interaction problems involving incompressible viscous flows and thin structures. The flow equations are discretised with isoparametric b-spline basis functions defined on a logically Cartesian grid. In addition, the previously proposed subdivision-stabilisation technique is used to ensure inf-sup stability. The beam equations are discretised with b-splines and the shell equations with subdivision basis functions, both leading to a rotation-free formulation. The interface conditions between the fluid and the structure are enforced with the Nitsche technique. The resulting coupled system of equations is solved with a Dirichlet-Robin partitioning scheme, and the fluid equations are solved with a pressure-correction method. Auxiliary techniques employed for improving numerical robustness include the level-set based implicit representation of the structure interface on the fluid grid, a cut-cell integration algorithm based on marching tetrahedra and the conservative data transfer between the fluid and structure discretisations. A number of verification and validation examples, primarily motivated by animal locomotion in air or water, demonstrate the robustness and efficiency of our approach. © 2013 John Wiley & Sons, Ltd.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.

Photodegradation dynamics of pure microcystin variants with illumination of fixed wavelength UV-lights

Photolysis of microcystins by UV irradiation and the effects of different environmental factors on efficiency of UV degradation were studied. The results indicated that the rates of the photolytical degradation reactions of microcystin-LR and RR-follow pseudo-first-order kinetic process. The results also showed that the concentrations of two microcystin variants decreased significantly by UV-C Irradiation; the wavelength and intensitiy of UV irradiation are two very important factors affecting the rate of degradation; temperature and pH value could also affect the half life of degradation rates. When irradiated by weaker UV-Iight, isomerization could be detected in the course of photolytical degradation. The concentrations of two isomers transformed from microcystin-LR reached its maximum at the third minute and decreased with the time afterwards. To simulate photolysis of microcystins in the field water body, microcystins with low concentration were used. It was found that UV-C illumination was capable of decomposing over 95% of microcystins within 40 min. In the presence of humic substances the photodecomposition slowed down to a certain extent. These results are valuable in using UV irradiation for elimination microcystins from raw water.

Effect of implantation of nitrogen into SIMOX buried oxide on its fixed positive charge density

In order to obtain greater radiation hardness for SIMOX (separation by implanted oxygen) materials, nitrogen was implanted into SIMOX BOX (buried oxide). However, it has been found by the C-V technique employed in this work that there is an obvious increase of the fixed positive charge density in the nitrogen-implanted BOX with a 150 out thickness and 4 x 10(15) cm(-2) nitrogen implantation dose, compared with that unimplanted with nitrogen. On the other hand, for the BOX layers with a 375 nm thickness and implanted with 2 x 10(15) and 3 x 10(15) cm(-2) nitrogen doses respectively, the increase of the fixed positive charge density induced by implanted nitrogen has not been observed. The post-implantation annealing conditions are identical for all the nitrogen-implanted samples. The increase in fixed positive charge density in the nitrogen-implanted 150 nm BOX is ascribed to the accumulation of implanted nitrogen near the BOX/Si interface due to the post-implantation annealing process according to SIMS results. In addition, it has also been found that the fixed positive charge density in initial BOX is very small. This means SIMOX BOX has a much lower oxide charge density than thermal SiO2 which contains a lot of oxide charges in most cases.