Self-organizing maps for pattern recognition in design of alloys

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.     

Coupling and comparing a spatially- and temporally-detailed eutrophication model with an ecosystem network model: An initial application to Chesapeake Bay

Coastal waters are modeled for a variety of purposes including eutrophication remediation and fisheries management. Combining these two approaches provides insights which are not available from either approach independently. Coupling is confounded, however, by differences in model formulations and “currencies.” We present here an initial coupling of a spatially- and temporally-detailed eutrophication model, CE-QUAL-ICM, with a network fisheries model, Ecopath. We list commonalities between the models and present algorithms and software for the exchange of information. The models are applied to the central portion of Chesapeake Bay for a contemporary summer period. After comparison of the representations of Chesapeake Bay by the two models, an illustrative example one-way, off-line, coupling is presented. In an initial examination of a 20% increase in predation on phytoplankton by a small, highly-exploited fish (Atlantic menhaden, Brevoortia tyrannus), computed reduction in phytoplankton biomass is accompanied by increased production due to enhanced nutrient recycling. Minimal impact on the structure of the food web or on biomass of higher-trophic level organisms is computed. The algorithms and software can be adapted to alternate eutrophication models and Ecopath applications and provide the first, necessary, steps for subsequent coupling with the time-variable Ecosim model.     

A high-density multi-point LAPS set-up using a VCSEL array and FPGA control

A new LAPS (light-addressable potentiometric sensor) set-up will be introduced, in which the light sources are miniaturised by the utilisation of a VCSEL (vertical-cavity surface-emitting laser) array to increase the measurement spot density. An FPGA (field-programmable gate array) is used to generate modulation signals for individual illumination of each measurement spot of the LAPS. The new set-up can operate a large number of measurement spots simultaneously by reading out the sum photocurrent and separate the signals of the individual measurement spots by an FFT analysis. The frequency, amplitude and offset of the modulation signal can be configured for each measurement spot by software. The new system can be combined with a positioning stage allowing the parallel read out of a single line of measurement spots and a scan perpendicular to that line in a similar manner, like for an optical scanner set-up. First measurements demonstrate the functionality of the new LAPS set-up as a chemical imaging system.     

Branch-and-Bound Methods for Euclidean Registration Problems

In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization theory, in particular convex underestimators in combination with branch-and-bound methods. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data. We also give examples of where traditional methods fail due to the local minima problem.     

A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.     

Racing to the precipice: a model of artificial intelligence development

This paper presents a simple model of an AI (artificial intelligence) arms race, where several development teams race to build the first AI. Under the assumption that the first AI will be very powerful and transformative, each team is incentivised to finish first—by skimping on safety precautions if need be. This paper presents the Nash equilibrium of this process, where each team takes the correct amount of safety precautions in the arms race. Having extra development teams and extra enmity between teams can increase the danger of an AI disaster, especially if risk-taking is more important than skill in developing the AI. Surprisingly, information also increases the risks: the more teams know about each others’ capabilities (and about their own), the more the danger increases. Should these results persist in more realistic models and analysis, it points the way to methods of increasing the chance of the safe development of AI.     

Optimal setpoint chasing in dynamic positioning of deep‐water drilling and intervention vessels

Conventional controller designs for dynamic positioning of ships and floating marine structures have so far been based on the principle on automatic positioning in the horizontal‐plane about desired position and heading co‐ordinates defined by the operator. A three degrees‐of‐freedom multivariable controller either of linear or nonlinear type, normally with feedback signals from surge, sway and yaw position and velocities, has been regarded as adequate for the control objective. For floating structures with small waterplane area such as semi‐submersibles, feedback from roll and pitch angular rotation velocity may also be included to avoid thrust‐induced roll and pitch motions that are caused by the hydrodynamic and the geometrical couplings between the horizontal and vertical planes. However, for certain marine operations this control philosophy may not be the most appropriate approach ensuring safety and cost effectiveness. For drilling and work‐over operations the main positioning objective is to minimize the bending stresses along the riser and the riser angle magnitudes at the well head on the subsea structure, and at the top joint as well. A positioning control strategy solely based on manual setting of the desired position co‐ordinates may not be the most optimal solution for these applications. In this paper a new hybrid dynamic positioning controller, that also accounts for riser angle offsets and bending stresses is proposed. It is shown that a significant reduction in riser angle magnitude can be achieved. Simulations with a drilling semi‐submersible demonstrate the effect of the proposed control strategy. Copyright © 2001 John Wiley & Sons, Ltd.