A service oriented architecture for decision making in engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design

High-power 1H composite pulse decoupling provides artifact free exchange-mediated saturation transfer (EST) experiments.

Exchange-mediated saturation transfer (EST) provides critical information regarding dynamics of molecules. In typical applications EST is studied by either scanning a wide range of (15)N chemical shift offsets where the applied (15)N irradiation field strength is on the order of hundreds of Hertz or, scanning a narrow range of (15)N chemical shift offsets where the applied (15)N irradiation field-strength is on the order of tens of Hertz during the EST period. The (1)H decoupling during the EST delay is critical as incomplete decoupling causes broadening of the EST profile, which could possibly result in inaccuracies of the extracted kinetic parameters and transverse relaxation rates. Currently two different (1)H decoupling schemes have been employed, intermittently applied 180° pulses and composite-pulse-decoupling (CPD), for situations where a wide range, or narrow range of (15)N chemical shift offsets are scanned, respectively. We show that high-power CPD provides artifact free EST experiments, which can be universally implemented regardless of the offset range or irradiation field-strengths

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks

Toward Self-Referential Autonomous Learning of Object and Situation Models

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach

A Comparative Study of Specific Enthalpy of Aromatic Hydrocarbons with Simple Carbohydrates

Calorimetry is an aspect of chemistry primarily focused on determining the enthalpy of reactions (∆Hrxn). In the bomb calorimetry technique, the heat of combustion of chemical compounds can be measured experimentally. From this data and the application of Hess’s Law, ∆the Hrxn of several chemical reactions can be determined. The technique of bomb calorimetry can be applied to food, fuels, pharmaceuticals, and many other fields. The objective of the present project is to determine the specific enthalpy of various simple carbohydrates (naturally occurring sugars) through bomb calorimetry and compare it with that of aromatic hydrocarbons. By performing benzoic acid standardization reactions with the Parr™ Model 1341 Oxygen Combustion Vessel, the calorimeter constant was found to be 10.2717 ± .0565 KJ/°C with a 95% confidence interval, allowing the accurate determination of specific enthalpy for each of the sugars at constant volume in a pure O2 vessel. As we proceed with the experiment several aromatic hydrocarbons (e.g Naphthalene, etc) and several simple carbohydrates (e.g Sucrose, Glucose, etc.) will be tested to obtain the enthalpy of combustion (∆Hcomb). We will perform quantum chemical calculations on the reactant and product molecules to determine the ∆Hcomb and compare the experimental values with computational data

Modelling of direct metal laser sintering of EOS DM20 bronze using neural networks and genetic algorithms

An attempt was made to predict the density and microhardness of a component produced by Laser Sintering of EOS DM20 Bronze material for a given set of process parameters. Neural networks were used for process-based-modelling, and results compared with a Taguchi analysis. Samples were produced using a powder-bed type ALM (Additive Layer Manufacturing)-system, with laser power, scan speed and hatch distance as the input parameters, with values equally spaced according to a factorial design of experiments. Optical Microscopy was used to measure cross-sectional porosity of samples; Micro-indentation to measure the corresponding Vickers’ hardness. Two different designs of neural networks were used - Counter Propagation (CPNN) and Feed-Forward Back-Propagation (BPNN) and their prediction capabilities were compared. For BPNN network, a Genetic Algorithm (GA) was later applied to enhance the prediction accuracy by altering its topology. Using neural network toolbox in MATLAB, BPNN was trained using 12 training algorithms. The most effective MATLAB training algorithm and the effect of GA-based optimization on the prediction capability of neural networks were both identified

Fuzzy-genetic algorithms and mobile robot navigation among static obstacles

The paper describes a fuzzy genetic algorithm in which a fuzzy logic controller (FLC) is used with genetic algorithms (GAs) to find obstacle-free paths in a number of find-path problems of a mobile robot. In this algorithm, an obstacle-free direction for the movement of a robot locally is created using an FLC and the extent of travel along obstacle-free direction is determined by a GA. Here, the fuzzy logic approach is used to create initial population and GA crossover and mutation operators. This algorithm is found to perform better than the popular steepest descent approach. The proposed algorithm also finds solutions close to the best known tangent graph with A algorithm from the accuracy point of view. However, the proposed algorithm finds a near-optimal solution faster than the tangent graph and A algorithm. Moreover, the proposed approach shows how genetic operators can be modified with problem-specific information to create a search algorithm which is efficient for the particular application

Simultaneous determination of fast and slow dynamics in molecules using extreme CPMG relaxation dispersion experiments.

Molecular dynamics play a significant role in how molecules perform their function. A critical method that provides information on dynamics, at the atomic level, is NMR-based relaxation dispersion (RD) experiments. RD experiments have been utilized for understanding multiple biological processes occurring at micro-to-millisecond time, such as enzyme catalysis, molecular recognition, ligand binding and protein folding. Here, we applied the recently developed high-power RD concept to the Carr–Purcell–Meiboom–Gill sequence (extreme CPMG; E-CPMG) for the simultaneous detection of fast and slow dynamics. Using a fast folding protein, gpW, we have shown that previously inaccessible kinetics can be accessed with the improved precision and efficiency of the measurement by using this experiment

Kinetics of the antibody recognition site in the third IgG-binding domain of protein G.

Protein dynamics occurring on a wide range of timescales play a crucial role in governing protein function. Particularly, motions between the globular rotational correlation time (τc ) and 40 μs (supra-τc window), strongly influence molecular recognition. This supra-τc window was previously hidden, owing to a lack of experimental methods. Recently, we have developed a high-power relaxation dispersion (RD) experiment for measuring kinetics as fast as 4 μs. For the first time, this method, performed under super-cooled conditions, enabled us to detect a global motion in the first β-turn of the third IgG-binding domain of protein G (GB3), which was extrapolated to 371±115 ns at 310 K. Furthermore, the same residues show the plasticity in the model-free residual dipolar coupling (RDC) order parameters and in an ensemble encoding the supra-τc dynamics. This β-turn is involved in antibody binding, exhibiting the potential link of the observed supra-τc motion with molecular recognition