Power system sensitivity analysis for probabilistic small signal stability assessment in a deregulated environment

Deregulations and market practices in power industry have brought great challenges to the system planning area. In particular, they introduce a variety of uncertainties to system planning. New techniques are required to cope with such uncertainties. As a promising approach, probabilistic methods are attracting more and more attentions by system planners. In small signal stability analysis, generation control parameters play an important role in determining the stability margin. The objective of this paper is to investigate power system state matrix sensitivity characteristics with respect to system parameter uncertainties with analytical and numerical approaches and to identify those parameters have great impact on system eigenvalues, therefore, the system stability properties. Those identified parameter variations need to be investigated with priority. The results can be used to help Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) perform planning studies under the open access environment.

Grain refinement of Mg-Al(-Mn) alloys by SiC additions

The addition of SiC particles effectively grain refined a range of Mg-Al alloys. The greatest reductions in grain size were found for the alloys with lower Al contents. The presence of Mg2Si in the microstructure after that SiC addition, and consideration of phase equilibria suggested that the SiC transforms to Al4C3, and this is the actual nucleant. The addition of Mn poisoned the grain refining effect of the SiC addition, probably due to the formation of less potent Al-Mn-carbides. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Effect of manganese on grain refinement of Mg-Al based alloys

Manganese is a grain refiner for high purity Mg-3%Al, Mg-6%Al, Mg-9%Al, and commercial AZ31 (Mg-3%Al-1%Zn) alloys when introduced in the form of an Al-60%Mn master alloy splatter but the use of pure Mn flakes and ALTAB (TM) Mn75 tablets shows no grain refinement. Long time holding of the melt at 730 degrees C leads to an increase in grain size. The mechanism is attributed to the presence of all epsilon-AlMn phase (hexagonal close-packed) in the master alloy splatter. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

Mimicking the action of GroEL in molecular dynamics simulations: Application to the refinement of protein structures

Bacterial chaperonin, GroEL, together with its co-chaperonin, GroES, facilitates the folding of a variety of polypeptides. Experiments suggest that GroEL stimulates protein folding by multiple cycles of binding and release. Misfolded proteins first bind to an exposed hydrophobic surface on GroEL. GroES then encapsulates the substrate and triggers its release into the central cavity of the GroEL/ES complex for folding. In this work, we investigate the possibility to facilitate protein folding in molecular dynamics simulations by mimicking the effects of GroEL/ES namely, repeated binding and release, together with spatial confinement. During the binding stage, the (metastable) partially folded proteins are allowed to attach spontaneously to a hydrophobic surface within the simulation box. This destabilizes the structures, which are then transferred into a spatially confined cavity for folding. The approach has been tested by attempting to refine protein structural models generated using the ROSETTA procedure for ab initio structure prediction. Dramatic improvements in regard to the deviation of protein models from the corresponding experimental structures were observed. The results suggest that the primary effects of the GroEL/ES system can be mimicked in a simple coarse-grained manner and be used to facilitate protein folding in molecular dynamics simulations. Furthermore, the results Sur port the assumption that the spatial confinement in GroEL/ES assists the folding of encapsulated proteins.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.

The effect of grain refinement and cooling rate on the hot tearing of wrought aluminium alloys

Using modifications to the Rappaz-Drezet-Gremaud hot tearing model, and using empirical equations developed for grain size and dendrite arm spacing (DAS) on the addition of grain refiner for a range of cooling rates, the effect of grain refinement and cooling rate on hot tearing susceptibility has been analysed. It was found that grain refinement decreased the grain size and made the grain morphology more globular. Therefore refining the grain size of an equiaxed dendritic grain decreased the hot tearing susceptibility. However, when the alloy was grain refined such that globular grain morphologies where obtained, further grain refinement increased the hot tearing susceptibility. Increasing the cooling decreased the grain size and made the grain morphology more dendritic and therefore increased the likelihood of hot tearing. The effect was particularly strong for equiaxed dendritic grain morphologies; hence grain refinement is increasingly important at high cooling rates to obtain more globular grain morphologies to reduce the hot tearing susceptibility.

Probabilistic world modelling for distributed team planning

This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that tbe action selection mechanism of a member in a robot team cm select am effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probsbilistie view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried ont to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.

Refinement laws for verifying library subroutine adaptation

In component-based software engineering programs are constructed from pre-defined software library modules. However, if the library's subroutines do not exactly match the programmer's requirements, the subroutines' code must be adapted accordingly. For this process to be acceptable in safety or mission-critical applications, where all code must be proven correct, it must be possible to verify the correctness of the adaptations themselves. In this paper we show how refinement theory can be used to model typical adaptation steps and to define the conditions that must be proven to verify that a library subroutine has been adapted correctly.

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.