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We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.
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Application of Orthogonal Projectors in Power System Dynamics
This paper explores the application of orthogonal projectors to simplify the analysis of complex power system dynamics. By leveraging modal information, orthogonal projections map high-dimensional dynamics onto a lower-dimensional subspace, enabling projected state variables to effectively depict the system's progression over time. A theoretical framework and its practical application are presented, illustrating how this approach enhances the analysis of high-dimensional dynamics. The method is validated using a two-machine infinite bus system and extended to the New England 39-bus system. Results demonstrate that complex trajectories in an n-dimensional space can be efficiently projected onto a 2D subspace, facilitating the observation of specific dynamics, such as those with an electromechanical nature.
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- Award ID(s):
- 2044629
- PAR ID:
- 10572059
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-2103-5
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- El Paso, TX, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NAPS61145.2024.10741727
