scholarly journals Order reduction of hierarchical interconnected dynamical systems

2018 ◽  
Vol 16 ◽  
pp. 89-97
Author(s):  
Michael Popp ◽  
Wolfgang Mathis
Keyword(s):  
Large Scale ◽  
Piecewise Linear ◽  
Hierarchical Structures ◽  
Order Reduction ◽  
System Structure ◽  
High Complexity ◽  
Interconnected System ◽  
Nonlinear Dynamical ◽  
Modern Engineering ◽  
Large Systems

Abstract. The simulation of large scale nonlinear dynamical interconnected systems, as they arise in all modern engineering disciplines, is a usual task. Due to the high complexity of the considered systems, the principle of thinking in hierarchical structures is essential and common among engineers. Therefore, this contribution proposes an approach for the numerical simulation of large systems, which keeps the hierarchical system structure alive during the entire simulation process while simultaneously exploiting it for order reduction purposes. This is accomplished by embedding the trajectory piecewise linear order reduction scheme in a modified variant of the component connection modeling for building interconnected system structures. The application of this concept is demonstrated by means of a widely used benchmark example and a modified variant of it.

Embedded order reduction of hierarchical systems within an mCCM framework

2018 ◽  
Vol 37 (4) ◽  
pp. 1535-1544
Author(s):  
Michael Popp ◽  
Wolfgang Mathis
Keyword(s):  
Dynamical Systems ◽  
Large Scale ◽  
Piecewise Linear ◽  
Hierarchical Structures ◽  
Order Reduction ◽  
Hierarchical Level ◽  
Hierarchical Systems ◽  
Content Type ◽  
Reduction Methods ◽  
Interconnected Dynamical Systems

Purpose The purpose of this paper is to present the embedding of linear and nonlinear order reduction methods in a theoretical framework for handling hierarchically interconnected dynamical systems. Design/methodology/approach Based on the component connection modeling (CCM), a modified framework called mCCM for describing interconnected dynamic systems especially with hierarchical structures is introduced and used for order reduction purposes. The balanced truncation method for linear systems and the trajectory piecewise linear reduction scheme are used for the order reduction of systems described within the mCCM framework. Findings It is shown that order reduction methods can be embedded into the context of interconnected dynamical systems with the benefit of having a further degree of freedom in form of the hierarchical level, on which the order reduction is performed. Originality/value The aspect of hierarchy within system descriptions is exploited for order reduction purposes. This distinguishes the presented approach from common methods, which already start with single large-scale systems without explicitly considering hierarchical structures.

Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits

2013 ◽  
Vol 14 (3) ◽  
pp. 639-663 ◽  
Author(s):  
Xiaoda Pan ◽  
Hengliang Zhu ◽  
Fan Yang ◽  
Xuan Zeng
Keyword(s):  
State Space ◽  
Model Order Reduction ◽  
Large Scale ◽  
Piecewise Linear ◽  
Order Reduction ◽  
Nonlinear Circuits ◽  
Model Order ◽  
Reduced Order ◽  
Dimensional State Space ◽  
Model Size

AbstractDespite the efficiency of trajectory piecewise-linear (TPWL) model order reduction (MOR) for nonlinear circuits, it needs large amount of expansion points for large-scale nonlinear circuits. This will inevitably increase the model size as well as the simulation time of the resulting reduced macromodels. In this paper, subspace TPWL-MOR approach is developed for the model order reduction of nonlinear circuits. By breaking the high-dimensional state space into several subspaces with much lower dimensions, the subspace TPWL-MOR has very promising advantages of reducing the number of expansion points as well as increasing the effective region of the reduced-order model in the state space. As a result, the model size and the accuracy of the TWPL model can be greatly improved. The numerical results have shown dramatic reduction in the model size as well as the improvement in accuracy by using the subspace TPWL-MOR compared with the conventional TPWL-MOR approach.

scholarly journals On the Root Causes of the Fukushima Daiichi Disaster from the Perspective of High Complexity and Tight Coupling in Large-Scale Systems

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 414
Author(s):  
Atsuo Murata ◽  
Waldemar Karwowski
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Large Scale ◽  
High Complexity ◽  
Tight Coupling ◽  
Large Scale Systems ◽  
Safety Issues ◽  
Loss Of Coolant ◽  
Station Blackout ◽  
Fukushima Daiichi

This study explores the root causes of the Fukushima Daiichi disaster and discusses how the complexity and tight coupling in large-scale systems should be reduced under emergencies such as station blackout (SBO) to prevent future disasters. First, on the basis of a summary of the published literature on the Fukushima Daiichi disaster, we found that the direct causes (i.e., malfunctions and problems) included overlooking the loss of coolant and the nuclear reactor’s failure to cool down. Second, we verified that two characteristics proposed in “normal accident” theory—high complexity and tight coupling—underlay each of the direct causes. These two characteristics were found to have made emergency management more challenging. We discuss how such disasters in large-scale systems with high complexity and tight coupling could be prevented through an organizational and managerial approach that can remove asymmetry of authority and information and foster a climate of openly discussing critical safety issues in nuclear power plants.

Neural system-enriched gene expression: relationship to biological pathways and neurological diseases

2004 ◽  
Vol 18 (2) ◽  
pp. 167-183 ◽  
Author(s):  
Jianhua Zhang ◽  
Amy Moseley ◽  
Anil G. Jegga ◽  
Ashima Gupta ◽  
David P. Witte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Intracellular Signaling ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene List ◽  
Structure And Function ◽  
System Structure ◽  
Psychiatric Disease ◽  
And Function

To understand the commitment of the genome to nervous system differentiation and function, we sought to compare nervous system gene expression to that of a wide variety of other tissues by gene expression database construction and mining. Gene expression profiles of 10 different adult nervous tissues were compared with that of 72 other tissues. Using ANOVA, we identified 1,361 genes whose expression was higher in the nervous system than other organs and, separately, 600 genes whose expression was at least threefold higher in one or more regions of the nervous system compared with their median expression across all organs. Of the 600 genes, 381 overlapped with the 1,361-gene list. Limited in situ gene expression analysis confirmed that identified genes did represent nervous system-enriched gene expression, and we therefore sought to evaluate the validity and significance of these top-ranked nervous system genes using known gene literature and gene ontology categorization criteria. Diverse functional categories were present in the 381 genes, including genes involved in intracellular signaling, cytoskeleton structure and function, enzymes, RNA metabolism and transcription, membrane proteins, as well as cell differentiation, death, proliferation, and division. We searched existing public sites and identified 110 known genes related to mental retardation, neurological disease, and neurodegeneration. Twenty-one of the 381 genes were within the 110-gene list, compared with a random expectation of 5. This suggests that the 381 genes provide a candidate set for further analyses in neurological and psychiatric disease studies and that as a field, we are as yet, far from a large-scale understanding of the genes that are critical for nervous system structure and function. Together, our data indicate the power of profiling an individual biologic system in a multisystem context to gain insight into the genomic basis of its structure and function.

scholarly journals Order Reduction based on Coulomb’s and Franklin’s laws Algorithm

2021 ◽  
Author(s):  
Ram Kumar ◽  
Afzal Sikander
Keyword(s):  
Large Scale ◽  
Linear Time ◽  
Order Reduction ◽  
Absolute Error ◽  
Search Space ◽  
Engineering Problem ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Linear Time Invariant

Abstract The Coulomb and Franklin laws (CFL) algorithm is used to construct a lower order model of higher-order continuous time linear time-invariant (LTI) systems in this study. CFL is quite easy to implement in obtaining reduced order model of large scale system in control engineering problem as it employs the combined effect of Coulomb’s and Franklin’s laws to find the best values in search space. The unknown coefficients are obtained using the CFLA methodology, which minimises the integral square error (ISE) between the original and proposed ROMs. To achieve the reduced order model, five practical systems of different orders are considered. Finally, multiple performance indicators such as the ISE, integral of absolute error (IAE), and integral of time multiplied by absolute error were calculated to determine the efficacy of the proposed methodology. The simulation results were compared to previously published well-known research.

scholarly journals Trajectory-User Linking via Variational AutoEncoder

2018 ◽  
Author(s):  
Fan Zhou ◽  
Qiang Gao ◽  
Goce Trajcevski ◽  
Kunpeng Zhang ◽  
Ting Zhong ◽  
...  
Keyword(s):  
Latent Variables ◽  
Large Scale ◽  
Human Mobility ◽  
Hierarchical Structures ◽  
High Dimensional ◽  
Generation Process ◽  
Mobility Patterns ◽  
Variational Autoencoder ◽  
Hidden States ◽  
Structural Semantics

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification. Existing works on mining mobility patterns often model trajectories using Markov Chains (MC) or recurrent neural networks (RNN) -- either assuming independence between non-adjacent locations or following a shallow generation process. However, most of them ignore the fact that human trajectories are often sparse, high-dimensional and may contain embedded hierarchical structures. We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic latent variables that span hidden states in RNN. TULVAE alleviates the data sparsity problem by leveraging large-scale unlabeled data and represents the hierarchical and structural semantics of trajectories with high-dimensional latent variables. Our experiments demonstrate that TULVAE improves efficiency and linking performance in real GTSM datasets, in comparison to existing methods.

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