Hierarchical/decomposition techniques for large-scale analogue diagnosis

2011 ◽  
pp. 113-140
Author(s):  
Peter Shepherd
Keyword(s):  
Large Scale ◽  
Hierarchical Decomposition ◽  
Decomposition Techniques

Hierarchical Structural Model of AC/DC Power Systems

2013 ◽  
Vol 313-314 ◽  
pp. 821-827
Author(s):  
Kai Feng Zhang ◽  
Hai Ming Zhou
Keyword(s):  
Power Systems ◽  
Decomposition Method ◽  
Large Scale ◽  
Structural Model ◽  
Modeling Method ◽  
Hierarchical Decomposition ◽  
New Model ◽  
Dc Power ◽  
The Difference ◽  
Dc Power Systems

The hierarchical decomposition and modeling method of large-scale power systems proposed previously is expanded to be suitable for AC/DC power systems in this paper. In the new model of AC/DC systems, DC systems will have the same position as AC systems. The components in AC/DC power systems are classified into three categories, namely conversion components, DC components and AC components. By analyzing the difference between DC interface and AC interface, the unified structural model suitable for any kind of component in AC/DC systems is built. Then, the hierarchical structural model is derived based on the hierarchical decomposition method. The main characteristics of the proposed AC/DC model are the same as that of previous AC model.

Set Based Hierarchical Design: A Constraint Satisfaction Approach

1995 ◽  
Author(s):  
Christian Bliek
Keyword(s):  
Constraint Satisfaction ◽  
Design Automation ◽  
Constraint Satisfaction Problems ◽  
Combinatorial Complexity ◽  
Hierarchical Decomposition ◽  
Hierarchical Design ◽  
Decomposition Techniques ◽  
New Approach ◽  
Candidate Solution ◽  
Literature Reviews

Abstract Many are the literature reviews where constraint satisfaction is rejected as a candidate solution for design automation. Some point to the combinatorial complexity associated with the solution of large constraint satisfaction problems, others claim it is inadequate to handle the uncertainty prominent in engineering design. In this paper we present a new approach in which hierarchical decomposition techniques exploit sensitivity to reduce combinatorial complexity and uncertainty is modeled using conservative enclosures of sets of possible solutions.

Decomposition techniques for the solution of large-scale scheduling problems

AIChE Journal ◽  
1996 ◽  
Vol 42 (12) ◽  
pp. 3373-3387 ◽  
Author(s):  
Matthew H. Bassett ◽  
Joseph F. Pekny ◽  
Gintaras V. Reklaitis
Keyword(s):  
Large Scale ◽  
Scheduling Problems ◽  
Decomposition Techniques

Hierarchical Decomposition and Multidomain Formulation for the Design of Complex Sustainable Systems

2010 ◽  
Vol 132 (9) ◽  
Author(s):  
Anas Alfaris ◽  
Afreen Siddiqi ◽  
Charbel Rizk ◽  
Olivier de Weck ◽  
Davor Svetinovic
Keyword(s):  
Design Parameter ◽  
Large Scale ◽  
Ad Hoc ◽  
Integrated Design ◽  
Hierarchical Decomposition ◽  
Transportation Energy ◽  
Sustainable Systems ◽  
The One ◽  
Parameter Dependency ◽  
And Behavior

Designing a large-scale complex system, such as a city of the future, with a focus on sustainability requires a systematic approach toward integrated design of all subsystems. Domains such as buildings, transportation, energy, and water are all coupled. Designing each one in isolation can lead to suboptimality where sustainability is achieved in one aspect but at the expense of other aspects. Traditional ad hoc allocations of design parameter precedence and dependence cannot be used for cases where new (instead of only mature) architectures are to be explored. A methodology is introduced for addressing design problems of complex sustainable systems that is comprised of, on the one hand, a hierarchical decomposition that includes multilevel abstraction and design parameter identification, and on the other hand, a multidomain formulation, which includes parameter dependency identification, design cycle identification and decision structuring, and scoping. The application of the methodology for the design of a new urban development, Masdar City in Abu Dhabi, with over 220 different form and behavior parameter sets is shown.

Optimal Synthesis of Large Systems Using Decomposition Techniques and Parallel Processing

1988 ◽  
Author(s):  
V. Bhatt ◽  
K. M. Ragsdell
Keyword(s):  
Parallel Processing ◽  
Developmental Stage ◽  
Large Scale ◽  
Optimal Synthesis ◽  
Automated Synthesis ◽  
Decomposition Techniques ◽  
Large Scale Systems ◽  
Complete Vehicle ◽  
Decomposition Scheme ◽  
Large Systems

Abstract Various approaches to multilevel decomposition of large-scale systems are considered in this paper. A linear decomposition scheme based on the Sobieski algorithm is selected as a vehicle for automated synthesis of a complete vehicle configuration in a parallel processing environment. The research is in a developmental stage. Preliminary numerical results are presented for several example problems.

Algorithms for Sparse Nonnegative Tucker Decompositions

2008 ◽  
Vol 20 (8) ◽  
pp. 2112-2131 ◽  
Author(s):  
Morten Mørup ◽  
Lars Kai Hansen ◽  
Sidse M. Arnfred
Keyword(s):  
Sparse Coding ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Two Dimensions ◽  
Decomposition Techniques ◽  
Data Decomposition ◽  
Parafac Model ◽  
Regular Factor ◽  
Tensor Data

There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used. The two most common decompositions for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as generalizations of the regular factor analysis to data of more than two modalities. Nonnegative matrix factorization (NMF), in conjunction with sparse coding, has recently been given much attention due to its part-based and easy interpretable representation. While NMF has been extended to the PARAFAC model, no such attempt has been done to extend NMF to the Tucker model. However, if the tensor data analyzed are nonnegative, it may well be relevant to consider purely additive (i.e., nonnegative) Tucker decompositions). To reduce ambiguities of this type of decomposition, we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse nonnegative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms for Tucker decompositions when the data and interactions can be considered nonnegative. We further illustrate how sparse coding can help identify what model (PARAFAC or Tucker) is more appropriate for the data as well as to select the number of components by turning off excess components. The algorithms for SN-TUCKER can be downloaded from Mørup (2007).

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