1,720,964 research outputs found
System Architecture Optimization: Function-Based Modeling, Optimization Algorithms, and Multidisciplinary Evaluation
Full text linkWhen designing complex systems, choices related to the system architecture, the description of the functions and components of a system, greatly influence to which extent design goals can be achieved. The architecture design space, the set of all possible architectures for a given design problem, can be extremely large due to the combinatorial nature of architectural choices. Additionally, the integration of innovative technologies for future systems requires the application of multidisciplinary, simulation-based evaluation. These two challenges are addressed by System Architecture Optimization (SAO): the combination of numerical optimization algorithms with multidisciplinary, simulation-based evaluation, to explore architecture design spaces without requiring the evaluation of all architectures.
A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.
SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.
Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed
System Architecture Optimization Experiments Dataset
No full text<p>This dataset contains experimental results for testing system architecture optimization algorithms. The results have been generated with <a href="https://github.com/jbussemaker/ArchitectureOptimizationExperiments/tree/hidden-constraints" target="_blank" rel="noopener">https://github.com/jbussemaker/ArchitectureOptimizationExperiments/tree/hidden-constraints</a>.</p>
<p>The results correspond to the following blocks of results:</p>
<ul>
<li>01_sampling_*: hierarchical sampling and correction algorithms</li>
<li>02_hier_*: hierarchical integration strategies</li>
<li>03_hc_07_*: engine architecture optimization application case</li>
</ul>
System architecture optimization : function-based modeling, optimization algorithms, and multidisciplinary evaluation
Full text linkWhen designing complex systems, choices related to the system architecture, the description of the functions and components of a system, greatly influence to which extent design goals can be achieved. The architecture design space, the set of all possible architectures for a given design problem, can be extremely large due to the combinatorial nature of architectural choices. Additionally, the integration of innovative technologies for future systems requires the application of multidisciplinary, simulation-based evaluation. These two challenges are addressed by System Architecture Optimization (SAO): the combination of numerical optimization algorithms with multidisciplinary, simulation-based evaluation, to explore architecture design spaces without requiring the evaluation of all architectures.
A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.
SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.
Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed.AlternativeReviewe
SBArchOpt: Surrogate-Based Architecture Optimization
No full text<ul>
<li>Add SEGOMOE ask-tell interface and pymoo Algorithm</li>
<li>Updated to pymoo 0.6.1</li>
</ul>If you use this software, please cite our article in the Journal of Open Source Software
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
System architecture optimization: an example application to space mission planning
Full text linkSpace mission planning involves coupled architecture decisions that non-trivially influence system-level performance metrics such as weight, power usage, and scientific value. We present an exemplary space mission planning problem involving several mission-level and spacecraft-level choices, and optimizing for the conflicting objectives of system mass and scientific value. The problem is solved using System Architecture Optimization (SAO): a technique where numerical optimization algorithms are used to explore an architecture design space and find a Pareto front of optimal architectures. The architecture design space is modeled using the Architecture Design Space Graph (ADSG) implemented in the ADORE editing and optimization tool. The design space model includes function-component allocation choices, component-level design variables, and system-level objectives and constraints to optimize for. Evaluation code is implemented in Python and linked to the design space model using class factories. The design space is explored using NSGA-II, a multi-objective evolutionary algorithm, resulting in a Pareto front trading-off system mass and total experiment duration
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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