scholarly journals A Clustering-based Visual Analysis Tool for Genetic Algorithm

2017 ◽  
Author(s):  
Habib Daneshpajouh ◽  
Nordin Zakaria
Keyword(s):  
Genetic Algorithm ◽  
Visual Analysis ◽  
Analysis Tool

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

scholarly journals Composer—Visual Cohort Analysis of Patient Outcomes

2019 ◽  
Vol 10 (02) ◽  
pp. 278-285 ◽  
Author(s):  
Jen Rogers ◽  
Nicholas Spina ◽  
Ashley Neese ◽  
Rachel Hess ◽  
Darrel Brodke ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Outcomes ◽  
Visual Analysis ◽  
Treatment Options ◽  
Cohort Analysis ◽  
Analysis Tool ◽  
Electronic Health Record Data ◽  
Patient Cohort ◽  
Orthopedic Surgeons ◽  
Patient Reported

Objective Visual cohort analysis utilizing electronic health record data has become an important tool in clinical assessment of patient outcomes. In this article, we introduce Composer, a visual analysis tool for orthopedic surgeons to compare changes in physical functions of a patient cohort following various spinal procedures. The goal of our project is to help researchers analyze outcomes of procedures and facilitate informed decision-making about treatment options between patient and clinician. Methods In collaboration with orthopedic surgeons and researchers, we defined domain-specific user requirements to inform the design. We developed the tool in an iterative process with our collaborators to develop and refine functionality. With Composer, analysts can dynamically define a patient cohort using demographic information, clinical parameters, and events in patient medical histories and then analyze patient-reported outcome scores for the cohort over time, as well as compare it to other cohorts. Using Composer's current iteration, we provide a usage scenario for use of the tool in a clinical setting. Conclusion We have developed a prototype cohort analysis tool to help clinicians assess patient treatment options by analyzing prior cases with similar characteristics. Although Composer was designed using patient data specific to orthopedic research, we believe the tool is generalizable to other healthcare domains. A long-term goal for Composer is to develop the application into a shared decision-making tool that allows translation of comparison and analysis from a clinician-facing interface into visual representations to communicate treatment options to patients.

scholarly journals miRTarVis: an interactive visual analysis tool for microRNA-mRNA expression profile data

2015 ◽  
Vol 9 (Suppl 6) ◽  
pp. S2 ◽  
Author(s):  
Daekyoung Jung ◽  
Bohyoung Kim ◽  
Robert J Freishtat ◽  
Mamta Giri ◽  
Eric Hoffman ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Expression Profile ◽  
Visual Analysis ◽  
Analysis Tool ◽  
Profile Data ◽  
Mrna Expression Profile ◽  
Interactive Visual Analysis

Kinneret watershed analysis tool: a cell-based decision tree model for watershed flowand pollutants predictions

2006 ◽  
Vol 53 (10) ◽  
pp. 29-35 ◽  
Author(s):  
A. Preis ◽  
A. Tubaltzev ◽  
A. Ostfeld
Keyword(s):  
Genetic Algorithm ◽  
Lake Kinneret ◽  
Analysis Tool ◽  
Rainfall Time Series ◽  
Tree Model ◽  
Watershed Analysis ◽  
Average Rainfall ◽  
A Cell ◽  
Surface Water Resource ◽  
Series Intensity

This paper presents the methodology and application underlying the Kinneret Watershed Analysis Tool (KWAT), developed for flow and contaminant predictions for Lake Kinneret (the Sea of Galilee) watershed located in northern Israel. Lake Kinneret watershed is about 2,730 km2 (2,070 in Israel, the rest in Lebanon), inhabited by about 200,000 people organized in 25 municipalities, and three cities (the Israeli part). The model aims to predict flow and contaminant transports within the watershed, down to its outlet – Lake Kinneret, the most important surface water resource in Israel. The model is comprised of two sections: quantity and quality. The objective of the quantity section is to tune the values of a vector of coefficients α that multiply the average rainfall time series intensity I(t) (the input) imposed on given sub-sets (i.e., cells) of the basin so as to calibrate their outlet flows Q(t); the quality section then uses these optimal flows Q(t) and the effective optimal rainfall intensities to adjust the values of a vector of coefficients β so as to calibrate the sub-watersheds outlet concentrations C(t). The model uses decision trees coupled with a genetic algorithm for optimally tuning the KWAT coefficients for each of the watershed cells, which taken together comprise the flow and contamination amounts measured at the watershed outlet.

TOPICVIEW: VISUAL ANALYSIS OF TOPIC MODELS AND THEIR IMPACT ON DOCUMENT CLUSTERING

2013 ◽  
Vol 22 (05) ◽  
pp. 1360008 ◽  
Author(s):  
PATRICIA J. CROSSNO ◽  
ANDREW T. WILSON ◽  
TIMOTHY M. SHEAD ◽  
WARREN L. DAVIS ◽  
DANIEL M. DUNLAVY
Keyword(s):  
Visual Analytics ◽  
Semantic Analysis ◽  
Visual Analysis ◽  
Document Clustering ◽  
Topic Models ◽  
Analysis Tool ◽  
Model Assessment ◽  
Text Corpora ◽  
The Impact ◽  
Document Relationships

We present a new approach for analyzing topic models using visual analytics. We have developed TopicView, an application for visually comparing and exploring multiple models of text corpora, as a prototype for this type of analysis tool. TopicView uses multiple linked views to visually analyze conceptual and topical content, document relationships identified by models, and the impact of models on the results of document clustering. As case studies, we examine models created using two standard approaches: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Conceptual content is compared through the combination of (i) a bipartite graph matching LSA concepts with LDA topics based on the cosine similarities of model factors and (ii) a table containing the terms for each LSA concept and LDA topic listed in decreasing order of importance. Document relationships are examined through the combination of (i) side-by-side document similarity graphs, (ii) a table listing the weights for each document's contribution to each concept/topic, and (iii) a full text reader for documents selected in either of the graphs or the table. The impact of LSA and LDA models on document clustering applications is explored through similar means, using proximities between documents and cluster exemplars for graph layout edge weighting and table entries. We demonstrate the utility of TopicView's visual approach to model assessment by comparing LSA and LDA models of several example corpora.

scholarly journals Reliable Shape Optimization of Structures Subjected to Transient Dynamic Loading Using Genetic Algorithms

2005 ◽  
Vol 12 (6) ◽  
pp. 407-424 ◽  
Author(s):  
Sabyasachi Chand ◽  
Anjan Dutta
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Analysis Tool ◽  
Time Step ◽  
Transient Dynamic ◽  
Boundary Definition ◽  
Automatic Mesh ◽  
Shape Changes

This paper presents a reliable method of solution of two dimensional shape optimization problems subjected to transient dynamic loads using Genetic Algorithms. Boundary curves undergoing shape changes have been represented by B-splines. Automatic mesh generation and adaptive finite element analysis modules are integrated with Genetic algorithm code to carry out the shape optimization. Both space and time discretization errors are evaluated and appropriate finite element mesh and time step values as obtained iteratively are adopted for accurate dynamic response. Two demonstration problems have been solved, which show convergence to the optimal solution with number of generations. The boundary curve undergoing shape optimization shows smooth shape changes. The combinations of automatic mesh generator with proper boundary definition capabilities, analysis tool with error estimation and Genetic algorithm as optimization engine have been observed to behave as a satisfactory shape optimization environment to deal with real engineering problems.

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