This Volume 4553 of the conference proceedings contains 66 papers. Topics discussed include visualization and optimization techniques, three dimensional computer vision, evaluation and systems, video and sequential im...
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This Volume 4553 of the conference proceedings contains 66 papers. Topics discussed include visualization and optimization techniques, three dimensional computer vision, evaluation and systems, video and sequential image processing and analysis.
High dimensional parameter space optimization is crucial in many applications. The parameters affecting this performance can be both numerical and categorical in their type. The existing techniques of black-box optimi...
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High dimensional parameter space optimization is crucial in many applications. The parameters affecting this performance can be both numerical and categorical in their type. The existing techniques of black-box optimization and visual analytics are good in dealing with numerical parameters but analyzing categorical variables in context of the numerical variables are not well studied. Besides, there are many application scenarios where users seek to explore the impact of the settings of several categorical variables with respect to one dependent numerical variable. For example, a computer systems analyst might want to study how the type of file system or storage device affects system performance. A usual choice is the method of Parallel Sets designed to visualize multivariate categorical variables for visually analyzing the parameter impacts. Also, direct black-box optimizationtechniques like Bayesian optimization and Simulated Annealing have been applied to find the near optimal configuration while optimizing for system performance or associated cost. However, we found that the magnitude of the parameter impacts on the numerical variable cannot be easily observed here. Also, the black-box optimizationtechniques are very slow to optimize storage systems because checking for the performance of a single configuration is a time consuming operation. We studied the existing experiments to optimize system’s performance ranging from Control theory to Deep Learning. We also attempted dimension reduction approaches based on Multiple Correspondence Analysis but found that the SVD-generated 2D layout resulted in a loss of information. We hence propose a novel approach, to create an auto-tuning framework for storage systems optimization combining both direct optimizationtechniques and visual analytics research. While the optimization algorithm will be the core of the system, visual analytics will provide a guideline with the help of an external agent (expert) to provide cruci
In this thesis we describe a number of data visualization and optimization techniques for urban planning. Many of these techniques originate from contributions to the Social Computing Group's "You Are Here&qu...
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In this thesis we describe a number of data visualization and optimization techniques for urban planning. Many of these techniques originate from contributions to the Social Computing Group's "You Are Here" project, which publishes maps intended to be viewed as a blend between art and urban planning tools. In particular these maps and this thesis focus on the topics of education and transportation. Eventually we hope to evolve these maps into social technologies that make it easier for communities to create the change they seek.
The dynamics of microbial communities in wastewater treatment reactors are crucial for enhancing treatment efficiency and promoting sustainability. This study highlights microorganisms' significant role in polluta...
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The dynamics of microbial communities in wastewater treatment reactors are crucial for enhancing treatment efficiency and promoting sustainability. This study highlights microorganisms' significant role in pollutant biodegradation, underpinning operational success in wastewater management. Recent advancements in imaging technologies, including super-resolution microscopy, Raman spectroscopy, and nanoscale imaging, have revolutionized the visualization of microbial cells and biofilms, facilitating real-time monitoring of microbial interactions. These methods offer valuable insights into microbial community dynamics and their adaptive responses to environmental changes. This review evaluates the application of these advanced imaging techniques in wastewater treatment research, pinpointing gaps in real-time microbial monitoring and suggesting strategies for their integration into operational systems. Additionally, it discusses the practical challenges of deploying these technologies in real-world scenarios and outlines future research directions. By leveraging innovative imaging approaches, researchers can develop sustainable and efficient wastewater treatment solutions to address critical environmental challenges.
Compared to classic ray marching-based approaches, Monte Carlo ray tracing for volume visualization can provide faster frame times through progressive rendering, improved image quality, and allows for advanced illumin...
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Compared to classic ray marching-based approaches, Monte Carlo ray tracing for volume visualization can provide faster frame times through progressive rendering, improved image quality, and allows for advanced illumination models more easily. techniques such as the view-dependent optimization of visibility and illumination of important regions, however, have been formulated for ray marching and rely on stepwise sampling along rays, and are thus incompatible with free-flight distance sampling of state-of-the-art Monte Carlo methods. In this paper we derive such a view-dependent optimization for Monte Carlo ray tracing where the visibility to the camera, the illumination and opacity of important regions is optimized for both single and multiple scattering rendering. For this we define a post-interpolative importance function, introduce an efficient data structure to sample, approximate and optimize the integrated extinction along rays, and devise an efficient Monte Carlo estimator for interactive visualization. Our method enables view-dependent visibility optimization with moderate memory overhead and unbiased, progressive Monte Carlo volume visualization. We demonstrate our method for various volume data sets as well as for data-dependent and spatially-dependent importance functions.
Managing the integration of Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) systems is complex, especially during peak hours due to high traffic volumes. While ETC is more efficient, an inappropriate...
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Managing the integration of Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) systems is complex, especially during peak hours due to high traffic volumes. While ETC is more efficient, an inappropriate proportion of ETC lanes can increase congestion, making optimal ETC lane selection crucial. This paper proposes a novel framework combining deep learning and multi-objective optimization to improve toll plaza efficiency. A Gated Recurrent Unit (GRU) model with Optuna hyperparameter tuning predicts average queue lengths for various ETC lane proportions. The predictions guide the identification of the ideal ETC lane configuration to minimize queue lengths and balance traffic flow between MTC and ETC lanes. The framework dynamically adjusts ETC lane proportions based on real-time traffic data to ensure optimal lane allocations during peak hours. A multi-objective optimization function is applied to balance key objectives such as minimizing queue length, reducing queue time, lowering operational costs, and optimizing ETC utilization. Simulations with real-world data from high-traffic toll plazas demonstrate the framework's effectiveness, reducing queue lengths by up to 95.03% during peak hours, decreasing operational costs by 28.72%, and improving overall toll plaza performance. The results are visualized with graphs showing predicted average queue lengths across different ETC lane proportions, aiding stakeholders in understanding the relationship between ETC adoption and congestion. The framework provides insights into operational costs and resource allocation, enhancing financial and operational planning. Validated through extensive simulations with real-time data, this research advances empirical studies with thorough model validation and introduces innovative visualizationtechniques for toll plaza management.
We applied two different flow visualizationtechniques to obtain detailed information on the inside flow of the diaphragm blood pump of our electrohydraulic total artificial heart system to determine the optimum washo...
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We applied two different flow visualizationtechniques to obtain detailed information on the inside flow of the diaphragm blood pump of our electrohydraulic total artificial heart system to determine the optimum washout effect that would result in better antithrombogenicity. Major orifice directions of the inflow and outflow Bjork-Shiley valves of the left blood pump were independently changed to create 17 varied patterns. The character and velocity of the main flow at the diaphragm-housing junction were acquired using a laser light sheet method with polyethylene tracers. Wall shear flow, a major factor governing washout in the blood pump, was estimated by a newly developed paint erosion method. In this method, quantitative evaluation for an index of washout effect was made by calculating the residual ratio of the paint on the blood pump inner surface at 30 sec of pumping. When a single circular flow was consistently observed by the laser light sheet method, the paint residual ratio become low, indicating washout was relatively good. At the lowest paint residual ratio, the center of the circular flow observed by the laser light sheet method was located at the geometric center of the blood chamber. In conclusion, the flow pattern inside the blood pump could be characterized by combined use of these two flow visualizationtechniques, and the significant role of circular flow in better washout was clarified.
In the last three decades, the focus of multi-criteria optimization has been solving problems containing two or three objectives. However, real-world problems generally involve multiple stakeholders and functionalitie...
In the last three decades, the focus of multi-criteria optimization has been solving problems containing two or three objectives. However, real-world problems generally involve multiple stakeholders and functionalities requiring relatively large number of objectives and decision variables to model these sophisticated problems. In the optimization field, multi-objective problems with four or more objectives are called many-objective problems. Although there are a number of highly successful multi-objective algorithms capable of solving complex two- or three-objective problems, the majority of these algorithms experience significant performance deterioration due-to an increase in the number of solutions required for approximating the entire Pareto-front and the loss of selection pressure required to move non-dominated candidate solutions towards the optimal Pareto- front. Moreover, as the number of objectives increases, visualization of the solution set becomes progressively challenging as well as the applicability of quantitative performance metrics capable of measuring the convergence and diversity of solution become computationally too expensive or unreliable. This thesis explores the challenges associated with solving many-objective optimization problems and proposes novel algorithms, performance measures, and visualizationtechniques to mitigate these challenges. Firstly, three multi- and many- objective visualizationtechniques are proposed. These visualizationtechniques are capable of showing the convergence and distribution of solutions on the Pareto-optimal front, the distribution of solutions along each objective, and relationship among decision variables and objective function values. Secondly, two novel performance measures capable of assessing the distribution and spread of solutions along each objective are proposed. Thirdly, a new reference-based hybrid optimization framework is proposed to allow multiple optimization algorithms to work together to tak
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