Optimal Design of Experiments for Dual-Response Systems

abstract: The majority of research in experimental design has, to date, been focused on designs when there is only one type of response variable under consideration. In a decision-making process, however, relying on only one objective or criterion can lead to oversimplified, sub-optimal decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical during the decision-making process in order to balance the tradeoffs of all potential solutions. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of interest does not have a clear answer, particularly when the response variables have different distributions. Responses with different distributions have different requirements of the design. Computer-generated optimal designs are popular design choices for less standard scenarios where classical designs are not ideal. This work presents a new approach to experimental designs for dual-response systems. The normal, binomial, and Poisson distributions are considered for the potential responses. Using the D-criterion for the linear model and the Bayesian D-criterion for the nonlinear models, a weighted criterion is implemented in a coordinate-exchange algorithm. The designs are evaluated and compared across different weights. The sensitivity of the designs to the priors supplied in the Bayesian D-criterion is explored in the third chapter of this work. The final section of this work presents a method for a decision-making process involving multiple objectives. There are situations where a decision-maker is interested in several optimal solutions, not just one. These types of decision processes fall into one of two scenarios: 1) wanting to identify the best N solutions to accomplish a goal or specific task, or 2) evaluating a decision based on several primary quantitative objectives along with secondary qualitative priorities. Design of experiment selection often involves the second scenario where the goal is to identify several contending solutions using the primary quantitative objectives, and then use the secondary qualitative objectives to guide the final decision. Layered Pareto Fronts can help identify a richer class of contenders to examine more closely. The method is illustrated with a supersaturated screening design example.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

High Efficiency and Wide Color Gamut Liquid Crystal Displays

Liquid crystal display (LCD) has become ubiquitous and indispensable in our daily life. Recently, it faces strong competition from organic light emitting diode (OLED). In order to maintain a strong leader position, LCD camp has an urgent need to enrich the color performance and reduce the power consumption. This dissertation focuses on solving these two emerging and important challenges. In the first part of the dissertation we investigate the quantum dot (QD) technology to improve the both the color gamut and the light efficiency of LCD. QD emits saturated color and grants LCD the capability to reproduce color vivid images. Moreover, the QD emission spectrum can be custom designed to match to transmission band of color filters. To fully take advantage of QD\u27s unique features, we propose a systematic modelling of the LCD backlight and optimize the QD spectrum to simultaneously maximize the color gamut and light efficiency. Moreover, QD enhanced LCD demonstrates several advantages: excellent ambient contrast, negligible color shift and controllable white point. Besides three primary LCD, We also present a spatiotemporal four-primary QD enhanced LCD. The LCD\u27s color is generated partially from time domain and partially from spatial domain. As a result, this LCD mode offers 1.5× increment in spatial resolution, 2× brightness enhancement, slightly larger color gamut and mitigated LC response requirement (~4ms). It can be employed in the commercial TV to meet the challenging Energy star 6 regulation. Besides conventional LCD, we also extend the QD applications to liquid displays and smart lighting devices. The second part of this dissertation focuses on improving the LCD light efficiency. Conventional LCD system has fairly low light efficiency (4%~7%) since polarizers and color filters absorb 50% and 67% of the incoming light respectively. We propose two approaches to reduce the light loss within polarizers and color filters. The first method is a polarization preserving backlight system. It can be combined with linearly polarized light source to boost the LCD efficiency. Moreover, this polarization preserving backlight offers high polarization efficiency (~77.8%), 2.4× on-axis luminance enhancement, and no need for extra optics films. The second approach is a LCD backlight system with simultaneous color/polarization recycling. We design a novel polarizing color filter with high transmittance ( \u3e 90%), low absorption loss (~3.3%), high extinction ratio (\u3e10,000:1) and large angular tolerance (up to ±50˚). This polarizing color filter can be used in LCD system to introduce the color/polarization recycling and accordingly boost LCD efficiency by ~3 times. These two approaches open new gateway for ultra-low power LCDs. In the final session of this dissertation, we demonstrate a low power and color vivid reflective liquid crystal on silicon (LCOS) display with low viscosity liquid crystal mixture. Compared with commercial LC material, the new LC mixture offers ~4X faster response at 20oC and ~8X faster response at -20°C. This fast response LC material enables the field-sequential-color (FSC) driving for power saving. It also leads to several attractive advantages: submillisecond response time at room temperature, vivid color even at -20oC, high brightness, excellent ambient contrast ratio, and suppressed color breakup. With this material improvement, LCOS display can be promising for the emerging wearable display market

High-dynamic-range Foveated Near-eye Display System

Wearable near-eye display has found widespread applications in education, gaming, entertainment, engineering, military training, and healthcare, just to name a few. However, the visual experience provided by current near-eye displays still falls short to what we can perceive in the real world. Three major challenges remain to be overcome: 1) limited dynamic range in display brightness and contrast, 2) inadequate angular resolution, and 3) vergence-accommodation conflict (VAC) issue. This dissertation is devoted to addressing these three critical issues from both display panel development and optical system design viewpoints. A high-dynamic-range (HDR) display requires both high peak brightness and excellent dark state. In the second and third chapters, two mainstream display technologies, namely liquid crystal display (LCD) and organic light emitting diode (OLED), are investigated to extend their dynamic range. On one hand, LCD can easily boost its peak brightness to over 1000 nits, but it is challenging to lower the dark state to \u3c 0.01 nits. To achieve HDR, we propose to use a mini-LED local dimming backlight. Based on our simulations and subjective experiments, we establish practical guidelines to correlate the device contrast ratio, viewing distance, and required local dimming zone number. On the other hand, self-emissive OLED display exhibits a true dark state, but boosting its peak brightness would unavoidably cause compromised lifetime. We propose a systematic approach to enhance OLED\u27s optical efficiency while keeping indistinguishable angular color shift. These findings will shed new light to guide future HDR display designs. In Chapter four, in order to improve angular resolution, we demonstrate a multi-resolution foveated display system with two display panels and an optical combiner. The first display panel provides wide field of view for peripheral vision, while the second panel offers ultra-high resolution for the central fovea. By an optical minifying system, both 4x and 5x enhanced resolutions are demonstrated. In addition, a Pancharatnam-Berry phase deflector is applied to actively shift the high-resolution region, in order to enable eye-tracking function. The proposed design effectively reduces the pixelation and screen-door effect in near-eye displays. The VAC issue in stereoscopic displays is believed to be the main cause of visual discomfort and fatigue when wearing VR headsets. In Chapter five, we propose a novel polarization-multiplexing approach to achieve multiplane display. A polarization-sensitive Pancharatnam-Berry phase lens and a spatial polarization modulator are employed to simultaneously create two independent focal planes. This method enables generation of two image planes without the need of temporal multiplexing. Therefore, it can effectively reduce the frame rate by one-half. In Chapter six, we briefly summarize our major accomplishments

Global, Multi-Objective Trajectory Optimization With Parametric Spreading

Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented

Designing optimal greenhouse gas observing networks that consider performance and cost

Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH₂FCF₃, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks

Large-Batch, Neural Multi-Objective Bayesian Optimization

Bayesian optimization provides a powerful framework for global optimization of black-box, expensive-to-evaluate functions. However, it has a limited capacity in handling data-intensive problems, especially in multi-objective settings, due to the poor scalability of default Gaussian Process surrogates. We present a novel Bayesian optimization framework specifically tailored to address these limitations. Our method leverages a Bayesian neural networks approach for surrogate modeling. This enables efficient handling of large batches of data, modeling complex problems, and generating the uncertainty of the predictions. In addition, our method incorporates a scalable, uncertainty-aware acquisition strategy based on the well-known, easy-to-deploy NSGA-II. This fully parallelizable strategy promotes efficient exploration of uncharted regions. Our framework allows for effective optimization in data-intensive environments with a minimum number of iterations. We demonstrate the superiority of our method by comparing it with state-of-the-art multi-objective optimizations. We perform our evaluation on two real-world problems - airfoil design and color printing - showcasing the applicability and efficiency of our approach. Code is available at: https://github.com/an-on-ym-ous/lbn\_mob

Architectural optimization results for a network of earth-observing satellite nodes

Earth observation satellite programs are currently facing, for some applications, the need to deliver hourly revisit times, sub-kilometric spatial resolutions and near-real-time data access times. These stringent requirements, combined with the consolidation of small-satellite platforms and novel distributed architecture approaches, are stressing the need to study the design of new, heterogeneous and heavily networked satellite systems that can potentially replace or complement traditional space assets. In this context, this paper presents partial results from ONION, a research project devoted to study distributed satellite systems and their architecting characteristics. A design-oriented framework that allows selecting optimal architectures for a given user needs is presented in this paper. The framework has been used in the study of a strategic use-case and its results are hereby presented. From an initial design space of 5586 unique architectures, the framework has been able to pre-select 28 candidate designs by an exhaustive analysis of their performance and by quantifying their quality attributes. This very exploration of architectures and the characteristics of the solution space, are presented in this paper along with the selected solution and the results of a detailed performance analysis.Postprint (published version

Architectural optimization framework for earth-observing heterogeneous constellations : marine weather forecast case

Earth observation satellite programs are currently facing, for some applications, the need to deliver hourly revisit times, subkilometric spatial resolutions, and near-real-time data access times. These stringent requirements, combined with the consolidation of small-satellite platforms and novel distributed architecture approaches, are stressing the need to study the design of new, heterogeneous, and heavily networked satellite systems that can potentially replace or complement traditional space assets. In this context, this paper presents partial results from ONION, a research project devoted to studying distributed satellite systems and their architecting characteristics. A design-oriented framework that allows selecting optimal architectures for the given user needs is presented in this paper. The framework has been used in the study of a strategic use-case and its results are hereby presented. From an initial design space of 5586 potential architectures, the framework has been able to preselect 28 candidate designs by an exhaustive analysis of their performance and by quantifying their quality attributes. This very exploration of architectures and the characteristics of the solution space are presented in this paper along with the selected solution and the results of a detailed performance analysis.Postprint (author's final draft

Simulation-Based Evaluation and Optimization of the Seismic Performance of Buildings with Passive Energy Dissipation System

Earthquakes are one of the major natural hazards that could directly cause damages to or collapse of buildings, leading to significant economic losses. In this dissertation research, analytical tools and simulation-based optimization framework are developed to improve our understanding of and the ability to design more seismic-resilient structures with passive energy dissipation systems. The main objectives of this dissertation are to (1) investigate the seismic performance of structures with energy dissipation systems and evaluate the effectiveness of damping coefficient dissipation methods using three-dimensional numerical models; (2) develop a simulation-based multi-objective optimization framework to evaluate and optimize the seismic performance of buildings with energy dissipation systems; (3) incorporate and evaluate the influence of soil-structure interaction in the performance-based seismic design of structures. Aiming at these objectives, this dissertation consists of three related studies. In the first study, the seismic performance of structures with energy dissipation systems, specifically fluid viscous dampers (FVD), was investigated using three-dimensional (3D) numerical models. Four different damping coefficient distribution methods for FVD were extended to 3D numerical models. Then, their effectiveness in terms of improving structural seismic performance was evaluated through a series of nonlinear dynamic analysis. The seismic performance of the structure has been significantly improved by applying the FVD, and this significance of the improvement depends on the distribution of damper\u27s damping coefficient within the 3d numerical model. Among the four different damping coefficient distribution methods, the story shear strain energy distribution (SSSED) method was found to be an optimal distribution method that can improve the inter-story drift of the structure while it can also provide the most uniformly distributed inter-story drift. In the second study, a performance-based optimization framework for the structural design was developed that considers multiple conflicting objectives: initial material cost, structural repair cost, and record-to-record variability of ground motions. The developed optimization framework was effective in improving the seismic performance of structures. All obtained optimum designs can dramatically decrease the inter-story drift and peak floor acceleration of the structure. This study also provided a practical approach to select the optimal design variables of the energy dissipation systems. The selected design can achieve the desired performance level of the structure with moderate initial material cost, structural repair cost, and robustness measure. In the third study, the effect of soil-structure interaction was incorporated into the optimization framework developed in the second study. Two scenarios were considered in the analysis: one with a fixed foundation, and the other one with a flexible foundation. In this study, the selection of soil properties was based on site class D. The frame with a flexible foundation was found to have a larger inter-story drift in each floor when compared to the frame with a fixed foundation. The guideline for selecting the best-performance design was developed based on the inter-story drift ratio. The improvement of the inter-story drift (compared to a bare frame without energy dissipation systems) and the uniformity of the inter-story drift, were proposed as two performance indices to evaluate the effectiveness of the selected designs. Finally, based on findings of this dissertation work, recommendations for seismic design of buildings with energy dissipation systems and directions for future research are given