No description available.

Minimax D-optimal designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible misspecification of the error variance in the model. We propose a flexible assumption for the error variance and use a minimax approach to define robust designs. As usual it is hard to find robust designs analytically, since the associated design problem is not a convex optimization problem. However, the minimax D-optimal design problem has an objective function as a difference of two convex functions. An effective algorithm is developed to compute minimax D-optimal designs under the least squares estimator and generalized least squares estimator. The algorithm can be applied to construct minimax D-optimal designs for any linear or nonlinear regression model with heteroscedastic errors. In addition, several theoretical results are obtained for the minimax D-optimal designs. / Graduate

Understanding the choice behavior of customers is crucial for effective service management and marketing in the hospitality industry. The first purpose of this dissertation is to examine the differential effects that cognitive, affective, and sensory attributes have on consumer hotel choice. The second purpose is to examine the moderating effects of consumer choice context on the relationship between the cognitive, affective, and sensory attributes and hotel choice. To achieve these two purposes, this dissertation includes the design of a choice experiment to examine how cognitive, affective, and sensory attributes predict consumer hotel choice using multinomial logit (MNL) and random parameter (or mixed) logit (RPL) models. For choice experiments, the main objectives are to determine the choice attributes and attribute levels to be used for the choice modeling and to create an optimal choice design. I used a Bayesian D-optimal design for the choice experiment, which I assess from the DOE (design of experiment) procedure outlined in JMP 8.0. The primary analysis associated with discrete choice analysis is the log-likelihood ratio (LR) test and the estimation of the parameters (known as part-worth utilities), using LIMDEP 9.0. The results showed that the addition of affective and sensory attributes to the choice model better explained hotel choice compared to the model with only cognitive attributes. The second purpose is to examine the moderating effects of choice context on the relationship between cognitive, affective, and sensory attributes and hotel choice. Using a stated choice model, respondents were randomly divided into two different groups and asked to evaluate their preference for two differently manipulated choice sets. For this purpose, it is necessary to include interaction effects in the choice model. This study identified the differences among choice criteria based on two different contexts. Among eight interaction effects, four interaction effects with the contexts -- price, comfortable, room quality, and atmosphere -- were statistically significant on hotel choice. The findings provide hotel managers with important insights and implications in terms of target segmentation, product development, and marketing communication strategy. / Ph. D.

abstract: Optimal design theory provides a general framework for the construction of experimental designs for categorical responses. For a binary response, where the possible result is one of two outcomes, the logistic regression model is widely used to relate a set of experimental factors with the probability of a positive (or negative) outcome. This research investigates and proposes alternative designs to alleviate the problem of separation in small-sample D-optimal designs for the logistic regression model. Separation causes the non-existence of maximum likelihood parameter estimates and presents a serious problem for model fitting purposes. First, it is shown that exact, multi-factor D-optimal designs for the logistic regression model can be susceptible to separation. Several logistic regression models are specified, and exact D-optimal designs of fixed sizes are constructed for each model. Sets of simulated response data are generated to estimate the probability of separation in each design. This study proves through simulation that small-sample D-optimal designs are prone to separation and that separation risk is dependent on the specified model. Additionally, it is demonstrated that exact designs of equal size constructed for the same models may have significantly different chances of encountering separation. The second portion of this research establishes an effective strategy for augmentation, where additional design runs are judiciously added to eliminate separation that has occurred in an initial design. A simulation study is used to demonstrate that augmenting runs in regions of maximum prediction variance (MPV), where the predicted probability of either response category is 50%, most reliably eliminates separation. However, it is also shown that MPV augmentation tends to yield augmented designs with lower D-efficiencies. The final portion of this research proposes a novel compound optimality criterion, DMP, that is used to construct locally optimal and robust compromise designs. A two-phase coordinate exchange algorithm is implemented to construct exact locally DMP-optimal designs. To address design dependence issues, a maximin strategy is proposed for designating a robust DMP-optimal design. A case study demonstrates that the maximin DMP-optimal design maintains comparable D-efficiencies to a corresponding Bayesian D-optimal design while offering significantly improved separation performance. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2019

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19310号 / 工博第4107号 / 新制||工||1633(附属図書館) / 32312 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 山川 宏, 教授 松尾 哲司, 准教授 中村 武恒 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

No description available.

No description available.

Nowadays, a high penetration level of distributed energy resources (DERs), such as renewables, energy storage, and electric vehicles, are integrated into modern electric power grids, especially power distribution sections, through inverter-based interfaces. Depending on the interfacing technologies and capacities of different DERs, the power distribution networks with inverter-based DERs feature different characteristics, which motivates this dissertation to investigate the modeling, design, and control of heterogeneous inverter-based power distribution networks. First, an example of a DER power distribution network, a PV system, is studied and an optimal design framework for PV systems is proposed considering two objectives, levelized cost of energy (LCOE) and power density (PD). Second, to further improve the performance of the inverter-based distribution networks, the harmonic characteristics of a generic grid-interactive inverter is investigated. A holistic mathematical harmonic state space (M-HSS) model of a grid-interactive inverter is derived to calculate each order of harmonics of grid-connected current. Moreover, to further reduce the computation burden caused by repetitive usage of the mathematical HSS model during the optimal design process, a data-driven HSS (D-HSS) modeling method is proposed by incorporating the data-driven techniques into the aforementioned M-HSS modeling. Based on the M- and D-HSS models, an effective optimal design framework is proposed to determine the closed-loop inverter system parameters. Furthermore, due to the increasing deployment of power electronic devices and nonlinear loads, power grids in the distribution network typically present certain degrees of low and/or high order harmonics. Thus, a harmonic compensation control (HCC) scheme is proposed to ensure that the inverter-based distribution network could provide high-quality grid current injection under distorted grid voltage conditions. Additionally, an energy-stored quasi-Z source converter (qZSC) based interlink converter is proposed for hybrid AC/DC microgrids in the distribution networks. The proposed system not only interlinks both AC and DC sub-microgrids but also incorporates energy storage. The operating principle, operating states as well as control schemes are presented in detail. Finally, another DER power distribution network, a medium voltage DC (MVDC) distribution network, is investigated in the study. First, the dissertation proposes an effective fault management scheme for MVDC networks, which includes a virtual-impedance-based fault current limiter (VI-FCL) on the DC side and a positive-negative-sequence (PNS) control scheme on the AC side. Finally, another DER power distribution network, a medium voltage DC (MVDC) distribution network, is investigated in the study. First, the dissertation proposes an effective fault management scheme for MVDC networks, which includes a virtualimpedance-based fault current limiter (VI-FCL) on the DC side and a positive-negativesequence (PNS) control scheme on the AC side. Then, a detailed 2ω mathematical model of the MVDC network under unbalanced AC voltage conditions is derived to investigate how the 2ω ripple propagates across the network and the corresponding control scheme is investigated to mitigate the 2ω ripple. / Electrical and Computer Engineering

No / As the COVID-19 pandemic unfolds, manually enhanced ad-hoc solutions have helped the physical space designers and decision makers to cope with the dynamic nature of space planning. Due to the unpredictable nature by which the pandemic is unfolding, the standard operating procedures also change, and the protocols for physical interaction require continuous reconsideration. Consequently, the development of an appropriate technological solution to address the current challenge of reconfiguring common physical environments with prescribed physical distancing measures is much needed. To do this, we propose a design optimization methodology which takes the dimensions, as well as the constraints and other necessary requirements of a given physical space to yield optimal redesign solutions on the go. The methodology we propose here utilizes the solution to the well-known mathematical circle packing problem, which we define as a constrained mathematical optimization problem. The resulting optimization problem is solved subject to a given set of parameters and constraints – corresponding to the requirements on the social distancing criteria between people and the imposed constraints on the physical spaces such as the position of doors, windows, walkways and the variables related to the indoor airflow pattern. Thus, given the dimensions of a physical space and other essential requirements, the solution resulting from the automated optimization algorithm can suggest an optimal set of redesign solutions from which a user can pick the most feasible option. We demonstrate our automated optimal design methodology by way of a number of practical examples, and we discuss how this framework can be further taken forward as a design platform that can be implemented practically. / University of Bradford's COVID-19 Response Fund, the Spanish Ministry of Science, Innovation, and Universities (Computer Science National Program) under grant #TIN2017-89275-Rof the Agencia Estatal de Investigacion and European Funds (AEI/FEDER, UE)

Sigmoid curves have found broad applicability in biological sciences and biopharmaceutical research during the last decades. A well planned experiment design is essential to accurately estimate the parameters of the model. In contrast to a large literature and extensive results on optimal designs for linear models, research on the design for nonlinear, including sigmoid curve, models has not kept pace. Furthermore, most of the work in the optimal design literature for nonlinear models concerns the characterization of minimally supported designs. These minimal, optimal designs are frequently criticized for their inability to check goodness of fit, as there are no additional degrees of freedom for the testing. This design issue can be a serious problem, since checking the model adequacy is of particular importance when the model is selected without complete certainty. To assess for lack of fit, we must add at least one extra distinct design point to the experiment. The goal of this dissertation is to identify optimal or highly efficient designs capable of checking the fit for sigmoid curve models. In this dissertation, we consider some commonly used sigmoid curves, including logistic, probit and Gompertz models with two, three, or four parameters. We use D-optimality as our design criterion. We first consider adding one extra point to the design, and consider five alternative designs and discuss their suitability to test for lack of fit. Then we extend the results to include one more additional point to better understand the compromise among the need of detecting lack of fit, maintaining high efficiency and the practical convenience for the practitioners. We then focus on the two-parameter Gompertz model, which is widely used in fitting growth curves yet less studied in literature, and explore three-point designs for testing lack of fit under various error variance structures. One reason that nonlinear design problems are so challenging is that, with nonlinear models, information matrices and optimal designs depend on the unknown model parameters. We propose a strategy to bypass the obstacle of parameter dependence for the theoretical derivation. This dissertation also successfully characterizes many commonly studied sigmoid curves in a generalized way by imposing unified parameterization conditions, which can be generalized and applied in the studies of other sigmoid curves. We also discuss Gompertz model with different error structures in finding an extra point for testing lack of fit. / Statistics