Browsing by Author "Wang, Yu"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Design, control and energy optimization of a rapid-prototyping hybrid powertrain research platform(2014-01) Wang, YuThis thesis focuses on the architecture design, dynamic modeling and system control of a rapid-prototyping hybrid automotive powertrain research platform and on this basis, conducts a series of research work on the powertrain control and energy/emissions optimization of the hybrid vehicle system. This hybrid powertrain research platform leverages the fast dynamic response of a transient hydrostatic dynamometer to mimic the dynamics of various hybrid power sources and hybrid architectures and therefore, creates an accurate and highly flexible emulation tool for hybrid powertrain operations. This design will greatly speed up the research progress and reduce the economic cost of the study on various hybrid architectures and control methodologies. The design, control and optimization of this research platform include the detailed research achievements in three levels of the proposed system:1) Low-level system (hydrostatic dynamometer) design and control:with regards to the low-level system, the design, modeling, nonlinear control and experimental validation of a transient hydrostatic dynamometer are accomplished, which provides the hardware ingredient for the research platform and ensures the dynamics emulation capability of the system.2) Mid-level system (hybrid powertrain system) design and control:with regards to the mid-level system, the design and experimental investigation of the multivariable hybrid powertrain control within the hybrid powertrain research platform are achieved; on this basis, the systematic integration and coordination of the energy optimization, hybrid powertrain control, hardware-in-the-loop vehicle simulation and hybrid torque emulation are conducted within a closed-loop architecture.3) High-level system (hybrid energy and emission management) control and optimization:the high-level system design consists of the fuel efficiency optimization ("Stochastic Dynamic Programming - Extremum Seeking" hybrid energy management strategy) and transient emission optimization (Two-Mode hybrid energy management strategy), which are not only the advanced studies in the core area of the hybrid powertrain technology development, but also can be considered as the functionality demonstrations of the designed hybrid powertrain research platform.Item Nonparametric Classification Method for Multiple-Choice Items in Cognitive Diagnosis(2023) Wang, Yu; Chiu, Chia-Yi; Köhn, Hans FThe multiple-choice (MC) item format has been widely used in educational assessments across diverse content domains. MC items purportedly allow for collecting richer diagnostic information. The effectiveness and economy of administering MC items may have further contributed to their popularity not just in educational assessment. The MC item format has also been adapted to the cognitive diagnosis (CD) framework. Early approaches simply dichotomized the responses and analyzed them with a CD model for binary responses. Obviously, this strategy cannot exploit the additional diagnostic information provided by MC items. De la Torre’s MC Deterministic Inputs, Noisy “And” Gate (MC-DINA) model was the first for the explicit analysis of items having MC response format. However, as a drawback, the attribute vectors of the distractors are restricted to be nested within the key and each other. The method presented in this study for the CD of DINA items having MC response format does not require such constraints. Another contribution of the proposed method concerns its implementation using a nonparametric classification algorithm (MC-NPC), which predestines it for use especially in small-sample settings like classrooms, where CD is most needed for monitoring instruction and student learning. In contrast, default parametric CD estimation routines that rely on EM- or MCMC-based algorithms cannot guarantee stable and reliable estimates—despite their effectiveness and efficiency when samples are large—due to computational feasibility issues caused by insufficient sample sizes. Results of simulation studies show that the MC-NPC method results in higher correct classification rates than the traditional CD methods for dichotomous data and outperforms the MC-DINA model when the samples are small.Item Three Essays On Political Economy And The Methods(2020-05) Wang, YuThis dissertation consists of three essays regarding political economy and the theoretical discussion of two empirical methods. Chapter 2 (Essay 1) discusses how the introduction of local direct elections, by providing local information, facilitates the fulfillment of the meritocratic selection of local leaders. Using the Bayesian framework, this paper finds that because local residents, the voters, communicate with the local leader candidates of more times than upper officials do, local residents infer each local leader candidate’s virtue or capacity more accurately and precisely. This paper then shows that due to the higher accuracy, the expectation of the competence (a weighted average of virtue and capacity) of the elected local leader is higher than that of the appointed leader; due to the higher precision, the variance of the competence of the elected local leader is lower than that of the appointed leader. Chapter 3 (Essay 2) discusses the lagged IV method, namely using the lagged endogenous explanatory variable as its instrumental variable (IV). This paper starts with a conceptual framework, and then conducts the numerical analysis. It shows that when the lagged IV only violates the independence assumption, the lagged IV estimate is consistent, and has lower bias than the OLS estimate; however, when the lagged IV violates both the independence assumption and the exclusion restriction, the lagged IV estimate is inconsistent, and has much higher bias than the OLS estimate. The simulation results support the numerical analysis. Chapter 4 (Essay 3) discusses the spatially lagged IV method, namely using the spatially lagged endogenous explanatory variable, namely the spatial weighting matrix, as its instrumental variable (IV). This paper introduces the spatially local average treatment effect (SLATE) theorem, which consists of two key properties: the spatial independence assumption and the spatial exclusion restriction. This paper demonstrates that when the spatially lagged IV satisfies the spatial independence assumption and the spatial exclusion restriction, its estimate is unbiased and consistent. Even if the treatment has multiple waves of implementation, the spatially lagged IV is still valid.