Parameric Decomposition for Evaluating Metamerism

In those industries in which materials are colored to close specifications, a means of evaluating the degree of metamerism of colored objects is of considerable importance. Based on Wyszecki\u27s hypothesis and its application to quantifying metamerism as described by Fairman, parameric decomposition is a technique to adjust one spectrum of a parameric match in order to achieve a perfect (metameric) match under a specific illumination and observer condition. This method can be viewed as batch correction using three colorants where the color-mixing model is linear in reflectance. The research in this thesis presented these methods using the basis functions from the CIE color-matching functions (CMFs) as well as alternative basis functions derived from dimensionality reduction techniques such as principal component analysis (PCA) and independent component analysis (ICA) for a pre-defined DuPont spectral dataset and Munsell dataset. 1,152 parameric pairs surrounding 24 color centers were synthesized using an automotive finish paint system and two-constant Kubelka-Munk turbid-media theory. Each parameric pair was corrected to a metameric pair using these various methods. The corrected spectra were compared with the formulated spectra using Kubelka-Munk theory to evaluate the parameric decomposition accuracy in terms of special and general metameric indices. The results showed that the estimated metameric indices from the CMFs-based process primaries presented relatively poor correlation to those from Kubelka-Munk theory. The process primaries from ICA for the Munsell IV dataset showed almost indentical performance in estimation of metameric indices to the process primaries from the PCA for Munsell dataset as well as those from ICA for the DuPont dataset. These three sets of process primaries showed slightly better performance in estimation of metameric indices than the process primaries from PCA for the DuPont dataset

Developments in Estimation and Control for Cloud-Enabled Automotive Vehicles.

Cloud computing is revolutionizing access to distributed information and computing resources that can facilitate future data and computation intensive vehicular control functions and improve vehicle driving comfort and safety. This dissertation investigates several potential Vehicle-to-Cloud-to-Vehicle (V2C2V) applications that can enhance vehicle control and enable additional functionalities by integrating onboard and cloud resources. Firstly, this thesis demonstrates that onboard vehicle sensors can be used to sense road profiles and detect anomalies. This information can be shared with other vehicles and transportation authorities within a V2C2V framework. The response of hitting a pothole is characterized by a multi-phase dynamic model which is validated by comparing simulation results with a higher-fidelity commercial modeling package. A novel framework of simultaneous road profile estimation and anomaly detection is developed by combining a jump diffusion process (JDP)-based estimator and a multi-input observer. The performance of this scheme is evaluated in an experimental vehicle. In addition, a new clustering algorithm is developed to compress anomaly information by processing anomaly report streams. Secondly, a cloud-aided semi-active suspension control problem is studied demonstrating for the first time that road profile information and noise statistics from the cloud can be used to enhance suspension control. The problem of selecting an optimal damping mode from a finite set of damping modes is considered and the best mode is selected based on performance prediction on the cloud. Finally, a cloud-aided multi-metric route planner is investigated in which safety and comfort metrics augment traditional planning metrics such as time, distance, and fuel economy. The safety metric is developed by processing a comprehensive road and crash database while the comfort metric integrates road roughness and anomalies. These metrics and a planning algorithm can be implemented on the cloud to realize the multi-metric route planning. Real-world case studies are presented. The main contribution of this part of the dissertation is in demonstrating the feasibility and benefits of enhancing the existing route planning algorithms with safety and comfort metrics.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120710/1/zhaojli_1.pd

Optimal Power Flow in Stand-alone DC Microgrids

Direct-current microgrids (DC-MGs) can operate in either grid-connected or stand-alone mode. In particular, stand-alone DC-MG has many distinct applications. However, the optimal power flow problem of a stand-alone DC-MG is inherently non-convex. In this paper, the optimal power flow (OPF) problem of DC-MG is investigated considering convex relaxation based on second-order cone programming (SOCP). Mild assumptions are proposed to guarantee the exactness of relaxation, which only require uniform nodal voltage upper bounds and positive network loss. Furthermore, it is revealed that the exactness of SOCP relaxation of DC-MGs does not rely on either topology or operating mode of DC-MGs, and an optimal solution must be unique if it exists. If line constraints are considered, the exactness of SOCP relaxation may not hold. In this regard, two heuristic methods are proposed to give approximate solutions. Simulations are conducted to confirm the theoretic results

Cross-modal Generative Model for Visual-Guided Binaural Stereo Generation

Binaural stereo audio is recorded by imitating the way the human ear receives sound, which provides people with an immersive listening experience. Existing approaches leverage autoencoders and directly exploit visual spatial information to synthesize binaural stereo, resulting in a limited representation of visual guidance. For the first time, we propose a visually guided generative adversarial approach for generating binaural stereo audio from mono audio. Specifically, we develop a Stereo Audio Generation Model (SAGM), which utilizes shared spatio-temporal visual information to guide the generator and the discriminator to work separately. The shared visual information is updated alternately in the generative adversarial stage, allowing the generator and discriminator to deliver their respective guided knowledge while visually sharing. The proposed method learns bidirectional complementary visual information, which facilitates the expression of visual guidance in generation. In addition, spatial perception is a crucial attribute of binaural stereo audio, and thus the evaluation of stereo spatial perception is essential. However, previous metrics failed to measure the spatial perception of audio. To this end, a metric to measure the spatial perception of audio is proposed for the first time. The proposed metric is capable of measuring the magnitude and direction of spatial perception in the temporal dimension. Further, considering its function, it is feasible to utilize it instead of demanding user studies to some extent. The proposed method achieves state-of-the-art performance on 2 datasets and 5 evaluation metrics. Qualitative experiments and user studies demonstrate that the method generates space-realistic stereo audio

Dimension Reduction for Efficient Data-Enabled Predictive Control

The recent data-enabled predictive control (DeePC) paradigm directly utilizes offline input/output data from an unknown system to predict its future trajectory and compute optimal control inputs online. In this scheme, the pre-collected input/output data needs to be sufficiently rich to represent the system behavior. This generally leads to an excessive amount of offline data, which consequently results in a high-dimension optimization problem in online predictive control. In this paper, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in DeePC. Specifically, in the case of linear time-invariant systems, the excessive input/output measurements can be rearranged into a smaller data library for the non-parametric representation of system behavior. Based on this observation, we use an SVD-based strategy to pre-process the offline data that achieves dimension reduction in DeePC. Numerical experiments confirm that the proposed method significantly enhances the computation efficiency without compromising the control performance.Comment: 9 pages, 4 figure

Finite Element Analysis and Lightweight Optimization Design on Main Frame Structure of Large Electrostatic Precipitator

The geometric modeling and finite element modeling of the whole structure of an electrostatic precipitator and its main components consisting of top beam, column, bottom beam, and bracket were finished. The strength calculation was completed. As a result, the design of the whole structure of the electrostatic precipitator and the main components were reasonable, the structure was in a balance state, its working condition was safe and reliable, its stress variation was even, and the stress distribution was regular. The maximum von Mises stress of the whole structure is 20.14 MPa. The safety factor was large, resulting in a waste of material. An optimization mathematical model is established. Using the ANSYS first-order method, the dimension parameters of the main frame structure of the electrostatic precipitator were optimized. After optimization, more reasonable structural design parameters were obtained. The model weight is 72,344.11 kg, the optimal weight is 49,239.35 kg, and the revised weight is 53,645.68 kg. Compared with the model weight, the optimal weight decreased by 23,104.76 kg and the objective function decreased by 31.94%, while the revised weight decreased by 18,698.43 kg and the objective function decreased by 25.84%