USING DEEP GENERATIVE MACHINE LEARNING METHODS TO GENERATE SYNTHETIC POPULATION

Population synthesis is an important area of research aiming at generating synthetic data about households and individuals that would be representative of real large populations. Scholars in different fields have worked on synthetic population generation: statisticians, computers scientists, economists, social scientists, and engineers. In transportation modeling, synthetic agents are a key input for agent-based models, that are gradually replacing zone-based aggregate four steps models. Traditional methods for population synthesis include Iterative Population Fitting (IPF), that weights sample data until marginals for the variables of interest match official statistics (often from CENSUS) at a certain geographical area. Recently, Machine Learning algorithms have been tested and compared to IPF, which suffers from several well-known limitations. In this M.S. thesis, advanced deep generative machine learning methods are applied to generate synthetic populations, including CTGAN and TVAE. CTGAN is an advanced GAN algorithm that models tabular data distribution and sample rows from the underlying distribution. It has been shown that CTGAN can solve issues that challenge conventional GAN model, including mixed data types, non-Gaussian distributions, multimodal distributions, learning from sparse one-hot-encoded vectors and highly imbalanced categorical columns. TVAE is also an advanced VAE model that adapts VAE to tabular data by using preprocessing and modifying the loss function. As a case study, this research applies these two machine learning methods to generate synthetic population based on a sample from the American Community Survey relative to the State of Maryland. To demonstrate the performance of the proposed methods, we compare our results to those obtained with IPF and Bayesian Network using metrics that evaluate the ability of the population synthetizer to reproduce the dependency structure and the marginals in the real population and to solve the problem of zero cells in IPF

Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning

Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response

Studies on glass fiber-reinforced poly(Ethylene-grafted-styrene)-based cation exchange membrane composite

To improve interfacial adhesion between glass fiber (GF) and poly(ethylene-grafted-styrene)-based cation exchange membranes (CEM), GF was modified by four coupling agents: [3-(Methacryloxy)propyl] trimethoxy silane (3-MPS), 1,6-bis (trimethoxysilyl) hexane (1,6 bis), Poly(propylene-graft-maleic anhydride) (PP-g-MA) and Triethoxyvinylsilane (TES). The results indicated the addition of modified GF increased tensile strength, tensile modulus, storage modulus and interfacial adhesion of GF/CEM composite but degraded the strains. The composite with 3-MPS modified GF obtained superior mechanical properties and interfacial adhesion, whereas the modified effect of TES was inconspicuous. The addition of unmodified GF even had negative effects on GF/CEM mechanical properties. The field emission scanning electron microscopes (FE-SEM) showed that the GF treated by 3-MPS and PP-g-MA have better compatibility with the CEM matrix than 1,6 bis and TES-treated GF. The Fourier-transform infrared spectroscopy (FT-IR) verified that the strengthening effects from modified GF were attributed to the formation of Si-O-Si and Si-O-C bonds. The additions of modified GF in CEM positively influence water uptake ability but negatively influence ion exchange capacity (IEC). This research provided a way of strengthening GF/CEM composite and pointed out which functional groups included in coupling agents could be useful to GF-reinforced composite