Conditional Markov chain and its application in economic time series analysis

Motivated by the great moderation in major U.S. macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long-run volatility change as a recurrent structure change, while short-run changes in the mean growth rate as regime switches. Both structure and regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure-dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short-run regime switches and long-run structure changes in the U.S. macroeconomic data.Markov regime switching; Conditional Markov chain

Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag

Simple Formula for Marcus-Hush-Chidsey Kinetics

The Marcus-Hush-Chidsey (MHC) model is well known in electro-analytical chemistry as a successful microscopic theory of outer-sphere electron transfer at metal electrodes, but it is unfamiliar and rarely used in electrochemical engineering. One reason may be the difficulty of evaluating the MHC reaction rate, which is defined as an improper integral of the Marcus rate over the Fermi distribution of electron energies. Here, we report a simple analytical approximation of the MHC integral that interpolates between exact asymptotic limits for large overpotentials, as well as for large or small reorganization energies, and exhibits less than 5\% relative error for all reasonable parameter values. This result enables the MHC model to be considered as a practical alternative to the ubiquitous Butler-Volmer equation for improved understanding and engineering of electrochemical systems

Over-limiting Current and Control of Dendritic Growth by Surface Conduction in Nanopores

Understanding over-limiting current (faster than diffusion) is a long-standing challenge in electrochemistry with applications in desalination and energy storage. Known mechanisms involve either chemical or hydrodynamic instabilities in unconfined electrolytes. Here, it is shown that over-limiting current can be sustained by surface conduction in nano pores, without any such instabilities, and used to control dendritic growth during electrodeposition. Copper electrode posits are grown in anodized aluminum oxide membranes with polyelectrolyte coatings to modify the surface charge. At low currents, uniform electroplating occurs, unaffected by surface modification due to thin electric double layers, but the morphology changes dramatically above the limiting current. With negative surface charge, growth is enhanced along the nanopore surfaces, forming surface dendrites and nanotubes behind a deionization shock. With positive surface charge, dendrites avoid the surfaces and are either guided along the nanopore centers or blocked from penetrating the membrane