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  1. Free, publicly-accessible full text available December 1, 2022
  2. Sea salt aerosols contribute significantly to the mass loading of ambient aerosol, which may serve as cloud condensation nuclei and can contribute to light scattering in the atmosphere. Two major chemical components commonly found in sea salts are ammonium sulfate (AS) and sodium chloride (NaCl). It has been shown that alkylamines, derivatives of ammonia, can react with ammonium salts in the particle-phase to displace ammonia and likely change the particle properties. This study investigated the effects of atmospheric alkylamines on the composition and properties of sea salt aerosols using a chemical system of methylamine (MA, as a proxy of alkylamines),more »AS and NaCl (as a proxy of sea salt aerosol). The concentrations of ammonia and MA in aqueous/gas phases at the thermodynamic equilibrium were determined using the Extended Aerosols and Inorganics Model (E-AIM) under varying initial inputs, along with the deliquescence relative humidity (DRH) and the corresponding particle water content. Our findings indicated a notable negative relationship between MA concentration and the DRH for both AS and NaCl while the effect of MA on NaCl is smaller than that on AS. The salt of MA in the particle phase may absorb water vapor and may lead to the displacement reaction between AS and NaCl due to the low solubility of sodium sulfate. The acidity in the particle phase also played a significant role in affecting the DRH of sea salt aerosols. Since both sea salt aerosol and alkylamines are emitted into the atmosphere from the ocean in large quantities, our study suggested the potential impact of alkylamines on the environment and the climate via the modification of sea salt aerosol properties.« less
    Free, publicly-accessible full text available August 24, 2022
  3. For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic controlmore »devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project.« less
  4. We propose an end-to-end optimized adversarial deep compressed imaging modality. This method exploits the adversarial duality of the sensing basis and sparse representation basis in compressed sensing framework and shows solid super-resolution results.