Vehicle Parameters Estimation and Driver Behavior Classification for Adaptive Shift Strategy of Heavy Duty Vehicles

Commercial vehicles fulfill the majority of inland freight transportation in the United States, and they are very large consumers of fuels. The increasingly stringent regulation on greenhouse-gas emission has driven manufacturers to adopt new fuel efficient technologies. Among others, advanced transmission control strategy can provide tangible improvement with low incremental cost. An adaptive shift strategy is proposed in this work to optimize the shift maps on-the-fly based on the road load and driver behavior while reducing the initial calibration efforts. In addition, the adaptive shift strategy provides the fleet owner a mean to select a tradeoff between fuel economy and drivability, since the drivers are often not the owner of the vehicle. In an attempt to develop the adaptive shift strategy, the vehicle parameters and driver behavior need to be evaluated first. Therefore, three research questions are addressed in this dissertation: (i) vehicle parameters estimation; (ii) driver behavior classification; (iii) online shift strategy adaption. In vehicle parameters estimation, a model-based vehicle rolling resistance and aerodynamic drag coefficient online estimator is proposed. A new Weighted Recursive Least Square algorithm was developed. It uses a supervisor to extracts data during the constant-speed event and saves the average road load at each speed segment. The algorithm was tested in the simulation with real-world driving data. The results have shown a more robust performance compared with the original Recursive Least Square algorithm, and high accuracy of aerodynamic drag estimation. To classify the driver behavior, a driver score algorithm was proposed. A new method is developed to represent the time-series driving data into events represented by symbolic data. The algorithm is tested with real-world driving data and shows a high classification accuracy across different vehicles and driving cycles. Finally, a new adaptive shift scheme was developed, which synthesizes the information about vehicle parameters and driver score developed in the previous steps. The driver score is used as a proxy to match the driving characteristics in real time. Drivability objective is included in the optimization through a torque reserve and it is subsequently evaluated via a newly developed metric. The impact of the shift maps on the objective drivability and fuel economy metrics is evaluated quantitatively in the vehicle simulation. The algorithms proposed in this dissertation are developed with practical implementation in mind. The methods can reduce the initial calibration effort and provide the fleet owner a mean to select an appropriate tradeoff between fuel economy and drivability depending on the vocation

Fractional-Order Hyperbolic Tangent Sliding Mode Control for Chaotic Oscillation in Power System

Chaotic oscillation will occur in power system when there exist periodic load disturbances. In order to analyze the chaotic oscillation characteristics and suppression method, this paper establishes the simplified mathematical model of the interconnected two-machine power system and analyzes the nonlinear dynamic behaviors, such as phase diagram, dissipation, bifurcation map, power spectrum, and Lyapunov exponents. Based on fractional calculus and sliding mode control theory, the fractional-order hyperbolic tangent sliding mode control is proposed to realize the chaotic oscillation control of the power system. Numerical simulation results show that the proposed method can not only suppresses the chaotic oscillation but also reduce the convergence time and suppress the chattering phenomenon and has strong robustness

Association Between Onset Age of Coronary Heart Disease and Incident Dementia: A Prospective Cohort Study

Background The association of age at coronary heart disease (CHD) onset with incident dementia remains unexplored. This study aimed to examine whether younger onset age of CHD is associated with a higher risk of incident dementia. Methods and Results Data were obtained from the UK Biobank. Information on the diagnosis of CHD and dementia was collected at baseline and follow‐ups. Propensity score matching method and Cox proportional hazards models were used to evaluate the association between different ages at CHD onset and incident dementia. A total of 432 667 adults (mean±SD age, 56.9±8.1 years) were included, of whom 11.7% had CHD. Compared with participants without CHD, participants with CHD exhibited higher risks of developing all‐cause dementia, Alzheimer's disease, and vascular dementia. More importantly, younger age at CHD onset (per 10‐year decrease) was significantly associated with elevated risks of all‐cause dementia (hazard ratio [HR], 1.25 [95% CI, 1.20–1.30]; P<0.001), Alzheimer's disease (HR, 1.29 [95% CI, 1.20–1.38]; P<0.001), and vascular dementia (HR, 1.22 [95% CI, 1.13–1.31]; P<0.001). After propensity score matching, patients with CHD had significantly higher risks of all‐cause dementia, Alzheimer's disease, and vascular dementia than matched controls among all onset age groups, and the HRs gradually elevated with decreasing age at CHD onset. Conclusions Younger onset age of CHD is associated with higher risks of incident all‐cause dementia, Alzheimer's disease, and vascular dementia, underscoring the necessity to pay attention to the neurocognitive status of individuals diagnosed with CHD at younger age to conduct timely interventions to attenuate subsequent risk of incident dementia

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter focal length to their high-resolution images captured at a longer focal length. With this challenge, we in-troduced a novel real-world super-resolution dataset (Re-alSR). The track had 403 registered participants, and 36 teams competed in the final testing phase. They gauge the state-of-the-art in real-world single image super-resolution