Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming

Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations require junction-level coordination, which has not been well studied. In this paper, we study the coordination of vehicle platooning at highway junctions. We consider a setting where CAVs randomly arrive at a highway junction according to a general renewal process. When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon. We formulate a Markov decision process to minimize the discounted cumulative travel cost, i.e. fuel consumption plus travel delay, over an infinite time horizon. We show that the optimal policy is threshold-based: the CAV will merge with the platoon if and only if the difference between the CAV's and the platoon's predicted times of arrival at the junction is less than a constant threshold. We also propose two ready-to-implement algorithms to derive the optimal policy. Comparison with the classical value iteration algorithm implies that our approach explicitly incorporating the characteristics of the optimal policy is significantly more efficient in terms of computation. Importantly, we show that the optimal policy under Poisson arrivals can be obtained by solving a system of integral equations. We also validate our results in simulation with Real-time Strategy (RTS) using real traffic data. The simulation results indicate that the proposed method yields better performance compared with the conventional method

High-quality Image Restoration from Partial Mixed Adaptive-Random Measurements

A novel framework to construct an efficient sensing (measurement) matrix, called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring a compressed image representation. The mixed sampling (sensing) procedure hybridizes adaptive edge measurements extracted from a low-resolution image with uniform random measurements predefined for the high-resolution image to be recovered. The mixed sensing matrix seamlessly captures important information of an image, and meanwhile approximately satisfies the restricted isometry property. To recover the high-resolution image from MAR measurements, the total variation algorithm based on the compressive sensing theory is employed for solving the Lagrangian regularization problem. Both peak signal-to-noise ratio and structural similarity results demonstrate the MAR sensing framework shows much better recovery performance than the completely random sensing one. The work is particularly helpful for high-performance and lost-cost data acquisition.Comment: 16 pages, 8 figure

Microenvironmental stimuli for proliferation of functional islet β-cells

Diabetes is characterized by high blood glucose level due to either autoimmune destruction of islet β-cells or insufficient insulin secretion or glucose non-responsive production of insulin by β-cells. It is highly desired to replace biological functional β-cells for the treatment of diabetes. Unfortunately, β-cells proliferate with an extremely low rate. This cellular property hinders cell-based therapy for clinical application. Many attempts have been made to develop techniques that allow production of large quantities of clinically relevant islet β-cells in vitro. A line of studies evidently demonstrate that β-cells can proliferate under certain circumstances, giving the hopes for generating and expanding these cells in vitro and transplanting them to the recipient. In this review, we discuss the requirements of microenvironmental stimuli that stimulate β-cell proliferation in cell cultures. We highlight advanced approaches for augmentation of β-cell expansion that have recently emerged in this field. Furthermore, knowing the signaling pathways and molecular mechanisms would enable manipulating cell proliferation and optimizing its insulin secretory function. Thus, signaling pathways involved in the enhancement of cell proliferation are discussed as well

A peak capacitor current pulse-train controlled buck converter with fast transient response and a wide load range

It is known that ripple-based control of a switching dc-dc converter benefits from a faster transient response than a conventional PWM control switching dc-dc converter. However, ripple-based control switching dc-dc converters may suffer from fast-scale oscillation. In order to achieve fast transient response and ensure stable operation of a switching dc-dc converter over a wide load range, based on a conventional pulse train control technique, a peak capacitor current pulse train (PCC-PT) control technique is proposed in this paper. With a buck converter as an example, the operating modes, steady-state performance and transient respond performance of a PCC-PT controlled buck converter are presented and assessed. To eliminate fast-scale oscillation, circuit and control parameter design consideration are given. An accurate discrete iteration model of a PCC-PT controlled buck converter is established, based on which, the effects of circuit parameters on stability of converter operating in a DCM mode, mixed DCM-CCM mode, and CCM mode are studied. Simulation and experimental results are presented to verify the analysis results