COMPUTATIONAL FLOW RATE FEEDBACK AND CONTROL METHOD IN HYDRAULIC ELEVATORS

The computational flow rate feedback and control method, which can be used in proportional valve controlled hydraulic elevators, is discussed and analyzed. In a hydraulic elevator with this method, microprocessor receives pressure information from the pressure transducers and computes the flow rate through the proportional valve based on pressure-flow conversion real time algorithm. This hydraulic elevator is of lower cost and energy consumption than the conventional closed loop control hydraulic elevator whose flow rate is measured by a flow meter. Experiments arc carried out on a test rig which could simulate the load of hydraulic elevator. According to the experiment results, the means to modify the pressure-flow conversion algorithm are pointed out.

Computation Rate Maximization for Wireless-Powered and Multiple-User MEC System with Buffer Queue

Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices.Precisely, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time slot.The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.

CT Image-based Analysis on the Defect of Polypropylene Fiber Reinforced High-Strength Concrete at High Temperatures

With the application of X-ray computed tomography（CT） technology of C80 high-strength concrete with polypropylene fiber at elevated temperatures, the microscopic damage evolution process observation and image building could be obtained, based on the statistics theory and numerical analysis of the combination of concrete internal defects extension and evolution regularity of microscopic structure. The expermental results show that the defect rate has changed at different temperatures and can determine the concrete degradation threshold temperatures. Also, data analysis can help to establish the evolution equation between the defect rate and the effect of temperature damage, and identify that the addition of polypropylene fibers in the high strength concrete at high temperature can improve cracking resistance.