Search

Considering the rapid urbanization and the road congestion, the development of smart parking solutions becomes more crucial, especially in terms of economic interests. Thanks to IoT-connectivity and the cloud-integrated platforms, drivers can easily find a vacant parking lot with smart parking services. This paper intervenes in the profit of parking management systems. The paper proposes a new technique “paging technique” which increases the utilization factor of parking slots. The proposed method takes advantage of the idle time that exists between two successful parking services in the same slot. Besides, it investigates the possibility of using the idle times from different parking slots to provide a continuous parking time for an additional car. The paging technique is optimally implemented using mixed-integer linear programming that maximizes the utilization factor for the parking slots with minimum car transitions. Moreover, a data model for the parking management system has been constructed while considering the three major customers, namely, regular, prepaid, and walk-in customers. The difference between fixed and dynamic pricing for parking has been investigated. The technique has been validated using GAMS optimization software and hardware using DSP with Coin-or branch and cut solver (CBC) under real-life conditions. The statistical results prove that the revenue for the proposed parking system has increased significantly. Finally, a comparative analysis is performed, benchmarking our proposed method against recent competing algorithms in real world applications to demonstrate its superiority.

This article proposes a new single-stage three-phase buck-boost inverter and control scheme, which remarkably reduces both the low and high-frequency ripple components in the input current. The low-frequency ripple component is eliminated by phase-shifting the output three-phase voltages by 120°. The high-frequency ripple component is decreased by doubling the effective switching frequencies and lowering the voltages of the input inductors. The proposed inverter has buck-boost dc-ac conversion ability and contains only six active switches. It is short-circuiting free and can avoid the reverse recovery losses of the body diodes of the mosfets. The proposed inverter has better spectral performance and has a lower distortion in the output waveforms due to no delays (equivalent to dead-time in the traditional three-phase inverter) in the switching signals. To validate the performance and advantages of the proposed inverter, detailed analytical, simulation, and experimental results are reported. Experiments have been conducted with a 1.5-kW laboratory prototype at a 50 kHz switching frequency for the line-line output voltage of 110–220 Vrms/60. At the end, integration of inductors is also suggested to decrease the magnetic volume.

This article describes an advanced tool that uses electromagnetic resonance coupling and machine learning techniques to detect and characterize metal loss on the inner surface of a metallic pipe. The proposed tool uses a transmitter coil placed along the axis of the pipe and four sensor coils installed around the transmitter coil. Any defect on the pipe surface leads to changes in the impedance of the transmitter and sensor coils as well as in the mutual coupling between them, thus creating a detectable variation in the outputs of one or multiple sensor coils. An artificial neural network is developed to reconstruct two-dimensional pipe cross-sections and to completely characterize the defects using these variations. The proposed tool is tested and validated via simulations, and data are collected using an experimental prototype. Results show that the tool can fully characterize the size, location (azimuthal angle), and level (thickness) of metal loss.