This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city ...
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This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (ips) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the ips algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the ips method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study.
The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a track quality measure ...
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The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a track quality measure for false track discrimination (FTD). This study investigates two IPF-based fixed-interval smoothing algorithms: the IP smoothing (ips) algorithm and the IP-Rauch-Tung- Striebel backward smoothing (IP-RTSBS) algorithm, both of which are capable of trajectory estimation and FTD. The ips algorithm fuses the propagations for each pair of forward IPF and backward IPF particles to obtain the smoothing propagation that is used to update the track state by applying all available measurements in the current scan. The IP-RTSBS algorithm employs the forward filtering backward smoothing approach to smooth the trajectory state, which is then applied to the RTS smoothing methodology to obtain the smoothing propagation used to update the PTE. As a result, both FTD and trajectory estimation are improved. The smoothing benefits of the two algorithms are validated in the simulations, where a sliding batch mode with overlapping measurements is utilised to limit the smoothing lag.
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