Laser tuning parameters and concentration retrieval technique for wavelength modulation spectroscopy based on the variable-radius search artificial bee colony algorithm

A novel wavelength modulation spectroscopy (WMS) laser tuning parameters and concentration retrieval technique based on the variable-radius-search artificial bee colony(VRS-ABC) algorithm is proposed. The technique imitates the foraging behavior of bees to achieve the retrieval of gas concentration and laser tuning parameters in a calibration-free WMS system. To address the problem that the basic artificial bee colony(ABC) algorithm tends to converge prematurely, we improve the search method of the scout bee. In contrast to prior concentration retrieval methods that utilized the Levenberg-Marquardt algorithm, the current technique exhibits a reduced dependence on the pre-characterization of laser parameters, leading to heightened precision and reliability in concentration retrieval. We validated the simulation with the VRS-ABC-based technique and the LM-based technique for the target gas C2H2. The simulation results show that the VRS-ABC-based technique performs better in terms of convergence speed and fitting accuracy, especially in the multi-parameter model without exact characterization

Concentration retrieval in a calibration-free wavelength modulation spectroscopy system using particle swarm optimization algorithm

This paper develops a spectral fitting technology based on the particle swarm optimization (PSO) algorithm, which is applied to a calibration-free wavelength modulation spectroscopy system to achieve concentration retrieval. As compared with other spectral fitting technology based on the Levenberg-Marquardt (LM) algorithm, this technology is relatively weakly dependent on the pre-characterization of the laser parameters. The gas concentration is calculated by fitting the simulated spectra to the measured spectra using the PSO algorithm. We validated the simulation with the LM algorithm and PSO algorithm for the target gas C2H2. The results showed that the convergence speed of the spectral fitting technique based on the PSO algorithm was about 63 times faster than the LM algorithm when the fitting accuracy remained the same. Within 5 seconds, the PSO algorithm can produce findings that are generally consistent with the values anticipated.Comment: arXiv admin note: text overlap with arXiv:2210.1654

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.</p