Two modified electrodes were fabricated by deposition of thin films of α-PbO2 and β-PbO2 on aluminum substrate electrodes. Both electrodes exhibit potentiometric response to the hydrogen ion concentration and, consequently, can be used as pH sensors. The potentiometric responses of the modified electrodes were close to the theoretical slope of Nerustian response and comparable with glass electrodes. The electrochemical properties and stability of the constructed electrode were investigated. The possibility of the pH sensor for using in flow injection analysis (FIA) was also studied, and the optimum conditions were obtained. 相似文献
In this paper, we study the effects of thermal noise on the time evolution of a weak light pulse (probe) in the presence of a strong light pulse (pump) within a gain medium which includes random scatterer particles. Suitable thermal noise term is added to a set of four coupled equations including three diffusion equations for energy densities and a rate equation for the upper level population in a four-level gain medium. These equations have been solved simultaneously by Crank–Nicholson numerical method. The main result is that the back-scattered output probe light is increased as the thermal noise strength is increased and simultaneously, with the same rate, the amplified spontaneous emission is decreased. Therefore, the amplified response of the random laser in diffusion regime for the input probe pulse is enhanced due to effect of the thermal noise. 相似文献
As a first attempt, Fourier series expansion (FSE), particle swarm optimization (PSO), and genetic algorithm (GA) methods are coupled for analysis of the static–dynamic performance and propagated waves in the magneto-electro-elastic (MEE) nanoplate. The FSE method is presented for solving the motion equations of the MEE nanoplate. For increasing the performance of genetic algorithms for solving the problem, the particle swarm optimization technique is added as an operator of the GA. Accuracy, convergence, and applicability of the proposed mixed approach are shown in the results section. Also, we prove that for obtaining the convergence results of the PSO and GA, we should consider more than 16 iterations. Finally, it is shown that if designers consider the presented algorithm in their model, the results of phase velocity of the nanosystem will be increased by 27%. A useful suggestion is that there is a region the same as a trapezium in which there are no effects from magnetic and electric potential of the MEE face sheet on the phase velocity of the smart nanoplate, and the region will be bigger by increasing the wavenumber.
Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP. 相似文献
This paper deals with ground roll attenuation using double density and dual-tree complex discrete wavelet transforms. These transforms use a new denoising method instead of well-known hard and soft thresholdings and Wavelet Domain Ground roll Analysis (WDGA) applied in finding and attenuating the ground roll energy in the wavelet domain. The dual-tree complex wavelet transform (DTCWT), and double density discrete wavelet transform (DDDWT) have two wavelets and one scaling function and are nearly shift-invariant. Also they have redundancy factors of 2, and their wavelets are smoother than other orthogonal wavelets. Based on this fact, they can be considered as replacement methods for such aims. Recognizing the performance of each method is required to show which method is more suitable and efficient. In the present study, distinct wavelets and scaling function of each method have been applied to the data, and also the data are denoised by Wavelet Domain Ground roll Analysis technique. Additionally, for the inverse transform, the coefficients of the noise are removed. And finally for concluding, the results of all methods are compared with the f-k method and band-pass filtering, and critically sampled Discrete Wavelet Transforms. Final results showed that the DTCWT is reliable in comparison with other methods for ground roll attenuation. 相似文献