Search Results (7)

The traditional approach to optical design faces limitations as photonic devices grow increasingly complex, requiring advanced functionalities. Recently, machine learning algorithms have gained significant interest for extracting structural designs from customized wavelength spectra, surpassing traditional simulation methods known for their time-consuming nature and resource-demanding computational requirements. This study focuses on the inverse design of a reflectionless multilayer thin-film structure across a specific wavelength region, utilizing a tandem neural network (TNN) approach. The method effectively addresses the non-uniqueness problem in training inverse neural networks. Data generation via the transfer matrix method (TMM) involves simulating the optical behavior of a multilayer structure comprising alternating thin films of silicon dioxide (SiO₂) and silicon (Si). This innovative design considers both reflection and absorption properties to achieve near-zero reflection. We aimed to manipulate the structure’s reflectivity by implementing low-index and high-index layers along with Si absorption layers to attain specific optical properties. Our TNN demonstrated an MSE accuracy of less than 0.0005 and a maximum loss of 0.00781 for predicting the desired spectrum range, offering advanced capabilities for forecasting arbitrary spectra. This approach provides insights into designing multilayer thin-film structures with near-zero reflection and highlights the potential for controlling absorption materials to enhance optical performance. Full article

Surface gravity inversion attempts to recover the density contrast distribution in the 3D Earth model for geological interpretation. Since airborne gravity is characterized by large data volumes, large-scale 3D inversion exceeds the capacity of desktop computing resources, making it difficult to achieve the appropriate depth/lateral resolution for geological interpretation. In addition, gravity data are finite and noisy, and their inversion is ill posed. Especially in the absence of a priori geological information, regularization must be introduced to overcome the difficulty of the non-uniqueness of the solutions to recover the most geologically plausible ones. Because the use of Haar wavelet operators has an edge-preserving property and can preserve the sensitivity matrix structure at each level of the multilevel method to obtain faster solvers, we present a multilevel algorithm for large-scale gravity inversion solved by the re-weighted regularized conjugate gradient (RRCG) algorithm to reduce the inversion computational resources and improve the depth/lateral resolution of the inversion results. The RRCG-based multilevel inversion was then applied to synthetic cases and airborne gravity data from the Quest-South project in British Columbia, Canada. Results from synthetic models and field data show that the RRCG-based multilevel inversion is suitable for obtaining density contrast distributions with appropriate horizontal and vertical resolution, especially for large-scale gravity inversions compared to Occam’s inversion. Full article

In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas. Full article

In this paper, an attempt at the energy optimization of perfect control systems is performed. The perfect control law is the maximum-speed and maximum-accuracy procedure, which allows us to obtain a reference value on the plant’s output just after a time delay. Based on the continuous-time state-space description, the minimum-error strategy is discussed in the context of possible solutions aiming for the minimization of the control energy. The approach presented within this study is focused on the nonunique matrix inverse-originated so-called degrees of freedom being the core of perfect control scenarios. Thus, in order to obtain the desired energy-saving parameters, a genetic algorithm has been employed during the inverse model control synthesis process. Now, the innovative continuous-time procedure can be applied to a wide range of multivariable plants without any stress caused by technological limitations. Simulation examples made in the MATLAB/Simulink environment have proven the usefulness of the new method shown within the paper. In the extreme case, the energy consumption has been reduced by approximately 80% in comparison with the well-known Moore–Penrose inverse. Full article

Airborne (or satellite) gravity measurement is a commonly used remote sensing method to obtain the underground density distribution. Airborne gravity gradiometry data have a higher horizontal resolution to shallower causative sources than airborne gravity anomaly, so joint exploration of airborne gravity and its gradient data can simultaneously obtain the anomaly feature of sources with different depths. The most commonly used joint inversion method of gravity and its gradient data is the data combined method, which is to combine all the components into a data matrix as mutual constraints to reduce ambiguity and non-uniqueness. In order to obtain higher resolution results, we proposed a cooperate density-integrated inversion method of airborne gravity and its gradient data, which firstly carried out the joint inversion using cross-gradient constraints to obtain two density structures, and then fused two recovered models into a result through Fourier transform; finally, data combined joint inversion of airborne gravity, and gradient data were reperformed to achieve high-resolution density result using fused density results as a reference model. Compared to the data combined joint inversion method, the proposed cooperate density-integrated inversion method can obtain higher resolution and more accurate density distribution of shallow and deep bodies meanwhile. We also applied it to real data in the mining area of western Liaoning Province, China. The results showed that the depth of the skarn-type iron mine in the region is about 900–1300 m and gives a more specific distribution compared to the geological results, which provided reliable data for the next exploration plan. Full article

In this paper, a new approach to the continuous-time perfect control algorithm is given. Focusing on the output derivative, it is shown that the discussed control law can effectively be implemented in terms of state-feedback scenarios. Moreover, the application of nonunique matrix inverses is also taken into consideration during the perfect control design process. Simulation examples given within this work allow us to showcase the main properties obtained for continuous-time perfect control closed-loop plants. Full article

The application of the switching control framework to the perfect control algorithm is presented in this paper. Employing the nonunique matrix inverses, the different closed-loop properties are obtained and further enhanced with possible switching methodology implementation. Simulation examples performed in the MATLAB/Simulink environment clearly show that the new framework can lead to benefits in terms of the control energy, speed, and robustness of the perfect control law. The possibility of transferring the new obtained results to the symmetrical nonlinear plants seems to be immediate. Full article