Optimal Power Flow Using Genetic Algorithm

This paper presents a genetic algorithm (GA) to solve Optimal Power Flow (OPF) problems, optimizing electricity generation fuel cost. The GA based OPF is a derivative free optimization technique that relies on the evaluation of several points in the parameter search space strictly on the objective function. A 3 bus system and the IEEE 30 bus test system are used to validate the developed GA based OPF by means of comparisons with an interior point based optimal power flow.

Digital nanophotonics: the highway to the integration of subwavelength-scale photonics

Nanophotonic devices with high densities are extremely attractive because they can potentially merge photonics and electronics at the nanoscale. However, traditional integrated photonic circuits are designed primarily by manually selecting parameters or employing semi-analytical models. Limited by the small parameter search space, the designed nanophotonic devices generally have a single function, and the footprints reach hundreds of microns. Recently, novel ultra-compact nanophotonic devices with digital structures were proposed. By applying inverse design algorithms, which can search the full parameter space, the proposed devices show extremely compact footprints of a few microns. The results from many groups imply that digital nanophotonics can achieve not only ultra-compact single-function devices but also miniaturized multi-function devices and complex functions such as artificial intelligence operations at the nanoscale. Furthermore, to balance the performance and fabrication tolerances of such devices, researchers have developed various solutions, such as adding regularization constraints to digital structures. We believe that with the rapid development of inverse design algorithms and continuous improvements to the nanofabrication process, digital nanophotonics will play a key role in promoting the performance of nanophotonic integration. In this review, we uncover the exciting developments and challenges in this field, analyse and explore potential solutions to these challenges and provide comments on future directions in this field.

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA and k-Means Clustering

The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem.

Estimating Range-Dependent Evaporation Duct Height

The refractivity from clutter (RFC) technique has been proved to be an effective way to estimate atmospheric duct structure. An important issue for RFC is how to make the estimate more robust, especially in range-dependent ducting conditions. Traditionally, statistical inversion methods need a large number of forward propagation model runs to obtain an acceptable result. Especially when the parameter search space is multidimensional, these methods are prone to being trapped into local optimal solutions. Recently published results (Zhao and Huang) indicate that the adjoint parabolic equation (PE) method holds promise for real-time estimation of one-dimensional refractive index structure from radar sea clutter returns. This paper is aimed at extending the adjoint PE method to range-dependent evaporation duct cases, with a log-linear relationship describing duct structures. Numerical simulations are used to test the performance of this method and the results are compared with that retrieved using a genetic algorithm. Both noise-free and 3-dB additive Gaussian noise clutter simulations are considered, as well as linearly and nonlinearly varying duct height with range.