Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively

Symbolic regression-based genetic approximations of the Colebrook equation for flow friction

Widely used in hydraulics, the Colebrook equation for flow friction relates implicitly to the input parameters; the Reynolds number, Re and the relative roughness of an inner pipe surface, epsilon/D with an unknown output parameter; the flow friction factor, ; = f (, Re, epsilon/D). In this paper, a few explicit approximations to the Colebrook equation; approximate to f (Re, epsilon/D), are generated using the ability of artificial intelligence to make inner patterns to connect input and output parameters in an explicit way not knowing their nature or the physical law that connects them, but only knowing raw numbers, {Re, epsilon/D}{}. The fact that the used genetic programming tool does not know the structure of the Colebrook equation, which is based on computationally expensive logarithmic law, is used to obtain a better structure of the approximations, which is less demanding for calculation but also enough accurate. All generated approximations have low computational cost because they contain a limited number of logarithmic forms used for normalization of input parameters or for acceleration, but they are also sufficiently accurate. The relative error regarding the friction factor , in in the best case is up to 0.13% with only two logarithmic forms used. As the second logarithm can be accurately approximated by the Pade approximation, practically the same error is obtained also using only one logarithm.Web of Science109art. no. 117

Convergence Properties of Two ({\mu} + {\lambda}) Evolutionary Algorithms On OneMax and Royal Roads Test Functions

We present a number of bounds on convergence time for two elitist population-based Evolutionary Algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.Comment: accepted for ECTA 201

On the prediction of pseudo relative permeability curves: meta-heuristics versus Quasi-Monte Carlo

International audienceThis article reports the first application of the Quasi-Monte Carlo (QMC) method for estimation of the pseudo relative permeability curves. In this regards, the performance of several meta-heuristics algorithms have also been compared versus QMC, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Artificial Bee Colony (ABC). The mechanism of minimizing the objective-function has been studied, for each method. The QMC has outperformed its counterparts in terms of accuracy and efficiently sweeping the entire search domain. Nevertheless, its computational time requirement is obtained in excess to the meta-heuristics algorithms

Elitism Levels Traverse Mechanism For The Derivation of Upper Bounds on Unimodal Functions

In this article we present an Elitism Levels Traverse Mechanism that we designed to find bounds on population-based Evolutionary algorithms solving unimodal functions. We prove its efficiency theoretically and test it on OneMax function deriving bounds c{\mu}n log n - O({\mu} n). This analysis can be generalized to any similar algorithm using variants of tournament selection and genetic operators that flip or swap only 1 bit in each string.Comment: accepted to Congress on Evolutionary Computation (WCCI/CEC) 201

Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion

Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gorithms that are sample efficient and inherently safe. Bayesian Optimization is a powerful sample-efficient tool for optimizing non-convex black-box functions. However, its performance can degrade in higher dimensions. We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking. This distance metric reflects some basic gait features of healthy walking and helps us quickly eliminate a majority of unstable controllers. With our approach we can learn policies for walking in less than 100 trials for a range of challenging settings. In simulation, we show results on two different costs and on various terrains including rough ground and ramps, sloping upwards and downwards. We also perturb our models with unknown inertial disturbances analogous with differences between simulation and hardware. These results are promising, as they indicate that this method can potentially be used to learn control policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids '2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally

Engineering the reciprocal space for ultrathin GaAs solar cells

III-V solar cells dominate the high efficiency charts, but with significantly higher cost than other solar cells. Ultrathin III-V solar cells can exhibit lower production costs and immunity to short carrier diffusion lengths caused by radiation damage, dislocations, or native defects. Nevertheless, solving the incomplete optical absorption of sub-micron layers presents a challenge for light-trapping structures. Simple photonic crystals have high diffractive efficiencies, which are excellent for narrow-band applications. Random structures a broadband response instead but suffer from low diffraction efficiencies. Quasirandom (hyperuniform) structures lie in between providing high diffractive efficiency over a target wavelength range, broader than simple photonic crystals, but narrower than a random structure. In this work, we present a design method to evolve a simple photonic crystal into a quasirandom structure by modifying the spatial-Fourier space in a controlled manner. We apply these structures to an ultrathin GaAs solar cell of only 100 nm. We predict a photocurrent for the tested quasirandom structure of 25.3 mA/cm$^2$, while a planar structure would be limited to 16.1 mA/cm$^2$. The modified spatial-Fourier space in the quasirandom structure increases the amount of resonances, with a progression from discrete number of peaks to a continuum in the absorption. The enhancement in photocurrent is stable under angle variations because of this continuum. We also explore the robustness against changes in the real-space distribution of the quasirandom structures using different numerical seeds, simulating variations in a self-assembly method