Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original ...HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.
Accurate estimation of the contrast transfer function (CTF) is critical for a near-atomic resolution cryo electron microscopy (cryoEM) reconstruction. Here, a GPU-accelerated computer program, Gctf, ...for accurate and robust, real-time CTF determination is presented. The main target of Gctf is to maximize the cross-correlation of a simulated CTF with the logarithmic amplitude spectra (LAS) of observed micrographs after background subtraction. Novel approaches in Gctf improve both speed and accuracy. In addition to GPU acceleration (e.g. 10–50×), a fast ‘1-dimensional search plus 2-dimensional refinement (1S2R)’ procedure further speeds up Gctf. Based on the global CTF determination, the local defocus for each particle and for single frames of movies is accurately refined, which improves CTF parameters of all particles for subsequent image processing. Novel diagnosis method using equiphase averaging (EPA) and self-consistency verification procedures have also been implemented in the program for practical use, especially for aims of near-atomic reconstruction. Gctf is an independent program and the outputs can be easily imported into other cryoEM software such as Relion (Scheres, 2012) and Frealign (Grigorieff, 2007). The results from several representative datasets are shown and discussed in this paper.
Feature-based electromagnetic (EM) optimization techniques can help avoid local minima in microwave design. Zeros of the transfer functions are recently used to help extract the features when the ...features of filter responses are not explicitly identifiable. This letter proposes a feature zero-adaptation approach to enlarge the surrogate range by overcoming the problem of varying orders of the transfer function w.r.t. the changes in design variables. In this way, the proposed technique allows larger step sizes for optimization, therefore, speeding up the overall EM optimization process. During each optimization iteration, parallel techniques are proposed to be used to generate multiple EM geometrical samples simultaneously for creating the feature-based surrogate model. The proposed technique is demonstrated using two microwave filter examples.
The flame transfer function (FTF) and acoustic transfer function (ATF) are estimated experimentally and numerically, respectively, and both are combined to predict combustion instability of a model ...rocket combustor with a single coaxial jet–swirl injector. FTF of the injector is measured experimentally by imposing external velocity excitation on the injector. ATF, i.e., model-chamber response to heat-release fluctuation from flames, is difficult to measure, and alternatively, an idea of numerical evaluation method is suggested by applying an equivalence-ratio perturbation to flames. Then, ATF is calculated by numerical simulations with a three-dimensional model and compared with ATF from the one-dimensional analytic equation. Results show that ATF estimated by the 3-D numerical model improves accuracy in predicting resonant frequencies and growth rates of pressure oscillations in the model chamber. Finally, a closed-loop system transfer function describing dynamic behaviors of the model chamber is formulated by combining FTF with ATF and it is applied to predict combustion instability of the chamber. This prediction is compared with experimental observation, which shows good agreement between them.
Quality assessment of pansharpening methods is not an easy task. Quality-assessment indexes, like Q4, spectral angle mapper, and relative global synthesis error, require a reference image at the same ...resolution as the fused image. In the absence of such a reference image, the quality of pansharpening is assessed at a degraded resolution only. The recently proposed index of Quality Not requiring a Reference (QNR) is one among very few tools available for assessing the quality of pansharpened images at the desired high resolution. However, it would be desirable to cross the outcomes of several independent quality-assessment indexes, in order to better determine the quality of pansharpened images. In this paper, we propose a method to assess fusion quality at the highest resolution, without requiring a high-resolution reference image. The novel method makes use of digital filters matching the modulation transfer functions (MTFs) of the imaging-instrument channels. Spectral quality is evaluated according to Wald's spectral consistency property. Spatial quality measures interscale changes by matching spatial details, extracted from the multispectral bands and from the panchromatic image by means of the high-pass complement of MTF filters. Eventually, we highlight the necessary and sufficient condition criteria for quality-assessment indexes by developing a pansharpening method optimizing the QNR spatial index and assessing the quality of fused images by using the proposed protocol.
Non-uniformly illuminated images often suffer from low visibility in dark areas. Traditional methods usually enhance non-uniformly illuminated images by bringing out the details in the dark areas, ...but easily result in over-enhancement. Motivated by the Weber contrast model, the authors propose a perceptually inspired image enhancement method, which treats an image as a product of a luminance mapping (LM) transfer function and a contrast measure (CM) transfer function. The contribution of this proposed method is two-fold. Firstly, they propose a progressive LM transfer function based on the sensitivity of the human visual system to emphasise changes at low brightness level and attenuates changes at high brightness levels. Secondly, they introduce a CM transfer function, which is based on a special implementation of a neural model of the human visual receptive field, to improve local intensity contrast. Experimental comparisons with some state-of-the-art methods show that the proposed method can achieve both contrast enhancement and visual fidelity preservation.
In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to ...achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. We devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.
The 3-D shape accuracy is a critical performance measure for products built via additive manufacturing (AM). With advances in computing and increased accessibility of AM product data, machine ...learning for AM (ML4AM) has become a viable strategy for enhancing 3-D printing performance. A proper description of the 3-D shape formation through the layer-by-layer fabrication process is critical to ML4AM, such as feature selection and AM process modeling. The physics-based modeling and simulation approaches present voxel-level description of an object formation from points to lines, lines to surfaces, and surfaces to 3-D shapes. However, this computationally intensive modeling framework does not provide a clear structure for machine learning of AM data. Significant progress has been made to model and predict the shape accuracy of planar objects under data analytical frameworks. In order to predict, learn, and compensate for 3-D shape deviations using shape measurement data, we propose a shape deviation generator (SDG) under a novel convolution formulation to facilitate the learning and prediction of 3-D printing accuracy. The shape deviation representation, individual layer input function, and layer-to-layer transfer function for the convolution modeling framework are proposed and derived. A deconvolution problem for identifying the transfer function is formulated to capture the interlayer interaction and error accumulation effects in the layer-by-layer fabrication processes. Physics-informed sequential model estimation is developed to fully establish the SDG models. The Gaussian process regression is adopted to capture spatial correlations. The printed 2-D and 3-D shapes via a stereolithography (SLA) process are used to demonstrate the proposed modeling framework and derive new process insights for AM processes. Note to Practitioners -With advances in computing and increased availability of product data, machine learning for additive manufacturing (ML4AM) has become a viable strategy for enhancing 3-D printing accuracy. This work establishes the shape deviation generator (SDG) as a novel data analytical framework through a convolution formulation to model the 3-D shape formation in the AM process. This new engineering-informed machine-learning framework will facilitate the learning of AM data to establish models for geometric shape accuracy prediction and control.
This paper addresses the resonance problem in a parallel-inverter-based grid-connected system. Harmonic interactions between inverters and the grid exhibit various number of complicated ...characteristics that threatens the system stability and power quality. In this paper, a method of harmonic resonance analysis is proposed to investigate the resonance problem in a multi-inverter grid-connected system based on resonance modal analysis (RMA) and participation factor (PF) calculations. Initially, an impedance-based analytical approach is employed and expanded to an electricity distribution network that is dominated by multiple inverters with LCL filters and proportional resonant controllers. Then, the RMA is introduced to investigate the resonant interactions among inverters and the grid. Specifically, harmonic resonance characteristics are addressed through transfer functions and the proposed method considering variation of inverter number, different inverter combinations, and critical system components. Finally, time domain simulations on the PSCAD/EMTDC platform are presented. The case study results validate the effectiveness of the proposed method.
In this paper the transfer functions from inner nodes of incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are analyzed. Such transfer functions are needed in order to predict the ...attenuation of error sources like noise and distortion from within the loopfilter (LF). In the state of the art, I-DS ADCs are often designed based on the theory of their free-running counterparts. However, the applied reset and reconstruction filter change the behavior of the incremental system and thus also alter its behavior with respect to inner error sources. A recently introduced methodology to describe I-DS ADCs in the frequency domain allows to analytically derive the internal transfer functions of I-DS ADCs. This enables the formulation of approximate error attenuation at inner nodes of I-DS ADCs and a comparison in form of a generalized noise-penalty factor to the known behaviour in their free-running counterparts.
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