Research on various PV arrays manufacturing data for power comparison and optimization through extremum seeking technique

The increasing environmental repercussions and depletion of nonrenewable energy resources have cautioned and enabled researchers to incorporate renewable energy systems. Amongst the renewable energy resources, the solar energy system has been utilized in most parts of the world due to cheaper, reliable, robust and sustainable energy resource than other resources. The Maximum Power Point Tracking (MPPT) techniques are used for increasing the power output of PV array. The Perturb and Observe (P&O) technique is widely used MPPT technique due to higher efficiency and ease in implementation. The proposed “Perturb and Observe (P&O)” MPPT technique is incorporated through Matlab Simulation software on PV arrays of various companies. The results are then compared through comparative analysis and optimum results are recommended for the manufacturing companies.

An Exhaustive Power Comparison of Normality Tests

A goodness-of-fit test is a frequently used modern statistics tool. However, it is still unclear what the most reliable approach is to check assumptions about data set normality. A particular data set (especially with a small number of observations) only partly describes the process, which leaves many options for the interpretation of its true distribution. As a consequence, many goodness-of-fit statistical tests have been developed, the power of which depends on particular circumstances (i.e., sample size, outlets, etc.). With the aim of developing a more universal goodness-of-fit test, we propose an approach based on an N-metric with our chosen kernel function. To compare the power of 40 normality tests, the goodness-of-fit hypothesis was tested for 15 data distributions with 6 different sample sizes. Based on exhaustive comparative research results, we recommend the use of our test for samples of size .

Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies

Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding of the transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression - DESeq2, edgeR, and limma - as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering, and generation of empirical null distribution of association p-values, and we apply the pipeline to compute empirical p-values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison, and the computation of quantile empirical p-values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical p-values. We provide the proposed pipeline with fast algorithms in R.

A Spatial-Temporal Approach Based on Antenna Array for GNSS Anti-Spoofing

The presence of spoofing signals poses a significant threat to global navigation satellite system (GNSS)-based positioning applications, as it could cause a malfunction of the positioning service. Therefore, the main objective of this paper is to present a spatial-temporal technique that enables GNSS receivers to reliably detect and suppress spoofing. The technique, which is based on antenna array, can be divided into two consecutive stages. In the first stage, an improved eigen space spectrum is constructed for direction of arrival (DOA) estimation. To this end, a signal preprocessing scheme is provided to solve the signal model mismatch in the DOA estimation for navigation signals. In the second stage, we design an optimization problem for power estimation with the estimated DOA as support information. After that, the spoofing detection is achieved by combining power comparison and cross-correlation monitoring. Finally, we enhance the genuine signals by beamforming while the subspace oblique projection is used to suppress spoofing. The proposed technique does not depend on external hardware and can be readily implemented on raw digital baseband signal before the despreading of GNSS receivers. Crucially, the low-power spoofing attack and multipath can be distinguished and mitigated by this technique. The estimated DOA and power are both beneficial for subsequent spoofing localization. The simulation results demonstrate the effectiveness of our method.