Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Worst-case Recovery Guarantees for Least Squares Approximation Using Random Samples

We construct a least squares approximation method for the recovery of complex-valued functions from a reproducing kernel Hilbert space on $$D \subset \mathbb {R}^d$$ D ⊂ R d . The nodes are drawn at random for the whole class of functions, and the error is measured in $$L_2(D,\varrho _{D})$$ L 2 ( D , ϱ D ) . We prove worst-case recovery guarantees by explicitly controlling all the involved constants. This leads to new preasymptotic recovery bounds with high probability for the error of hyperbolic Fourier regression on multivariate data. In addition, we further investigate its counterpart hyperbolic wavelet regression also based on least squares to recover non-periodic functions from random samples. Finally, we reconsider the analysis of a cubature method based on plain random points with optimal weights and reveal near-optimal worst-case error bounds with high probability. It turns out that this simple method can compete with the quasi-Monte Carlo methods in the literature which are based on lattices and digital nets.

A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population

Background Birth weight (BW) is one of the most widely studied anthropometric traits in humans because of its role in various adult-onset diseases. The number of loci associated with BW has increased dramatically since the advent of whole-genome screening approaches such as genome-wide association studies (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating the genetic architecture of BW, we analyzed a genotyped Norwegian dataset with information on child’s BW (N=9,063) using a slightly modified version of a wavelet-based method by Shim and Stephens (2015) called WaveQTL. Results WaveQTL uses wavelet regression for regional testing and offers a more flexible functional modeling framework compared to conventional GWAS methods. To further improve WaveQTL, we added a novel feature termed “zooming strategy” to enhance the detection of associations in typically small regions. The modified WaveQTL replicated five out of the 133 loci previously identified by the largest GWAMA of BW to date by Warrington et al. (2019), even though our sample size was 26 times smaller than that study and 18 times smaller than the second largest GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL performed better in regions of high LD between SNPs. Conclusions This study is the first adaptation of the original WaveQTL method to the analysis of genome-wide genotypic data. Our results highlight the utility of the modified WaveQTL as a complementary tool for identifying loci that might escape detection by conventional genome-wide screening methods due to power issues. An attractive application of the modified WaveQTL would be to select traits from various public GWAS repositories to investigate whether they might benefit from a second analysis.

Wavelet regression estimation over Lp risk based on negatively associated sample

This paper considers wavelet estimations of a regression function based on negatively associated sample. We provide upper bound estimations over [Formula: see text] risk of linear and nonlinear wavelet estimators in Besov space, respectively. When the random sample reduces to the independent case, our convergence rates coincide with the optimal convergence rates of classical nonparametric regression estimation.

China’s First Demonstration of Cobalt-rich Manganese Crust Thickness Measurement in the Western Pacific with a Parametric Acoustic Probe

Cobalt-rich manganese crusts (CRCs) are important as a potential mineral source that could occur throughout the Pacific on seamounts, ridges, and plateaus. We built a prototype parametric acoustic probe to complete the task of in-situ thickness measurements to estimate the volumetric distribution of deep-sea mineral. The prototype is designed with dual-channels for receiving the primary and secondary signal, which lays a foundation for improving the thickness extraction algorithm. Considering that the signal quality is degraded by the system interference and ambient noise, some improvements to the algorithm are proposed by including the wavelet-based envelope extraction method and the adaptive estimation strategy based on the dual-channel information. Additionally, wavelet regression is applied to reduce the measuring noise assuming that the CRCs have local thickness invariability. The algorithm is suitable for the CRCs with the structure of the multilayers at the top surface and one single layer at the bottom surface. A laboratory experiment is performed to validate the effectiveness of the algorithm. The experiments carried out on the China Ocean 51th voyage in the Western Pacific Ocean on Aug 30, 2018, are described and the data obtained by using the sit-on-bottom stationary measurement are processed to validate the design of the prototype.