scholarly journals Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Animals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 2238
Author(s):  
Zhigang Sun ◽  
Mengmeng Gao ◽  
Guotao Wang ◽  
Bingze Lv ◽  
Cailing He ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Signal To Noise Ratio ◽  
Sound Signal ◽  
Spectral Subtraction ◽  
Wiener Filtering ◽  
Spectrum Estimation ◽  
Multiple Perspectives ◽  
Data Set ◽  
Signal Filtering ◽  
Filtering Method

Broiler sounds can provide feedback on their own body condition, to a certain extent. Aiming at the noise in the sound signals collected in broiler farms, research on evaluating the filtering methods for broiler sound signals from multiple perspectives is proposed, and the best performer can be obtained for broiler sound signal filtering. Multiple perspectives include the signal angle and the recognition angle, which are embodied in three indicators: signal-to-noise ratio (SNR), root mean square error (RMSE), and prediction accuracy. The signal filtering methods used in this study include Basic Spectral Subtraction, Improved Spectral Subtraction based on multi-taper spectrum estimation, Wiener filtering and Sparse Decomposition using both thirty atoms and fifty atoms. In analysis of the signal angle, Improved Spectral Subtraction based on multi-taper spectrum estimation achieved the highest average SNR of 5.5145 and achieved the smallest average RMSE of 0.0508. In analysis of the recognition angle, the kNN classifier and Random Forest classifier achieved the highest average prediction accuracy on the data set established from the sound signals filtered by Wiener filtering, which were 88.83% and 88.69%, respectively. These are significantly higher than those obtained by classifiers on data sets established from sound signals filtered by other methods. Further research shows that after removing the starting noise in the sound signal, Wiener filtering achieved the highest average SNR of 5.6108 and a new RMSE of 0.0551. Finally, in comprehensive analysis of both the signal angle and the recognition angle, this research determined that Wiener filtering is the best broiler sound signal filtering method. This research lays the foundation for follow-up research on extracting classification features from high-quality broiler sound signals to realize broiler health monitoring. At the same time, the research results can be popularized and applied to studies on the detection and processing of livestock and poultry sound signals, which has extremely important reference and practical value.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Error Correction for FSI-Based System without Cooperative Target Using an Adaptive Filtering Method and a Phase-Matching Mosaic Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1954 ◽  
Author(s):  
Xing-Ting Xiong ◽  
Xing-Hua Qu ◽  
Fu-Min Zhang
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Incident Angle ◽  
External Cavity ◽  
Phase Matching ◽  
Maximum Deviation ◽  
External Cavity Laser ◽  
Filtering Method ◽  
System A ◽  
Sampling Points

In our frequency scanning interferometry-based (FSI-based) absolute distance measurement system, a frequency sampling method is used to eliminate the influence of laser tuning nonlinearity. However, because the external cavity laser (ECL) has been used for five years, factors such as the mode hopping of the ECL and the low signal-to-noise ratio (SNR) in a non-cooperative target measurement bring new problems, including erroneous sampling points, phase jumps, and interfering signals. This article analyzes the impacts of the erroneous sampling points and interfering signals on the accuracy of measurement, and then proposes an adaptive filtering method to eliminate the influence. In addition, a phase-matching mosaic algorithm is used to eliminate the phase jump, and a segmentation mosaic algorithm is used to improve the data processing speed. The result of the simulation proves the efficiency of our method. In experiments, the measured target was located at eight different positions on a precise guide rail, and the incident angle was 12 degrees. The maximum deviation of the measured results between the FSI-based system and the He-Ne interferometer was 9.6 μm, and the maximum mean square error of our method was 2.4 μm, which approached the Cramer-Rao lower bound (CRLB) of 0.8 μm.

scholarly journals A search for transiting planets in the β Pictoris system

2018 ◽  
Vol 615 ◽  
pp. A145 ◽  
Author(s):  
M. Mol Lous ◽  
E. Weenk ◽  
M. A. Kenworthy ◽  
K. Zwintz ◽  
R. Kuschnig
Keyword(s):  
Signal To Noise Ratio ◽  
Large Fraction ◽  
Young Star ◽  
High Signal ◽  
Data Set ◽  
Transiting Planets ◽  
Using Data ◽  
Exoplanet Systems ◽  
Transit Signals

Context. Transiting exoplanets provide an opportunity for the characterization of their atmospheres, and finding the brightest star in the sky with a transiting planet enables high signal-to-noise ratio observations. The Kepler satellite has detected over 365 multiple transiting exoplanet systems, a large fraction of which have nearly coplanar orbits. If one planet is seen to transit the star, then it is likely that other planets in the system will transit the star too. The bright (V = 3.86) star β Pictoris is a nearby young star with a debris disk and gas giant exoplanet, β Pictoris b, in a multi-decade orbit around it. Both the planet’s orbit and disk are almost edge-on to our line of sight. Aims. We carry out a search for any transiting planets in the β Pictoris system with orbits of less than 30 days that are coplanar with the planet β Pictoris b. Methods. We search for a planetary transit using data from the BRITE-Constellation nanosatellite BRITE-Heweliusz, analyzing the photometry using the Box-Fitting Least Squares Algorithm (BLS). The sensitivity of the method is verified by injection of artificial planetary transit signals using the Bad-Ass Transit Model cAlculatioN (BATMAN) code. Results. No planet was found in the BRITE-Constellation data set. We rule out planets larger than 0.6 RJ for periods of less than 5 days, larger than 0.75 RJ for periods of less than 10 days, and larger than 1.05 RJ for periods of less than 20 days.

Variations in noise and signal levels in a pair of deep boreholes near Amarillo, Texas

1994 ◽  
Vol 84 (5) ◽  
pp. 1593-1607
Author(s):  
Christopher J. Young ◽  
Eric P. Chael ◽  
David A. Zagar ◽  
Jerry A. Carter
Keyword(s):  
Noise Level ◽  
Signal To Noise Ratio ◽  
Body Wave ◽  
Working Hours ◽  
Data Set ◽  
Frequency Independent ◽  
Deep Boreholes ◽  
Strong Body ◽  
Borehole Seismic ◽  
40 Hz

Abstract To better understand the depth dependence of improvements in signal-to-noise ratio for borehole seismic data, we have collected and analyzed a data set recorded from a pair of high-fidelity, broadband (1 to 80 Hz) seismometers sited in two closely separated deep boreholes near Amarillo, Texas. The total decrease (surface to 1951 m) in minimum noise level is up to 30 dB; the total decrease in maximum noise level is up to 35 dB; and the range of noise values (maximum to minimum) at a given depth decreases from 30 to 10 dB. The majority of the noise reduction occurs within the first few hundred meters below the surface, but the decrease continues with depth and there is no indication that even lower noise levels do not exist below our deepest recording depth (1951 m). Cultural work-day noise (prominent during normal working hours) from 0 to 40 Hz is observed at all depths, suggesting a strong body-wave component. Wind-generated 15 to 60-Hz noise is strongest at the surface, and is observed as deep as 367 m without a properly shielded hole. With the addition of shielding to reduce the coupling between the borehole casing and/or cable with the wind, wind-generated noise above 40 Hz can be eliminated at 367 m. Events with near-vertical propagation paths and signal power above 1 Hz show signal strength decreasing uphole, with a steady winnowing of high frequencies that can be fit by a simple frequency-independent Q of 35.5 ± 8 for the material between 1951 and 367 m.

Refraction wavefield migration

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. Q27-Q37
Author(s):  
Yang Shen ◽  
Jie Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Surface Velocity ◽  
Data Set ◽  
Near Surface ◽  
Depth Migration ◽  
Reflection Data ◽  
Imaging Tool ◽  
Imaging Results ◽  
Migration Method ◽  
Single Method

Refraction methods are often applied to model and image near-surface velocity structures. However, near-surface imaging is very challenging, and no single method can resolve all of the land seismic problems across the world. In addition, deep interfaces are difficult to image from land reflection data due to the associated low signal-to-noise ratio. Following previous research, we have developed a refraction wavefield migration method for imaging shallow and deep interfaces via interferometry. Our method includes two steps: converting refractions into virtual reflection gathers and then applying a prestack depth migration method to produce interface images from the virtual reflection gathers. With a regular recording offset of approximately 3 km, this approach produces an image of a shallow interface within the top 1 km. If the recording offset is very long, the refractions may follow a deep path, and the result may reveal a deep interface. We determine several factors that affect the imaging results using synthetics. We also apply the novel method to one data set with regular recording offsets and another with far offsets; both cases produce sharp images, which are further verified by conventional reflection imaging. This method can be applied as a promising imaging tool when handling practical cases involving data with excessively weak or missing reflections but available refractions.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

scholarly journals Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5202 ◽  
Author(s):  
Yu ◽  
Ji ◽  
Xue ◽  
Wang
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Gaussian Noise ◽  
Quotient Space ◽  
Signal To Noise Ratio ◽  
Phase Error ◽  
Phase Shifting ◽  
Signal To Noise ◽  
Partial Differential ◽  
Filtering Method

Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.

scholarly journals Public HARPS radial velocity database corrected for systematic errors

2020 ◽  
Vol 636 ◽  
pp. A74 ◽  
Author(s):  
Trifon Trifonov ◽  
Lev Tal-Or ◽  
Mathias Zechmeister ◽  
Adrian Kaminski ◽  
Shay Zucker ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Signal To Noise Ratio ◽  
Auxiliary Information ◽  
Easy Access ◽  
High Signal ◽  
M Dwarfs ◽  
Data Set ◽  
New Public ◽  
Systematic Effects ◽  
User Friendly

Context. The High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph has been mounted since 2003 at the ESO 3.6 m telescope in La Silla and provides state-of-the-art stellar radial velocity (RV) measurements with a precision down to ∼1 m s−1. The spectra are extracted with a dedicated data-reduction software (DRS), and the RVs are computed by cross-correlating with a numerical mask. Aims. This study has three main aims: (i) Create easy access to the public HARPS RV data set. (ii) Apply the new public SpEctrum Radial Velocity AnaLyser (SERVAL) pipeline to the spectra, and produce a more precise RV data set. (iii) Determine whether the precision of the RVs can be further improved by correcting for small nightly systematic effects. Methods. For each star observed with HARPS, we downloaded the publicly available spectra from the ESO archive and recomputed the RVs with SERVAL. This was based on fitting each observed spectrum with a high signal-to-noise ratio template created by coadding all the available spectra of that star. We then computed nightly zero-points (NZPs) by averaging the RVs of quiet stars. Results. By analyzing the RVs of the most RV-quiet stars, whose RV scatter is < 5 m s−1, we find that SERVAL RVs are on average more precise than DRS RVs by a few percent. By investigating the NZP time series, we find three significant systematic effects whose magnitude is independent of the software that is used to derive the RV: (i) stochastic variations with a magnitude of ∼1 m s−1; (ii) long-term variations, with a magnitude of ∼1 m s−1 and a typical timescale of a few weeks; and (iii) 20–30 NZPs that significantly deviate by a few m s−1. In addition, we find small (≲1 m s−1) but significant intra-night drifts in DRS RVs before the 2015 intervention, and in SERVAL RVs after it. We confirm that the fibre exchange in 2015 caused a discontinuous RV jump that strongly depends on the spectral type of the observed star: from ∼14 m s−1 for late F-type stars to ∼ − 3 m s−1 for M dwarfs. The combined effect of extracting the RVs with SERVAL and correcting them for the systematics we find is an improved average RV precision: an improvement of ∼5% for spectra taken before the 2015 intervention, and an improvement of ∼15% for spectra taken after it. To demonstrate the quality of the new RV data set, we present an updated orbital solution of the GJ 253 two-planet system. Conclusions. Our NZP-corrected SERVAL RVs can be retrieved from a user-friendly public database. It provides more than 212 000 RVs for about 3000 stars along with much auxiliary information, such as the NZP corrections, various activity indices, and DRS-CCF products.

scholarly journals Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bo Liu ◽  
Qilin Wu ◽  
Yiwen Zhang ◽  
Qian Cao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Computing Time ◽  
Experimental Results ◽  
Data Set ◽  
Compression Effect ◽  
Model Based ◽  
The Relationship

Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.

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