Assessment of scale interactions associated with wake meandering using bispectral analysis methodologies

Large atmospheric boundary layer fluctuations and smaller turbine-scale vorticity dynamics are separately hypothesized to initiate the wind turbine wake meandering phenomenon,a coherent,dynamic,turbine-scale oscillation of the far wake.Triadic interactions,the mechanism of energy transfers between scales,manifest as triples of wavenumbers or frequencies and can be characterized through bispectral analyses.The bispectrum,which correlates the two frequencies to their sum,is calculated by two recently developed multi-dimensional modal decomposition methods:scale-specific energy transfer method and bispectral mode decomposition.Large-eddy simulation of a utility-scale wind turbine in an atmospheric boundary layer with a broad range of large length-scales is used to acquire instantaneous velocity snapshots.The bispectrum from both methods identifies prominent upwind and wake meandering interactions that create a broad range of energy scales including the wake meandering scale.The coherent kinetic energy associated with the interactions shows strong correlation between upwind scales and wake meandering.

A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treatments require quantitative protein identification and function.Despite technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved.Protein function inference from amino acid sequences is the main biological data challenge.This study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family discrimination.Training and validation employed 70%of the dataset,while 30%was used for testing.This paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all phases.The initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning classification.The single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%F1-score.The two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%F1-score.This work provides balanced,reliable,and precise forecasts for all families in all measures.It ensured that the new model was resilient to family variances and provided high-scoring results.

An Innovative Bispectral Deep Learning Method for Protein Family Classification

Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein characteristics are vital for improving therapies and precision medicine.The automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence data.Inferring protein function from amino acid sequences is crucial in biology.This study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector representation.The power of two representations was used to identify each amino acid,and a coding technique was established for each sequence family.Subsequently,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding techniques.Initially,the dataset was split 70/30 for training and testing.Correspondingly,the dataset was utilized for 70%training,15%validation,and 15%testing.The suggested methods are evaluated using accuracy,precision,and recall.The non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after validation.Meanwhile,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%recall.Validation showed 90%accuracy,91.2%precision,and 89.7%recall.Note that both algorithms outperform the rest.The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture.They offered the best inference performance as the proposed approach improves categorization and prediction.Several instances show successful multi-class prediction in molecular biology’s massive data.

A nonlinear wave coupling algorithm and its programing and application in plasma turbulences

The fully developed turbulence can be regarded as a nonlinear system,with wave coupling inside,which causes the nonlinear energy to transfer,and drives the turbulence to develop further or be suppressed.Spectral analysis is one of the most effective methods to study turbulence system.In order to apply it to the study of the nonlinear wave coupling process of edge plasma turbulence,an efficient algorithm based on spectral analysis technology is proposed to solve the nonlinear wave coupling equation.The algorithm is based on a mandatory temporal static condition with the nonideal spectra separated from the ideal spectra.The realization idea and programing flow are given.According to the characteristics of plasma turbulence,the simulation data are constructed and used to verify the algorithm and its implementation program.The simulation results and experimental results show the accuracy of the algorithm and the corresponding program,which can play a great role in the studying the energy transfer in edge plasma turbulences.As an application,the energy cascade analysis of typical edge plasma turbulence is carried out by using the results of a case calculation.Consequently,a physical picture of the energy transfer in a kind of fully developed turbulence is constructed,which confirms that the energy transfer in this turbulent system develops from lower-frequency region to higher-frequency region and from linear growing wave to damping wave.

The analysis of heart sounds by using bispectral technique

An advanced signal processing technique, higher-order spectra, is proposed to in vestigate the nonlinear coupling phenomena of the heart sounds. To extract more higher-order information of the heart sounds, a non-Gaussian AR model is selected for parametric bispectral estimation in analyzing several kinds of heart sounds. The non-Gaussian AR model of the sound signals is llsed to detect quadratic nonlinear interactions and to classify two patterns of heart sounds in terms of the parametric bispectral estimate. The bispectral cross-correlation is employed to the order determination of the model. Several real heart sound data are imple mented to show that the quadratic nonlinearity exist in both normal and clinical heart sounds.It was found that bispectral techniques are effective and useful tools in analyzing heart sounds and other acoustical signals

Global structure and seasonal variations of the migrating 6-h tide observed by SABER/TIMED

Ten years of SABER/TIMED temperature data are used to analyze the global structure and seasonal variations of the migrating 6-h tide from the stratosphere to the lower thermosphere. The amplitudes of the migrating 6-h tide increase with altitudes. In the stratosphere, the migrating 6-h tide peaks around 35°N/S. The climatologically annual mean of the migrating 6-h tide clearly shows the manifestation of the(4, 6) Hough mode between 70 and 90 km that peaks at the equator and near 35°N/S. Above 90 km, the 6-h tide shows more than one Hough mode with the(4, 6) mode being the dominant one. The migrating 6-h tide is stronger in the southern hemisphere. Annual, semiannual, 4-, and 3-month oscillations are the four dominant seasonal variations of the tidal amplitude. In the stratosphere and stratopause, the spring enhancement of the 6-h tide at middle latitudes is the most conspicuous feature. From the mesosphere to the lower thermosphere, the tidal amplitude at low latitudes is gradually in the scale of that at middle latitudes and exhibits different temporal variations at different altitudes and latitudes. Both ozone heating in the stratosphere and the background atmosphere probably affect the generation and the seasonal variations of the migrating 6-h tide. In addition, the non-linear interaction between different tidal harmonics is another possible mechanism.