Spectral Analysis Method Research Articles

Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets.

Exploring Rotational Isomerism of Fluorescent Diarylethenes Comprising 2-Naphthyl and Regioisomeric Oxazoyl Groups. A Combined Experimental and Computational Study

In the paper a phenomenon of rotational (conformational) isomerism within 1,2-diarylethenic compounds (DAE) comprising heteroarene ring of oxazole is explored on the example of the fluorescent naphthyl-vinyl-oxazole framework. In this regard three regioisomers of a n-(2-(naphthalen-2-yl)vinyl)oxazole molecule (nNVOx), differing in the oxazole substitution pattern (n = 2, 4, 5), are synthesized and methodically investigated by means of electronic UV-Vis spectroscopy combined with chemometric methods for spectral data analysis. Design of the experiments and interpretation of their output is additionally supported by quantum-chemical simulations based on the (TD) DFT methodology. As the result, all the studied nNVOx compounds are unveiled to simultaneously occur in form of at least two non-equivalent rotational isomers, varying in the conformation taken by the arene moieties with respect to the central vinyl fragment. The individual rotamers are then demonstrated to exhibit non-identical spectroscopic properties expressed by differences in corresponding absorption and fluorescence profiles, thereby ultimately confirming the impact of rotamerism onto the investigated molecular systems.

Long time behavior for the two-dimensional magnetohydrodynamic flows in a general domain

By utilizing spectral analysis methods and properties of fractional order operators, some new estimates have been established for the nonlinear terms appearing from the two-dimensional non-stationary magnetohydrodynamic flows in a general domain. Based on these, a series of energy decay properties are given. Furthermore, the large time decay behavior of the solution and that of its gradient have also been addressed in the L∞ and L2 spaces, respectively.

О возможности проведения контроля состояния асинхронных электродвигателей с частотно-регулируемым приводом методом спектрального анализа

One of the important tasks of current energy power industry is to increase the reliability of induction motors operation. A promising direction for monitoring their technical condition is application of methods of spectral analysis of signals. However, very little attention is paid to the diagnostic assessment of induction motors as a part of a variable frequency drive, as well as the use of frequency converters for diagnostic purposes. Therefore, the issues related to monitoring the condition of induction motors with variable frequency drives are relevant and require scientific research. The purpose of the study is experimental evaluation of the possibility to use the spectral analysis method to assess the technical condition of induction motors driven by frequency converters. The research has been carried out on AIR71A6 induction motor with a serviceable rotor winding and one broken rotor bar rotor winding. The internal magnetic field is considered as a diagnostic signal in steady-state and start-up mode. The condition has been evaluated based on the determination of harmonics in the amplitude spectrum from the dummy rotor winding. A Short Time Fourier Transform is used for signal processing. The spectra of the internal magnetic field have been obtained at start-up and in steady-state mode when the engine is fed by an autotransformer and frequency converter. A diagnostic sign of the presence of broken bars, that is an increase of harmonic amplitudes from the dummy rotor winding, manifested itself not only when the electric motor is fed by an autotransformer, but also frequency driven. Comparing the amplitudes of harmonics in the presence and absence of a frequency converter allows us to conclude that interference of the converter slightly affect the amplitudes of specific harmonics. Using the example of an internal magnetic field signal, it is shown that spectral analysis methods can be used to diagnose the induction motors condition with a variable speed drive.

Mining PANI/Ti3AlC2/CeO2 ternary composite CO sensor: Material synthesis, gas sensing performance and mechanism research

To address the issue of poor gas sensitivity in existing mining semiconductor CO sensor materials like PANI at room temperature, this study employed an ice-water bath in conjunction with an in-situ polymerization method to incorporate 2D sheet-like Ti3AlC2 and nano-CeO2 materials with oxygen vacancies into PANI. As a result, nanometer-sized PANI/Ti3AlC2/CeO2 ternary composite nanomaterial was synthesized. The morphology, synthesis mechanism, and thermal stability of the ternary composite material were investigated using scanning electron microscopy-energy dispersive analysis (SEM-EDS), transmission electron microscopy analysis (TEM), spectral analysis, and thermogravimetric methods. Furthermore, the CO gas sensing mechanism of the ternary composite material was also examined. The study discovered that the nano-PANI/Ti3AlC2/CeO2 ternary composite nanomaterial, when deposited on the Au interdigital electrode of the IDE substrate, exhibits superior gas transmission of 400 ppm CO compared to gas sensors based on nano-PANI, nano-PANI/Ti3AlC2, and nano-PANI/CeO2. The sensing response was enhanced by 1.75 times, 1.4 times, and 1.2 times, respectively. This improvement in sensing mechanism can be attributed to the pn heterojunction and interfacial interaction forces between the components. Hence, the nano-PANI/Ti3AlC2/CeO2 ternary composite nanomaterial holds potential as a high-performance CO detection sensing material at room temperature. Additionally, it can serve as a theoretical basis for further research on low-power, integrable sensor materials in coal mines.

Spectral element method for random vibration analysis of the vehicle-track coupling system

In this paper, we propose a new random vibration analysis model for the vertical vehicle-track coupling system, which combines the symplectic method and the spectral element method (SEM) to enhance calculation efficiency and accuracy. The model assumed that the vehicle is composed of a multi-rigid body with 10 DOFs, and its motion equation is derived using the traditional frequency analysis method. Tracks are considered periodic structures, and the part between sleeper support points is considered a substructure. The motion equation of the periodic track-substructure is established by spectral element method (SEM), and the propagation of vibration wave between the substructures is calculated by the symplectic method. The frequency responses of the vehicle-track coupling system are calculated by using the German low-interference irregularity spectrum and short-wave irregularity spectrum as excitation. Then, the rationality of this method is verified by comparing it with the results from the traditional symplectic method and simulations using Universal Mechanism software. The comparison shows that this method surpasses the traditional symplectic method in high-frequency accuracy while maintaining high computational efficiency. Although the finite element method also has the capability for high-frequency analysis, this method can calculate the high-frequency vibration of an infinitely long track using just one element, making it more suitable for the efficient analysis of vehicle-track coupling systems. Finally, the model is applied to analyze the distribution characteristics of the vibration response of the coupled system in the frequency domain.

Impact of rogue waves on semi-submersible platforms: An experimental investigation of wave run-up and air-gap responses

The wave run-up and air-gap responses of semi-submersible platforms under extreme sea conditions are crucial design considerations for ensuring the structural integrity and safety of marine engineering operations. This study investigated the impact of rogue waves on the wave run-up and air-gap responses of semi-submersible platforms using a transient focusing wave group to accurately simulate irregular seas. Comparative model tests were conducted on a semi-submersible platform under both rogue and non-rogue-wave conditions. The platform motions and air-gap responses were analysed using spectral analysis, extreme-value statistics, and wavelet methods. Rogue waves were found to significantly affect the air-gap response spectrum, increasing the nonlinearity of both the wave run-up and air-gap responses. Different locations on the platform exhibited varying statistical and spectral response characteristics. Rogue waves modified the instantaneous properties of wave elevation, thereby increasing the energy levels in the system. This study confirmed that rogue waves significantly influence the air-gap responses and wave run-up of semi-submersible platforms, suggesting that they should be considered when designing and planning the operations of these platforms. This study offers a comprehensive understanding of the complex interactions between rogue waves and large maritime structures and provides insights for improving safety and performance under extreme sea conditions.

Enhanced numerical resolution of the Duffing and Van der Pol equations via the spectral homotopy analysis method employing chebyshev polynomials of the first kind

Enhanced numerical resolution of the Duffing and Van der Pol equations via the spectral homotopy analysis method employing chebyshev polynomials of the first kind

The effect of complex extra-furnace treatment of metal melts on the formation of non-metallic inclusions in large-sized ingots

Abstract The article presents the results of studying non-metallic inclusions (NI) after extra-furnace treatment of the 13Cr9Mo2Co1NiVNbNB steel melt to produce large-sized ingots. The objects of the study were laboratory samples melted in a semi-industrial induction furnace with the capacity of 20 tons (the first batch), and samples obtained after melting in an electric arc furnace with the capacity of 120 tons, followed by modification and double vacuuming (the second batch).
Previously, using the Fact Sage software, modeling of the NI formation processes was carried out. The data were obtained on the possible NI composition and amount in this steel.
Studying NIs (the composition, the contamination index, inclusion parameters) was carried out on an ASPEX Explorer electron microscope using the method of automatic X-ray spectral analysis of particles (Automatic Features Analysis, AFA). It was established that in the samples of the first batch, the main part of the NIs consisted of chromium- and manganese-containing oxides: chromium-manganese spinels (Cr-Mn-O and Mn-Cr-Si-O groups), which occupy more than 94% of the total area of the NIs. In the samples of the second batch, the main part of the Nis was represented by Cr-Mn-O; Al-Cr-O; Cr-Al-Mn-O systems.
It was shown that the average size of non-ferrous substances in the samples of the laboratory batch and in industrial samples after extra-furnace treatment differs slightly (5-12%), however, the contamination index in industrial samples after vacuum treatment and especially after modification and repeated vacuum treatment was reduced by more than 10 times. The results obtained allow recommending vacuum treatment and complex modification with calcium and boron as measures to prevent the formation of non-ferrous metals and to improve metallurgical quality when smelting large ingots using complex alloy steels.

Spectral and optic-metric methods of monitoring parameters of plasma channels caused by discharge currents between metals granules in working liquids

Introduction. Spark-erosion processing of metals and alloys granules in working liquids is the basis of a several technological processes. Efficiency of energy use in them and parameters of the resulting product largely depend on the accuracy of stabilization and regulation of pulse power in each plasma channel between the granules. To achieve this, until now only the voltage and current of the discharge pulses in the entire layer of granules have been controlled. Problem. The measurement methods, which are used, are not effective enough for monitoring the parameters of individual plasma channels and predicting the size distribution of eroded metals particles at the stage of their formation. The aim of the work is to develop a method for determining the volumes of components of plasma channels in layers of metals granules during their spark-erosion treatment to predict the size distribution of eroded metal particles at the stage of their formation, as well as to simplify the method of spectrometric analysis of the elemental composition of substances surrounding plasma channels for the operational prediction of the chemical composition of resulting products. Methodology. A series of experiments were carried out on spark-erosion processing of Al and Ag granules layers in distilled water. Using a digital camera, images of the plasma channels in them were obtained. Based on the theory of pulsed electrical breakdown of liquid dielectrics, an analysis of the components of plasma channels was carried out. Using the specialized ToupView program, the volumes of equivalent ellipsoids of rotation were determined, approximating the halos of colored radiation likely arising from streamers, as well as the spark cores of plasma channels emitting white light. The shades of the resulting radiation were studied for several metals and working liquids. The obtained data were compared with the known results of spectrometric studies for the same elements excited by similar mechanisms. Results. The theory of discharge-pulse systems for spark-erosion processing of granular conductive media has been developed in the direction of new methods for monitoring the parameters of discharge pulses and predicting the chemical composition and size distribution parameters of eroded metal particles at the stage of their production. An optic-metric method has been developed for determining the volumes of halos and cores of plasma channels. A simplified spectral method for determining the chemical composition of erosion particles based on the shade of the resulting radiation was proposed. Originality. The developed new optic-metric method makes it possible to obtain information about almost every plasma channel, which refines predictions of the size distribution of erosion particles. To implement the method, general-purpose hardware and specialized software that is freely available are used. The developed method of simplified spectral analysis of excited atoms makes it possible to make preliminary predictions of the chemical composition of the obtained erosion particles already at the stage of their formation without the use of expensive specialized equipment. Practical significance. The ratio of the volumes of halos and cores of plasma channels between Al and Ag granules in distilled water was measured. An analysis of the emission spectra of plasma channel halos between Al, Ag and Cu granules in distilled water, Fe in ethyl alcohol, Ni-Mn-Ga and Ti-Zr-Ni alloys in liquid nitrogen, and Ti-Zr-Ni in liquid argon was carried out. Based on spectrometry data, the resulting shades of these radiations were substantiated and their description in the RGB system is given. References 56, table 1, figures 4.

A Review of Hypergraph Neural Networks

In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships. However, in many real-world networks, the relationships between entities are complex and go beyond simple pairwise connections, as seen in scientific collaboration networks, protein networks, and similar domains. If these complex relationships are directly represented as pairwise relationships using graph structures, it can lead to information loss. A hypergraph, as a special kind of graph-structured data, can represent higher-order relationships that cannot be fully captured by graphs, thereby addressing the limitations of graphs. In light of this, researchers have begun to focus on how to design neural networks on hypergraphs, leading to the proposal of hypergraph neural network (HGNN) models for downstream tasks. Therefore, this paper reviews the existing hypergraph neural network models. The review is conducted from two perspectives: spectral analysis methods and neural network methods on hypergraphs, discussing both unfolded and non-unfolded methods, and further subdividing them based on their algorithm characteristics and application scenarios. Subsequently, the design concepts of various algorithms are analyzed and compared, and the advantages and disadvantages of each type of algorithm are summarized based on experimental results. Finally, potential future research directions in hypergraph learning are discussed.

Quantification of nonlinear effect in seismic signal using Holo-Hilbert spectral analysis

Affected by excitation conditions, absorption, and attenuation of stratum and receiving instruments, the seismic signal is non-stationary and nonlinear. Time-frequency spectral analysis methods are effective and widely used in describing the time-varying features of seismic signals. However, these methods are all based on additive expansions, and cannot represent the nonlinearity of signal, which is achieved through a multiplicative process. Therefore, the information provided by the time-frequency spectrum is incomplete. To show the nonlinearity of seismic signal, that is, the modulation effect of low-frequency components on high-frequency components, the Holo-Hilbert spectral analysis method is introduced in this paper. Holo-Hilbert spectral analysis (HHSA) contains nested empirical mode decomposition and Hilbert transform to achieve a full informational spectrum represented by two frequency dimensions. In the Holo-Hilbert spectrum, one dimension represents frequency-modulated (FM) frequency, which characterizes the non-stationarity of the signal, and the other dimension represents the amplitude-modulated (AM) frequency, which indicates the nonlinear effect in the signal. Synthetic and field data examples verify the feasibility of HHSA in characterizing the cross-frequency interaction of non-stationary seismic signals as a potential tool to detect reservoirs.

Lifetime minimally invasive assessment of iron in the pig liver

Microelements are an integral part of the mammalian body, and their content in organs and tissues is associated with other components of a complex biological system. Based on this, it is feasible to evaluate the concentration of specific chemical elements within the structures of the body in non-invasive or minimally invasive methods. The meat and by-products of farm animals serve as a readily assimilateable source of iron, which is one of the reasons for potentially defining the quality of agricultural products in conditions of widespread iron deficiency. Landrace pigs were raised in standard conditions at an industrial complex located in the Altai Territory in order to fatten up to a live weight of 100 kg. Venous blood was collected using the acute method from the jugular vein in accordance with the principles of asepsis and pre-analytical guidelines. The hematologic and biochemical examination of the blood and serum of animals was performed by apparatus. After slaughter, liver samples were collected, and the method of atomic emission spectral analysis using inductively coupled plasma on iCAP-PRO equipment (Thermo Fisher Scientific) was used to estimate the iron level in them. To manipulate the data, Microsoft Office Excel software and RStudio data analysis environment version 2023.03.1 (RStudio, PBC) were employed. For regression analysis, the least squares approach was used. The model was fitted using a stepwise selection of predictors in both directions using the Akaike information criterion, Bayesian information criterion, and adjusted coefficient of determination. The linear regression assumptions were evaluated. The final regression model used for determining iron levels in pig liver contains mean hemoglobin content in erythrocytes, hemoglobin, and serum inorganic phosphorus as predictors. There is no evidence that there is multicollinearity between the predictors of the final model. The proposed model satisfies the requirements for a normal distribution of residuals, the absence of their correlation, and influential observations. The proposed multiple regression model has the capability to estimate iron levels in pig liver in vivo for various purposes.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness.

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patients capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Maximum entropy spectral analysis: an application to gravitational waves data analysis

The maximum entropy spectral analysis (MESA) method, developed by Burg, offers a powerful tool for spectral estimation of a time-series. It relies on Jaynes’ maximum entropy principle, allowing the spectrum of a stochastic process to be inferred using the coefficients of an autoregressive process AR(p) of order p. A closed-form recursive solution provides estimates for both the autoregressive coefficients and the order p of the process. We provide a ready-to-use implementation of this algorithm in a Python package called memspectrum, characterized through power spectral density (PSD) analysis on synthetic data with known PSD and comparisons of different criteria for stopping the recursion. Additionally, we compare the performance of our implementation with the ubiquitous Welch algorithm, using synthetic data generated from the GW150914 strain spectrum released by the LIGO-Virgo-Kagra collaboration. Our findings indicate that Burg’s method provides PSD estimates with systematically lower variance and bias. This is particularly manifest in the case of a small (O(5000)) number of data points, making Burg’s method most suitable to work in this regime. Since this is close to the typical length of analysed gravitational waves data, improving the estimate of the PSD in this regime leads to more reliable posterior profiles for the system under study. We conclude our investigation by utilising MESA, and its particularly easy parametrisation where the only free parameter is the order p of the AR process, to marginalise over the interferometers noise PSD in conjunction with inferring the parameters of GW150914

Sound quality characteristics of reciprocating hermetic refrigeration compressors

In recent years, sound quality (SQ) has become an increasingly important factor in the home appliance industry, enhancing the customer experience and comfort. Manufacturers that prioritize SQ in their designs can gain a competitive advantage in the market and increase customer satisfaction. The compressor is one of the main sources of noise in a refrigerator. While manufacturers typically provide refrigerator sound quality and noise labels, research that specifically evaluates the compressor sound quality metrics at a component level is not common. Therefore, this study aims to isolate the compressor from the refrigerator to investigate SQ metrics for hermetic refrigeration compressors. To achieve this, compressors with different capacities are operated and recorded at different working speeds for different thermal conditions. Objective parameters are calculated and tested using time domain signals and analyzed with spectral analysis methods. In addition to the objective parameters, subjective results are obtained through jury tests consisting of 75 people of different genders and age ranges. By considering objective parameters of sound quality as input and sound quality evaluation values as output, a regression model is established. It is found that Loudness, Sharpness, and Degree of Modulation play an important role in compressor sound quality evaluations consider.

Research on optimizing the optical local-oscillator power of Coherent Doppler LiDAR to enhance wind velocity measurements

Due to weak echo signals that become progressively overwhelmed by noise, measurement accuracy and effective detection range of the Coherent Doppler wind LiDAR (CDL) are often compromised. While increasing the optical local-oscillator power (OLP) can amplify the echo signal, it is constrained by the nonlinear effects of the detector. This paper introduces a method for optimizing the OLP in CDL systems. Theoretical analysis has been proposed to explore the amplification effect of OLP on echo signals, and the nonlinear effects of detectors have been studied. Simulations are performed to explore the influence of varying OLP on the signal-to-noise ratio (SNR) across different detector α (quadratic nonlinear coefficient) values. The spectral analysis method is used to directly compute the SNR of actual atmospheric wind field signals under various OLP settings. Results demonstrate consistency between calculated and simulated values, enabling determination of optimal OLP and the detector α values from fitted curves. Comparative experiments confirm significant enhancement in effective detection range (> 1.5 km) with ± 0.5 m/s accuracy. The innovation of this study lies in combining the OLP optimization method with real atmospheric wind field echo signal experiments. It addresses the challenges of directly measuring the nonlinear parameters of the detector and determining the optimal OLP. This study offers valuable theoretical and experimental insights for enhancing the wind measurement performance of CDL.

Acoustic flow resonances in installed rectangular jets

Zonal Large Eddy Simulations (LES) of complex installed rectangular jet flows are performed for subsonic jet conditions of the Central Institute for Aviation Motors (CIAM) experiment, where a strong tone was reported. For far-field noise modelling, the zonal LES solutions are coupled with the permeable formulation of the Ffowcs Williams and Hawkings (FW-H) method. The obtained noise spectra predictions are compared with the acoustic measurements in the CIAM facility. To reduce statistical noise of the relatively short LES time series, contributions due to several dominant low frequency tones and the remaining broadband signal are computed separately, using a tailored Fourier transform technique for each signal type. For cross-validation, noise spectra measurements of an equivalent isolated round jet in the CIAM facility are compared with the empirical sJet model calibrated on the NASA Small Hot Jet Acoustic Rig (SHJAR) jet noise database. While the two datasets agree reasonably well for mid to high frequencies, larger discrepancies are observed for low frequencies, which are attributed to acoustic reflections in the non-anechoic CIAM facility. The differences in noise levels between the CIAM dataset and the sJet model representing the NASA jet noise data are used to determine the experimental uncertainty of the CIAM noise measurements for each frequency and observer angle. For the installed rectangular jet, it is shown that far-field noise spectra predictions of the zonal LES-FW-H method for both the fundamental tone, its sixth harmonic corresponding to the jet Strouhal number of around 1, and the broadband component are broadly within the experimental error bar for most observer angles. Some discrepancies between predictions of the zonal LES method and the CIAM measurements for the main low frequency tone for the 30o and 90o, where the experimental uncertainty at low frequencies is largest are also noted. After validating the acoustic solutions, spectral and conditional averaging analysis methods are applied to elucidate mechanisms of the acoustic-flow resonance in the installed rectangular jet flow. The numerical dispersion relation is obtained to analyse phase velocities of the upstream and downstream travelling pressure waves in the stream-wise direction. The conditional analysis of pressure fluctuations inside the jet reveals the emergence of internal reflection points corresponding to a rapid change of the acoustic wave phase as well as the saddle points interpreted as localised zones of vorticity shed from the side-wall edges. To independently investigate the effect of trailing edges and corner points of the jet embodiment geometry on closing the acoustic-flow resonance cycle, several modifications of the baseline rectangular nozzle are considered, such as introducing chevron-like lobes and cuts on the trailing edges of the side walls as well as smoothing the sharp corner points of the wall edges. Following the series of additional zonal LES runs with the modified installed nozzle geometries, an optimized tone-less modification of the original installed nozzle was obtained, in broad agreement with the conditional analysis results. The obtained tone-less modification of the installed rectangular jet is further analyzed to provide insights into its similarity and differences from the aeroacoustics of the reference isolated round jet in the same CIAM facility.

Non-Destructive Identification of Virgin Cashmere and Chemically Modified Wool Fibers Based on Fractional Order Derivative and Improved Wavelength Extraction Algorithm Using NIR Spectroscopy and Chemometrics

ABSTRACT Virgin cashmere is highly prized for its superior quality, and chemically modified wool is often used to fake it. However, traditional spectral analysis and image processing methods struggle to identify the two fibers. To address this, combining chemometrics with near-infrared (NIR) spectroscopy is proposed to identify virgin cashmere and chemically modified wool based on fractional order derivatives and improved wavelength extraction algorithm. The original spectra of the two fibers are very close, making identification difficult. Therefore, fractional order derivatives are used to amplify tiny spectral differences, which are often overlooked in conventional analysis. Meanwhile, common wavelength extraction algorithms few consider inherent relationship between spectral features and chemical properties, so an improved wavelength extraction algorithm using Shuffled Frog Leaping Algorithm (SFLA) and Beluga Whale Optimization (BWO) is employed to explore the connection of the two. The proposed method with Partial Least Squares Discriminant Analysis (PLS-DA) achieves an accuracy of 94.4%. Experimental results demonstrate that fractional order derivatives enhance tiny spectral features and differences, while extracted feature wavelengths correspond to main chemical characteristic bands of the fibers. This study shows the feasibility of identifying virgin cashmere and chemically modified wool and offers a novel idea for identifying fibers in the textile industry.

Bias‐Eliminating Techniques in the Computation of Power Spectra for Characterizing Gravity Waves: Interleaved Methods and Error Analyses

AbstractObservational data inherently contain noise which manifests as uncertainties in the measured parameters and creates positive biases or noise floors in second‐order products like variances, fluxes, and spectra. Historical methods estimate and subsequently subtract noise floors, but struggle with accuracy. Gardner and Chu (2020, doi.org/10.1364/AO.400375) proposed an interleaved data processing method, which inherently eliminates biases from variances and fluxes, and suggested that the method could also eliminate noise floors of power spectra. We investigate the interleaved method for spectral analysis of atmospheric waves through theoretical studies, forward modeling, and demonstration with lidar data. Our work shows that calculating the cross‐power spectral density (CPSD) from two interleaved subsamples does reduce the spectral noise floor significantly. However, only the Co‐PSD (the real part of CPSD) eliminates the noise floor completely, while taking the absolute magnitude of CPSD adds a reduced noise floor back to the spectrum when the sample number is finite. This reduced noise floor can be further minimized through averaging over more observations, completely different from traditional spectrum calculations whose noise floor cannot be reduced by incorporating more samples. We demonstrate the first application of the interleaved method to spectral data, successfully eliminating the noise floor using the Co‐PSD in a forward model and in lidar observations of the vertical wavenumber of gravity waves at McMurdo, Antarctica. This high accuracy is gained by sacrificing precision due to photon‐count splitting, requiring additional observations to counter this effect. We provide quantitative assessment of accuracy and precision as well as application recommendations.