Accurate Frequency Research Articles

Evaluation of Modeled Diurnal Warming Estimates for Application to Producing Sea Surface Temperature Analyses

AbstractAccurate knowledge of the amount of diurnal warming present in sea surface temperature (SST) observations at different times and effective depths is important for multiple applications including the production of blended SST analyses. This work explores the ability of a modified Kantha‐Clayson‐type one‐dimensional mixed layer ocean model with wave effects to accurately simulate the observed diurnal warming amplitude over a global grid when forced with coarse resolution numerical weather prediction (NWP) outputs. The sensitivity of the modeled diurnal amplitudes to multiple adjustable parameters and model configurations is evaluated to determine whether a preferred configuration can be identified that yields reliable predictions. The accuracy of the predictions is determined through comparison against estimates from operational SST retrievals from geostationary satellites. The results show that a single configuration can yield predictions that reproduce the observed range of diurnal warming amplitudes across a range of seasons and locations and an accurate occurrence frequency of the largest amplitude events. Simulated amplitudes fall along the one‐to‐one line with observations but with significant scatter due to factors including positioning of the NWP fluxes. The identified configuration is also shown to favorably reproduce diurnal warming observations from multiple research cruises. Overall uncertainty of the simulated diurnal warming amplitude across the different tests ranges between 0.4 K for all observations to ∼1 K for the largest warming events. While the focus is not on model comparisons, the results show improved performance relative to other models. Use of the model appears warranted but the associated uncertainty must be considered.

A combination of improved spline-kernelled chirplet transform and multi-synchrosqueezing technique for analyzing nonstationary signals and structural instantaneous frequency identification

To improve the accuracy of structural instantaneous frequency (IF) identified by the spline-kernelled chirplet transform, a combination of an improved spline-kernelled chirplet transform and a multi-synchrosqueezing technique is proposed for analyzing nonstationary signals and structural IF identification in this paper. Firstly, an improved spline-kernelled chirplet transform (ISCT) is put forward by introducing a three parameter Gaussian window function for an existing spline-kernelled chirplet transform. These three parameters are successfully obtained through an optimization algorithm and the energy concentration measure (CM). Then, combined with a multi-synchrosqueezing technique, a so-called improved multi-synchrosqueezing spline-kernelled chirplet transform (IMSSSCT) approach is further presented to improve the time–frequency energy concentration. Finally, the accuracy and practicability of the proposed IMSSSCT method are validated by analytical signals, a single-degree-of-freedom Duffing system, a three-layer shear frame, as well as a cantilever beam experiment. The research results indicate that the IMSSSCT technique can effectively and accurately identify the IFs of nonlinear and time-varying structures.

Target localization algorithm based on improved short-time Fourier transform

The properties of Doppler through-wall radar (TWR) in target localization applications mainly depend on the accuracy of instantaneous frequency (IF) estimations obtained from the time-frequency distribution (TFD) of target echo. However, when the time-frequency aliasing occurs in TFD, it is difficult to accurately estimate the target IF curves. To solve this problem, an improved short-Time Fourier transform is proposed in this paper. Firstly, a series of rough estimation time-frequency information (IF and chirp rate) of target echoes are obtained by fractional Fourier transform. Secondly, we update the time-frequency window according to the rough time-frequency information of the target signal. Meanwhile, we define a new distribution estimator based on the local maximum. Finally, the target IF curve is obtained by the local maximum operator as well as the updated window function. The experimental results show that the method significantly improves the target IF estimation and localization accuracy by 76.5 % and 74.6 %, respectively, compared with STFT.

Online Mechanical Resonance Frequency Identification Method Based on an Improved Second-Order Generalized Integrator—Frequency-Locked Loop

To address the issue of mechanical resonance frequency detection in dual-inertia servo systems, this paper proposes an online identification method for mechanical resonance frequency using a low-pass filter and cascaded second-order generalized integrator—frequency-locked loop (LPF-CSOGI-FLL). Initially, the cascaded second-order generalized integrator—frequency-locked loop (CSOGI-FLL) is employed to eliminate the interference of direct current (DC) bias in resonance frequency identification. From a dual-stage structural perspective, the first second-order generalized integrator (SOGI-FLL) acts as a band-pass pre-filter to extract the mechanical resonance signal from the signal to be tested. The second SOGI-FLL generates a signal with equal amplitude and frequency to the mechanical resonance and obtains the frequency of the resonance signal through the frequency-locked loop. Subsequently, a low-pass filter (LPF) is applied to the frequency feedback loop of the second-stage SOGI-FLL, effectively reducing the oscillation of the estimated frequency. Finally, combining the CSOGI-FLL with an LPF forms a novel structure, namely, LPF-CSOGI-FLL. The results demonstrate that the proposed method significantly improves the detection accuracy of mechanical resonance frequency under various conditions. Compared to traditional offline techniques, this method overcomes the impact of resonance frequency drift and enhances system stability.

Improving the frequency resolution of distribution of relaxation times by integrating elastic net regularization and quantum particle swarm optimization

The distribution of relaxation times (DRT) method is an efficient approach for analyzing electrochemical impedance spectroscopy (EIS) of fuel cells. However, facing electrochemical processes with close characteristic times/frequencies, low frequency resolution of DRT puts a damper on the analysis of results. To address this problem, a new DRT method integrating elastic net regularization and quantum particle swarm optimization (QPSO) algorithm is proposed in this study. Elastic net regularization combines the advantages of Lasso's feature selection and Tikhonov's coefficient shrinkage, thereby enhancing the frequency resolution of DRT. The use of the QPSO algorithm avoids the manual selection of parameters. A comprehensive evaluation of the developed method is conducted using both synthetic EIS and experimental EIS. The results show that the developed DRT method shows good performance and the reconstructed DRT functions exhibit high frequency resolution and accuracy. Overall, the proposed DRT method holds promise for advancing the characterization of fuel cells.

Nonlinear dynamic analysis and vibration reduction of two sandwich beams connected by a joint with clearance

The dynamics and vibration reduction characteristics of the clamped–clamped two sandwich beams jointed with clearance is studied theoretically and experimentally. A transverse and torsional spring system with clearance is used to equivalent the joint model. The homogenization method is used to equivalent the core layer and Rayleigh-Ritz method is utilized to derive the mode function of the interconnected sandwich beam by using a sequence of orthogonal polynomials. The nonlinear motion equation of the two jointed sandwich beam structure with clearance is derived by the application of the Hamilton principle and then solved using an improved Newmark integration approach. In order to validate the accuracy of the natural frequency and vibration mode, the finite element model is established. This paper examines the impact of clearance on the amplitude frequency response and vibration transmission of the two jointed sandwich beam structure, it is found the jointed sandwich beams show obvious nonlinear characteristics and intermittent vibration transmission phenomenon due to the clearance. Moreover, the vibration transmission analysis reveals that the existed clearance demonstrates significant vibration reduction effect, for which an experiment is conducted to validate the results. In general, this work proposes a novel approach for modeling sandwich structures with clearance with improved vibration reduction performance.

EEG workload estimation and classification: a systematic review.

Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. ML and DL techniques have been increasingly employed to develop accurate workload estimation and classification models based on EEG data. The goal of this systematic review is to compile the body of research on EEG workload estimation and classification using ML and DL approaches. The PRISMA procedures were followed in conducting the review, searches were conducted through databases at SpringerLink, ACM Digital Library, IEEE Explore, PUBMED, and Science Direct from the beginning to the end of February 16, 2024. Studies were selected based on predefined inclusion criteria. Data were extracted to capture study design, participant demographics, EEG features, ML/DL algorithms, and reported performance metrics. Out of the 125 items that emerged, 33 scientific papers were fully evaluated. The study designs, participant demographics, and EEG workload measurement and categorization techniques used in the investigations differed. SVM, CNN, and hybrid networks are examples of ML and DL approaches that were often used. Analyzing the accuracy scores achieved by different ML/DL models. Furthermore, a relationship was noted between sample frequency and model accuracy, with higher sample frequencies generally leading to improved performance. The percentage distribution of ML/DL methods revealed that SVMs, CNNs, and RNNs were the most commonly utilized techniques, reflecting their robustness in handling EEG data. The comprehensive review emphasizes how ML may be used to identify mental workload across a variety of disciplines using EEG data. Optimizing practical applications requires multimodal data integration, standardization efforts, and real-world validation studies. These systems will also be further improved by addressing ethical issues and investigating new EEG properties, which will improve human-computer interaction and performance assessment.

Coarse frequency offset estimation and compensation based on short-time spectrum analysis in FSO communication system

Due to the high-speed movement of low-orbit satellites, the Doppler frequency offset in 1550-nm wavelength satellite-to-ground coherent laser communication is as high as ±4.5 GHz, which greatly increases the difficulty of carrier frequency acquisition. Low complexity, short capture time, and stable coarse frequency offset estimation and compensation algorithms are technical bottlenecks that must be addressed. In this work, a coarse frequency offset estimation and compensation algorithm based on short-time spectrum analysis is proposed. In the proof-of-principle system, the feasibility of the coarse frequency offset capture is verified based on a 2.5-GBaud data rate PM-QPSK modulation FPGA-based coherent receiver. The results show that the frequency offsets capture range is extended to ±4.5 GHz, covering the Doppler frequency offset range. Moreover, the standard deviation of the residual frequency offset after coarse frequency offset compensation is less than 140 MHz, which is smaller than the accurate frequency offset compensation range of 312.5 MHz.

A Network Approach for the Accurate Characterization of Water Lines Observable in Astronomical Masers and Extragalactic Environments.

The water molecule, crucial to the chemical composition and dynamics of the universe, is typically identified in its gas phase via radio and submillimeter transitions, with frequencies up to a few THz. To understand the physicochemical behavior of astronomical objects, accurate transition frequencies are required for these lines. From a set of 26 new and 564 previous Lamb dip measurements, utilizing our ultrasensitive laser-based spectrometers in the near-infrared region, ultrahigh-precision spectroscopic networks were set up for H2 16O and H2 18O, augmented with 40 extremely accurate frequencies taken from the literature. Based on kHz-accuracy paths of these networks, considerably improved line-center frequencies have been obtained for 35 observed or predicted maser lines of H2 16O, as well as for 14 transitions of astronomical significance of H2 18O. These reference frequencies, attached with 5-25 kHz uncertainties, may help future studies in various fields of astrochemistry and astrophysics, in particular when precise information is demanded about Doppler-velocity components, including the gas flows of galactic cores, the kinematics of planetary nebulae, or the motion in exoplanetary atmospheres.

An improved derivative‐based phase‐locked loop for single‐phase grid synchronization under abnormal grid conditions

SummaryGenerating a quadrature signal in a single‐phase system using a second‐order generalized integrator (SOGI) requires accurate frequency information. The standard SOGI phase‐locked loop (PLL) includes frequency feedback to the SOGI. However, during grid abnormalities such as voltage sag/swell and phase angle jumps, the SOGI‐PLL faces frequency disturbances that propagate to the phase detector (PD) and affects the quadrature signal generator (QSG). Furthermore, the SOGI‐PLL having two loops dependent on each other creates loop coupling phenomena; either a change in phase or frequency affects each other. SOGI‐PLL is tuning sensitive, as the SOGI block has a gain that needs to be adjusted, increases the complexity, and affects the performance of the system. There is a trade‐off between SOGI gain and PLL parameters that needs to be considered for adequate parameter design to provide accurate grid synchronization while maintaining the stability of the system. To attain better performance, researchers have proposed derivative‐based PLL (DPLL). The conventional DPLL faces challenges to noise and harmonics amplification. This paper presents an improved DPLL for single‐phase grid synchronization under adverse grid conditions. The improved derivative‐based PLL (IDPLL) comprises two improved derivative‐based quadrature signal generator (IDQSG) blocks to extract the phase‐error information for accurately estimating phase and frequency. The detailed mathematical modeling and bode plot for the IDQSG and IDPLL are presented. The proposed IDQSG eliminates the requirement of gain tuning, hence reducing complexity. Moreover, there is no interdependent loop in the IDPLL, which significantly improves the dynamic performance. A hardware setup is developed to evaluate the performance of the system in real‐time. The experimental results are obtained using an field programmable gate array (FPGA)‐based controller.

A cross-comparison of different extreme value modeling techniques for traffic conflict-based crash risk estimation

Extreme Value Theory (EVT) models have recently gained increasing popularity for crash risk estimation using traffic conflict data. Extreme value modeling consists of two fundamental approaches: the block maxima approach and the peak-over-threshold approach, each with several variants. However, a comprehensive comparison of these two approaches and their variants in crash risk estimation is lacking. This study bridges this gap by comparing different extreme value modeling techniques and evaluating their performance in estimating crash frequencies. Within a non-stationary Bayesian hierarchical modeling framework, the analyzed models include the block maxima model, the r largest order statistic model, and the peak-over-threshold model with the fixed and dynamic threshold, across univariate and bivariate traffic conflict cases. The analysis utilizes modified time-to-collision and post-encroachment time conflict indicator data collected from four signalized intersections in the City of Surrey, British Columbia, Canada. The results show that incorporating additional order statistics in the r largest order statistic model improves predictive performance, particularly with limited extreme conflict samples. Moreover, employing the dynamic threshold within the peak-over-threshold model enhances model goodness-of-fit and yields more accurate crash frequency estimates compared to using the fixed threshold. While the performance of the block maxima and peak-over-threshold models varies with the selected conflict indicator in the univariate case, the bivariate peak-over-threshold model with the dynamic threshold exhibits superior overall prediction accuracy over the corresponding block maxima model. This is likely due to the effectiveness of the dynamic threshold in precisely identifying truly critical extreme conflicts.

Research on optimization strategy of grid carbon emission factor for promoting electricity-carbon market coupling

Abstract The grid emission factor is an essential parameter for accounting for carbon emissions caused by electricity consumption, and its update frequency and spatial accuracy affect the quality of the emission factor. Based on the existing regional grid emission factors, this study proposes an improved calculation method to get the revised emission factors. The revised emission factors can address barriers such as delayed updating, inequitable accounting between regions, and double measurement of green power emission reduction benefits. Therefore, the study can support the construction of the carbon market and the realization of carbon neutrality goals.

High-Precision Digital Clock Steering Method Based on Discrete Σ-Δ Modulation for GNSS

A high-precision time reference is fundamental to the positioning, navigation, and timing (PNT) of global navigation satellite systems (GNSS). The precision of clock steering determines the accuracy of practical applications that rely on the time–frequency reference. With the invention of direct digital synthesizer (DDS) technology, digital clock steering (DCS) has gradually become a mainstream technology. However, the key factor limiting DCS accuracy is the system quantization noise, which leads to a low frequency and phase adjustment accuracy. Here we propose a DCS method based on Σ-Δ modulation to address the issue of low resolution of DAC through shaping the quantization noise. A simulated GNSS time–frequency reference system experimental platform is constructed to validate the effectiveness of the proposed method. The experimental results demonstrate that this method achieves a phase adjustment accuracy of 0.48 ps and a frequency adjustment accuracy better than 0.48 pHz, which is two orders of magnitude higher than that of existing GNSS time–frequency reference systems. Thus, the proposed method offers a significant improvement in time–frequency reference systems, leading to better performance, reliability, and accuracy in a wide range of practical applications.

A modified droop-based decentralized control strategy for accurate power sharing in a PV-based islanded AC microgrid

This paper introduces a novel droop-based decentralized control scheme to address the power-sharing challenges within a PV-fed islanded AC microgrid. This novel approach integrates both conventional (P-f/Q-V) and virtual impedance concepts to optimize and manage the precise distribution of active and reactive power among parallel operating inverters posing a significant research challenge. The conventional droop control methods encounter limitations such as voltage and frequency deviations and inaccuracies in power-sharing due to line impedance disparities. To overcome these limitations, the proposed solution integrates an enhanced virtual impedance control loop alongside the conventional control loop (P-f/Q-V). The efficacy of this approach is showcased through simulations conducted using the OPAL-RT OP4510 simulator within the MATLAB/Simulink platform. The Real-time simulation outcomes confirm the efficiency of the suggested control strategy, guaranteeing precise distribution of both active and reactive power while upholding stable voltage and frequency profiles within the system.

Approximate modeling of cross-beam multi-axis force/moment sensor through Gaussian process and partial dependence plots for design optimization including stress topology

This study introduces a novel approximate model (AM) aimed at optimizing the design of cross-beam multi-axis force/moment sensor. It considers the characteristics of flexure hinges in elastic beams and the flexibility of beam joints, resulting in improved accuracy and broader solutions for equivalent stresses and natural frequencies compared to existing approximate models in the literature. Model validation is carried out by comparing the results with the finite element model in different cases that each case contains 1000 simulations. Validation cases combine different sensor dimensions with different forces, moments, material properties, and center hub shapes. Each validation case typically requires approximately 19 h using the finite element method (FEM) and just 1 sec with the AM. Furthermore, by using the Gaussian process and partial dependence plots, the sensitivity of the model accuracy on normalized sensor dimensions is investigated. The evaluation of model linearity and dimension normalization allows for the utilization of the novel approximate model beyond the provided dimension range, while still adhering to the normalized parameters. The implementation of AM in the design optimization problem demonstrates its suitability and advantages compared to existing literature and FEM results. When compared to finite element solutions within the sensor workspace, the approximate model exhibits a stiffness error of 5%, a strain error of 2%, a fundamental frequency error of 3%, and an equivalent stress error of 8%, while maintaining correlations of axial sensor properties above 98%.

A modified dispersed frequency and phase consensus algorithm based on differential evolution and credibility weighting matrix

In distributed antenna array systems, accurate synchronization of the frequency and phase of the transmission signals among subarrays is a premise for beamforming. To solve this problem, a modified dispersed frequency and phase consensus (MDFPC) algorithm, which is based on differential evolution (DE) and the credibility weighting matrix, is proposed in this paper. DE algorithm is adopted to optimize the mixing matrix of MDFPC algorithm, and the convergence speed of consensus algorithm can be improved. Moreover, Lanczos method is adopted to calculate the fitness of individuals during optimization, thus the computation amount of the eigenvalue decomposition of the large-scale, sparse and symmetric mixing matrix can be effectively reduced. In addition, the influence of the transmission distance between subarrays on the received Signal-to-Noise Ratio (SNR) is considered, and the credibility weighting matrix can be obtained. Then the contribution of low-credible frequency and phase messages to the iterative updated values can be decreased by the credibility weighting matrix, thus more accurate frequency and phase updated values can be obtained in each iteration. It is shown from simulation results that the proposed algorithm can further reduce the phase synchronization error by at least 8% and achieve at least 18% convergence speed improvement compared to dispersed frequency and phase consensus (DFPC) algorithm.

Broadband stepped-frequency radar waveform generation by Fourier domain mode-locking period-one laser dynamics.

A stepped-frequency (SF) radar waveform generation method based on Fourier domain mode-locking (FDML) period-one laser dynamics is proposed and demonstrated. By fast controlling the optical injection strength of a semiconductor laser through electro-optical modulation, a broadband SF signal is generated. By further introducing an optoelectronic feedback loop with its round trip time delay matched with the temporal period of the SF signal, FDML is enabled through which the frequency stability, accuracy, and in-band signal-to-noise ratio are greatly improved. In the experiment, SF signals with a bandwidth of 6 GHz (12-18 GHz) and a frequency step of 150 MHz are generated. By comparing the qualities of signals generated with and without FDML, advantages of the proposed SF signal generation method are verified. Based on the proposed SF signal generation method, high-resolution inverse synthetic aperture radar (ISAR) imaging is also demonstrated, in which the 2D imaging resolution reaches 2.6 cm × 3.82 cm.

Application of Generalized S-Transform in the Measurement of Dynamic Elastic Modulus

Resonance is commonly used for in situ measurement of the dynamic elastic modulus to evaluate the strength of concrete samples. Many researchers are also exploring the application of this convenient measurement technology for safety monitoring. Nevertheless, the presence of cracks and variations in curing conditions within samples can impact the resonance frequency range, potentially leading to potential inaccuracies in measurements. In order to improve the measurement accuracy of resonance frequency, this study introduces the Generalized S-Transform (GST) algorithm for measuring the dynamic elastic modulus, which utilizes its high time-frequency resolution to scan the power peak-point in non-stationary and transient excitation signals to determine the resonance frequency. For concrete materials with lower consistency, the experimental results verify the high accuracy of this algorithm in measuring resonance frequency compared with Fast Fourier Transform (FFT). This provides a reference for using the algorithm to measure the dynamic elastic modulus in civil engineering applications, such as buildings and bridges.

Millimeter to THz Spectroscopy of HC18O+ and HC17O+: Accurate Rest Frequencies for Astrophysical Studies

Heavy oxygen isotopic species of HCO+ are important optically thin astrophysical tracers. The ground-state rotational spectrum of HC18O+, DC18O+, HC17O+, and DC17O+ has been recorded in the laboratory in the frequency range from 85 GHz to 1.3 THz. The ions have been produced in the negative column of a glow-discharge plasma, and their spectrum has been recorded in absorption using a frequency-modulation submillimeter-wave spectrometer. Various sources of systematic error have been carefully accounted for in order to obtain highly accurate line-position measurements. Theoretical estimates of the molecular parameters and of the collision effects on the line shape have been obtained by high-level ab initio calculations. The analysis yielded much improved rotational and centrifugal distortion constants, thus bringing the spectroscopic characterization of these rare isotopic variants to the same level of the parent species. Also, the first experimental rotational data for DC17O+ have been provided. These results allow for the calculation of an updated set of rest frequencies to support current and future astrophysical studies. The derived data set for the widely used HC18O+ tracer reaches an accuracy of a few parts in 109 up to 1.5 THz. Such accuracy is important for the analysis of astrophysical objects targeted by Atacama Large Millimeter/submillimeter Array observations at the submillimeter regime.

Association of pre-pregnancy anthropometric factors and mediterranean diet score with hyperemesis gravidarum: Results from a hospital-based case-control study

BackgroundHyperemesis gravidarum (HG) is the most widespread reason for admission to the hospital during the first half of pregnancy. HG etiology is not clear in many cases. However, maternal body composition and pre-pregnancy diet are suggested as possible factors. This hospital-based case-control study aims to determine the link between the Mediterranean diet score (MDS) and pre-pregnancy anthropometric indicators with Hyperemesis gravidarum. MethodsA total of 45 women with incident HG and 126 controls were entered into this case-control study between April 2020 and June 2021. A repeatable and accurate 168-item semi-quantitative food frequency questionnaire was used to define the MDS. The association between MDS and HG odds was examined through adjustment by possible confounders by the estimation of multivariate regression models. ResultsThe median (interquartile range) pre-pregnancy body mass index of participants was 22.7 (21.7-25.9) and 22.9 (21.4-25) Kg/m2 in cases and controls, respectively (P=0.284). The adjusted analyses found that the MDS was associated with a 25% reduced odds of HG (odds ratio (OR): 0.75, 95% confidence interval (95%CI): 0.24-0.91). Body mass index and waist circumference before pregnancy were not significantly associated with HG odds in both regression models. ConclusionsThe odds of HG during pregnancy are lower in women with a higher MDS before pregnancy. Also, a high intake of fruits and nuts, vegetables, legumes, and fish before pregnancy is liked with a lower HG odds.