Low-frequency Data Research Articles

Investigation of Low-Frequency Data Significance in Electric Vehicle Drivetrain Durability Development

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs.

A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements

A comprehensive understanding of wind turbine wake characteristics is vital, particularly in the context of expanding large offshore wind farms. Existing wake measurement techniques provide only spatially sparse wake measurement data, limiting their utility in precise wind turbine design and control. This paper introduces a data-driven approach that combines proper orthogonal decomposition (POD) with machine learning (ML) techniques, designing a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. This framework establishes a nonlinear mapping between sensor measurements and low-dimensional POD coefficients. Two distinct sensor placements, wall-mounted and wake-mounted, are investigated for real measurement scenarios. The results highlight the effectiveness of the proposed wake flow estimation method in reconstructing a complete flow field from exceptionally sparse sensor data, with both wall-mounted and wake-mounted strategies, exhibiting promising results with maximum relative errors of 6.37% and 4.51%, respectively. From the reliability assessments considering various configurations of POD modes and sensor numbers, the ROM-WFE framework demonstrates its capability to estimate wake flow effectively, offering a cost-effective tool for practical applications. Furthermore, the framework maintains accuracy even with high-noise and low-frequency data, demonstrating robustness and generalization. This method significantly contributes to wind turbine wake prediction controller design, promising accurate and robust wake flow field estimation, potentially revolutionizing active wake control and enhancing wind farm operational efficiency.

Characterize fracture geometry and propagation with low frequency distributed acoustic sensing strain data

Low-frequency distributed acoustic sensing (LF-DAS) strain data from offset wells are widely used to characterize hydraulic fracture geometry. In this paper, we propose a new optimization model for DAS interpretation. The model quantifies the fracture propagation trajectory and fracture geometry variation with time from low-frequency DAS strain data of offset wells. First, the optimization model is developed by combining the Green function and the Sneddon fracture width function to correlate parameters such as fracture width distribution, fracture length and fracture location with the far-field strain distribution. Second, the optimization model is solved by the Levenberg-Marquardt nonlinear least squares algorithm. The model was validated by generating cases. In addition, the model was applied to the interpretation of fracture propagation characteristics for the fiber optic well case of HFTS-2 offset wells. The interpretation results can visually determine the fracture modification effect and help to improve the production efficiency.

Macroeconomic and financial mixed frequency factors in a big data environment

Abstract In this paper, we evaluate the predictive content of 3 new business condition indexes and uncertainty measures that are estimated using high-frequency financial and low-frequency macroeconomic time series data. More specifically, our measures are defined as latent factors that are extracted from a state space model that includes multiple different frequencies of non-parametrically estimated components of quadratic variation, as well as mixed frequency macroeconomic variables. When forecasting growth rates of various monthly financial and macroeconomic variables, use of our new mixed frequency factors is shown to result in significant improvement in predictive performance, relative to a number of benchmark models. Additionally, when used to forecast corporate yields, predictive gains associated with the use of our measures are shown to be monotonically increasing, as one moves from predicting higher to lower rated bonds. This is consistent with the existence of a natural pricing channel wherein financial risk (as measured using our volatility factors) contains more predictive information for lower grade bonds. We also find that a variety of extant risk factors including the Aruoba et al. [(2009a). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417427] business conditions index also contain marginal predictive content for the variables that we examine, although their inclusion does not reduce the usefulness of our measures.

Data transmission via hybrid fuzzy time slot scheduling in long-range communication

ABSTRACT Several communication protocols have been tuned for LoRaWAN since the development of LoRa technology more than 10 years ago. LPWAN technology can address issues like overlooked delivery ratio, reducing low frequency and limited data availability, thereby improving system effectiveness, and enormous packet drops due to collisions will exist. Hence the hybrid fuzzy time slot scheduling is proposed to avoid collision in the packet with secure data transmission in the LoRa communication. The fuzzy C means clustering algorithm is suggested to cluster nodes and select the Cluster head based on distance and energy usage. The packet transmission is carried out using the time slot scheduling mechanism by TDMA, which sends the packet without collision. The proposed hybrid method can improve data transmission reliability by minimising collisions, optimising time slots, improving network capacity and decreasing idle time. The outcomes reveal that the proposed hybrid method attains high bandwidth with frequency, throughput and PDR also the PLR attains low without collision of a packet received when related to other prior techniques.

An AstroSat/UVIT study of galaxies in the cluster Abell 2199

We present the newly acquired data for an AstroSat/UVIT field centred on a face-on spiral starburst galaxy UGC 10420, located in the X-ray bright cluster Abell 2199 (z = 0.031). We have analysed the FUV BaF2 data for this field along with the archival FUV and NUV data from the GALEX mission, optical photometric data from the SDSS, spectroscopic data from the literature, and low-frequency radio data from the LoTSS survey, respectively. The stars were separated from the galaxies using the SDSS photometric pipeline classification, while the spectroscopic redshifts available for 35% of the detected UVIT sources were used to identify member galaxies of the cluster Abell 2199. We find that (a) the non-cluster galaxies are on average fainter than the cluster galaxies at fixed magnitude, (b) stars and galaxies are indistinguishable in the r vs. NUV–r plane, and (c) bright stars are ∼ 1.5 mag bluer than the galaxies in the FUV–r vs. NUV–r colour-colour plane. Besides UGC 10420 which is the only known cluster galaxy with an extended-UV disk, we identify five more galaxies with asymmetric FUV morphology and extended radio emission in this field. All the asymmetric member galaxies of Abell 2199, lie within the virial boundaries of the cluster. This observation, together with the fact that these asymmetric cluster galaxies have low-frequency radio tails or FUV emission pointing away from the cluster centre leads us to hypothesise that these galaxies are likely undergoing ram-pressure stripping (RPS) under the influence of cluster-environment related mechanisms. A comparison of optical and FUV star formation rate of UVIT detected galaxies shows enhanced star formation in half of the RPS candidates, suggesting that environment-related mechanisms may lead to a burst of star formation in RPS galaxies.Our analysis indicates the presence of at least two more groups or clusters at z ∼ 0.077 and 0.260, coincident with Abell 2199 along the line of sight of the field of view studied here.

Measuring anxiety disorder in bipolar disorder using EVestG: broad impact of medication groups.

Anxiety disorder is present in approximately half of all bipolar disorder (BD) patients. There are neurologic bases for the comorbidity of balance (vestibular) disorders and anxiety. Our objective is to use electrovestibulography (EVestG), which is predominantly a measure of vestibular neural activity to not only quantitatively detect and measure comorbid anxiety disorder but also to quantitatively measure the impacts of anti-depressant, anti-psychotic, and mood stabilizer medication groups on anxiety measures in BD patients. In a population of 50 (24 with anxiety disorder) depressive phase BD patients, EVestG signals were measured. Participants were labeled depression-wise as anxious or non-anxious using standard questionnaires. Analyses were conducted on the whole dataset as well as on matched (age/gender/MADRS) and "modeled medication-free" subsets. Modulations of the low-frequency EVestG firing pattern data were measured. For BD, the main anxious minus non-anxious difference was the presence of an increase in spectral power proximal to 8-9 Hz, which was best attenuated by mood stabilizers. This is the first study to use an oto-acoustic physiological measure to quantify anxiety disorder in BD wherein it appears to manifest as a peak proximal to 8-9 Hz which we hypothesize as likely linked to hippocampal theta.

A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.

Blood coagulation system in experimental acute lung injury and its treatment with dexamethasone

The effects of short- and long-term administrations of dexamethasone on survival, severity of pulmonary edema, and hemostasis on experimental lipopolysaccharide-induced acute lung injury in rats were analyzed. Acute lung injury in rats was modeled by the intratracheal injection of lipopolysaccharide from the Salmonella enterica cell wall. White male rats were randomly divided into nine groups: the intact group consisted of 10 animals; two control groups of 20 animals each, in which acute lung injury was simulated without further treatment and removed from the experiment on day 3 or 7; six comparison groups of 20 animals each, in which, 3 h after modeling of acute lung injury and then once a day for 3 days (short mode of administration) or 7 days (long mode of administration), dexamethasone solution was administered intraperitoneally in the following doses: 0. 52 (equivalent to 6 mg/day for humans), 1. 71 (20 mg/day for humans), and 8 mg/kg/day (94 mg/day, pulse therapy for humans). On days 3 and 7, the survival rate, coagulogram values (active partial thromboplastin time, prothrombin time, activity of antithrombin, and soluble fibrin monomer complexes), and low-frequency piezotromboelastography data were assessed in the surviving animals. The results revealed that dexamethasone reduces mortality in acute lung injury and has a dose-dependent effect on the hemostasis system: with an increase in the dose administered, blood clotting processes increase and fibrinolysis is inhibited. Low-frequency piezothromboelastography with a conventional coagulogram allows for a comprehensive assessment of the hemostasis system, identifying violations, and timely drug correction.

Quantifying seasonal to multi-decadal signals in coastal water quality using high- and low-frequency time series data

Abstract To inform water quality monitoring techniques and modeling at coastal research sites, this study investigated seasonality and trends in coastal lagoons on the eastern shore of Virginia, USA. Seasonality was quantified with harmonic analysis of low-frequency time-series, approximately 30 years of quarterly sampled data at thirteen mainland, lagoon, and ocean inlet sites, along with 4–6 years of high-frequency, 15-min resolution sonde data at two mainland sites. Temperature, dissolved oxygen, and apparent oxygen utilization (AOU) seasonality were dominated by annual harmonics, while salinity and chlorophyll-a exhibited mixed annual and semi-annual harmonics. Mainland sites had larger seasonal amplitudes and higher peak summer values for temperature, chlorophyll-a and AOU, likely from longer water residence times, shallower waters, and proximity to marshes and uplands. Based on the statistical subsampling of high-frequency data, one to several decades of low-frequency data (at quarterly sampling) were needed to quantify the climatological seasonal cycle within specified confidence intervals. Statistically significant decadal warming and increasing chlorophyll-a concentrations were found at a sub-set of mainland sites, with no distinct geographic patterns for other water quality trends. The analysis highlighted challenges in detecting long-term trends in coastal water quality at sites sampled at low frequency with large seasonal and interannual variability.

Macroeconomic Prediction with High Frequency Data of Electricity Consumption based on MIDAS model

In recent years, China’s macroeconomic landscape has been characterized by a confluence of challenges, including intensified global economic headwinds and the complexities associated with domestic structural economic transformation. Consequently, the macroeconomic environment has become increasingly intricate, with heightened sensitivity and rapid responsiveness to external shocks, posing formidable tests for China’s macroeconomic early warning capabilities. Traditional macroeconomic forecasting methodologies, predominantly reliant on monthly and quarterly economic and industrial statistics, exhibit inherent limitations due to their low frequency and sluggish data updates, rendering them inadequate for accurately predicting short-term macroeconomic fluctuations. Electricity consumption, being a pivotal component of terminal energy utilization across most industries, holds robust representativeness for the broader macroeconomic landscape. Leveraging the salient characteristics of electricity consumption data, this study amalgamates it with other high-frequency data to forecast industrial added value through the employment of the MIDAS (Mixed Data Sampling) model. The findings indicate that the integration of daily electricity consumption data into the MIDAS model can significantly enhance short-term prediction accuracy.

Prediction of Low-Frequency Seismic Data Without Relying on Wavelet Using Deep Learning

Prediction of Low-Frequency Seismic Data Without Relying on Wavelet Using Deep Learning

Low-Frequency Magnetotelluric Data Denoising Using Improved Denoising Convolutional Neural Network and Gated Recurrent Unit

Low-Frequency Magnetotelluric Data Denoising Using Improved Denoising Convolutional Neural Network and Gated Recurrent Unit

Regularized full waveform inversion for low frequency ultrasound tomography with a structural similarity EIT prior

In previous work by Rueter et al., low frequency (10–750 kHz) ultrasound was shown to penetrate the lung, motivating its use as a nonionizing tomographic technique for pulmonary monitoring. Here, we present a method for regularized full waveform inversion for low-frequency tomographic ultrasound data. A novel structural similarity index metric (SSIM)-based regularization term is introduced that compares the structural correlation of the current sound speed iterate to a prior reconstruction computed by electrical impedance tomography (EIT). Full waveform reconstructions of sound speeds from numerically simulated low frequency ultrasound data with 0.1% additive Gaussian noise are computed, and the results are compared using Tikhonov regularization, total variation regularization, and both terms combined with the SSIM-EIT regularization term. The EIT reconstruction was computed from simulated voltage data on the same phantom using one step of a Newton-Raphson method with a high-pass Gaussian filter as a regularizer. Reconstructions including the SSIM-EIT regularization term converged fastest, and an adaptive regularization parameter provided the most accurate reconstructions. The method is then iterated by computing an EIT reconstruction using the USCT result as a prior, and a subsequent USCT reconstruction is computed using the SSIM-EIT regularization term with the updated EIT reconstruction.

Enhancing Effectiveness and Security in Microservices Architecture

Microservices have allowed the decomposition of large monoliths and introduced the simple responsibility principle into its essence. It allowed complex systems to be more cost efficient and easily scalable. With this new architecture, new vulnerabilities were introduced, such as the large surface that started to get exposed to the internet. Instead of having one monolithic surface exposed, we started to have several small services exposed. New patterns were introduced to try to diminish these threats and among them the “API Gateway” pattern was introduced. The purpose of this article is to further explore the API Gateway pattern, and to further enhance the microservices communication security as part of that pattern. This research proposes a hybrid approach, using HTTPS and JWT only when strictly necessary, towards overcoming those recognized limitations of security and performance.Furthermore, proof of concept on top of NB-IoT communication protocol was developed. It intends to explore the transmission of a large quantity of low-frequency data from devices to the cloud, while the communication maintains effective and with low latency. The created supporting microservices API followed the proposed architecture and was tested against security attacks towards mitigating its impacts.

Lower-ionosphere anomalies prior to strong earthquakes that occurred in north-central mainland Greece on March 2021 as revealed by multi-method analysis of VLF sub-ionospheric propagation data

In this work we present the multi-method analysis of very low frequency (VLF) data, acquired by the radio receiver with call name UWA, located in Athens (Greece), in the University of West Attica, focusing on two strong ( ) earthquakes (EQs) that occurred in north-central mainland Greece sequentially, on 3 and 4 March 2021, with very close epicenters. Specifically, we used the data acquired from seven VLF transmitters located in Europe, North/North-West to UWA, and their propagation paths include the specific EQs epicenters. We analyzed these data using multiple analysis methods in order to investigate for possible EQ-related anomalies, taking also into account all the other possibly ionosphere-influencing extreme events that occurred during the studied period. Especially, we applied the “nighttime fluctuation method” (NFM), as well as, the “terminator time method” (TTM) in order to reveal any statistical anomaly in the nighttime amplitude recordings of VLF sub-ionospheric propagation data within 15 days before each one examined EQs. Also, we calculated the scalogram (wavelet power spectrum over time) using Morlet mother wavelet of the same nighttime data searching for possible imprints of wave-like structures during the same time period. In terms of criticality analysis, first we applied the “natural time” (NT) analysis method to the daily-valued NFM VLF propagation quantities, and subsequently applied the “method of critical fluctuations” (MCF) to the raw nighttime amplitude VLF recordings, to check for any criticality signatures up to two weeks before the examined EQs. Taking into account all the above-mentioned analysis results, we conclude that there are multiple indications that the lower ionosphere was indeed disturbed due to the preparation processes of the above-mentioned EQs, offering different types of seismogenic indications.

Characteristic analysis and data comparative of linear and nonlinear low-frequency sweep in vibroseis

Abstract Low-frequency seismic data plays a crucial role in seismic data processing and seismic wave inversion. At present, there are two methods to realize the low-frequency excitation of vibrators: one is exciting low-frequency vibrators by linear sweep signals, and the other is exciting conventional vibrators by nonlinear low-frequency sweep signals. The cost of exploration using low-frequency vibroseis is high, and it is challenging to obtain sufficient low-frequency information using traditional vibrators. To this end, this paper comparatively studies the low-frequency sweep signal characteristics and data effects of low-frequency and traditional vibrators. Therefore, three kinds of linear and nonlinear low-frequency sweep signal are designed. Theoretical analysis shows that there are certain differences between linear and nonlinear signal in design methods, signal shapes, etc., but after correlation calculations the signal spectra reflecting the seismic response and the related wavelet shapes are basically consistent. Besides, the actual force signal data shows that the linear and nonlinear harmonic distortion are basically equivalent. Finally, based on the forward simulation of three sweep signals and the comparative analysis of field test data, it can be considered that the linear and nonlinear low-frequency sweep signals of vibrators have almost the same denoising ability under the basic conditions of the spectrum and wavelet. Both can achieve low-frequency excitation and obtain rich low-frequency information, and the quality of seismic data is basically the same, so they can be applied in practical production.

High-frequency data significantly enhances the prediction ability of point and interval estimation

Accurate prediction of dissolved oxygen (DO) dynamics is crucial for understanding the influence of environmental factors on the stability of aquatic ecosystem. However, limited research has been conducted to determine the optimal frequency of water quality monitoring that ensures continuous assessment of water health while minimizing costs. To address these challenges, the present study developed a hybrid stochastic hydrological model (i.e., ARIMA-GARCH hybrid model) and machine learning (ML) models. The objective of this study is to identify the best-performing model and establish the optimal monitoring frequency. Results revealed that high-frequency DO monitoring data exhibit greater variability compared to low-frequency data. Moreover, the ARIMA-GARCH model demonstrates promising potential in predicting DO concentrations for low-frequency monitoring data, surpassing ML models in performance. Furthermore, increasing the monitoring frequency significantly improves the prediction accuracy of models, regardless of whether point (with lower R2 values of 0.64 and 0.51 for daily detection than these of every 15 min (0.96 and 0.99) at CHQ and LHT, respectively) or interval predictions (with RIW higher values of 2.00 and 1.55 for daily detection higher than these of 0.02 and 0.16 in every 15 min at CHQ and LHT, respectively) are considered. Additionally, a 4 hourly monitoring frequency was found to be optimal for water quality assessment using each model. These findings identify the superior performing of the ARIMA-GARCH model and highlight the crucial role of monitoring frequency in enhancing DO prediction and improving model performance.

Soft imitation reinforcement learning with value decomposition for portfolio management

Imitation learning has been recognized as a method to accelerate the training process of deep reinforcement learning agents in search of optimal strategies. Nevertheless, existing imitation learning algorithms have limitations in effectively leveraging expert demonstrations when confronted with dynamic environments, as the behavior cloning loss weight cannot be adaptively updated. To overcome this challenge, we propose a novel approach called Soft Imitation Reinforcement Learning (SIRL), which combines imitation learning and reinforcement learning to guide the training of reinforcement learning agents in an adaptive manner. Additionally, we addressed the challenge of high-dimensional action spaces for reinforcement learning in portfolio management with value decomposition, and provide theoretical proof of convergence for this method. To validate the effectiveness of the SIRL algorithm, we conduct extensive experiments using stock market data from emerging (China) and developed (the US) countries. Our experiments indicate the versatility of the proposed SIRL across different types of trading data, encompassing both high-frequency (5-minute interval) and low-frequency (daily and weekly) data.

Evaluation of magnetic inhomogeneities in non-stoichiometric Mg0.5Ca0.5Fe2O4 nanoferrite

Present work reports a systematic study on the evaluation of magnetic inhomogeneities in non-stoichiometric Mg0·5Ca0·5Fe2O4 nanoferrite (MCNF) by conducting exhaustive dc-magnetization, ac-susceptibility and 57Fe Mössbauer spectroscopic measurements and exchange bias investigations using training protocol down to 6 K. Rietveld fitting to PXRD established the formation of anticipated spinel fcc phase of MCNF (non-stoichiometric) along with a minute impurity phase of calcite. Scherrer method and HRTEM micrographs illustrated broad size distribution of MCNF nanoparticles with an average nanocrystallite size of ∼15 nm. Combined 57Fe Mössbauer spectroscopic and dc-magnetization analysis establishes coexistence of ferrimagnetic (67 %) & superparamagnetic (33 %) states at 300 K with notable Ms = 22 emu/g, Mr = 4 emu/g & Hc = 130 Oe and blocking of most of the nanoparticles of MCNF below 300 K. The coercivity followed the size-modified Kneller law for ferrimagnetic nanoparticles and the saturation magnetization abides the Bloch law. Moreover the frequency-dependent ac-susceptibility investigations revealed two magnetic transitions: (i) A transition at ∼ 330 K in the low frequency data attributed to the relaxation of blocked particles of bigger sizes under the superparamagnetic (SPM) regime and (ii) an irregularity at low temperatures is assigned to surface spin glass freezing. Surface spin glass freezing was further affirmed by the ageing experiments and dynamic scaling law. Furthermore, even the best fit to the dynamic scaling couldn't assert the existence of conventional spin glass phase due to slower spin-flip time of surface spins. A soft ferrimagnetic core of MCNF is enveloped with disordered surface spins, which manifest spin glass state. Concurrently, the findings of exchange bias at 30 K and training effect at 6 K affirmed that MCNF nanoparticles are presenting themselves as FM core- SG shell system. Our experimental findings suggested magnetic inhomogeneities comprised of superparamagnetism, ferrimagnetism and disordered surface spin glass state in the non-stoichiometric MCNF.