Optimal Sampling Frequency Research Articles

Processing Magnetometer Signals for Accurate Wide-Area Geomagnetic Disturbance Monitoring and Resilience Analysis

Geomagnetic disturbances (GMDs) can disrupt the operation of power systems by inducing a quasi-dc voltage and generating geomagnetically induced currents (GICs) in a vast area of the power systems. This gives rise to the importance of wide-area monitoring of magnetic field on earth's surface. Assessment of power system resiliency against GMDs requires an accurate calculation of GIC flows, which is achieved by wide-area monitoring of the magnetic field B, and processing the B signals that are recorded by magnetometers on the earth's surface. In this paper, a method is proposed to denoise the B signal. Spikes in the signal are detected using a stationary wavelet transform and then replaced. Time derivative of B signal is taken by a continuous wavelet transform to prevent amplification of the noises. Furthermore, a quantitative analysis is performed to detect the optimum sampling frequency to overcome the practical limitations associated with transmitting the recorded B signal and to modify peaks of dB/dt signal negligibly. It is demonstrated that a sampling frequency of 1/15 Hz satisfies these conditions. Finally, GICs in a 118-bus benchmark power system are calculated with respect to a realistic geomagnetic storm to demonstrate the effectiveness of the proposed signal processing method.

Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Abstract. Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

Significant Sampling for Shortest Path Routing: A Deep Reinforcement Learning Solution

Significant sampling is an adaptive monitoring technique proposed for highly dynamic networks with centralized network management and control systems. The essential spirit of significant sampling is to collect and disseminate network state information when it is of significant value to the optimal operation of the network, and in particular when it helps identify the shortest routes. Discovering the optimal sampling policy that specifies the optimal sampling frequency is referred to as the significant sampling problem. Modeling the problem as a Markov Decision process, this paper puts forth a deep reinforcement learning (DRL) approach to tackle the significant sampling problem. This approach is more flexible and general than prior approaches as it can accommodate a diverse set of network environments. Experimental results show that, 1) by following the objectives set in the prior work, our DRL approach can achieve performance comparable to their analytically derived policy φ' - unlike the prior approach, our approach is model-free and unaware of the underlying traffic model; 2) by appropriately modifying the objective functions, we obtain a new policy which addresses the never-sample problem of policy φ', consequently reducing the overall cost; 3) our DRL approach works well under different stochastic variations of the network environment - it can provide good solutions under complex network environments where analytically tractable solutions are not feasible.

Method of diagnostics of the rotor eccentricity of an induction motor

The main type of electric motors used in the electric drive of technological processes are induction motors with a squirrel-cage rotor. With proper operation, they have a higher MTBF (Mean Time Between Failures). It was found that one of the common causes of engine failure is bearing wear and, as a result, increased rotor eccentricity. We used the oscillating amplitude of the rotor speed in the transient state as a diagnostic parameter. The paper presents a method for this troubleshooting, a block scheme and appearance of the experimental installation, and a single-line diagram of the transmitter for measuring rpm. The experience has established the optimal ADC sampling frequency for experimentation to be carried out. The results of studies of changes in the rotor speed oscillating from time to time under load at rotor eccentricity have been given.

Sampling frequency of climate data for the determination of daily temperature and daily temperature extrema

AbstractThe sampling frequency of temperature data is examined. The range of sampling from hourly to twice daily are explored to determine the uncertainty that is introduced by sampling less frequently than hourly in the determination of daily temperature and daily temperature extrema. The standards for comparison for the daily average temperature are the average of hourly data and the average of the daily maximum and minimum temperature. Hourly temperature data from 12 Canadian climate stations are examined for several decades leading up to 2017. Daily average temperatures were calculated using data sampled 24 times a day (hourly), 12 times, 8 times, 6 times, 4 times and twice daily. Two triad algorithms from the literature and an experimental one are assessed relative to these sampling frequencies. The sampling frequency analysis was remarkably consistent across all climate stations. The departure from the hourly estimate ranged from 0.1°C for the bi‐hourly sampling to ~1°C for the twice daily sampling. The uncertainty associated with the min/max method consistently fell within that of three and four samples per day. Comparison of triad algorithms, based on a quantitative criterion for determination of best sampling hours, revealed a station specific triad that outperforms algorithms from the literature and thrice daily evenly spaced sampling. Minimum and maximum estimates were compared across the different sampling frequencies for all stations as well. The accuracy of estimating temperature extrema decreases with lower sampling rates with the exception of the 8 hr sampling where hour of sampling influences accuracy. The results demonstrate that the local climate characteristics needs to be considered when choosing the optimal sampling frequency and calculation method for daily means and extrema.

Suppression of sweeping fluctuation of Fabry-Perot filter in fiber Bragg grating interrogation using PSO-based self-adaptive sampling

Random fluctuations of the nonlinearity curves of Fabry–Perot (F–P) tunable filters cause instabilities in fiber Bragg grating (FBG) interrogation systems. FBG sensors are widely used in structural health monitoring (SHM), and suppressing random fluctuations can provide engineers with more precise information on the conditions of structures. While F–P etalon and acetylene (C2H2) are most commonly used to calibrate the wavelength of the F-P filter, in medium–high sweeping situations, they suffer from drastic fluctuations in the interrogation results. In addition, these two methods require more interrogation components and render the interrogation system more complex and expensive. This study investigates the fluctuation of interrogation errors caused by sampling frequency. A novel self-adaptive sampling approach based on particle swarm optimization (PSO) is proposed to automatically search the optimal sampling frequency with the smallest fluctuation. The experimental results show that the proposed approach successfully detected the optimal sampling frequency. The drastic fluctuations of micro strains caused by high sampling frequency are avoided completely, and the stability of interrogation system is guaranteed. The proposed approach is thus helpful for suppressing sweeping fluctuations by appropriately choosing the optimal sampling frequency.

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.

User-Driven Adaptive Sampling for Massive Internet of Things

Energy conservation techniques are crucial to achieving high reliability in the Internet of Things (IoT) services, especially in the Massive IoT (MIoT), which stringently requires cost-effective and low-energy consumption for battery-powered devices. Most of the proposed techniques generally assume that data acquiring and processing consume significantly lower than that of communication. Unfortunately, this assumption is incorrect in the MIoT scenario, which mostly involves the low-power wide-area network (LPWAN) and complex data sensing operations (e.g., biological and seismic sensing) using “power-hungry” sensors (e.g., gas sensors, seismometers). Thus, sensing actions may consume even more energy than transmission. In addition, none of them support end-users in controlling the trade-off between energy conservation and data precision. To deal with these issues, we propose an adaptive sampling algorithm that estimates the optimal sampling frequencies in real-time for IoT devices based on the changes of collected data. Given a user's saving desire, our algorithm could minimize the device's energy consumption while ensuring the precision of collected information. Practical experiments over IoT datasets have shown that our algorithm can reduce the number of acquired samples up to 20 times compared with a traditional fixed-rate approach at extremely low Normal Mean Error value around 3.45%.

Challenges of integrated variance estimation in emerging stock markets

Estimating integrated variance, using high frequency data, requires modelling experience and data crunching skills. Although intraday returns have attracted much attention in recent years, handling these data is challenging because of their unique characteristics. When dealing with ultra-high frequency or tick-by-tick observations the enormous amount of data needs to be processed prior to estimation of integrated variance for two reasons: eliminating microstructure noise and finding appropriate unbiased estimator. This paper contributes to the existing literature in a two ways. First, we propose how to handle quality issues of the high frequency data due to non-frequent trading and lower liquidity of emerging markets. Second, we find the optimal sampling frequency at slow time scale that should be used to obtain two-time scale estimator of integrated variance for each emerging market under consideration: Romania, Hungary, Bulgaria and Croatia. Empirical results indicate that intraday returns should be sampled every 7 to 10 minutes at slow time scale while the fast time scale should be fixed at the highest possible frequency. Realized variance estimator at the fast time scale mostly overestimates the integrated variance on all stock markets except Bulgaria; on average between 70% and 90% of the time. Moreover, the robustness of the results with respect to the price jumps has been verified for Romania and Hungary, unlike Croatia and Bulgaria, for which we recommend a robust version of two-time scale estimator of integrated variance within truncation technique. It is additionally found that intraday returns should be sampled more frequently in a highly volatile periods. These findings offer valuable information to market participants, as they are able to apply the most accurate ex-post volatility measure, as unbiased and consistent estimate of integrated variance.

Intelligent identification for working-cycle stages of excavator based on main pump pressure

Abstract An identification of the working stages of the excavator is used as the basis to realize a staged energy-saving control and reduce the fuel consumption. However, existing methods based on computer vision and multi-sensor information fusion technologies have limitations in practical applications. In this paper, an intelligent identification method for the working-cycle stages of an excavator is proposed based on the relationship between the working stages and the main pump pressure waveform. Three machine learning algorithms, a support vector machine (SVM), back propagation neural network (BPNN), and logistic regression (LR), were used to establish the intelligent recognition model. In addition, an intelligent calibration system was established to reduce the effects of human-induced factors and pressure fluctuations on the accuracy when using the original main pump pressure signals. The relationships among the identification accuracy, time window width, and signal sampling frequency were also studied. The results show that the library for support vector machine (LIBSVM) model outperforms the BPNN and LR, recording an accuracy of 94.64% when using the test set and an accuracy of 93.82% with an intelligent calibration system when using the original main pump pressure signals. The optimal time window width and signal sampling frequency are 0.5 s and 20 Hz, respectively. The proposed method is a practical approach to realize the real-time supervision of the working stage of an excavator, providing theoretical support for a staged energy-saving control.

Deterministic Methodology for Determining the Optimal Sampling Frequency of Water Quality Monitoring Systems

This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF, taking into consideration all the potential changes in the boundary conditions of a water body. Two-dimensional contaminant transport simulation models (RMA4) were implemented to estimate the distribution patterns of some effective physiochemical parameters within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 models to determine the minimum SF of the monitoring stations with a monitoring accuracy (MA) level of detectable change in contaminant concentration ranging from the standard level to 50% (stepwise 5%). For the study area, the proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. An exponential relationship between SF and MA was obtained. This relationship shows that increasing the MA to ±10%, ±25%, and ±50% increases the SF by approximately 14%, 28%, and 93%, respectively. However, the proposed methodology includes all the potential values and cases of flow and contaminant transport boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

Intraday efficiency-frequency nexus in the cryptocurrency markets

This study investigates the nexus between weak-form efficiency and intraday sampling frequency for the highest capitalized cryptocurrencies. Applying a battery of long memory tests, we provide evidence of major discrepancies on the predictability of cryptocurrency returns for alternative high frequency intervals. Accordingly, efficiency demonstrates a U-shaped pattern with respect to alternative sampling frequencies, hence there exists an optimal intraday sampling frequency that maximizes the market efficiency. These findings have important implications for portfolio analysis, risk management, regulations and administrative rulings in the cryptocurrency markets.

What temporal resolution is required for remote sensing of regional aerosol concentrations using the Himawari-8 geostationary satellite

Few studies have directly addressed the question of what temporal resolution is required for air quality studies using geostationary remote sensing data. If timescales are too large, there is a risk that events affecting air quality may be missed; and if too small, there is a possibility that large data files may be processed frequently, at significant computing cost and potentially without concomitant improvements in the monitoring of air quality. The problem is particularly significant in sparsely populated regional areas such as the Pilbara in Western Australia, where air quality issues arising from a range of events, dispersed over a vast area, increase the risk of environmental health and ecosystems impacts and where the use of conventional monitoring is impractical. This study aimed to establish an optimum temporal sampling interval for air quality studies using geostationary data and determine the impact of different timescales on ground level concentrations. The study was based on an analysis of Himawari-8 satellite data relating to a dust storm within a roll cloud which occurred near Onslow in Western Australia on 8 March 2017. Data from the Himawari-8 satellite were obtained from the Australian Bureau of Meteorology relating to the event and were used to: (1) assess the probability of a satellite overpass coinciding with the event; (2) determine scale factors of different time periods during the event; and (3) undertake an analysis of event duration using remote sensing data. The analysis identified numerous sub-phases of the dust event, each lasting between 30 and 50 min. Data analysis considered all thermal infrared bands and Taylor plot analysis reduced the ten wavelength bands to six independent bands. Principal component analysis of brightness temperature difference between these six bands identified the rate of aerosol compositional change and established that an optimal geostationary sampling frequency of five to 10 min would be required to quantify these temporal changes effectively.

Application of multi-criteria decision analysis to assess sampling strategies in eutrophic urbanized waterbodies.

Different water quality sampling practices such as location selection or frequency can inform future watershed management strategies. The objective of this work was to compare water quality sampling strategies based on different weighted criteria to determine the optimal sampling frequency and sampling location for an urbanized, eutrophic, freshwater system. Weekly water sampling was conducted over a 2-year period at five locations for six water quality parameters. This high frequency (HF) dataset was then deconstructed into a lower frequency (LF) dataset to simulate a monthly sampling strategy. Statistical analyses conducted showed that for all sampling locations the LF datasets were not significantly different from the HF datasets, suggesting monthly sampling is sufficient to capture the overall water quality conditions in this system. A multi-criteria decision analysis was constructed for statistical and operational criteria to determine the optimal sampling locations given different criteria weights. Results showed that the optimal sampling location changed depending on the criteria weighting, suggesting that statistical analyses alone would not be sufficient to determine optimal sampling locations in this system. This analysis was then used if optimal sampling location depended on specific water quality monitoring goals. Results showed that the optimal location depends on the particular water quality monitoring goals and that this effect should also be considered in the design of future sampling programs.

Optimal Sampling Frequency for Wearable Sensor Data in Arthroplasty Outcomes Research. A Prospective Observational Cohort Trial

BackgroundWearable sensors can track patient activity after surgery. The optimal data sampling frequency to identify an association between patient-reported outcome measures (PROMs) and sensor data is unknown. Most commercial grade sensors report 24-hour average data. We hypothesize that increasing the frequency of data collection may improve the correlation with PROM data. MethodsTwenty-two total joint arthroplasty (TJA) patients were prospectively recruited and provided wearable sensors. Second-by-second (Raw) and 24-hour average data (24Hr) were collected on 7 gait metrics on the 1st, 7th, 14th, 21st, and 42nd days postoperatively. The average for each metric as well as the slope of a linear regression for 24Hr data (24HrLR) was calculated. The R2 associations were calculated using machine learning algorithms against individual PROM results at 6 weeks. The resulting R2 values were defined having a mild, moderate, or strong fit (R2 ≥ 0.2, ≥0.3, and ≥0.6, respectively) with PROM results. The difference in frequency of fit was analyzed with the McNemar’s test. ResultsThe frequency of at least a mild fit (R2 ≥ 0.2) for any data point at any time frame relative to either of the PROMs measured was higher for Raw data (42%) than 24Hr data (32%; P = .041). There was no difference in frequency of fit for 24hrLR data (32%) and 24Hr data values (32%; P > .05). Longer data collection improved frequency of fit. ConclusionIn this prospective trial, increasing sampling frequency above the standard 24Hr average provided by consumer grade activity sensors improves the ability of machine learning algorithms to predict 6-week PROMs in our total joint arthroplasty cohort.

Signal reconstruction in diffusion‐based molecular communication

AbstractMolecular communication (MC) is an important nanoscale communication paradigm, which is employed for the interconnection of the nanomachines (NMs) to form nanonetworks. A transmitter NM (TN) sends the information symbols by emitting molecules into the transmission medium and a receiver NM (RN) receives the information symbols by sensing the molecule concentration. In this paper, a model of how an RN measures and reconstructs the molecular signal is proposed. The signal around the RN is assumed to be a Gaussian random process instead of the less realistic deterministic approach. After the reconstructed signal is derived as a doubly stochastic poisson process, the distortion between the signal around the RN and the reconstructed signal is derived as a new performance parameter in MC systems. The derived distortion, which is a function of system parameters such as RN radius, sampling period, and the diffusion coefficient of the channel, is shown to be valid by employing random walk simulations. Then, it is shown that the original signal can be satisfactorily reconstructed with a sufficiently low level of distortion. Finally, optimum RN design parameters, namely, RN radius, sampling period, and sampling frequency, are derived by minimizing the signal distortion. The simulation results reveal that there is a trade‐off among the RN design parameters which can be jointly set for a desired signal distortion.

Modeling and optimization of closed-loop retinal motion tracking in scanning light ophthalmoscopy.

A model of closed-loop retinal motion tracking in an adaptive optics scanning light ophthalmoscope (AOSLO) was built, and the tracking performance was optimized by minimizing the root-mean-square of residual motion. We started with an evaluation of the fidelity of the retinal motion measurement, and then analyzed the transfer function of the system and power spectral density of retinal motion from human eyes, to achieve optimal control gain and sampling frequency. The performance was further enhanced by incorporating retinal motion prediction during the period in which the slow scanner was retracing. After optimization, residual image motion performance was improved by 33% with a nearly 50% reduction in computational cost in comparison to our previous setup, reaching a 3dB bandwidth of 15-17Hz, which is close to the frame rate (∼21 fps) of this AOSLO system.

Digital cardiotocography: What is the optimal sampling frequency?

Cardiotocography (CTG) is the most popular prenatal diagnostic test for establishing fetal health and consists in simultaneous recording of fetal heart rate (FHR, bpm) and maternal uterine contraction (UC, mmHg) traces. Typically, FHR and UC traces are visually analyzed and interpreted by clinicians. Recently, software applications like CTG Analyzer have been developed to support visual CTG interpretation by making it more objective and independent from clinician’s experience. Automatic CTG analysis requires CTG-traces digitalization and thus assessment of a correct sampling frequency (SF). Thus, this paper aims to investigate dependency of automatic CTG analysis on SF in order to identify optimal SF (OSF) for FHR and UC traces that minimizes computational efforts without jeopardizing CTG interpretation. To this aim, the “CTU-CHB intra-partum CTG database” was considered and visually annotated by an expert gynecologist. FHR and UC traces, originally sampled at 4 Hz, were down sampled at 2 Hz, 1 Hz, 0.4 Hz and 0.2 Hz, and automatically analyzed using CTG Analyzer. Eventually, results obtained through automatic analysis were compared to visual annotations, which were taken as reference. A cumulative statistical index (CSI), ranging from 0.00% to 100.00%, was defined as a linear combination of positive-predictive value, sensitivity, false-positive rate and false-negative rate. OSF was defined as the one that maximizes CSI. If CSI was showing the same value for more than one SF, the lowest SF was selected as the optimal since minimizing computational efforts. Results indicate that OSF for FHR is 2 Hz (CSI ≥ 85.41%), whereas OSF for UC is 0.2 Hz (CSI = 75.21%).

Modified CRLB Based Optimal Sampling Frequency Selection in Phase and Frequency Estimation

Modified CRLB Based Optimal Sampling Frequency Selection in Phase and Frequency Estimation

Optimal design of orders of DFrFTs for sparse representations

This study proposes an optimal design of the orders of the discrete fractional Fourier transforms (DFrFTs) and construct an overcomplete transform using the DFrFTs with these orders for performing the sparse representations. The design problem is formulated as an optimisation problem with an L 1 -norm non-convex objective function. To avoid all the orders of the DFrFTs to be the same, the exclusive OR of two constraints are imposed. The constrained optimisation problem is further reformulated to an optimal frequency sampling problem. A method based on solving the roots of a set of harmonic functions is employed for finding the optimal sampling frequencies. As the designed overcomplete transform can exploit the physical meanings of the signals in terms of representing the signals as the sums of the components in the time-frequency plane, the designed overcomplete transform can be applied to many applications.