Current Sampling Strategies Research Articles

A Low-Speed Position Sensorless Scheme From Standstill for Low-Cost SRM Drives Based on Triple Current Slope Difference Threshold

In this paper, a triple current slope difference threshold based low-speed sensorless scheme for low-cost SRM drives is proposed. Firstly, slope based methods, including the current slope difference method, are proposed and the advantages in terms of anti-interference ability over traditional instantaneous value based methods have been clarified. Secondly, the traditional single/double threshold methods and corresponding outgoing/incoming phase injection types are introduced and comprehensively compared. The limitations of conventional methods while adopted in low-cost SRM drives are stated. Accordingly, to solve the problem that the current cannot be obtained during the demagnetization stage in the resistor-based current sampling scheme, the triple current slope difference threshold method is proposed, whose 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> threshold indicates the zero-current position. Thirdly, a general threshold value determination method considering the three-phase unbalanced inductances is proposed, which only requires the unsaturated inductance vs. rotor position characteristics that is very easy to be obtained. The proposed method only needs to store six threshold values in the microprocessor and needs no complex calculations and additional hardware, which can be realized in low-cost drive systems. Finally, the experiments of both the steady-state and starting transient-state under over-load conditions are implemented to verify the feasibility and accuracy of the proposed method.

High-Precision Injection Current Sampling Scheme for Direct Drive Low-Speed Position Sensorless Control of Switched Reluctance Machine

High-Precision Injection Current Sampling Scheme for Direct Drive Low-Speed Position Sensorless Control of Switched Reluctance Machine

A Closer Look at Video Sampling for Sequential Action Recognition

In recent years, sequential action recognition has attracted increasingly attention as it requires long-term sequential and compositional reasoning of human actions and object interactions. Existing methods perform reasoning either by using snippets that cover very short consecutive frames or key frames sampled from segments, which take a bias process of local and global temporal information. We also find ad-hoc training and ensembling of two separate networks using existing sampling strategies can easily outperform complex state-of-the-art methods, which reveals the complementary nature of current sampling strategies. Motivated by this observation, we propose a simple yet efficient strategy named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dense Segmental Sampling (DSS)</i> and a novel network architecture named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Temporal Dense Segment Network (TDSN)</i> to capture the complementary information from DSS. Our TDSN achieves excellent results on benchmark action recognition datasets, which not only validate the proposed strategy but also help highlight the importance along this direction for sequential video reasoning.

Surrogate models for twin-VAWT performance based on Kriging and artificial neural networks

Though twin vertical axis wind turbines (VAWTs) have great potential in the application in oceanic energy harvest, their aerodynamic characteristics is quite complex, especially in the case of sufficient wake-blade interactions. At design stages, the prediction of their power performance usually relies on high-fidelity unsteady simulations based on computational fluid dynamics, whose time budget is high. In this paper, two surrogate models, i.e., Kriging and artificial neural networks (ANN), were adopted for the performance prediction of a twin-VAWT with a close staggered arrangement. Turbines’ pitch angles and their averaged torques at the best tip speed ratio were taken as the input and output, respectively. The numerical study shows that both Kriging and ANN models can provide satisfactory predictions using only 22.45% of CFD observations as training set, and the R2 values for both upstream and downstream turbine models reach more than 0.99 and 0.98, respectively. Among them, the Kriging-based models appear to be more time-efficient and stable than those based on ANN under the moderate dataset in hand. In addition, the current sampling strategy was tested to be modest and robust through sensitivity analysis.

The “Gandalf” soil sampling project at a former industrial site in Copenhagen, Denmark: evaluating soil classification reliability

Handling and transport of contaminated soil from industrial sites in Denmark requires classification based on concentrations of selected metals and organic contaminants. Reliable soil classification is needed for defensible remedial decision-making. Today’s sampling process in Denmark is based on grab sampling of prescribed standard volumes of soil; 30 tons is typically used as the basic sampling Decision Unit. Soil classification follows a number of varying systems, but classification into five classes (class 0 to class 4) based on analytical results from sub-samples of 50 g is the most common. In this study we investigate the sampling uncertainty obtained by sampling of &gt; 1800 samples at a former industrial site in Copenhagen, Denmark. The aim of the study was to conduct a critical assessment of the current sampling strategy by determination of soil classification errors obtained for duplicate primary samples collected from the same truck- load of soil, but with different distances from the original primary sample. It is also discussed which contaminants are the major parameters responsible for final soil classification designations. The original primary samples for the soil classification were taken at the centre of a 7 × 7 m grid for every 33 cm to a depth of 1 m or 3 m. In addition - for approximately half of the positions - samples were taken at a distance of 1, 2 or 3 m from the original sample. The classifications for the original samples were compared to the classifications for the additional samples in the same Decision Unit (7 × 7 × 0.33 m box) and from duplicate original samples. The results show that at least 50 % of all samples were misclassified, 20 % were mis-classified by two or more classes. This study demonstrates that the risk of misclassification is highest for less mobile parameters, metals and PAHs compared to the volatile organic solvents.

Water Quality Sampling Frequency Analysis of Surface Freshwater: A Case Study on Bristol Floating Harbour

Water quality monitoring is essential to understanding the complex dynamics of water ecosystems, the impact of human infrastructure on them and to ensure the safe use of water resources for drinking, recreation and transport. High frequency in-situ monitoring systems are being increasingly employed in water quality monitoring schemes due to their much finer temporal measurement scales possible and reduced cost associated with manual sampling, manpower and time needed to process results compared to traditional grab-sampling. Modelling water quality data at higher frequency reduces uncertainty and allows for the capture of transient events, although due to potential constraints of data storage, inducement of noise, and power conservation it is worthwhile not using an excessively high sampling frequency. In this study, high frequency data recorded in Bristol's Floating Harbour as part of the local UKRIC Urban Observatory activities is presented to analyse events not captured by the current manual sampling and laboratory analysis scheme. The frequency components of the time-series are analysed to work towards understanding the necessary sampling frequency of temperature, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), turbidity and conductivity as indicators of water quality. This study is the first of its kind to explore a statistical approach for determining the optimum sampling frequency for different water quality parameters using a high frequency dataset. Furthermore, it provides practical tools to understand how different sampling frequencies are representative of the water quality changes.

Diagnostic accuracy and clinical implications of robotic assisted MRI-US fusion guided target saturation biopsy of the prostate

MRI-targeted prostate biopsy improves detection of clinically significant prostate cancer (PCa). However, up to 70% of PCa lesions display intralesional tumor heterogeneity. Current target sampling strategies do not yet adequately account for this finding. This prospective study included 118 patients who underwent transperineal robotic assisted biopsy of the prostate. We identified a total of 58 PCa-positive PI-RADS lesions. We compared diagnostic accuracy of a target-saturation biopsy strategy to accuracy of single, two, or three randomly selected targeted biopsy cores and analysed potential clinical implications. Intralesional detection of clinically significant cancer (ISUP ≥ 2) was 78.3% for target-saturation biopsy and 39.1%, 52.2%, and 67.4% for one, two, and three targeted cores, respectively. Target-saturation biopsies led to a more accurate characterization of PCa in terms of Gleason score and reduced rates of significant cancer missed. Compared to one, two, and three targeted biopsy cores, target-saturation biopsies led to intensified staging procedures in 21.7%, 10.9, and 8.7% of patients, and ultimately to a potential change in therapy in 39.1%, 26.1%, and 10.9% of patients. This work presents the concept of robotic-assisted target saturation biopsy. This technique has the potential to improve diagnostic accuracy and thus individual staging procedures and treatment decisions.

Three-Level Unidirectional Rectifiers under Non-Unity Power Factor Operation and Unbalanced Split DC-Link Loading: Analytical and Experimental Assessment

Three-phase three-level unidirectional rectifiers are among the most adopted topologies for general active rectification, achieving an excellent compromise between cost, complexity and overall performance. The unidirectional nature of these rectifiers negatively affects their operation, e.g., distorting the input currents around the zero-crossings, limiting the maximum converter-side displacement power factor, reducing the split DC-link mid-point current capability and limiting the converter ability to compensate the low-frequency DC-link mid-point voltage oscillation. In particular, the rectifier operation under non-unity power factor and/or under constant zero-sequence voltage injection (i.e., when unbalanced split DC-link loading occurs) typically yields large and uncontrolled input current distortion, effectively limiting the acceptable operating region of the converter. Although high bandwidth current control loops and enhanced phase current sampling strategies may improve the rectifier input current distortion, especially at light load, these approaches lose effectiveness when significant phase-shift between voltage and current is required and/or a constant zero-sequence voltage must be injected. Therefore, this paper proposes a complete analysis and performance assessment of three-level unidirectional rectifiers under non-unity power factor operation and unbalanced split DC-link loading. First, the theoretical operating limits of the converter in terms of zero-sequence voltage, modulation index, power factor angle, maximum DC-link mid-point current and minimum DC-link mid-point charge ripple are derived. Leveraging the derived zero-sequence voltage limits, a unified carrier-based pulse-width modulation (PWM) approach enabling the undistorted operation of the rectifier in all feasible operating conditions is thus proposed. Moreover, novel analytical expressions defining the maximum rectifier mid-point current capability and the minimum peak-to-peak DC-link mid-point charge ripple as functions of both modulation index and power factor angle are derived, the latter enabling a straightforward sizing of the split DC-link capacitors. The theoretical analysis is verified on a 30 kW, 20 kHz T-type rectifier prototype, designed for electric vehicle ultra-fast battery charging. The input phase current distortion, the maximum mid-point current capability and the minimum mid-point charge ripple are experimentally assessed across all rectifier operating points, showing excellent performance and accurate agreement with the analytical predictions.

Modeling Customer Experience in a Contact Center through Process Log Mining

The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data.

Leakage locating and sampling optimization of small-rate leakage on medium-and-low-pressure unground natural gas pipelines

The leakage on underground natural gas pipelines leads to not only energy loss but also a significant threat to the nearby region. Because of atmospheric dilution, strong wind, and complex underground conditions, small-rate leakage is particularly challenging to locate accurately for reparation. In this study, a series of field experiments with real-gas are carried out. Base on experimental data, a steady dispersion model of underground gas leakage is established and calibrated. This model with inversion method can be used to guide the locating of leakage. To furtherly improve the locating accuracy, two optimized sampler placing strategies based on the Fisher information matrix (FIM) and the covariance matrix are compared to the currently-used method in both simulations and field experiments. with optimized sampling strategies, the locating errors can be reduced by 14–54% with the same number of sampling points. This study show that first-principle steady-distribution model can be used to locate the leakage base on underground concentration samples and achieve the required precision. The current sampling strategy can also be optimized to improve the accuracy. Overall, this work could improve the safety of gas supply and reduce the emission of greenhouse gas.

Fine-scale spatial variability in organic carbon in a temperate mangrove forest: Implications for estimating carbon stocks in blue carbon ecosystems

Mangrove ecosystems have the potential to store large amounts of carbon but detailed studies on the fine scale spatial variability of biomass components and sediment organic carbon (SOC) and their relationships are lacking. Here we investigated the fine-scale spatial variation of SOC, aboveground and belowground biomass using systematic grid sampling in a temperate Avicennia marina ((Forssk.) Vierh) forest in Australia. We estimated aboveground biomass in a 9 × 9 sampling grid within a 0.16 ha plot and measured SOC, fine root biomass and necromass down to 1 m at 81 sampling points in an A. marina forest in Western Port Bay near Melbourne, Australia. We detected large differences in all measured carbon pools. SOC increased from low to high intertidal position but showed no consistent trend with depth. Aboveground biomass carbon was highly variable (mean 84 ± 56 Mg C ha−1), and showed no consistent trends with intertidal position. The average total organic carbon stock at the study site was 207 ± 80 Mg C ha−1. Aboveground biomass contributed 41% and coarse roots around 6% to the total carbon content of the forest. The bulk of organic carbon in the ecosystem was SOC (53%) and necromass was an important contributor to SOC. Aboveground biomass was a poor predictor of all belowground carbon pools. Our data indicate that 43 sediment cores would be required to determine mean SOC content at the level required by international carbon accounting standards. The high spatial variability of all carbon stocks in the system indicates that current sampling strategies for SOC need to be revised in order to improve predictions of the amount of carbon stored in blue carbon ecosystems.

Sliced optimal transport sampling

In this paper, we introduce a numerical technique to generate sample distributions in arbitrary dimension for improved accuracy of Monte Carlo integration. We point out that optimal transport offers theoretical bounds on Monte Carlo integration error, and that the recently-introduced numerical framework of sliced optimal transport (SOT) allows us to formulate a novel and efficient approach to generating well-distributed high-dimensional pointsets. The resulting sliced optimal transport sampling, solely involving repeated 1D solves, is particularly simple and efficient for the common case of a uniform density over a d -dimensional ball. We also construct a volume-preserving map from a d -ball to a d -cube (generalizing the Shirley-Chiu mapping to arbitrary dimensions) to offer fast SOT sampling over d -cubes. We provide ample numerical evidence of the improvement in Monte Carlo integration accuracy that SOT sampling brings compared to existing QMC techniques, and derive a projective variant for rendering which rivals, and at times outperforms, current sampling strategies using low-discrepancy sequences or optimized samples.

Evaluation of sample size for laboratory detection during HAdVs outbreaks in China: a systematic review and model research

ABSTRACT Human adenovirus infection is a common cause of upper and lower respiratory illness and easily breaks out in schools and the army. In an adenovirus epidemic, a large number of samples would be collected for laboratory diagnosis, and it is urgent to optimize the current sampling strategy. We researched the application of laboratory detection in the adenovirus epidemic and optimized the range of laboratory pathogen detection in the adenovirus epidemic by summarizing previous theoretical achievements, research reports, and experts’ opinions and by using mathematical model tools. Under certain assumptions, a susceptible-infectious-quarantined-recovered (SIQR) model was established to describe the adenovirus epidemic and optimize the range of laboratory pathogen detection. Some standards and implementation rules suggest that when the number of cases is less than 10 or 20, all patients should be sampled for laboratory examination, and when the number of cases is more than 10 or 20, at least 10 or 20 samples should be collected. In practice, the sampling range can be appropriately expanded. A total of 21 studies were analyzed, and the sampling rate of adenovirus infection was 31% (95%CI: 24%~38%). The mathematical model suggested that the screening of asymptomatic people in the latent stage can slow the spreading of the epidemic, but the detection range will be too large. These findings may be helpful for policymaking during an adenovirus epidemic and to avoid proceeding with laboratory testing blindly. Furthermore, it may also provide some guidance for optimizing the sampling strategy of other diseases, especially for respiratory tract infections.

Calculation of sampling size for non-zero tolerance level

The tolerance level of 0 and the confidence level of 0.95 are widely applied in current investigation and sampling strategies for the crime, food, environment, and biodiversity fields. Although some researchers recommend the non-zero tolerance level, few relevant sampling plans have been proposed. In this report, I used the binomial distribution as an example to show the estimate of sampling size, considering different detection efficacies, proportion of concerned units, and confidence level. The results indicate that the required sampling size based on non-zero tolerance is larger than that based on zero tolerance when other parameters are the same. High detection efficacy and high proportion of concerned units can decrease the sampling size. Especially, large sampling size can not only increase the confidence level, but also decrease the proportion of concerned regulated units with effective policy measurement.

Seasonal variability in metal and metalloid burdens of mussels: using data from the German Environmental Specimen Bank to evaluate implications for long-term mussel monitoring programs

BackgroundMetal and metalloid concentrations in mussels can vary between seasons. In biota monitoring, the sampling time is therefore an important issue. Within the German Environmental Specimen Bank (ESB) program blue mussels (Mytilus edulis Complex) are sampled regularly since the 1980s. The samples are collected in two-monthly intervals at two North Sea sites and in 6-month intervals at one Baltic Sea site. All samples from one site and year are combined to annual composite samples and archived as sub-samples under cryogenic conditions. In order to investigate a possible reduction of the number of annual sampling intervals while maintaining comparability with the long-term composite sample data, the seasonal variability of metals/metalloids was analyzed based on the half-yearly and bimonthly samples of 2013, 2015 and 2017.ResultsIn mussels from the North Sea site Eckwarderhoerne seasonality of metals/metalloids was comparable in all 3 years (arsenic being the only exception). At the North Sea site Koenigshafen seasonality of cobalt, nickel, cadmium, copper, lead, and arsenic was comparable in 2013 and 2015 but not in 2017, while selenium showed the same seasonality in all 3 years. Within 1 year, concentrations of metals and metalloids can vary by the same order of magnitude as observed between annual composite samples of different years making it impossible to select just one representative sampling time point per year that would provide the same information as the respective annual composite sample.ConclusionsThe findings highlight the importance of carefully selecting the sampling time point when using mussels in biota monitoring. For the German ESB program it is recommended to continue with the current sampling strategy and analyze annual composite samples in order to maintain comparability with the long-term data series, which are a special feature of the ESB.

Determination of DNA yield rates in six different skeletal elements in ancient bones

Current sampling strategy for laboratories typing bones for human identification include samples obtained from femur, tooth and temporal bone. Latest studies suggest that the small bones of the hands and feet were very similar or even better in DNA yield. These bones can be easily sampled with a disposable scalpel and thus reduce potential DNA contamination. The aim of our study was to determine the suitability of metatarsals, metacarpals and phalanges for genetic identification. 48 bone samples from 8 different skeletons (six from 18th century and two from 3rd century) were obtained from 5 archaeological sites in Slovenia. In each skeleton, 6 different skeletal elements were sampled (temporal bone, molar, femur, metacarpal bone, metatarsal bone and proximal phalanx of the hand), and strict precautions followed to prevent contamination. Half of gram of bone powder was decalcified using full demineralization extraction method. The DNA was purified in a Biorobot EZ1 (Qiagen), DNA content determined with the PowerQuant kit (Promega), and autosomal STR typing performed with the Investigator ESSplex Plus kit (Qiagen). Up to 8.75 ng DNA/g of powder was obtained from samples analyzed. The highest yields were detected in temporal bone and the lowest in femur. The success rate of STR typing was evaluated according to the number of successfully typed loci and a strong correlation between the success rate of STR typing and the amount of extracted DNA was confirmed. For all eight skeletons full consensus genetic profiles were determined from skeletal elements analyzed. Our findings suggest it would be suitable to include metatarsal and metacarpal bones in sampling strategy for human identification although further research is needed to substantiate the findings of this study.

A Prediction-Based Current Sampling Scheme Using Three Resistors for Induction Motor Drives

In the traditional three-resistor-based current sampling scheme, the sampled currents will become inaccurate when the duration of zero-voltage vectors of an inverter is too short, which leads to a limited modulation index (MI). To overcome this problem, an improved three-resistor sampling method is proposed by establishing a predictive current model of an inverter load, which is that of an induction motor on the synchronous rotating frame for the demonstration purpose in this paper. The principle of this method is detailed in the paper. Then a simulation model of a vector-controlled induction motor drive that utilizes the proposed current sampling scheme is built in the Simulink environment. Finally, the experiment on the new sampling scheme is performed in both steady-state and dynamic conditions. The results verify that the existing current sampling problems can be solved effectively and the motor can perform well with higher MI under the new scheme. As a result, the loading capacity of the system can be enhanced notably.

An assessment of the Brazil Current baroclinic structure and variability near 22° S in Distinct Ocean Forecasting and Analysis Systems

The Brazil Current (BC) is the Western Boundary Current of the South Atlantic subtropical gyre, the dominant dynamic feature in the South Atlantic Ocean. The importance of this current lies in that it is the main conduit of subtropical waters to higher latitudes in the South Atlantic Ocean. This study assesses the structure and variability of the BC across the nominal latitude of 22° S using data from the high-density eXpendable BathyThermograph (XBT) AX97 transect and from three numerical ocean models with data assimilation. This XBT transect was implemented in 2004 and represents one of the longer-term monitoring systems of the BC in existence. The goal of this work is to enhance the understanding of the temporal and spatial variability of the ocean dynamics in the southwestern South Atlantic Ocean by using a suite of hydrographic observations and numerical model outputs. In the present work, 37 XBT transect realizations using data collected between 2004 and 2012 are used. Daily outputs covering the same time period are evaluated from Hybrid Coordinate Ocean Model with the Navy Coupled Ocean Data Assimilation (HYCOM-NCODA) with a 1/12° horizontal resolution, and GLORYS2V3 and FOAM, both with a 1/4° horizontal resolution. These Ocean Forecasting and Analysis Systems (OFAS) are able to capture the mean observed features in the 22° S region, showing a BC core confined to the west of 39° W and an Intermediate Western Boundary Current between the depths of 200 and 800 m. However, the OFAS tend to overestimate the mean BC baroclinic volume transport across the AX97 reference transect and underestimate its variability. The OFAS show that the coastal region between the coastline and the western edge of the AX97 transect plays an important role in the mean BC total transport, contributing to up to 23 % of its value, and further that this transport is not sampled by the XBT observations with its current sampling strategy. In order to understand the variability of the BC, a statistical classification of the BC is proposed, with the identification of three different events: weak, intermediate, and strong.

Advanced induction motor drive control with single current sensor

This paper proposes induction motor drive control method which uses minimal number of sensors, providing only DC-link current as a feedback signal. Improved DC-link current sampling scheme and modified asymmetrical switching pattern cancels characteristic waveform errors which exist in all three reconstructed motor line-currents. Motor linecurrent harmonic content is reduced to an acceptable level, eliminating torque and speed oscillations which were inherent for conventional single sensor drives. Consequently, use of single current sensor and line-current reconstruction technique is no longer acceptable only for low and medium performance drives, but also for drives where priority is obtaining a highly accurate, stable and fast response. Proposed control algorithm is validated using induction motor drive hardware prototype based on TMS320F2812 digital signal processor.

Current Limit Strategy for BLDC Motor Drive With Minimized DC-Link Capacitor

Brushless dc motor drive system employing minimized dc-link capacitor is becoming popular for the reason of compactness and long lifetime. However, once the capacitance is reduced, reversal dc-link current will lead to excessive voltage rise in the dc link when conventional current limit strategy is used. In order to avoid the damage caused by oversize pumping-up voltage, a novel current limit strategy without reversal dc-link current is necessary. In this paper, the traditional all-turn-off current limit logic is replaced by half-turn-off logic. Then, an improved current sampling scheme is presented to replace the cycle-by-cycle mode, which induces failure of current limiting after the elimination of reversal current. Finally, the proposed strategy is verified by simulation and experiment.