Minimum Total Error Research Articles

Complex-Valued Minimum Total Error Entropy Adaptive Algorithm With Fiducial Point for Power System Frequency Estimation

Complex-Valued Minimum Total Error Entropy Adaptive Algorithm With Fiducial Point for Power System Frequency Estimation

Minimum total complex error entropy for adaptive filter

As a powerful tool, the minimum error entropy (MEE) criterion has aroused extensive attention within the field of adaptive filtering recently. However, most MEE-based methods cannot be applied to the complex domain. Although the maximum total complex correntropy (MTCC) algorithm has been applied in errors-in-variables (EIV) model successfully when the noise is impulsive, it would degenerate obviously in some non-Gaussian noise cases, especially in the multi-peak distributed noise case. In this paper, we take the EIV model and complex domain into consideration and then propose a minimum total complex error entropy (MTCEE) algorithm. More importantly, the local stability and steady-state behavior of MTCEE are analyzed theoretically. Finally, we apply the MTCEE algorithm to the system identification and channel estimation, considering situations where noise affects both input and output. Through these applications, we demonstrate the effectiveness and superiority of MTCEE under EIV model in complex domain.

Total error in lymphocyte subpopulations by flow cytometry-based in state of the art using Spanish EQAS data.

Flow cytometry analyses of lymphocyte subpopulations (T, B, NK) are crucial for enhancing clinical algorithms and research workflows. Estimating the total error (TE) values for the percentage and absolute number of lymphocyte subpopulations using the state-of-the-art (SOTA) approach with real data from an external proficiency testing (EPT) scheme was performed. A comparison with previously published Biological Variability (BV)-based specifications was carried out. A total of 44,998 results from 86 laboratories over 10 years were analysed and divided into two five-year periods (2012-2016) and (2017-2021). Data come from the IC-1 Lymphocytes scheme of the Spanish External Quality Assurance System (EQAS) GECLID Program. This quantitative scheme includes percentages and absolute numbers of CD3+, CD3+CD4+, CD3+CD8+, CD19+, and CD3-CD56+CD16+ NK cells. The percentage of TE was calculated as: |reported value-robust mean|*100/robust mean for each laboratory and parameter. The cut-off for TE is set at 80 % best results of the laboratories. A significant reduction in the SOTA-based TE for all lymphocyte subpopulations in 2017-2021 was observed compared to 2012-2016. The SOTA-based TE fulfils the minimum BV-based TE for percentages of lymphocyte subpopulations. The parameter with the best analytical performance calculated with SOTA (2017-2021 period)-based TE was the percentage of CD3+ (TE=3.65 %). The values of SOTA-based specifications from external quality assurance program data are consistent and can be used to develop technical specifications. The technological improvement, quality commitment, standardization, and training, reduce TE. An update of TE every five years is therefore recommended. TE assessment in lymphocyte subsets is a helpful and reliable tool to improve laboratory performance and data-based decision-making trust.

A Calculation Method for the Optimal Initial Polar Distance of Capacitive Force Sensors

For capacitive sensors, the initial polar distance of capacitors has a vital influence on the performances of capacitive sensors; however, there are few current studies on the method of calculating the initial polar distance of capacitors, leading to a lack of corresponding theoretical basis for the design of capacitive sensors. In this article, a calculation method for the optimal initial polar distance based on minimum error is proposed. The total error expression is derived from error analysis, which clarifies the complex relationship between the total error and the initial polar distance, and the optimal initial polar distance is solved by Newton’s method. A capacitive torque sensor is used in the experiment to verify the calculation method for the optimal initial polar distance, the theoretical error and experimental error of the sensor are compared, and the results show that the experimental error is consistent with the theoretical error curve. The optimal initial polar distance obtained by the calculation method is 0.8479 mm, which is within the range of 0.8–0.9 mm determined by the experiment. The theoretical minimum total error is 0.211% and the experimental minimum total error is 0.208%. The total error expression proposed in this article is accurate, and the calculation method of the initial polar distance is effective. In addition, theoretical analysis reveals that the differential parallel plate capacitor has the characteristic that any small deformation of the moving plate can be equivalent to a translation whose magnitude is the mean value of the deformation.

Diffusion Mixture Minimum Total Error Entropy Adaptive Filtering Algorithm and Its Performance Analysis

The distributed adaptive filtering algorithms applied in the error-in-variables (EIV) model consider that both input and output signals suffer from noise disturbance for practical applications. However, the traditional distributed adaptive filtering algorithms applied in the EIV model cannot combat non-Gaussian noises well. To address this issue, this paper develops a mixture minimum total error entropy (MMTE) method and its diffusion mixture minimum total error entropy (DMMTE) algorithm. The proposed DMMTE has the significant performance improvement in non-Gaussian noises, and is more general than the diffusion minimum total error entropy (DMTE) algorithm. To select mixture coefficient effectively, a variable mixture coefficient method is further proposed to generate a variable DMMTE (VDMMTE) algorithm. Moreover, the local mean stability analysis and steady-state performance analysis of DMMTE are presented. Simulations verify the correctness of the theoretical analysis and illustrate the performance superiority of the proposed DMMTE.

Performance of HDL-C measurements assessed by a 4-year trueness-based EQA/PT program in China.

A trueness-based EQA/PT program for high density lipoprotein cholesterol (HDL-C) was initiated. We analyzed the 4year EQA/PT program to overview the measurement standardization for HDL-C in China. Two levels of freshly frozen, commutable serum external quality assessment/proficiency testing (EQA/PT) materials were prepared and determined by reference measurement procedure each year. The samples were delivered to clinical laboratories and measured 15 times in 3days. The precision [coefficient of variation (CV)], trueness (bias), and accuracy [total error (TE)] were calculated and used to evaluate measurement performance. The pass rates of individual laboratories and peer groups were analyzed using the acceptable performance from the National Cholesterol Education Program (NCEP) and biological variation as the evaluation criteria. More than 60% of laboratories use heterogeneous systems, and there was a decrease in the percentage from 2016 to 2019. About 95, 78, and 33% of laboratories met the minimum, desirable and optimum TE criteria derived from biological variation. The pass rates were 87.0% (84.7-88.8%), 58.7% (55.3-62.4%), and 97.3% (95.6-98.3%) that met the acceptable performance of TE, bias, and CV of NCEP. The homogeneous systems had higher pass rates of TE, bias, and CV than the heterogeneous groups in 2016, but they did not show apparent advantages in 2017-2019. The trueness-based EQA/PT program can be used to evaluate the accuracy, reproducibility, and trueness of results. For some IVD manufacturers and individual laboratories, accuracy, especially trueness, are still problems. Efforts should be made to improve the situation and achieve better HDL-C measurement standardization.

Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise

Accurate knowledge of transmission line parameters is essential for a variety of power system monitoring, protection, and control applications. The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented. However, existing literature on PMU-based TLPE implicitly assumes the measurement noise to be Gaussian. Recently, it has been shown that the noise in PMU measurements (especially in the current phasors) is better represented by Gaussian mixture models (GMMs), i.e., the noises are non-Gaussian. We present a novel approach for TLPE that can handle non-Gaussian noise in the PMU measurements. The measurement noise is expressed as a GMM, whose components are identified using the expectation-maximization (EM) algorithm. Subsequently, noise and parameter estimation is carried out by solving a maximum likelihood estimation problem iteratively until convergence. The superior performance of the proposed approach over traditional approaches such as least squares and total least squares as well as the more recently proposed minimum total error entropy approach is demonstrated by performing simulations using the IEEE 118-bus system as well as proprietary PMU data obtained from a U.S. power utility.

Enhancement of Spectrum Sensing Performance via Cooperative Cognitive Radio ‎Networks at Low SNR

The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achieved for cognitive users (secondary users) of the authorized unused dynamic spectrum holes (primary users) while operating in a very low signal-to-noise ratio with the proper condition of minimum total error rate.

An offline map matching algorithm based on shortest paths

ABSTRACT Offline map matching identifies corresponding roads to a GPS trajectory represented by a series of recorded geographic coordinates (GPS points) to the road network. This paper defines matching error as cost on the corresponding road-link to matched GPS points and formulates the offline map matching problem as a shortest path problem with resource constraints. By regarding matched points on one link as a type of resource consumed, the resource constraint indicates that the number of matched GPS points equals the total number of points in the given trajectory. We propose an offline map matching algorithm based on shortest paths by calculating the matching error on each link and extending the classic label-setting shortest path algorithm to find the path with the minimum total matching error for all GPS points. We use real-world taxi trajectories to compare our algorithm with three state-of-the-art map matching algorithms. Our algorithm outperforms all benchmark algorithms in terms of both matching accuracy and computational efficiency. Our algorithm achieves greater matched length (5.36 to 12.27% larger) and lower mis-matched length (3.72 to 75.30% smaller) at a very high matching speed (60.59 points per second on average over thirteen sampling intervals).

A deferring strategy to improve schedulability for the imprecise convergence on-line tasks

The imprecise real-time scheduling can be used for minimizing the bad effects of timing faults by leaving less important tasks unfinished if necessary when a transient overload occured. In the imprecise scheduling, every time-critical task can be logically decomposed into two tasks : a mandatory task and an optional task. Recently, some studies in this field showed good schedulability performance and minimum total error by deferring the optional tasks. But the schedulability performance of the studies can be shown only when the execution time of each optional task was less than or equal to the execution time of its corresponding mandatory task. Therefore, in this paper, a new deferring strategy is proposed under the reverse execution time restriction to the previous studies. Nevertheless, the strategy produces comparable or superior schedulability performance to the previous studies and can minimize the total error also.

Covert Communications With Channel Inversion Power Control in the Finite Blocklength Regime

In this letter, we investigate covert communications with finite blocklength under the channel inversion power control (CIPC) scheme, where a transmitter is allowed to communicate with a receiver without transmitting the pilot signals, which facilitates the hidden of the transmitter from the warden. Considering both the conventional and truncated CIPC schemes, we provide the novel expressions of the optimal detection threshold and the minimum total error probability at the warden. Furthermore, the optimization of the power control parameter at the transmitter is presented to maximize the effective covert throughput, which quantifies the amount of the information bits transmitted from the transmitter to the receiver subject to the covert constraint, under two kinds of CIPC schemes.

Double yolk eggs detection using fuzzy logic.

Chicken egg products increased by 60% worldwide resulting in the farmers or traders egg industry. The double yolk (DY) eggs are priced higher than single yolk (SY) eggs around 35% at the same size. Although, separating DY from SY will increase more revenue but it has to be replaced at the higher cost from skilled labor for sorting. Normally, the separation of double yolk eggs required the expertise person by weigh and shape of egg but it is still high error. The purpose of this research is to detect double-yolked (DY) chicken eggs with weight and ratio of the egg’s size using fuzzy logic and developing a low cost prototype to reduce the cost of separation. The K-means clustering is used for separating DY and SY, firstly. However, the error from this technique is still high as 15.05% because of its hard clustering. Therefore, the intersection zone scattering from using the weight and ratio of the egg’s size to input of DY and SY is taken into consider with fuzzy logic algorithm, to improve the error. The results of errors from fuzzy logic are depended with input membership functions (MF). This research selects triangular MF of weight as low = 65 g, medium = 75 g and high = 85 g, while ratio of the egg is triangular MF as low = 1.30, medium = 1.40 and high = 1.50. This algorithm is not provide the minimum total error but it gives the low error to detect a double yolk while the real egg is SY as 1.43% of total eggs. This algorithm is applied to develop a double yolk egg detection prototype with Mbed platform by a load cell and OpenMV CAM, to measure the weight and ratio of the egg respectively.

A method to select the optimal equivalent electrical circuit applied to study corrosion system of composite coating on magnesium alloy

Mg-Al layered double hydroxides/micro-arc oxidation (Mg-Al LDHs/MAO) composite coating on AZ31 magnesium alloy is fabricated by hydrothermal treatment. Corrosion system is described by ideal equivalent circuit, in which each layer or interface is regarded as the corresponding electronic component. The optimal equivalent circuit is determined by the minimum total error area of electrical parameters. Error areas of phase angle and modulus for experimental and fitting data are calculated for the selected four different kinds of circuit models such as EC1, EC2, EC3 and EC4. The results show that circuit model EC2 is the best because its total error is 1/2, 2/19 and 1/15 of error values for other three kinds of circuit models, respectively. This work reveals that the optimal equivalent electrical circuit can well reflect corrosion system of Mg-Al LDHs/MAO composite coating with the help of electrochemical impedance spectroscopy (EIS) and scanning electron microscope (SEM).

A simple approach to modelling the soil water budget in cool temperate mineral topsoils

A simple soil water budget model is introduced and evaluated against data collected at four sites at Gourdie, near Dundee, Scotland, during 2000, using both short-term (2000-only) and long-term (1961–2011) averaged climate data. The performance of the model is compared using five different pedotransfer function sets to derive parameters for cool temperate mineral soils.Simulated soil water values were significantly correlated with measurements for all pedotransfer functions and climate-terms. The minimum total error achieved between simulations and measurements for the best performing pedotransfer function was 19%, suggesting that predictions are accurate to within 20% of the average measurement. A low bias was observed in the simulations when compared to measured values, suggesting only a small systematic overestimate (2.8% of average measurement). Some model assumptions may have been invalidated where extreme events such as storms or anomalous dry spells were not captured in the monthly time-step led to small model errors.

Timely Classification and Verification of Network Traffic Using Gaussian Mixture Models

We present a novel approach for timely classification and verification of network traffic using Gaussian Mixture Models (GMMs). We generate a separate GMM for each class of applications using component-wise expectation-maximization (CEM) to match the network traffic distribution generated by these applications. We apply our models for both traffic classification, where the goal is to identify the source application from which the traffic originates, by evaluating the maximum posterior probability, and for traffic verification, where the goal is to verify whether the application that claims to be the source of the traffic is as expected, by likelihood testing. Our models use only the first initial packets of truncated flows in order to provide more efficient and timely traffic classification and verification. This allows for triggering timely countermeasures before the end of flows. We demonstrate the effectiveness of our approach by experiments on a public dataset collected from a real network. Our traffic classification approach outperforms other state-of-the-art approaches that are based on machine learning, and achieves up to 97.7% flow classification accuracy when using only 9 first initial packets of flows. We show that 96.6% flow classification accuracy can still be obtained when training the GMMs using only 0.5% of all flows. Our traffic verification approach achieves a minimum Half Total Error Rate (HTER) of 7.65% when using only 6 first initial packets of flows.

Advection without compounding errors through flow map composition

We propose a novel numerical methodology to compute the advective transport and diffusion-reaction of tracer quantities. The tracer advection occurs through flow map composition and is super-accurate, yielding numerical solutions almost devoid of compounding numerical errors, while allowing for direct parallelization in the temporal direction. It is computed by implicitly solving the characteristic evolution through a modified transport partial differential equation and domain decomposition in the temporal direction, followed by composition with the known initial condition. This advection scheme allows a rigorous computation of the spatial and temporal error bounds, yields an accuracy comparable to that of Lagrangian methods, and maintains the advantages of Eulerian schemes. We further show that there exists an optimal value of the composition timestep that yields the minimum total numerical error in the computations, and derive the expression for this value. We develop schemes for the addition of tracer diffusion, reaction, and source terms, and for the implementation of boundary conditions. Finally, the methodology is applied in three flow examples, namely an analytical reversible swirl flow, an idealized flow exiting a strait undergoing sudden expansion, and a realistic ocean flow in the Bismarck sea. New benchmark problems for advection-diffusion-reaction schemes are developed and used to compare and contrast results with those of classic schemes. The results highlight the theoretical properties of the methodology as well as its efficiency, super-accuracy with minimal numerical errors, and applicability in realistic simulations.

Impact of testosterone assay standardization efforts assessed via accuracy-based proficiency testing

BackgroundWe reported observations on analytical performance in testosterone measurements of various methods/assays from the study carried out using accuracy-based proficiency testing (PT) during 2012–2013. In 2016, we re-evaluated analytical performance of testosterone assays using accuracy-based PT to assess effectiveness of CDC efforts toward standardization. MethodsFive single-donor human serum samples from female and male adult donors were analyzed for testosterone by New York State Department of Health-certified clinical laboratories using 16 immunoassays and LC-MS/MS methods. Target values were determined using the CDC reference measurement procedure. ResultsTestosterone targets for the 5 samples were 43.5, 160, 294, 457, and 534 ng/dL. The biases of individual result of the 65 participant laboratories against the target for each sample were calculated. Of participants, 87.7% had ≥4 of the 5 results within the minimum allowable total error limits (± 25.1%), a 14.7% increase from the previous study. The improved PT scores were attributed to better analytical accuracy and precision, and laboratories' selection of more accurate assays/methods. ConclusionsImproved analytical accuracy and precision for testosterone assays were demonstrated over a 3.5-year period after the first CDC-directed accuracy-based proficiency testing. Additional effort is needed to improve accuracy/precision of measurements, especially at low concentrations.

Вопросы оптимизации кадастровых измерений с применением спутниковых изображений сверхвысокого разрешения

Satellite images of high and super high resolution make it possible utilization of these images for purpose of cadastre measurements. Preprocessing of satellite data include also carrying out of ortho-rectification operation which does mean removal of geometric distortions occurred due tosensors orientation and the relief non-evennes. It is shown that curves of distribution of measurements error on number of land plots developed on the basis of such satellite data as CARTOSAT-2and GEO-EYEcan be characterized using unique limitation condition. The optimization task taking into account the said limitation condition and allowing to calculate the total amount of error of serial satellite measurements with increasing number of studied land plots is formulated. The solution of formulated optimization task does show that upon such organization of serial measurements utilization of else more geometric resolution make it possible to achieve the minimum total error of cadastre measurements.

Maximum total correntropy adaptive filtering against heavy-tailed noises

Total least squares (TLS) method has been widely used in errors-in-variables (EIV) modeling tasks in which both input and output data are disturbed by noises, and adaptive filtering algorithm using TLS has shown significantly superior performance to classical least squares (LS) method in EIV system. The TLS essentially extends the minimum mean square error (MMSE) criterion to EIV model, which, however, may work poorly when noise is non-Gaussian (especially heavy-tailed distribution). Recently, an information theoretic learning (ITL) based minimum total error entropy (MTEE) adaptive filtering algorithm has been proposed, which extends the minimum error entropy (MEE) criterion to EIV model and shows desirable performance in non-Gaussian noise environments. However, due to complex mathematical expression, MTEE is computationally expensive and difficult to carry out the theoretical analysis. In this paper, we propose a new ITL-based criterion called maximum total correntropy (MTC) and develop a gradient-based MTC adaptive filtering algorithm. We analyze theoretically the local stability and steady-state performance of the proposed algorithm. Simulation results confirm the theoretical analysis and show the superior performance of MTC in heavy-tailed noises. Further, simulation comparisons between MTC and MTEE are presented. Compared with the MTEE, the MTC is mathematically more tractable and computationally much simpler while achieving similar or even better performance.

On the Performance of Cooperative Spectrum Sensing in Random Cognitive Radio Networks

This paper investigates the performance of cooperative spectrum sensing in cognitive radio networks using the stochastic geometry tools. In order to cope with the diversity of received signal-to-noise ratios at secondary users, a practical and efficient cooperative spectrum sensing model is proposed and investigated based on the generalized likelihood ratio test detector. In order to investigate the cooperative spectrum sensing system, the theoretical expressions of the probabilities of false alarm and detection of the local decision are derived. The optimal number of cooperating secondary users is then investigated to achieve the minimum total error rate of the final decision by assuming that the secondary users follow a homogeneous Poisson point process. Moreover, the theoretical expressions for the achievable ergodic capacity and throughput of the secondary network are derived. Furthermore, the technique of determining an appropriate number of cooperating secondary users is proposed in order to maximize the achievable ergodic capacity and throughput of the secondary network based on a target total error rate requirement. The analytical and simulation results validate the chosen optimal number of collaborating secondary users in terms of spectrum sensing, achievable ergodic capacity, and throughput of the secondary network.