When the alternating current (AC) chassis dynamometer system measures the motion parameters of a test vehicle, it is subject to interference from measurement noise, leading to an increase in testing errors. An innovat...
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When the alternating current (AC) chassis dynamometer system measures the motion parameters of a test vehicle, it is subject to interference from measurement noise, leading to an increase in testing errors. An innovative adaptive Kalman Filtering (KF) algorithm based on innovation covariance is proposed. This algorithm facilitates the optimal estimation of vehicle motion parameters without necessitating prior error statistics. The loading model of the measurement and control system is optimized, enabling the precise loading of the dynamometer. The test results indicate that the testing error of the optimized algorithm for the loading model decreases from 6.4% to 1.8%. This improvement establishes a foundation for achieving accurate control of the chassis dynamometer and minimizing testing errors.
Electrically excited synchronous machines have become one of the potential alternatives to permanent magnet synchronous machines in electric vehicles to avoid rare-earth materials. Utilizing high-frequency brushless e...
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Electrically excited synchronous machines have become one of the potential alternatives to permanent magnet synchronous machines in electric vehicles to avoid rare-earth materials. Utilizing high-frequency brushless exciters for rotor excitation is a promising choice to reduce friction losses and maintenance effort and cost. However, with the usage of brushless exciters, the field current and temperature cannot be measured directly. In this article, an algorithm is proposed to dynamically estimate the field current as well as the field winding temperature. The dc-link current is utilized as a feedback to correct the estimation. The performance of the estimation algorithm is initially evaluated in simulations and then verified by experimental measurements in a prototype. Based on the estimation algorithm, closed-loop control of the field current is developed and verified experimentally.
An estimation algorithm for Butyrylcholinesterase (BChE) in Kochi Medical School Hospital is proposed for early prediction of cirrhosis. It provides automatic calculation of similarity of input BChE for known learning...
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ISBN:
(纸本)9781889335384
An estimation algorithm for Butyrylcholinesterase (BChE) in Kochi Medical School Hospital is proposed for early prediction of cirrhosis. It provides automatic calculation of similarity of input BChE for known learning data constructed by Neural Network, which estimates subsequent value of input value based on instance-based reasoning. Experimental estimation results for real data show that mean difference of day under normal values for estimated value is 178 in 4320 interval compared with true data and that the proposed algorithm distinguish cirrhosis patients from other patients. The proposed algorithm constructs learning database automatically and provides early prediction using screening study in hospital.
The paper presents 20-year time series (1975-1994) of annual mass loads discharged from the aggregated freshwater-monitored area of Great Britain for suspended solids, total nitrogen, orthophosphate and zinc. Correspo...
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The paper presents 20-year time series (1975-1994) of annual mass loads discharged from the aggregated freshwater-monitored area of Great Britain for suspended solids, total nitrogen, orthophosphate and zinc. Corresponding time series are also given for regional groups of catchments draining towards five sea-areas recognised for monitoring UK fluvial inputs to the North-East Atlantic. A mass load computational scheme that effectively merges the national river flow and water quality databases is used to derive the mass flow time series, and their utility is considered within the context of strategic information needs and the capabilities of current national monitoring programmes. While the aggregated annual suspended solids loads presented in the paper are often severe underestimates of the actual load, because infrequent sampling tends to miss high concentrations at high flows, the loads for total nitrogen and orthophosphate, which exhibit less variability in concentration than suspended solids, are of a more acceptable quality. Mass loads for zinc, and other determinands often recorded as 'less than a limit of detection' (< LOD), can be very uncertain. Bearing in mind the uncertainties involved, the time series presented are assessed for the presence or absence of trends. Not surprisingly, annual loads exhibit correlation with annual runoff. Except for zinc, however, there is no obvious evidence for temporal patterns in the time series that are not associated with variations in annual runoff. Zinc loadings have decreased as a result of improved environmental management in industrialized catchments. Discussion of the sources of uncertainty in regional mass flows is followed by some general recommendations related to capitalising most effectively on available mass flow time series. (c) 2004 Published by Elsevier B.V.
Conventional LPC algorithms give biased estimates for voiced speech signals in the case of pitch-asynchronous analysis. This paper presents a new estimation algorithm for an all-pole model, named as homomorphic linear...
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Conventional LPC algorithms give biased estimates for voiced speech signals in the case of pitch-asynchronous analysis. This paper presents a new estimation algorithm for an all-pole model, named as homomorphic linear predictive coding (HLPC) which models the vocal tract transfer function as an all-pole filter, but performs the estimation of the filter coefficients in the cep-strum domain by a minimum mean squared error method. By limiting the summation interval of squared errors in the cepstrum domain to the low time portion that is not affected by pitch components, the estimation results obtained by HLPC are unbiased and independent of the exciting signal type. Experiments on spectrum estimation and formant estimation under several conditions have been carried out for comparison. It is shown that for pitch-asynchronous analysis, HLPC has a higher accuracy than LPC in spectrum estimation as well as formant frequency and bandwidth estimation, especially for speech signals with high pitch frequencies.
The State of Charge (SoC)-Open Circuit Voltage (OCV) curve and the quality of estimation algorithm are two important factors that infect the accuracy of SoC estimation for lithium-ion batteries in electric vehicles. T...
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The State of Charge (SoC)-Open Circuit Voltage (OCV) curve and the quality of estimation algorithm are two important factors that infect the accuracy of SoC estimation for lithium-ion batteries in electric vehicles. The purpose of this study is to improve the accuracy of SoC estimation for the lithium-ion battery. The battery management system is established to monitor the state of lithium-ion batteries to ensure the safety and reliability of the battery system. Firstly, the specific experiments were designed to analyze the relationship between the SoC-OCV curve and experimental conditions (e.g., ambient temperature and current rate) and battery states (e.g., State of Health and positive materials). A series of conclusions was found and used to correct the process of SoC estimation. Secondly, by analyzing the reasons that the SoC estimation error increased in the low-capacity period and the late-stage of estimation using the extended Kalman filter (EKF), an improved estimation algorithm was proposed. In the improved estimation algorithm, the ampere-hour counting was used in the low-capacity period, and the EKF was used in the rest. The accuracy of the improved estimation algorithm was verified by two experiments. Verification results show that the improved estimation algorithm makes up for the drawback of the EKF, the estimation error in constant current discharge experiment is less than 2 %, and the estimated error under dynamic conditions is less than 3 %. Therefore, the improved estimation algorithm has a higher accuracy than the EKF for the SoC estimation and can meet the operation requirements of a lithium-ion battery. This study contributes to the improvement of the safety and reliability of battery systems in electric vehicles by improving the accuracy of SoC estimation for lithium-ion batteries.
Smoothing cubic normalized B splines are used in synthesizing a suboptimal algorithm for estimation Oil Bayes criteria, maximum likelihood, and a posteriori density without constraint 017 the gaussial behavior of the ...
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Smoothing cubic normalized B splines are used in synthesizing a suboptimal algorithm for estimation Oil Bayes criteria, maximum likelihood, and a posteriori density without constraint 017 the gaussial behavior of the corresponding distribution densities. The potential accuracy of the algorithm is evaluated in accordance with the Crainer-Rao inequality.
In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is stu...
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In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multidimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability.
The principles for the development of portable instrumentation equipment (multimeters, frequency meters, etc.) using quantised sine signals, with a common unknown fixed frequency, and an estimation algorithm implement...
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The principles for the development of portable instrumentation equipment (multimeters, frequency meters, etc.) using quantised sine signals, with a common unknown fixed frequency, and an estimation algorithm implemented on digital signal processors (DSPs) are described. An unknown internal sine signal is generated. For measurements, analog to digital converters (ADCs) acquire multiple samples of sine signals, which are quantised and transmitted to the DSP. A precise quantised sine signal, with a common unknown fixed frequency, signal estimation algorithm, for DSP-based instrumentation, is presented. An iterative process is performed to obtain amplitudes, phases, dc components, common frequency and to control the ADC number of bits. Final solutions depend on the initial values, obtained using different classical estimation algorithms. The accuracy of the initial values of iterations and the number of used points have a large influence on the speed of convergence. The algorithm stops when quantisation conditions are satisfied.
For estimating the missing data of wireless sensor networks, an estimation algorithm called HD method, which can make use of sensoring space-time correlation of the data, was proposed based on mathematical Hermite and...
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For estimating the missing data of wireless sensor networks, an estimation algorithm called HD method, which can make use of sensoring space-time correlation of the data, was proposed based on mathematical Hermite and DESM statistical models. The algorithm not only can adaptively adjust the time and space weights, but also can accurately estimate the missing or unavailable data. The experimental results show that the algorithm has good stability and relatively high estimation accuracy.
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