Adaptive Identification Research Articles

Stream diatom assemblages as environmental indicators – A cross-regional assessment

Abstract Ongoing climatic change and anthropogenic pressure highlight the importance of reliable assessment of the ecological status of freshwaters. Bioindicators are used widely in ecological assessments as biotic assemblages reflect the environmental conditions in current ecosystems. However, the robustness of bioindicators relies on the transferability of indices and models outside the regions they were derived from. We tested the reliability of stream diatom assemblages as indicators of water chemistry and climatic factors in a cross-regional assessment by developing a predictive model with diatom assemblage data from Sweden and using it to model stream conditions in Finland. The inference models and predictions were performed using the Boosted Regression Trees (BRT) method, separately in species and genus levels. The predictive performance of the calibration models in Sweden were good or moderate for both water chemistry and climatic variables, both at species and genus levels. The most important climatic (growing degree days, r2 = 0.57) and water chemistry variables (pH, r2 = 0.65; and total phosphorus (TP), r2 = 0.52) could be inferred from diatom assemblages relatively well. However, predictive performances of the cross-regional models were low (r2 ≤ 0.13). Nevertheless, water chemistry variables, conductivity (r2 = 0.13) and TP (r2 = 0.12), were predicted the best. The most important diatom indicators for climatic and environmental variables varied between Sweden and Finland. The study showed that diatom assemblages can be robust indicators of water chemistry and climatic variables within the region where the inference models are calibrated. However, their indicator ability may be weak between regions. The reason for the low transferability of the diatom inference models may stem from between-region differences in species realized niches, species pools and/or ecosystems, local adaptation or species identification. Hence, models should only be used with caution in geographical contexts other than the one where they were developed. The calibration data should cover as large geographical area as possible to give reliable predictions when applied in smaller regions.

ДИАГНОСТИЧЕСКИЕ КРИТЕРИИ ВЫДЕЛЕНИЯ ФИЛЬТРАЦИОННЫХ ПОТОКОВ В ПРОЦЕССЕ ГИДРОДИНАМИЧЕСКИХ ИССЛЕДОВАНИЙ ГОРИЗОНТАЛЬНЫХ СКВАЖИН

Актуальность работы обусловлена необходимостью выделения фильтрационных режимов течения для определения гидродинамических параметров нефтяных пластов в процессе проведения нестационарных испытаний горизонтальных скважин по кривым восстановления забойного давления. Целью исследования является разработка диагностических критериев детерминированных моментов давлений для выделения фильтрационных режимов течения в процессе нестационарных гидродинамических испытаний горизонтальных скважин нефтяных месторождений по кривым восстановления давления. Методы исследования основаны на теории и практике нестационарных испытаний горизонтальных скважин, линейной алгебре, идентификации систем, системном анализе. Апробация предложенных диагностических критериев осуществлялась с использованием промысловых данных нестационарных испытаний нефтяных горизонтальных скважин по кривым восстановления забойного давления. Результаты. Разработаны диагностические критерии выделения на кривой восстановления забойного давления участков раннего радиального, линейного и позднего радиального (псевдорадиального) потоков в процессе проведения гидродинамических исследований горизонтальных скважин нефтяных месторождений. В основе диагностических критериев использованы оценки детерминированных моментов давлений, полученные в процессе проведения испытаний горизонтальных скважин. Для определения неизвестных значений пластовых и забойных давлений на недовосстановленном участке кривой восстановления забойного давления использованы феноменологические прогнозирующие модели забойного давления с параметрами, зависящими от времени, с учетом экспертной оценки пластового давления, и адаптивные алгоритмы идентификации. Проводилась обработка результатов испытаний двух горизонтальных скважин нефтяного месторождения. Показано, что предложенные диагностические критерии практически не уступают по точности графоаналитическому методу выделения режимов фильтрации и позволяют определять время начала и завершения фильтрационных потоков, а также время завершения испытаний скважин в процессе их проведения без участия квалифицированного интерпретатора.

Creation of a learning microprocessor system for protection of contact network feeders using adaptive parametric identification methods

Handling of high-weight trains, double trains, packet traffic schedule, recuperation on electric locomotives often leads to false protection relays, downtime of trains and increased wear of power switching and protective equipment of traction substations. The paper proposes methods to improve the selectivity of microprocessor relay protection of feeders of AC traction substations contact network by increasing the accuracy of extraction of the first current and voltage harmonic based on phase locked circuits, integration of learning subroutines by fixing the electric parameters and creating the sets of dynamic setpoints in accordance with the current train situation and adaptive parametric identification of dynamic assessment of current electric parameters of the traction network. It also analyzes structural diagrams of hardware and software of the proposed microprocessor protection system of contact network feeders.

An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

A Quotient Function Method for Selecting Adaptive Dynamic Load Identification Optimal Regularization Parameter

A Quotient Function Method for Selecting Adaptive Dynamic Load Identification Optimal Regularization Parameter

Recursive parameter estimation and its convergence for bilinear systems

This study develops an adaptive filtering-based recursive identification algorithm for joint estimation of states and parameters of bilinear state-space systems with an autoregressive moving average noise. In order to handle the correlated noise and unmeasurable states in parameter estimation, an adaptive filter is established to whiten the coloured noise and a bilinear state observer is constructed to update the unavailable states recursively. Then a hierarchical generalised extended least squares (HGELS) algorithm and an adaptive filtering-based HGELS algorithm are developed for simultaneously estimating the unknown states and parameters. The convergence analysis indicates that the parameter estimates can converge to their true values. A numerical example illustrates the convergence results.

Hawkeye: Adaptive Straggler Identification on Heterogeneous Spark Cluster With Reinforcement Learning

It is a common sense that people harbors the belief that stragglers exert huge influence upon the performance conducted by the analysis systems of big data for the reason of poor performance made by some computing nodes, data skew and so on. Accordingly, stragglers have been billed as an indispensable bottleneck in Map-Reduce framework processing. However, existing studies on stragglers identification are targeting coarse-grained detection, schedule level optimization and off-line log based cause analysis. Accuracy identifying the stragglers in time for each job, however, is an extremely tough because (1) Auite a number of root causes for stragglers in data analytics frameworks;(2) The number of key parameters affecting stragglers identification; and (3) The different clusters configurations, and their impact on the stragglers detection, vary among different job types and sizes. Either existing solutions adopt a “tweak-and-pray” manual tuning approach, which is complex, time-consuming and error-prone, or only most of them fix theirs eyes upon coarse-grained straggler detection. In this paper, we systematically conduct the exploration on the fundamental problem of automatic, adaptive straggler identification on big data analytics platform. Under the inspiration of the recent triumphs over implementing Reinforcement Learning (RL) techniques for solving complex online optimal problems, we conducted investigation that Reinforcement learning are reasonably employed to adaptively opt the optimal parameters to identify stragglers free of the intervention of human beings. Specifically, we propose Hawkeye, a general adaptive speculative execution system which identifies stragglers by reinforcement are learning to launch speculative tasks on heterogeneous cluster at runtime. In accordance with the experimental conclusions, Hawkeye manages to cut down the job completion time over the distinct type applications. An instance is that it reveals as many as nearly 37% decrease average job completion time based on an improvement of 23% on the preciseness of the present resolutions to the heterogeneous cluster.

Speed-gradient Method in Adaptive Control and Identification. Historical Overview

The paper provides a historical overview of the Speed-gradient method and its applications to adaptive control and identification problems since mid-1970-th, when the method was originated, till the present days. It is demonstrated that it is an efficient and an useful tool for solving a wide range of engineering problems.

Remote Measurement of Human Vital Signs Based on Joint-Range Adaptive EEMD

Remote techniques for measuring human vital signs have attracted great interests due to the benefits shown in medical monitoring and military applications. Compared with continuous-wave Doppler radar, frequency-modulated continuous-wave (FMCW) radar which can discriminate vital signs from different distances, shows potential for reducing the interferences from other targets and the environment. However, in the state-of-the-art algorithms, only one chirp per frame is utilized for FMCW-based vital sign monitoring. Moreover, the vital signal is extracted from only one range bin of the fast Fourier transform. Which does not make full utilization of the long system idle time, and loses the power distributed on other range bins. By exploiting the relationship between respiration and heartbeat vibrations, an adaptive identification embedded ensemble empirical mode decomposition (EEMD) method for joint-range spectral estimation is proposed to measure the heart rate. First, A multi-chirp processing is presented for a 2-dimensional phase accumulation. Then the phase signals from a sequence of range bins are decomposed with a fast adaptive identification process. Finally, with the identified heartbeat components, we solve a multiple measurement vectors problem to estimate the heart rate. Experimental results showed that, at the detection range from 1 m~ 2.5 m, the proposed method can robustly distinguish the heartbeat from respiration and its harmonics and accurately estimate the heart rate with a root mean square error less than 6 bpm.

Fuzzy MRAS rotor resistance identification of BL-IM based on reactive power

As for the inverse decoupling control system of a bearingless induction motor (BL-IM), in order to eliminate the influence of rotor resistance variation on its control performance, on the basis of the reactive power calculation of torque system, a novel fuzzy model reference adaptive (MRAS) identification method of rotor resistance is proposed. The reference model and adjustable model of instantaneous reactive power are derived in detail. In order to improve the identification performance of rotor resistance, a fuzzy PI adaptive law based on popov super stability theory is constructed. On this basis, a rotor resistance identifier is constructed, and it is used to on-line correct the rotor resistance parameter in the inverse system mathematical model of a BL-IM system. Based on the inverse decoupling control system of a BL-IM, the simulation experimental analysis and verification are carried out. The simulation experimental results have shown that when the proposed identification method of rotor resistance is used, not only the identification- and tracking-speed of rotor resistance can be effectively improved, but also the identification accuracy of rotor resistance can be improved; as for the BL-IM system, after the rotor resistance parameter is on-line corrected, not only the inverse dynamic decoupling control performance can be effectively improved, but also the robustness of BL-IM system to the variation of rotor resistance parameters can be improved.

К настройке алгоритма вычисления барометрической высоты летательного аппарата системой воздушных сигналов

Introduction. In conditions of increasing requirements for the accuracy of determining the altitude and speed parameters of aircraft, the task of tuning up the algorithmic support of their air signal systems (SHS) based on real flight data is quite relevant. Aim. Consider the task of assessing the real value of the temperature gradient along the height of the atmosphere for the purpose of operational adjustment of the standard algorithmic support of SHS. Materials and methods. The traditional approach is to use the numerical value of the temperature gradient, adopted for the international standard atmosphere (ISA). A feature of the proposed approach is that the temperature gradient in height is determined by current measurements of atmospheric pressure and temperature, and not height and temperature. Results. Adaptive and non-adaptive parametric identification algorithms are developed. An improved version of the adaptive algorithm is proposed, which ensures proper tuning of its filtering properties to a specific statistical situation. Algorithms were tested both according to the characteristics of the ISA using computer-generated random interference, and according to the results of real measurements known in the literature obtained using a meteorological rocket. The properties of adaptive and non-adaptive algorithms are compared. It is shown that repeated “running” of the same experimental data through an adaptive algorithm leads to almost the same accuracy of the gradient estimate, as when using a non-adaptive algorithm. Conclusion. The implementation of the algorithms improves the accuracy of determining the barometric altitude of the aircraft (AC), does not require any modernization of the hardware of the SHS, only some addition of the algorithmic support of the system is necessary. The adaptive algorithm is recommended for use in the case of a significant drift of atmospheric characteristics for the operational adjustment of the system.

Adaptive parameters identification of lithium-ion batteries with adaptive linear neuron and state-of-charge estimation based on open circuit voltage

The state of charge (SOC) is a critical parameter of a Li-ion battery. An accurate online estimation of the SOC is important for forecasting the electric vehicle driving range. A good estimation of the SOC results from a good identification of the battery parameters. Reducing the algorithm complexity is important to improve the accuracy of SOC estimation results. We propose in this work an original structure of an adaptive linear neuron (ADALINE) to estimate the SOC. The ADALINE provides the weighted sum of the inputs based on an online identification of the open-circuit voltage (OCV). The advantage of this approach is its adaptable capability and the speed of execution (fast training) as well as the possibility of interpreting these weights. The simulation results indicate that the proposed method can ensure an acceptable accuracy of SOC estimation for online application with a maximum error being less than 5%.

Researching of the low-precision SINS research for an unmanned vehicle

A small-sized inertial navigation system (SINS) Gyrolab GL VG 109 is researched. It is shown that this system has low accuracy; therefore it cannot be used to determine the parameters of an unmanned vehicle in an autonomous mode. Correction of the system from the satellite navigation system significantly increases the accuracy of determining the parameters of an unmanned vehicle, but only under conditions of stable signals from the satellite navigation system (SNS). The algorithmic support for the correction facility of the navigation system based on the scalar adaptive estimation algorithm and identification procedure is formed. The use of algorithmic correction of SINS from SNS using an estimation algorithm allows achieving an accuracy that corresponds to systems of the third accuracy class. Keywords inertial navigation system without platform; unmanned vehicle; correction; satellite navigation system; scalar estimation algorithm; scalar identification; analysis of accuracy

Locomotion Control of Quadruped Robots With Online Center of Mass Adaptation and Payload Identification

Most of the controllers for quadruped robots are based on the planning of their centers of mass (CoM), with the assumption that the CoM locates at the geometrical center of the robot trunk. However, this assumption rarely meets the fact of the robot prototype and introduces apparent influences to the system. This article proposes a CoM estimation and adaptation method for quadruped robots in dynamic trot gait based on the model predictive control (MPC) of the trunk. The influences of the CoM offset on the robot states are inherently analyzed. The CoM components on the horizontal plane are taken into consideration and estimated based on the speed errors and the contact force differences, which are compensated to the robot controller. In the case of payload changes, the identification method for the mass of payload is proposed based on the estimated CoM. Simulation results verify that with the proposed approach, the robot successfully adapts to the CoM payload changes while the locomotion is not interrupted, and the state errors are significantly reduced.

A sparse system identification algorithm based on fractional order LMS

In this paper, a zero-attracting fractional order least mean squares (ZA-FLMS) algorithm is proposed for adaptive sparse system identification. l1-norm of the filter coefficients is considered and zero-attracting correction is introduced into the updating equation. The extension of the algorithm is also proposed for distributed local sensors of information-centric IoT. The convergence speed and MSE performance are investigated in the simulations, which show effective improvement for sparse system identification compared with traditional LMS, zero-attracting LMS and fractional order LMS, especially with lower sparsity and smaller fractional order. But the decrement of fractional order and step size will lead to slower convergence speed, while the bigger fractional order and step size will lead to bigger variation of MSE. Therefore, optimised ZA-FLMS is further introduced, which uses l0-norm in the initial stage of the algorithm and shows improvement of convergence speed for smaller fractional order.

An Adaptive Snow Identification Algorithm in the Forests of Northeast China

Northeast China is one of the primary snow-covered regions, and its forest coverage is over 40%. Forest snow identification is usually a challenging problem, and the SNOMAP algorithm tends to underestimate the amount of snow cover in forest regions for the lower normalized difference snow index. In this article, an improved method of the snow-cover identification based on the Landsat operational land imager is proposed. One improvement includes using the normalized difference forest snow index (NDFSI) to discriminate between snow-covered and snow-free forests. The threshold value of the NDFSI in different forest types is set according to the normalized difference vegetation index. On the other hand, the sun elevation is very low in winter in Northeast China with high latitude; as a result, the snow in shadow areas is usually classified as liquid water for its low near-infrared reflectance in the current SNOMAP algorithm. Then, another improvement is introducing the land surface temperature, which is retrieved from the thermal infrared band to distinguish liquid water from snow in shadow areas. We applied this improved method to evaluate forest areas in the Daxinganling, Xiaoxinganling, and Changbai Mountain areas in different seasons. The total classification accuracy reached 97.5%, and the pixels that introduce omission error and commission error were mainly distributed in areas of dense forest shadows. This improved method retains the computational simplicity and effectiveness of the SNOMAP algorithm in nonforest areas and improves the underestimation of snow cover in forest regions and shadow areas.

Comparison of Rotor Wake Identification and Characterization Methods for the Analysis of Wake Dynamics and Evolution

Optimal wind power plant design requires understanding of wind turbine wake physics and validation of engineering wake models under wake-controlled operating conditions. In this work, we have developed and investigated several different wake identification and characterization methods for analyzing wake evolution and dynamics. The accuracy and robustness of these methods, based on Gaussian function fitting and adaptive contour identification, have been assessed by application to a large-eddy simulation data set. A new contour-based method based on downstream momentum deficit has been considered. Uncertainties arising from wake-identification errors result in characterizations of the wake expansion, recovery, and meandering motion that differ by 19% of the rotor area, 4% of the freestream, and 15% rotor diameter, respectively.

Adaptive parameters identification of lithium-ion batteries with adaptive linear neuron and state-of-charge estimation based on open circuit voltage

The state of charge (SOC) is a critical parameter of a Li-ion battery. An accurate online estimation of the SOC is important for forecasting the electric vehicle driving range. A good estimation of the SOC results from a good identification of the battery parameters. Reducing the algorithm complexity is important to improve the accuracy of SOC estimation results. We propose in this work an original structure of an adaptive linear neuron (ADALINE) to estimate the SOC. The ADALINE provides the weighted sum of the inputs based on an online identification of the open-circuit voltage (OCV). The advantage of this approach is its adaptable capability and the speed of execution (fast training) as well as the possibility of interpreting these weights. The simulation results indicate that the proposed method can ensure an acceptable accuracy of SOC estimation for online application with a maximum error being less than 5%.

Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

A New Robust Identification Method for Transmission Line Parameters Based on ADALINE and IGG Method

Accurate transmission line parameters are the basis of power system calculations. Aiming at obtaining accuracy online transmission line parameters in the case of large random noises even bad data in Phasor Measurement Unit (PMU) measurements, which occurs frequently in the practice, a new adaptive robust identification method combining adaptive linear neuron (ADALINE) and traditional robust IGG (Institute of Geodesy & Geophysics, Chinese Academy of Sciences) method is proposed. In detail, first, the identification model of transmission line parameters is presented based on the multi-period PMU measurements at both ends of the transmission line. Then, a parameter solving model based on ADALINE is established. Furthermore, to fully use measurement information, the adaptive robust ADALINE (ARA) are proposed, which applies the robust IGG weight function (Scheme I) to ADALINE to realize “three segments” robust identification. In addition, to improve the robustness, the expectation and variance of the equation residual sequence are estimated adaptively with the median principle to adjust the threshold for the IGG function to assure robustness (TAR), which is independent of the known information for the error of the measurement equipment. The cases based on PSCAD simulated data and measured data show the effectiveness and engineering practicality of the proposed method.