Traditional Adaptive Methods Research Articles

A double-ended Raman temperature measurement method for hazardous chemicals warehouse

An adaptive calibration method for distributed temperature sensor is proposed to eliminate the influence of fiber attenuation and APD dark current fluctuation. The two ends of one fiber are connected to the different measuring channels of the system. In a measurement period, two measuring channel alternately inject pulse signal into optical fiber. With the help of the measured data obtained from the two channels, the attenuation function part in demodulation formula which is a function related to the distance is converted to a constant. The mean filtering method is used to restrain the influence of APD output fluctuation on the measurement results. The experiment shows that compared with the traditional adaptive demodulation method, the temperature measurement curve is smoother and the error is reduced from about 1 °C to less than 0.5 °C.

Modifying CMAC adaptive control with weight smoothing in order to avoid overlearning and bursting

This paper proposes a method to prevent overlearning (weight drift) and bursting in adaptive control using the cerebellar model arithmetic computer (CMAC). Traditional robust adaptive methods such as deadzone, projection, e-modification are not necessarily suitable for CMAC control. The proposed method relies on the idea of weight smoothing, where the difference between adjacent weights in the CMAC is penalized in the adaptation. Previously proposed weight smoothing schemes are only suitable for one or two-input CMACs and do not have stability guarantees. This work extends the method for use with n-input CMACs and develops the adaptive scheme within a Lyapunov framework to guarantee uniformly ultimately bounded signals. Simulations with a two-link, flexible-joint robot subject to sinusoidal disturbance demonstrate the performance and stability of the new method.

Fast Adaptive BSS Algorithm for Independent/Dependent Sources

Blind separation for dependent sources and multi-Gaussian sources is a challenging problem. This letter proposes a new adaptive blind source separation algorithm, which is referred to as vector auto-regressive diagonalization (VAR-JD), to deal with this problem. This algorithm estimates demixing matrix by matrix joint JD of a set of matrices containing the estimated VAR coefficients. Compared with traditional adaptive BSS method, which assumes that source signals are mutual independent and no more than one Gaussian source signal exists, the proposed VAR-JD algorithm is not only able to separate independent source signals but also dependent source signals, and there is no demand for the number of Gaussian source signals. Simulation results on the separation of synthetic and communication signals demonstrate the effectiveness of the proposed approach. This latter is important to anti-jamming communications. Moreover, VAR-JD is suitable for determined mixtures and can extend easily to overdetermined mixtures.

Fuzzy adaptive control of two totally different chaotic systems with complicated structures by novel pragmatically adaptive control strategy

In this paper, a set of novel fuzzy adaptive control strategy is proposed to control two totally different nonlinear systems with complicated structure and different numbers of nonlinear terms. This novel adaptive control strategy is composed of three main points—(1) novel fuzzy model, proposed via Li and Ge (IEEE Trans Syst Man Cybern Part B Cybern 41(4):1015–1026, 2011), provides a set of simple and effective modeling strategy to reduce the total numbers of fuzzy rules through modeling process, and only two linear subsystems are needed; (2) novel control Lyapunov function is set as an exponential form of error states and tries to speed up the variance of states in this article; and (3) pragmatical asymptotically stability theorem: this theorem can be applied to proof that the error of parameters can achieve the original point strictly. Mathieu–Van der Pol system and quantum cellular neural networks nanosystem are used for illustrations in numerical simulation results to show the effectiveness and feasibility of the new fuzzy adaptive approach. Traditional fuzzy modeling and adaptive control method are given for further comparison.

Amplitude-preserving nonlinear adaptive multiple attenuation using the high-order sparse Radon transform

The Radon transform is widely used for multiple elimination. Since the Radon transform is not an orthogonal transform, it cannot preserve the amplitude of primary reflections well. The prediction and adaptive subtraction method is another widely used approach for multiple attenuation, which demands that the primaries are orthogonal with the multiples. However, the orthogonality assumption is not true for non-stationary field seismic data. In this paper, the high-order sparse Radon transform (HOSRT) method is introduced to protect the amplitude variation with offset information during the multiple subtraction procedures. The HOSRT incorporates the high-resolution Radon transform with the orthogonal polynomial transform. Because the Radon transform contains the trajectory information of seismic events and the orthogonal polynomial transform contains the amplitude variation information of seismic events, their combination constructs an overcomplete transform and obtains the benefits of both the high-resolution property of the Radon transform and the amplitude preservation of the orthogonal polynomial transform. A fast nonlinear filter is adopted in the adaptive subtraction step in order to avoid the orthogonality assumption that is used in traditional adaptive subtraction methods. The application of the proposed approach to synthetic and field data examples shows that the proposed method can improve the separation performance by preserving more useful energy.

Domain Adaptation With Preservation of Manifold Geometry for Hyperspectral Image Classification

Adapting a pretrained classifier with unlabeled samples from an image for classification of another related image is a common domain adaptation strategy. However, traditional adaptation methods are not effective when the drift of spectral signatures is significant. Instead of iteratively redefining classifier parameters or decision boundaries, we exploit similar data geometries of images and preserve essential common data characteristics in a joint manifold space where similar samples are clustered. The proposed classification framework is based on aligning two global data manifolds with bridging pairs. In addition to global structures, we also consider the local scale by incorporating similar local clusters into the alignment process. In experiments with challenging temporal and spatially disjoint hyperspectral data sets, the proposed framework provides favorable classification results compared to two baseline methods, naive k-NN in both the original space and the manifold derived from pooled data. In comparisons with four state-of-the-art domain adaptation benchmark methods, the proposed method is demonstrated to be a competitive domain adaptation method, especially for the case when spectral changes between two data domains are significant. Results also provide insights related to the usefulness of incorporating global and local geometric characteristics of remote sensing data for domain adaptation studies.

Smooth Adaptive Internal Model Control Based on U Model for Nonlinear Systems with Dynamic Uncertainties

An improved smooth adaptive internal model control based on U model control method is presented to simplify modeling structure and parameter identification for a class of uncertain dynamic systems with unknown model parameters and bounded external disturbances. Differing from traditional adaptive methods, the proposed controller can simplify the identification of time-varying parameters in presence of bounded external disturbances. Combining the small gain theorem and the virtual equivalent system theory, learning rate of smooth adaptive internal model controller has been analyzed and the closed-loop virtual equivalent system based on discrete U model has been constructed as well. The convergence of this virtual equivalent system is proved, which further shows the convergence of the complex closed-loop discrete U model system. Finally, simulation and experimental results on a typical nonlinear dynamic system verified the feasibility of the proposed algorithm. The proposed method is shown to have lighter identification burden and higher control accuracy than the traditional adaptive controller.

Rolling bearing feature frequency extraction using extreme average envelope decomposition

The vibration signal contains a wealth of sensitive information which reflects the running status of the equipment. It is one of the most important steps for precise diagnosis to decompose the signal and extracts the effective information properly. The traditional classical adaptive signal decomposition method, such as EMD, exists the problems of mode mixing, low decomposition accuracy etc. Aiming at those problems, EAED(extreme average envelope decomposition) method is presented based on EMD. EAED method has three advantages. Firstly, it is completed through midpoint envelopment method rather than using maximum and minimum envelopment respectively as used in EMD. Therefore, the average variability of the signal can be described accurately. Secondly, in order to reduce the envelope errors during the signal decomposition, replacing two envelopes with one envelope strategy is presented. Thirdly, the similar triangle principle is utilized to calculate the time of extreme average points accurately. Thus, the influence of sampling frequency on the calculation results can be significantly reduced. Experimental results show that EAED could separate out single frequency components from a complex signal gradually. EAED could not only isolate three kinds of typical bearing fault characteristic of vibration frequency components but also has fewer decomposition layers. EAED replaces quadratic enveloping to an envelope which ensuring to isolate the fault characteristic frequency under the condition of less decomposition layers. Therefore, the precision of signal decomposition is improved.

Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction

Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise. In order to increase the calculation speed and improve the weak feature detection performance, a new bistable model has been built. With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper. By adding the second-order differential item into the traditional bistable model, noise utilization can be increased and the quality of SR output signal can be improved. The iteration algorithm for implementing the adaptive SR is given. Compared with the traditional adaptive SR method, this algorithm does not need to set up the searching range and searching step size of the system parameters, but only requires a few iterations. The proposed method, discrete wavelet transform and the traditional adaptive SR method are applied to analyzing simulated vibration signals and extracting the fault feature of a rotor system. The contrastive results verify the superiority of the proposed method, and it can be effectively applied to weak mechanical fault feature extraction.

A Frequency Tracking Adaptive Power Line Interference Canceller for Electrocardiogram

Power line interference (PLI) may lead to the signal-to-noise ratio (SNR) decline sharply on biomedical signals, including the electrocardiogram (ECG). The proposed method employs the relationship of frequency and weights in adaptive filter to track the frequency variation of PLI. Real ECG signals from MIT-BIH database was used in the experiment, and they were corrupt by an artificial PLI signal for experiment. Correction performances of the proposed method and traditional adaptive method were compared by SNR in the paper. The results showed that the proposed method is consistently superior to the traditional one when the power line interference is vary with time, and the proposed method can track the variation of power line interference effectively.

Peformance Analysis of Mixture Approaches and Tracking Performance of Adaptive Filter using Adaptive Neural Network

paper mainly concentrates on different mixture structures which include affine and convex combinations of several parallel running adaptive filters. The mixture structures are investigated using their final MSE values and the tracking of the nonlinear system is done using an ANN model that updates the filter weights using nonlinear learning strategies(it uses stochastic gradient descent to update the filter weights based on MSE's of mixture structures).the mixture structures greatly improve the convergence and performance of the of the constituent filters compared to traditional adaptive methods. The mixture structures employed in this paper can be applied to the constituent filters that employ different adaptation algorithms. We describe an adaptive neural network model that updates the weights of the filter using nonlinear methods.

An integrated approach to active model adaptation and on-line dynamic optimisation of batch processes

In the application of on-line, dynamic process optimisation, adaptive estimation of the system states and parameters is usually needed to minimise the unavoidable model-process mismatch. This work presents an integrated approach to optimal model adaptation and dynamic optimisation, with specific focus on batch processes. An active approach is proposed whereby the input variables are designed so as to maximise the information content of the data for optimal model adaptation. Then, this active adaptation method is combined with the objective of process performance to form a multi-objective optimisation problem. This integrative approach is in contrast to the traditional adaptation method, where only the process performance is considered and adaptation is passively carried out by using the data as is. Two strategies for solving the multi-objective problem are investigated: weighted average and constrained optimisation, and the latter is recommended for the ease in determining the balance between these two objectives. The proposed methodology is demonstrated on a simulated semi-batch fermentation process.

Bayesian Optimal Adaptive Designs for Delayed-Response Dose-Finding Studies

Bayesian adaptive design has been broadly recognized as a method of improving the efficiency of determining dose-response relationships in clinical trials, thus leading to reliable dose selection for phase III clinical trials. However, in some disease areas such as diabetes and obesity, patients may need to be studied for several weeks or months for a drug effect to emerge. These delayed-response studies provide challenges for using traditional adaptive design methods. Many current methods for analyzing the data at the time of the interim analysis only use the last observation from patients who have completed the study. Data for those patients who have not completed the study are often ignored or imputed via last observation carried forward (LOCF) or other imputation method. Therefore, data collected at intermediate timepoints are not fully used for decision making. These approaches are useful for studies where the final responses can be quickly observed. However, in delayed-response studies, where longitudinal data are normally collected for each patient, using all available information instead of just endpoint values is critical to improving efficiency. Fu and Manner (2010) proposed an integrated two-component prediction (ITP) model for delayed-response adaptive design. In this paper, we extend their ITP model to incorporate a dose-response model in it and propose an ITP Emax model. Furthermore, we derive a method to find the minimum effective dose (MED) for our newly proposed model by using an optimal design theorem. By using the proposed method, a better understanding of the dose-response relationship and the MED was achieved more efficiently. Potential sample size reduction is also discussed in this paper.

Supersonic Missile Control with PI Sliding Mode Method

Comparing with traditional adaptive methods, a very simple PI sliding mode controller is designed for uncertain nonlinear missile systems with the help of two main assumptions which are also testified to be reasonable.The construction of PI control for sliding surface is novel and effective. Also, the proof of PI type control law is unique in this paper.

Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling

Diffuse optical imaging (DOI) allows the recovery of the hemodynamic response associated with evoked brain activity. The signal is contaminated with systemic physiological interference which occurs in the superficial layers of the head as well as in the brain tissue. The back-reflection geometry of the measurement makes the DOI signal strongly contaminated by systemic interference occurring in the superficial layers. A recent development has been the use of signals from small source-detector separation (1cm) optodes as regressors. Since those additional measurements are mainly sensitive to superficial layers in adult humans, they help in removing the systemic interference present in longer separation measurements (3cm). Encouraged by those findings, we developed a dynamic estimation procedure to remove global interference using small optode separations and to estimate simultaneously the hemodynamic response. The algorithm was tested by recovering a simulated synthetic hemodynamic response added over baseline DOI data acquired from 6 human subjects at rest. The performance of the algorithm was quantified by the Pearson R2 coefficient and the mean square error (MSE) between the recovered and the simulated hemodynamic responses. Our dynamic estimator was also compared with a static estimator and the traditional adaptive filtering method. We observed a significant improvement (two-tailed paired t-test, p<0.05) in both HbO and HbR recovery using our Kalman filter dynamic estimator compared to the traditional adaptive filter, the static estimator and the standard GLM technique.

Universal Access for Universal Value: Creating Disabled Access at Heritage Sites for those with Mobility Impairments

In today's society it is recognized that all people, regardless of disability, should be welcomed at heritage sites. As mobility impairments are one of the most common types of disability faced by visitors to heritage sites, this study will look at how the changing views on disability discrimination have affected heritage sites by reviewing current legislation and comparing it with visits to heritage sites in the UK and the US. For this purpose, site visits looking at adaptations for mobility impairments were carried out at fifteen sites in the UK and six sites in the US. There are additionally three case studies: a comparison between a UK and a US early nineteenth-century naval vessel, a site with traditional adaptation methods, and one that has creatively designed access. Overall, both the UK and the US have adopted similar methods for creating disabled access. Yet, the research shows that although many sites have designed some type of access, there is no conformity as to how this access is achieved. In addition, many sites use the loopholes in legislation to ensure that little is changed in the physical material. In the end, it is evident that more must be done to find a compromise between accessibility and preservation.

Adaptive Nulling with Time-Modulated Antenna Arrays Using a Hybrid Differential Evolution Strategy

A flexible approach based on a hybrid differential evolution strategy was developed for adaptive pattern nulling with time-modulated linear antenna arrays. The hybrid differential evolution strategy adjusts only the least significant bits of the digital phase shifters and the on—off time sequence to minimize the total output power; deep nulls can then be placed in the direction of the interferences without much compromise of the shape of the main beam. Thanks to the new design freedom introduced in time-modulated antenna arrays, this adaptive nulling approach exhibits better performance over the traditional phase-only, amplitude-only, and phase-amplitude adaptive nulling methods, in terms of the practical implementation, nulling depth, and pattern deterioration. Numerical examples are presented to demonstrate the effectiveness and robustness of the proposed approach.

Error estimation and adaptation for functional outputs in time-dependent flow problems

The paper presents an adjoint-based approach for determining global error in the time domain that is relevant to functional outputs from unsteady flow simulations. The algorithm is derived for the unsteady Euler equations that are discretized for second-order accuracy in both space and time and takes into account the effect of dynamic meshes. In addition to error due to temporal resolution, the formulation also takes into account algebraic error arising from partial convergence of the governing equations at each implicit time-step. The resulting error distributions are then used to drive adaptation of the temporal resolution and the convergence tolerances for the governing equations at each time-step. The method is demonstrated in the context of both time-integrated and instantaneous functionals and the results are compared against traditional adaptation methods.

RazorII: In Situ Error Detection and Correction for PVT and SER Tolerance

Traditional adaptive methods that compensate for PVT variations need safety margins and cannot respond to rapid environmental changes. In this paper, we present a design (RazorII) which implements a flip-flop with in situ detection and architectural correction of variation-induced delay errors. Error detection is based on flagging spurious transitions in the state-holding latch node. The RazorII flip-flop naturally detects logic and register SER. We implement a 64-bit processor in 0.13 mum technology which uses RazorII for SER tolerance and dynamic supply adaptation. RazorII based DVS allows elimination of safety margins and operation at the point of first failure of the processor. We tested and measured 32 different dies and obtained 33% energy savings over traditional DVS using RazorII for supply voltage control. We demonstrate SER tolerance on the RazorII processor through radiation experiments.

Efficient implementation of robust adaptive beamforming based on worst-case performance optimisation

Traditional adaptive beamforming methods undergo serious performance degradation when a mismatch between the presumed and the actual array responses to the desired source occurs. Such a mismatch can be caused by desired look direction errors, distortion of antenna shape, scattering due to multipath, signal fading as well as other errors. This mismatch entails robust design of the adaptive beamforming methods. Here, the robust minimum variance distortionless response (MVDR) beamforming based on worst-case (WC) performance optimisation is efficiently implemented using a novel ad hoc adaptive technique. A new efficient implementation of the robust MVDR beamformer with a single WC constraint is developed. Additionally, the WC optimisation formulation is generalised to include multiple WC constraints which engender a robust linearly constrained minimum variance (LCMV) beamformer with multiple-beam WC (MBWC) constraints. Moreover, the developed LCMV beamformer with MBWC constraints is converted to a system of nonlinear equations and is efficiently solved using a Newton-like method. The first proposed implementation requires low computational complexity compared with the existing techniques. Furthermore, the weight vectors of the two developed adaptive beamformers are iteratively updated using iterative gradient minimisation algorithms which eliminate the estimation of the sample matrix inversion. Several scenarios including angle-of-incidence mismatch and multipath scattering with small and large angular spreads are simulated to study the robustness of the developed algorithms.