Leaky Factor Research Articles

Hidden Markov Model Decorrelated Diffusion Leaky LMS With Optimized Leaky Factor

Hidden Markov Model Decorrelated Diffusion Leaky LMS With Optimized Leaky Factor

A constrained adaptive active noise control filter design method via online convex optimization

In practical active noise control (ANC) applications, various types of constraints may need to be satisfied, e.g., robust stability, disturbance enhancement, and filter output power constraint. Some adaptive filters such as leaky LMS have been developed to apply required constraints indirectly. However, when multiple constraints are required simultaneously, satisfactory noise performance is difficult to achieve by tuning only one leaky factor. Another filter design approach that may achieve better noise control performance is to solve a constrained optimization problem. But the computational complexity of solving such a constrained optimization problem for ANC applications is usually too high even for offline design. Recently, a convex optimization reformulation is proposed which significantly reduces the required computational effort in solving constrained optimization problems for active noise control applications. In the current work, a constrained adaptive ANC filter design method is proposed. The previously proposed convex formulation is improved so that it can be implemented in real-time. The optimal filter coefficients are then redesigned continuously using online convex optimization when the environment is time-varying.

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

A power generating system should be able to generate and feed quality power to the loads which are connected to it. This paper suggests a very efficient controlling technique, supported by an effective optimization method, for the control of voltage and frequency of the electrical output of an isolated wind power harnessing unit. The wind power unit is modelled using MATLAB/SIMULINK. The Leaky least mean square algorithm with a step size is used by the proposed controller. The Least Mean Square (LMS) algorithm is of adaptive type, which works on the online modification of the weights. LMS algorithm tunes the filter coefficients such that the mean square value of the error is the least. This avoids the use of a low pass filter to clean the voltage and current signals which makes the algorithm simpler. An adaptive algorithm which is generally used in signal processing is applied in power system applications and the process is further simplified by using optimization techniques. That makes the proposed method very unique. Normalized LMS algorithm suffers from drift problem. The Leaky factor is included to solve the drift in the parameters which is considered as a disadvantage in the normalized LMS algorithm. The selection of suitable values of leaky factor and the step size will help in improving the speed of convergence, reducing the steady-state error and improving the stability of the system. In this study, the leaky factor, step size and controller gains are optimized by using optimization techniques. The optimization has made the process of controller tuning very easy, which otherwise was carried out by the trial-and-error method. Different techniques were used for the optimization and on result comparison, the Antlion algorithm is found to be the most effective. The controller efficiency is tested for loads that are linear and nonlinear and for varying wind speeds. It is found that the controller is very efficient in maintaining the system parameters under normal and faulty conditions. The simulated results are validated experimentally by using dSpace 1104. The laboratory results further confirm the efficiency of the proposed controller.

Optimal Output-Constrained Active Noise Control Based on Inverse Adaptive Modeling Leak Factor Estimate

Output saturation, mainly caused by the power amplifier, is a critical issue influencing the performance and stability of an adaptive system, such as in active noise control. In this paper, a quadratically constrained quadratic program (QCQP) is defined to achieve optimal control under the averaging-output-power constraint, which ensures the output of the system operates linearly and hence, avoids the output saturation. To solve this QCQP problem recursively in practice, this paper utilizes one of the leaky-based filtered-x least mean square algorithm with an optimal leak factor. However, this method only can be applied when the statistical feature of the control signal with maximum output-power is known, which is difficult to obtain in practice. Hence, by incorporating the adaptive inverse modeling technique, we can derive a practical estimation of the optimal leaky factor, which is applicable to different noise types. Furthermore, as the optimal output-constraint control forces the output to operate linearly, the nonlinear amplifier model is not required for the leak factor estimate. The simulation of the proposed algorithm is carried out on measured nonlinear paths to validate its efficacy.

A Study on Evaluation Method for Micro Defects on Surface based on Leaky Rayleigh Wave

Among the various non-destructive test techniques, one of the ultrasonic techniques, ultrasonic testing using leaky Rayleigh wave, are very useful for measuring micro-defects that occur on surfaces. However, it is difficult to determine the depth of the actual flaw, because the amplitudes of the collected signals vary depending on the location of the signal acquisition due to the characteristics of the leaky Rayleigh wave occurring at liquid-solid interface. Therefore, in this study, the phenomenon and characteristics of leaky Rayleigh wave were analyzed through FEM simulation. In addition, transmissions factors that depend only on the size of the defect and leaky factors that depend only on the collection distance were distinguished. Then, a defect calculation algorithm has been developed that can quantitatively assess the depth of defects regardless of its acquisition location.

Derivation of a groundwater flow model within leaky and self-similar aquifers: Beyond Hantush model

Scientists conducted researches on leaky aquifers, developed groundwater models and equations for this type of aquifer. These equations are developed from classical formulas like Darcy's Law. The classical equations cannot be used to assess heterogeneous and porous aquifers. A fractal differential operator is then defined for fractal geometry and it is now widely used by many scientists to allow better understanding of such aquifers, which cannot be based on Euclid geometry or assessed using classical formulas. Fractals have a property called self-similarity and there are examples in nature like fractured aquifers. A self-similar leaky aquifer is visualized as a heterogeneous media, which cannot be solved or assessed using the classical equation. This article entails a new groundwater equation derived for flow within a self-similar leaky aquifer. We provided in this paper new models of groundwater flowing within a leaky aquifers with self-similarities. The new model is more representative that one suggested by Hantush as it includes in the mathematical formulation the scaling effect of a geological formation. Thus the solution is not only function of time-space and the well-known aquifer's parameters including the transmissivity, storativity and leaky factor, but there is a new factor that takes into account the scaling of the aquifer. We presented the existence and uniqueness of the solutions. We used a newly established numerical scheme to solve the new equations. We presented some numerical simulations and observed very interesting features that are observed in real world problems.

A generalized leaky FxLMS algorithm for tuning the waterbed effect of feedback active noise control systems

Abstract To tune the noise amplification in the feedback system caused by the waterbed effect effectively, an adaptive algorithm is proposed in this paper by replacing the scalar leaky factor of the leaky FxLMS algorithm with a real symmetric Toeplitz matrix. The elements in the matrix are calculated explicitly according to the noise amplification constraints, which are defined based on a simple but efficient method. Simulations in an ANC headphone application demonstrate that the proposed algorithm can adjust the frequency band of noise amplification more effectively than the FxLMS algorithm and the leaky FxLMS algorithm.

An adaptive current control-detuned harmonics elimination schemes for enhancement of power quality in RES interfaced AC-grid network

This paper presents a robust natural-frame based renewable energy source (RES) interfacing control scheme utilizing Neural Network (NN) identified adaptive leaky least mean square (ALLMS). The integration of RES in distributed network leads to quandaries on power quality and stability. These quandaries directly demands precise generation of current references under sundry operating scenarios. The self-learning facility of proposed NN identified ALLMS control model along with a detuned LC filtering circuit can make felicitous shaping of the VSC output under various system perturbance. The proposed methodology utilizes a variable leaky factor to eschew wavering of weights involved in the estimation and extraction process of current references. To achieve most expeditious and precise convergence of the controller, accentuation is made to cull a congruous value for step-size utilized in controlling actions. Emolument of reactive power, load balancing and mitigation of harmonics are taken care of in the proposed control scheme. The comparative study on power quality in-between proposed and conventional adaptive current control techniques, modelled in MATLAB/Simulink, is also presented and it justifies the sound performances of the proposed control scheme under diverse system dynamics.

ANN‐based adaptive current controller for on‐grid DG system to meet frequency deviation and transient load challenges with hardware implementation

To maximise functional efficacy and reliability of distributed generations (DGs), this study leads to modelling, control, stability analysis, and hardware validation of a new adaptive current controller in the application of an on-grid voltage source converter (VSC) system. For effective mitigation of power system hindrances without affecting the power quality (PQ), self-tuning of weights associated with the proposed variable leaky adaptive step-size-least mean square (VLAS-LMS) control algorithm based on artificial neural network (ANN) is updated in natural frame and felicitous shaping of VSC outputs are witnessed. The selection of a constant step-size associated with proposed controller usually yields updating of same weights by all the sampling periods and hardly takes care of rate of convergence factor, which decides the stability of the controller. It can no longer avoid wavering of weights during grid disturbances, resulting in high-filtering gains. Again, a constant leaky factor may lead to over- or under-parameterisation of regularisation component. These disputes can be overcome in the proposed algorithm by the introduction of an adaptive step size along with a variable leaky factor. Furthermore, PQ is maintained as trade-off by the inclusion of detuned LC filter. Experimental outcome ensures the validation and effectiveness of the proposed controller.

A Variable Leaky Entropy-Based Whitening Algorithm for Blind Decision Feedback Equalization

This paper addresses the joint entropy maximization algorithm constrained by the variable leaky factor (JEM-VL) aiming at mitigating the feedback filter (FBF) mismatch effects characterizing the operation of the decision feedback equalizer which, at the start of adaptation, swaps positions of feedforward and feedback filters so that the latter acts as a linear all-pole whitener of the received signal. The FBF mismatch is a result of disparity between the FBF setup achieved in the blind mode by observing channel outputs and an excepted FBF setup in the tracking mode which is driven by detected data symbols. Depending on the given signal complexity and inter-symbol interference severity, the FBF filter mismatch is typically manifested by the equalizer convergence instability or even the catastrophic error propagation effects arising at the time of the equalizer structure-criterion switching from the blind to the tracking operation mode. The constraint of superfluous coefficients of the FBF filter by means of the JEM-VL algorithm eliminates the equalizer convergence instability at the time of its switching and, consequently, increases equalization successfulness. The efficiency of the JEM-VL algorithm is verified by simulations using the 64-QAM signal.

Study on Leaky Fault Mechanism and Influential Factors of Hydraulic System of Artillery

Hydraulic technique is more and more applied in weapon equipments; hydraulic system is becoming one of the important subsystems of modern artillery. In this paper, considering effects of operating pressure, temperature and mixed air rate of hydraulic system, the mathematical model of hydraulic fluid internal leakage rate of artillery is built to analyze leaky fault form and leaky reason. The conclusion shows that the internal leakage rate of the hydraulic system of artillery is depend on temperature, pressure, dynamic viscosity, and bulk modulus and thermal expansion coefficient of hydraulic system. The variation rule of hydraulic fluid internal leakage rate is gotten according to operating parameters. Special reference will be given to leakage detection and location of the hydraulic system of artillery.

A New TURBO LMS Beamforming for Mobile Communication

processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing indepe ndent beams towards various users, as well as its ability to provide estimate of directions of radiating sources, make it attractive to the mobile communications system designer. In this paper existing Least Mean Square (LMS) algorithm is modified to obtain lesser Mean Square Error (MSE) by using leaky factor in the weight updation.A novel approach named as TURBO LMS is proposed which reduces the Mean Square Error and increases the convergence speed by a large amount as compared to existing beamforming algorithms. Simulation of Beamforming algorithms namely Least Mean Square (LMS), Leaky Least Mean Square (LLMS) and novel TURBO LMS algorithm are done for various look directions and jammer configurations and their MSE characteristics are compared. MATLAB simulation shows that the proposed TURBO LMS algorithm has improved convergence as compared to existing beamforming algorithms. Performance of TURBO LMS algorithm is measured by varying number of array elements.

Robust Video Region-of-Interest Coding Based on Leaky Prediction

A video region-of-interest (ROI) scalable coding scheme can ensure the priority of ROI. Error protection schemes can be used to guarantee the correct receipt of the ROI stream when transporting ROI scalable video over an error-prone network. However, we find that the correct receipt of ROI bitstreams cannot ensure the correct decoding of ROI due to the unique issue of the cross error propagation between ROI and background in ROI scalable coding. In this letter, we propose an ROI scalable coding framework based on leaky prediction (LP) for robustly transporting video over an error-prone network. Although several LP approaches have been proposed to improve layered coding, they cannot be applied to ROI scalable coding straightforwardly due to the cross error propagation issue. We deploy a leaky factor to weigh the two predictions: one from the constrained motion estimation (ME) within the ROI layer of the reference frame, and the other from the unrestricted ME in the overall reference frame. Simulation results show that the proposed scheme enhances the robustness of ROI scalability while maintaining coding efficiency.

FGS Coding Using Cycle-Based Leaky Prediction Through Multiple Leaky Factors

This paper proposes a fine granularity scalable (FGS) coding using cycle-based leaky prediction, in which the multiple leaky factors are used to yield enhancement layer prediction to make a good compromise between coding efficiency and drift error. In this proposed method, first, the error propagation for leaky prediction with two leaky factors is theoretically analyzed in case only the base-layer bitstream and part of the enhancement-layer bitstream are available at the decoder. Based on this analysis, in this paper, we investigate how to effectively introduce enhancement-layer information into the prediction loop for enhancement-layer coding by the proper leaky factors to constrain drift error while keeping high coding efficiency. Furthermore, a coefficient scaling approach in the transform domain is proposed to address the decoding complexity issue for multiple reconstructions of partial enhancement layers at different quality levels. Finally, an encoder optimization approach is presented to further control drift error for multiple FGS layers coding. The experimental results show that compared to AR-FGS in JSVM, the proposed method can significantly improve the coding performance over a wide range of bitrates.

Efficient Fine Granularity Scalability Using Adaptive Leaky Factor

Fine Granularity Scalability (FGS) video coding has been adopted by the MPEG-4 standard for video streaming applications. In this paper, we propose a novel FGS coding scheme, which applied adaptive leaky factors for the enhancement layer prediction to further improve the coding efficiency of FGS. A flexible method that can dynamically determine the leaky factors according to the network conditions is also presented. With the proposed method, a better trade-off between the coding efficiency and drifting reduction can be achieved, and the coding performance is further improved compared with using a fixed leaky factor. Experimental results show that the proposed method can further improve the coding efficiency over a wide range of bitrate and packet loss ratio, and still keep the original characteristics, such as fine granularity, and bandwidth adaptation.

Evaluation of beryllium stiffening on zirconium plate using backscattered leaky Lamb waves

The ultrasonic backscattering profiles of the Zr plates with/without Be–Zr alloy layer were measured at various incidence positions to evaluate the embedded Be diffusion layer on the Zr plate. Four principal subprofiles of backward ultrasound radiated from leaky Lamb waves were observed. The angles and the intensities of the subprofile peaks decreased due to the stiffening effect of Be layer. The generation and change of subprofiles can be explained by the influence of acoustical properties, collective group velocities and leaky factors of plates. It is suggested that the backward radiation subprofile is an effective tool for the evaluation of thin diffusion layers on plates.

An Analytical Evaluation for Airflow to Inlet Wells in Vapor Extraction Systems Under Leaky Conditions

Air inlet wells have been employed to enhance the fresh air flushing through the target‐contaminated soils in many soil vapor extraction systems. The analytical solution presented here evaluates the efficiency of the airflow to these inlet wells under the conditions of leaky surface boundary, anisotropic soils, and partially screened extraction. Using the analytical solution, the efficiency of air inlet wells is illustrated through sensitivity studies by varying system parameters (number of inlet wells, well spacing, well radius, and screen condition) and soil parameters (leaky factor and anisotropy).