Conventional Adaptive Algorithms Research Articles

New robust adaptive beamforming for imaging damages scattering coherent Lamb waves

The Lamb wave imaging approach based on adaptive beamforming method, notably the minimum variance distortionless response beamforming algorithm, is analyzed to explain its serious performance degradation or even complete invalidation, when the scattering signals generated by damages are coherent. It is demonstrated that in such case, the covariance matrix of the received scattering signals becomes rank-deficient, and when decomposed into the description form of the array manifold matrix left and conjugate right multiplied by a matrix P, P is a non-diagonal matrix with off-diagonal elements representing the coherences among scattering signals. These factors result in the failure of the conventional adaptive beamforming algorithm. To break the above limitations, a covariance matrix fitting method using a weighted least squares criterion is proposed by constructing P as a diagonal matrix. Based on the fitted covariance matrix, a robust adaptive beamforming Lamb wave imaging approach for coherent scattering signals is presented. Extensive numerical simulations and imaging experiments conducted on an aluminum plate corroborate the effectiveness of the proposed algorithm. The results affirm that the proposed method preserves the performance levels achieved by non-coherent scattering signals Lamb wave imaging approaches.

Routing Optimization of Two-Echelon Two-Way Logistics in Rural Areas Based on E-Commerce Environment

This is a study of the two-echelon two-way logistics vehicle routing problem in rural areas while accounting for government subsidies to alleviate the high distribution costs associated with “long transportation chain + low consumption density” in rural logistics. To address the issue, an improved adaptive ant colony optimization (IACO) algorithm was developed and simulations were run to validate it. The experimental findings demonstrate that, in comparison with the conventional adaptive ant colony optimization algorithm method, the IACO algorithm offers superior optimization capabilities. Finally, a case study was conducted in a rural area of China to demonstrate that the two-echelon two-way logistics network outperforms the current single-echelon logistics network by shortening the total driving distance and reducing distribution costs. This research can inform the optimization of rural logistics networks and provide a reference for the rural e-commerce logistics industry.

Evolution-guided value iteration for optimal tracking control

In this article, an evolution-guided value iteration (EGVI) algorithm is established to address optimal tracking problems for nonlinear nonaffine systems. Conventional adaptive dynamic programming algorithms rely on gradient information to improve the policy, which adheres to the first order necessity condition. Nonetheless, these methods encounter limitations when gradient information is intricate or system dynamics lack differentiability. In response to this challenge, evolutionary computation is leveraged by EGVI to search for the optimal policy without requiring an action network. The competition within the policy population serves as the driving force for policy improvement. Therefore, EGVI can effectively handle complex and non-differentiable systems. Additionally, this innovative method has the potential to enhance exploration efficiency and bolster the robustness of algorithms due to its population-based characteristics. Furthermore, the convergence of the algorithm and the stability of the policy are investigated based on the EGVI framework. Finally, the effectiveness of the established method is comprehensively demonstrated through two simulation experiments.

Bone remodelling prediction using mechanical stimulus with bone connectivity theory in porous implants

Strain energy density (SED) is considered to be the primary remodelling stimulus influencing the process of bone growth into porous implants. A bone remodelling algorithm incorporating the concept of bone connectivity, that newly formed bone should only grow from existing bone, was developed to provide a more biologically realistic simulation of bone growth. Results showed that the new algorithm prevented the occurrence of unconnected mature bone within porous implants, an unrealistic phenomenon observed using conventional adaptive elasticity theories. The bone connectivity algorithm had minimal effect (0.67% difference) on the final bone density distribution for standard bending and torsional moment cases. For a porous implant model, both algorithms, with and without bone connectivity implementation, reached the same final stiffness, with a difference of less than 0.01%. The bone connectivity algorithm predicted a slower and more gradual bone remodelling process, requiring at least 50% additional time for full remodelling compared to the conventional adaptive elasticity algorithm, which should be accounted for in the planning of rehabilitation strategies. The developed modelling can be employed to improve porous implant designs to achieve better clinical outcomes.

Adaptive Modulation Based on Nondata-Aided Error Vector Magnitude for Smart Systems in Smart Cities

A smart city involves big data transmission (BDT) between smart systems, which increases queue delays and leads to difficulty in enhancing the spectral efficiency. Adaptive modulation is an effective technique for enhancing data transmission rates in smart systems. However, traditional adaptive modulation approaches are not suitable for BDT in smart systems because the delays caused by the large amount of transmitted data lead to difficulty in evaluating the channel quality. In this paper, we propose a nondata-aided error vector (NDA-EVM) that can be employed in adaptive modulation over wireless channels. The proposed NDA-EVM can be used to evaluate the channel quality and symbol error rate (SER), which reflect the quality of service (QoS) of the system. We formulated the relationship between the NDA-EVM and SER, which provides a basis for designing adaptive modulation techniques for smart systems. To address the low average spectrum efficiency (ASE) caused by BDT queue delays, an adaptive modulation strategy based on the finite-state Markov chain (FSMC) of the NDA-EVM (i.e., NDA-EVM-AM) was designed. This method simplifies the adaptive modulation algorithm for smart systems to search for the optimal transfer probability in the FSMC matrix based on two typical states: the resident state and transient state. Moreover, we proposed an analytical procedure to describe queuing behavior to analyze the performance of the NDA-EVM-AM algorithm for smart systems in smart cities. The performance is compared with that of a conventional adaptive modulation algorithm through simulations. The results show that compared with traditional adaptive modulation, NDA-EVM-AM obtains a lower packet loss rate and higher spectral efficiency for smart systems.

A combined method for multi-channel active noise control based on online adaptive estimation and offline convolutional neural network

Multi-channel active noise control (ANC) has been widely used to attenuate low-frequency noise in relatively large spatial areas. Traditional multi-channel systems are achieved by optimizing the controller weights with adaptive algorithms that minimize the power of all error signals. However, nonlinearity in ANC systems can significantly degrade the performance of conventional adaptive algorithms, which is even more severe in multi-channel systems. Researchers suggested using neural networks (NN) to deal with the nonlinear problems. However, applying NN to multi-channelANC systems is challenging in real-time processing because of the high computational burden. Therefore, a multi-channel ANC method that combines NN and adaptive method is proposed. The proposed controller consists of both linear and nonlinear parts, where the linear part is estimated adaptively to track the change of primary noise in real applications while the nonlinear part is modeled offline with a small-scale convolutional NN (CNN). This method has the advantages of solving the nonlinear problem in ANC systems and achieving the capability in tracking primary noise source positions. At last, the performance of the proposed algorithm is verified through simulation experiments.

Validation of the adaptive scan method in the quest for time-efficient methods of testing auditory processes

A major barrier to the clinical application of psychophysical testing of central auditory processes is the time required to obtain precise estimates of different listening abilities. In this study, we validate a novel adaptive scan (AS) method of threshold estimation that is designed to adapt on a range of values around threshold rather than on a single threshold value. This method has the advantage of providing the listener with greater familiarity with the stimulus characteristics near threshold while maintaining precise measurement and increasing time-efficiency. Additionally, we explore the time-efficiency of AS through comparison with two more conventional adaptive algorithms and the method of constant stimuli in two common psychophysical tasks: the detection of a gap in noise and the detection of a tone in noise. Seventy undergraduates without hearing complaints were tested using all four methods. The AS method provided similar threshold estimates with similar precision to those from the other adaptive methods and, thus, it is a valid adaptive method of psychophysical testing. We also provide an analysis of the AS method based on precision metrics to propose a shortened version of the algorithm that maximizes the time/precision tradeoff and can achieve similar thresholds to the adaptive methods tested in the validation. This work lays the foundation for using AS across a wide variety of psychophysical assessments and experimental situations where different levels of precision and/or time-efficiency may be required.

Ultrasonic adaptive plane wave high-resolution imaging based on convolutional neural network

Components with complex surfaces can pose a challenge for achieving phased array ultrasonic testing. Although coupling issues can be addressed by immersing or adding plexiglass wedges, obtaining high-quality images in a short time remains difficult under such conditions. This paper proposes an adaptive plane wave imaging scheme to address these challenges. First, the interface is reconstructed using a plane wave priority echo estimation method. Then, a beamforming algorithm adapted to the irregular interface is used to generate low-resolution (LR) images, which are then reconstructed into high-resolution (HR) images in a short time with convolutional neural network (CNN). The CNN is trained with simulation data, and the defects of test blocks and wheel rim are tested. Experimental results show that the reconstruction method combined with CNN significantly improves imaging quality and speed compared to the conventional adaptive plane wave beamforming algorithm based on the time-domain physical model.

Adaptive parallel filter method for active cancellation of road noise inside vehicles

Active noise cancellation (ANC) is an efficient and effective method for suppressing low frequency disturbances generated by tyre–road interaction in a vehicle. There are significant differences in road noise inside the vehicle under different driving conditions. However, fixed-filter ANC strategies are unable to achieve superior noise attenuation for different types of noise, and conventional adaptive algorithms require prolonged adaptation which is detrimental to dynamic noise reduction. Thus, this study proposes an adaptive parallel filter method (APFM) that quickly responds to and suppresses varying vehicle interior road noises. The APFM is divided into two phases: the tuning phase and control phase. In the tuning phase, a set of potential noises is adopted to train the corresponding optimal fixed filters. During the control phase, the most appropriate parallel control filter is first selected for each frame based on a selection mechanism to minimise the energy of the estimated error signal. Then, a delayless variable step size normalised frequency-domain block adaptive filter algorithm is adopted to update the selected control filter. In addition, the computational complexity of the proposed APFM is analysed. Numerous simulations and real time in-vehicle experiments conducted using multi-channel ANC headrests have confirmed the feasibility and effectiveness of the proposed algorithm. It is evident that the APFM achieves fast noise reduction response and low steady-state error in attenuating varying in-vehicle road noise, and retains superior performance in the presence of non-pre-trained disturbances.

Sparse echo cancellation using variants of least mean fourth and least mean square algorithms

Echo cancellation is the most essential and indispensable component of telephone networks. The impulse responses of most of the networks are sparse in nature; that is, the impulse response has a small percentage of its components with a significant magnitude (large energy), while the rest are zero or small. In these sparse environments, conventional adaptive algorithms like least mean square (LMS) and normalized LMS (NLMS) show substandard and inferior performances. In this paper, the performances of the normalized least mean square (NLMS) algorithm, the normalized least mean fourth (NLMF) and the proportionate normalized least mean fourth (PNLMF) are compared for sparse echo cancellation. The sparseness of both the echo response and the input signal is exploited in this algorithm to achieve improved results at a low computational cost. The PNLMF algorithm showed better results and faster convergence in sparse and non sparse systems, but its results in sparse environments are more impressive. The NLMF algorithm shows good results in sparse environments but not in non-sparse environments. The PNLMS algorithm can be considered superior to the NLMF and NLMS algorithms with respect to the error profile. A modified algorithm, the sparse controlled modified proportionate normalized LMF (SCMPNLMF) algorithm, is proposed, and its performances are compared with the other algorithms.

Maximum total generalized correntropy adaptive filtering for parameter estimation

In this study, we consider the parameter estimation problem for an errors-in-variables (EIV) model with impulse noise. New adaptive filtering, called the maximum total generalized correntropy (MTGC) adaptive filtering algorithm, is developed to further improve the robustness of conventional adaptive filtering algorithms. Specifically, the proposed approach is derived by integrating the generalized maximum correntropy (GMC) criterion into the total least square (TLS) framework. The local stability and steady-state properties are investigated with the aid of the generalized Gaussian process. Numerical simulations are presented to illustrate the effectiveness of the proposed method in the presence of impulse noise.

Design of continuous-time model reference adaptive and super-twisting sliding mode controller

This paper presents a continuous-time Robust Model Reference Adaptive and Super-Twisting Sliding Mode controller and its stability analysis by means of Lyapunov stability theory. The controller law is composed by two control structures: a Robust Model Reference Adaptive Controller (RMRAC) and an Adaptive Super-Twisting Sliding Mode (STSM) Controller. The main benefit of this novel control algorithm is to present, simultaneously, high robustness and fast dynamic response, which are conflicting with each other in conventional adaptive algorithms. Furthermore, as the STSM Controller is also adaptive, the dynamic response has relevant chattering mitigation without additional efforts on controller design, implementation, or execution. Besides, the union of the STSM with RMRAC also improved sliding accuracy in steady state. Thereby, in steady state, the STSM control contribution is almost zero, which avoids the chattering phenomenon, once it acts only in the transient regimes. The stability and robustness analysis of the control structure are presented, in continuous-time, considering the overall plant, that is, in presence of matched and unmatched dynamics. The continuous-time stability analysis is important in an adaptive system, since it features an intermediary step for control system stability analysis in discrete-time and its application in real plants. In addition, to present the feasibility and benefits of the proposed controller, simulations results of two case studies are presented: one is the controller application in an unstable non-minimum phase plant, and the second is its application for current regulation of a single-phase grid-tied power converter with an LCL filter. Moreover, to corroborate the robustness and tracking performance of the proposed controller a comparison with an adaptive first order Sliding Mode is also presented.

Modification of reconstruction threshold algorithm in two-dimensional LFMCW compressive radar

Nowadays, the Compressive Sensing theory has an important effect on many communication system’s performances, and one of these applications is the radar system. Applying CS in the radar such as Linear Frequency Modulation Continuous Wave (LFMCW) radar signals has many advantages but suffers from the processing time in the two-dimensional processing. The performance of the LFMCW radar signals is achieved by using both conventional Complex Approximate Message Passing (CAMP) and adaptive recovery algorithms using a suitable reduction factor in both range and Doppler directions. In this paper, a modification is made to the adaptive CAMP algorithm to enhance the radar detection performance compared to both the conventional and adaptive algorithms under the same conditions. One of the main problems in radar detection is off-pin targets, which can be overcome using a certain filter in both range and Doppler directions. A comparison is achieved among these algorithms concerning detection performance using Receiver Operating Characteristic curves and the resolution performance in both range and Doppler directions. During the system analysis, it was found that an enhancement was achieved using the modified algorithm in the radar performance without any degradation in both range and Doppler resolution compared with the other algorithms.

Hadamard matrix calibration and fuzzy proportional integral differential closed-loop control of adaptive optics system

Conventional adaptive optics algorithms are studied through calibration or closed-loop control point of view. A Hadamard matrix algorithm was used to calculate the interaction matrix from the wavefront slope to the deformable mirror voltage. A fuzzy proportional integral differential (PID) control algorithm was used to adjust the control parameters to complete the closed-loop control of the adaptive optics. The simulation shows that when the atmospheric turbulence intensities D / r0 are 1, 10, and 20, respectively, the mean wavefront peak to valley (PV) values are are 0.38, 0.93, and 3.53 μm, respectively, after wavefront correction by Hadamard + PID algorithm; the wavefront PV mean values are 0.38, 0.93, and 3.48 μm, after wavefront correction by PushPull + FuzzyPID algorithm; and the wavefront PV mean values are 0.36, 0.92, and 3.30 μm after wavefront correction by Hadamard + FuzzyPID algorithm. The experimental results show that: the wavefront PV mean values are 3.84, 3.56, and 3.36 μm for Hadamard + PID algorithm, PushPull + FuzzyPID algorithm, and Hadamard + FuzzyPID algorithm, respectively. The Hadamard algorithm significantly improves the interaction matrix measurement accuracy, and the FuzzyPID control algorithm improves the adaptive ability under strong turbulence conditions. The wavefront correction using two advanced algorithms is better than that of the combination of advanced and conventional algorithms.

Adaptive broadband frequency invariant beamforming using nulling-broadening and frequency constraints

The performance of conventional adaptive broadband beamforming algorithms is degraded because of rapidly moving interferences and look-direction errors. In this paper, a number of techniques are proposed to overcome this issue. By proposing an adaptive frequency invariant beamformer (FIB) that uses frequency constraints, we can obtain undistorted responses to broadband signal of interests (SOIs) and constant beamwidths over an entire frequency band without pre-steering delays. And the influence of look-direction errors can also be receded. Furthermore, a singular value decomposition of the frequency constraints is added to the proposed algorithm in view of releasing numerous degrees of freedom (DOFs), as this approach can simultaneously maintain the enhanced anti-interference performance and hold the undistorted response to SOIs. Finally, the nulling-broadening method involving the use of virtual interferences is adopted to construct a taper matrix and ensure robust performance versus rapidly moving interferences. This robust response to rapidly moving interferences and look-direction errors allows the proposed algorithm to achieve satisfactory broadband beamforming performance. Simulation results highlight the correctness and effectiveness of the proposed methods.

Affine Projection Algorithm for Censored Regression

Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in the paper. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware.

A Simple and Robust Equalization Algorithm for Variable Modulation Systems

This paper proposes a simple and robust variable modulation-decision-directed least mean square (VM-DDLMS) algorithm for reducing the complexity of conventional equalization algorithms and improving the stability of variable modulation (VM) systems. Compared to conventional adaptive equalization algorithms, known information was used as training sequences to reduce the bandwidth consumption caused by inserting training sequences; compared with conventional blind equalization algorithms, the parameters and decisions of the equalizer were determinate, which was conducive to a stable equalization performance. The simulation and implementation results show that the proposed algorithm has a better bit error rate (BER) performance than that of the constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA) while maintaining the same level of consumption of hardware resources. Compared to the conventional decision-directed least mean square (DDLMS) algorithm, the proposed algorithm only needs to make quadrature phase shift keying (QPSK) symbol decisions, which reduces the computational complexity. In parallel 11th-order equalization algorithms, the operating frequency of VM-DDLMS can reach up to 333.33 MHz.

Selective fixed-filter active noise control based on convolutional neural network

Active noise control (ANC) technology is increasingly ubiquitous in wearable audio devices, or hearables. Owing to its low computational complexity, high robustness, and exemplary performance in dealing with dynamic noise, the fixed-coefficient control filter strategy plays a central role in portable ANC implementation. Unlike its traditional adaptive counterpart, the fixed-filter strategy is unable to attain optimal noise reduction for different types of noise. Hence, we propose a selective fixed-filter ANC method based on a simplified two-dimensional convolution neural network (2D CNN), which is implemented on a co-processor (e.g., in a mobile phone), to derive the most suitable control filter for different noise types. To further reduce classification complexity, we designed a lightweight one-dimensional CNN (1D CNN), which can directly classify noise types in time domain. A numerical simulation based on measured paths in headphones demonstrates the proposed algorithm’s efficacy in attenuating real-world non-stationary noise over conventional adaptive algorithms.

Minimizing printing time and volumetric error by GPU-accelerated adaptive slicing

Staircase defects in additive manufacturing significantly reduce the surface roughness, dimensional accuracy, and fatigue strength of printed parts. Adaptive layer thicknesses are promising to reduce the printing time and staircase defects simultaneously. However, conventional adaptive slicing algorithms aim to minimize the number of layers, instead of the actual printing time, and are computationally expensive to evaluate all possible slicing combinations and find the optimal ones. This paper presents a GPU-accelerated slicing algorithm to minimize both the printing time and the volumetric error explicitly and find all the optimal solutions among all possible combinations of layer thicknesses. The experimental results validated that the algorithm could obtain not only the optimal slicing but also the dimension accuracy along the printing direction.

Composite adaptive backstepping controller design and the energy calculation for active suspension system.

The half-car suspension has the coupling of pitch angle and front and rear suspension. Especially when the suspension model has a series of uncertainties, the traditional linear control method is difficult to be applied to the half-car suspension model. At present, there is no systematic method to solve the suspension power. According to the energy storage characteristics of the elastic components of the suspension, the power calculation formula is proposed in this paper. This paper proposes a composite adaptive backstepping control scheme for the half-car active suspension systems. In this method, the correlation information between the output error and the parameter estimation error is used to construct the adaptive law. According to the energy storage characteristics of the elastic components of the suspension, the power calculation formula is introduced. The compound adaptive law and the ordinary adaptive law have good disturbance suppression, both of which can solve the pitching angle problem of the semi-car suspension, but the algorithm of the compound adaptive law is superior in effect. In terms of vehicle comfort, the algorithm of the general adaptive law can achieve stability quickly, but compared with the composite adaptive law, its peak value and jitter are higher, while the algorithm of the composite adaptive law is relatively gentle and has better adaptability to human body. In terms of vehicle handling, both control algorithms can maintain driving safety under road excitation, and the compound adaptive algorithm appears to have more advantages. Compared with the traditional adaptive algorithm, the power consumption of the composite adaptive algorithm is relatively lower than that of the former in the whole process. The simulation results show that the ride comfort, operating stability and safety of the vehicle can be effectively improved by the composite adaptive backstepping controller, and the composite adaptive algorithm is more energy-saving than the conventional adaptive algorithm based on projection operator.