Adaptive Iterative Algorithm Research Articles

Fast reconstruction of the magnetization of a Halbach magnet in EMAT using experimental measurements

The paper presents a fast and accurate algorithm to reconstruct the magnetization model of parallelepiped magnet (parts of a Halbach magnet array) or the Halbach magnet array used in an electromagnetic acoustic (EMAT) transducer. The model accuracy is validated against measurements of the magnetic flux distributions above surface of block magnets or Halbach magnet array using 2D/3D theoretical formula to compute magnetic flux distribution, validated by 2D/3D FEM simulations, and based on a non-uniform distribution of reconstructed magnetization model (using only main magnetization component). The illness of inverse reconstruction model is controlled through very small enough increments steps in an adaptive iterative algorithm but large enough increments to assure faster reconstruction, and converging to the same magnetization model of magnet blocks or Halbach array magnet, independent of the initial magnetization values.

Research on Optimal Electrical Energy Management Method of Smart Grid Based on Value Iteration

Abstract Based on the smart grid residential load data and energy storage terminal data, this paper expands a new optimal control method of smart grid supply and demand matching and optimization.This paper describes the operation principle and defines the required optimization parameters of the control object—electric energy storage management system. Aiming at the characteristics of discrete non linearity of the smart grid, a value iteration algorithm is selected to establish a control strategy,and an adaptive value iteration optimization scheme is derived.We use the BP neural network to export the control strategy of the electric energy management system by reducing the back propagation error. Finally,we use the day-ahead electricity price data to achieve numerical simulation.The result shows that the adaptive value iteration algorithm methods have higher efficiency and superior adaptability compared with the traditional BP neural network control method.

AnomalyNet: An Anomaly Detection Network for Video Surveillance

Sparse coding-based anomaly detection has shown promising performance, of which the keys are feature learning, sparse representation, and dictionary learning. In this paper, we propose a new neural network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks. Specifically, to learn better features, we design a motion fusion block accompanied by a feature transfer block to enjoy the advantages of eliminating noisy background, capturing motion, and alleviating data deficiency. Furthermore, to address some disadvantages (e.g., nonadaptive updating) of the existing sparse coding optimizers and embrace the merits of neural network (e.g., parallel computing), we design a novel recurrent neural network to learn sparse representation and dictionary by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM). To the best of our knowledge, this could be one of the first works to bridge the $\ell _{1}$ - solver and LSTM and may provide novel insight into understanding LSTM and model-based optimization (or named differentiable programming), as well as sparse coding-based anomaly detection. Extensive experiments show the state-of-the-art performance of our method in the abnormal events detection task.

Adaptive Iterative Control of Temperature in Greenhouse

Introduction. Creation and development of efficient agricultural complexes providing high yields at minimal time, material, and energy costs is impossible without the use of automatic control systems (ACS), which allow for maintaining the microclimate of the greenhouse with high accuracy. Improvements of the microclimate by ASC are aimed at neutralizing the influence of parametric perturbations of processes inside and outside the greenhouse. Using the example of a temperature control channel in a greenhouse with a heating circuit based on hot piped water supply, an adaptive iterative (search) algorithm for adjusting the components of a proportional-integral-differential (PID) controller in the heating circuit is proposed to ensure the required quality of the control process. Materials and Methods. The management is based on a parametrically uncertain model of temperature in the greenhouse, the structure of which, based on the principle of superposition, is transformed into a form with control and disturbances concentrated on an output coordinate. The use of an adaptive PID controller is based on the real-time analysis of a database containing trends of the controlled process. Using operators of Database Managment System or SQL language, queries evaluate regulation quality in accord with quality assessment. Proportional and differential components of the PID controller are adjusted so that the control system works on the verge of switching to auto-oscillation mode. The resulting static error is compensated by a change in the driving force. Results. Simulation of the real structure of a single-circuit automatic control system with temperature in the greenhouse with built-in regulating, executive and measuring elements, with a delay of a coolant movement was carried out using the software MVTU (SimIn- Tech). The proposed adaptation algorithm, consisting of the additive adjustment of the PID controller coefficients and being conveniently implemented within the SCADA system, was shown to provide the minimum oscillatory temperature maintenance for arbitrary parametric perturbations in the presence of the delay. Discussion and Conclusion. The proposed adaptation algorithm provides for compensation for model uncertainty and disturbances, while achieving the required accuracy of maintaining the temperature in the greenhouse. Results of the study will serve as the basics for development of multi-contour ACS microclimate greenhouses with the examination of the impact and compensation of parametric and structural uncertainty, inertia and nonlinearities of real elements. Our results may be used in many sectors of the national economy to study the general and applied problems of digital adaptive process control.

Adaptive Scheduling of Stochastic Task Sequence for Energy-Efficient Mobile Cloud Computing

Mobile cloud computing (MCC) is an attractive technique to improve energy efficiency and computing performance by offloading complex applications from resource-constrained mobile devices. This paper presents an adaptive scheme for scheduling randomly arriving heterogeneous tasks in MCC to minimize energy consumption while satisfying quality-of-service (QoS) requirements. A queuing with trigger mechanism is introduced to characterize the sequential dynamics of interdependent tasks. The energy-efficient QoS-guaranteed task scheduling is formulated as a constrained stochastic optimization problem based on continuous-time Markov decision processes. An adaptive policy iteration algorithm that combines potentials estimation and stochastic approximation is proposed to find the optimal scheduling policy online in unknown dynamic environments. Simulation results demonstrate the effectiveness of the proposed method.

Fuzzy singular value shrinkage for Poisson image denoising

Poisson noise is a fundamental problem in various imaging applications, such as low-light photography, computed tomography and fluorescence microscopy. To remove Poisson noise, an adaptive iterative singular value shrinkage algorithm based on variance-stabilizing transformation (VST) and fuzzy logic classification is proposed in this paper. Since Poisson noise is signal dependent, we use the VST to convert it into signal independent Gaussian noise. The transformed image is divided into a number of blocks, and the similarity of these blocks is well judged according to the similarity criterion of an approximate Kullback–Leibler (KL) distance, and they are arranged to form a low-rank matrix. Then, the proposed algorithm uses singular value decomposition and adaptive soft-thresholding contraction operator to reduce noise, because large singular values point to the position of interesting information, and small singular values point to the position of the noise. In addition, to better preserve the structural information of the image, an adaptive iterative regularization technique based on fuzzy logic classification is proposed. Finally, a potentially noise-free image is obtained by unbiased inverse VST. Experimental results show that the proposed algorithm is competitive with several popular Poisson denoising techniques in both visual and objective metrics.

Adaptive asynchronous time-stepping, stopping criteria, and a posteriori error estimates for fixed-stress iterative schemes for coupled poromechanics problems

In this paper we develop adaptive iterative coupling schemes for the Biot system modeling coupled poromechanics problems. We particularly consider the space–time formulation of the fixed-stress iterative scheme, in which we first solve the problem of flow over the whole space–time interval, then exploiting the space–time information for solving the mechanics. Two common discretizations of this algorithm are then introduced based on two coupled mixed finite element methods in-space and the backward Euler scheme in-time. Therefrom, adaptive fixed-stress algorithms are build on conforming reconstructions of the pressure and displacement together with equilibrated flux and stresses reconstructions. These ingredients are used to derive a posteriori error estimates for the fixed-stress algorithms, distinguishing the different error components, namely the spatial discretization, the temporal discretization, and the fixed-stress iteration components. Precisely, at the iteration k≥1 of the adaptive algorithm, we prove that our estimate gives a guaranteed and fully computable upper bound on the energy-type error measuring the difference between the exact and approximate pressure and displacement. These error components are efficiently used to design adaptive asynchronous time-stepping and adaptive stopping criteria for the fixed-stress algorithms. Numerical experiments illustrate the efficiency of our estimates and the performance of the adaptive iterative coupling algorithms.

Dynamic optimization method of secondary cooling water quantity in continuous casting based on three-dimensional transient nonlinear convective heat transfer equation

The defects of a slab, such as shrinkage and crack, are related to non-uniform cooling in secondary cooling zone (SCZ) of continuous casting, hence setting the value of water flow rate in the SCZ plays a vital role for the quality of slab. The traditional scheme can lead to the great fluctuation of temperature when the casting speed changes. Therefore, based on 3-dimensional transient nonlinear convective heat transfer equation of a slab, this paper develops a dynamic optimization method for setting the value of water flow rate. However, it is difficult to realize this method due to the following two causes. 1. It costs too much time to calculate the 3-dimensional transient nonlinear convective heat transfer equation. 2. It is not easy to select a suitable step size for the optimization algorithm. So this paper presents a modified adaptive step size quasi-Newton parallel iterative algorithm, which can not only reduce the computing time but also select a suitable step size. Actual data in a steel plant are used to demonstrate its validity, and the results clearly show that this scheme can greatly reduce the running time and give a more stable temperature distribution.

Optimal synchronization controller design for complex dynamical networks with unknown system dynamics

In this paper, the optimal synchronization controller design problem for complex dynamical networks with unknown system internal dynamics is studied. A necessary and sufficient condition on the existence of the optimal control minimizing a quadratic performance index is given. The optimal control law consists of a feedback control and a compensated feedforward control, and the feedback control gain can be obtained by solving the well-known Algebraic Riccati Equation (ARE). Especially, in the presence of unknown system dynamics, a novel adaptive iterative algorithm using the information of system states and inputs is proposed to solve the ARE to get the optimal feedback control gain. Finally, a simulation example shows the effectiveness of the theoretical results.

An adaptive inner iterative pressure-based algorithm for steady and unsteady incompressible flows

PurposePressure-based methods have been demonstrated to be powerful for solving many practical problems in engineering. In many pressure-based methods, inner iterative processes are proposed to get efficient solutions. However, the number of inner iterations is set empirically and kept fixed during the whole computation for different problems, which is overestimated in some computations but underestimated in other computations. This paper aims to develop an algorithm with adaptive inner iteration processes for steady and unsteady incompressible flows.Design/methodology/approachIn this work, with the use of two different criteria in two inner iterative processes, a mechanism is proposed to control inner iteration processes to make the number of inner iterations vary during computing according to different problems. By doing so, adaptive inner iteration processes can be achieved.FindingsThe adaptive inner iterative algorithm is verified to be valid by solving classic steady and unsteady incompressible problems. Results show that the adaptive inner iteration algorithm works more efficient than the fixed inner iteration one.Originality/valueThe algorithm with adaptive inner iteration processes is first proposed in this paper. As the mechanism for controlling inner iteration processes is based on physical meaning and the feature of iterative calculations, it can be used in any methods where there exist inner iteration processes. It is not limited for incompressible flows. The performance of the adaptive inner iteration processes in compressible flows is conducted in a further study.

An Adaptive Iterative Thresholding Algorithm for Distributed MIMO Radars

In this paper, a Block Iterative Method with Adaptive Thresholding for Sparse Recovery (BIMATSR) is proposed to recover the received signal in an under-sampled distributed multiple-input multiple-output radar. The BIMATSR scheme induces block sparsity with the aid of a signal-dependent thresholding operator which increases the accuracy of the target parameter estimation task. We have proved that under some sufficient conditions, the suggested scheme converges to a stable solution. Moreover, different simulation scenarios confirm that the BIMATSR algorithm outperforms its counterparts in terms of the target parameter estimation. This superiority is achieved at the expense of slightly more computational complexity. It is also shown that in this application, random sampling scheme offers better performance than random Gaussian matrix, while it has simpler structure.

An Adaptive Iterative Algorithm for Motion Deblurring Based on Salient Intensity Prior

An Adaptive Iterative Algorithm for Motion Deblurring Based on Salient Intensity Prior

Flood disaster evaluation based on adaptive fuzzy clustering iterative model and hybrid differential evolution algorithm

Abstract In order to reasonably and rapidly evaluate flood disaster, based on a fuzzy clustering iterative model (FCI) and differential evolution algorithm (DE), an adaptive fuzzy clustering iterative model using a hybrid differential evolution algorithm (AFCI-HDE) is proposed, which has three advantages: firstly, the decision-maker's subjective preference was considered to flexibly modify the objective function; secondly, HDE was introduced to optimize the index weight vector of AFCI; thirdly, the validity of its clustering effect was more credible than that of FCI. Finally, the case study revealed that AFCI-HDE is feasible and effective by comparing the optimal fitness and clustering validity values with other approaches, which could reflect various decision-maker's preferences by simple adaptive adjustments and rapidly obtain reasonable evaluation results, thus providing a new effective approach in flood risk management.

Implicit surface reconstruction based on generalized radial basis functions interpolant with distinct constraints

• We propose a generalized radial basis function interpolant with difference constraints. • Various types of constraints are derived to satisfy the interpolation conditions . • An adaptive iterative algorithm is used for approximating gradient and tangent constraints. • The numerical results reveal the improved performance of our method. In this work, we present a new algorithm for reconstruction of implicit surfaces from a set of cloud points with normals (Hermite data), based on the generalized radial basis functions interpolant with various types of constraints. Our key contribution is a novel construction of difference constraints in the interpolant to satisfy certain linear functional data, named as the domain constraints, the difference constraints of the gradient and the difference constraints of the tangent. To avoid ambiguous constraints, we apply an iterative algorithm to determine the adaptive distance in the difference constraints. The extended interpolant can not only provide an exact interpolation to the function values, but also approximate the function's derivatives by constructing a difference operator along the gradient or tangent direction. We interpolate the sampling points using our method by constructing a signed distance field in the geometry domain. Compared to the Hermite–Birkhoff interpolation with RBFs, the interpolant we use has faster solution efficiency. The experimental results of several data sets show the reliability and performance of our method in a variety of practical scenarios.

Distributed optimal coordination control for nonlinear multi-agent systems using event-triggered adaptive dynamic programming method

This paper is concerned with the design of distributed optimal coordination control for nonlinear multi-agent systems (NMASs) based on event-triggered adaptive dynamic programming (ETADP) method. The method is firstly introduced to design the distributed coordination controllers for NMASs, which not only avoids the transmission of redundant data compared with traditional time-triggered adaptive dynamic programming (TTADP) strategy and minimizes the performance function of each agent. The event-triggered conditions are proposed based on Lyapunov functional method, which is deduced by guaranteeing the stability of NMASs. Then a new adaptive policy iteration algorithm is presented to obtain the online solutions of the Hamiton–Jocabi–Bellman (HJB) equations. In order to implement the proposed ETADP method, the fuzzy hyperbolic model based critic neural networks (NN) are utilized to approximate the value functions and help calculate the control policies. In critic NNs, the NN weight estimations are updated at the event-triggered instants leading to aperiodic weight tuning laws so that computation cost is reduced. It is proved that the weight estimation errors and the local neighborhood coordination errors is uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to show the effectiveness of the proposed ETADP method.

Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus

Depth from focus (DFF) is a technique for estimating the depth and three-dimensional (3D) shape of an object from a multi-focus image sequence. At present, focus evaluation algorithms based on DFF technology will always cause inaccuracies in deep map recovery from image focus. There are two main reasons behind this issue. The first is that the window size of the focus evaluation operator has been fixed. Therefore, for some pixels, enough neighbor information cannot be covered in a fixed window and is easily disturbed by noise, which results in distortion of the model. For other pixels, the fixed window is too large, which increases the computational burden. The second is the level of difficulty to get the full focus pixels, even though the focus evaluation calculation in the actual calculation process has been completed. In order to overcome these problems, an adaptive window iteration algorithm is proposed to enhance image focus for accurate depth estimation. This algorithm will automatically adjust the window size based on gray differences in a window that aims to solve the fixed window problem. Besides that, it will also iterate evaluation values to enhance the focus evaluation of each pixel. Comparative analysis of the evaluation indicators and model quality has shown the effectiveness of the proposed adaptive window iteration algorithm.

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis.

The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed.

Iterative coupling algorithms for large multidomain problems with the boundary element method

SummaryA new parallel Robin‐Robin adaptive iterative coupling algorithm with dynamic relaxation parameters is proposed for the boundary element method (BEM), and relaxation parameters are derived for other existing iterative coupling algorithms. The performances of the new algorithm and of the modified existing algorithms are investigated in terms of convergence properties with respect to the number of subdomains, mesh density, interface mesh conformity, and BEM element types. Results show that the number of subdomains and the refinement level of the mesh are the two dominant factors affecting the performances of the considered algorithms. The proposed parallel Robin‐Robin algorithm shows the best overall convergence behavior for the tested large problems, thanks to its effectiveness in handling complex boundary conditions and large number of subdomains, thus resulting to be very promising for efficient parallel BEM computing and large coupling problems.Source code is available at https://github.com/BinWang0213/PyBEM2D.

FMCW multiple‐input multiple‐output radar with iterative adaptive beamforming

This work presents the design of a frequency modulated continuous wave (FMCW) multiple-input multiple-output radar system with collocated antennas. A key part of this article is the modification and application of the iterative adaptive algorithm (IAA) for digital beamforming to an FMCW radar system. The implemented system operates in the 2.4 GHz Industry, Scientific and Medical band with 100 MHz of bandwidth and consists of a transmit (TX) site with four antennas and a receive (RX) site with four antennas. The angular resolution is greatly improved from 15° with the common delay-and-sum beamforming to only 5° with IAA beamforming, which was verified by the results from a radar simulator and by a field test with the implemented hardware. The challenge of synchronisation and maintaining phase coherence in systems with remote TX and RX sites is also addressed by proposing a low-frequency clock and trigger distribution system. This approach allows for cost-efficient systems which can be distributed over longer distances.

Craniocervical computed tomography angiography with adaptive iterative dose reduction 3D algorithm and automatic tube current modulation in patients with different body mass indexes.

The aim of this study was to investigate the feasibility of head and neck computed tomography angiography (CTA) using the 80-kV tube voltage and the adaptive iterative dose reduction (AIDR) 3D algorithm in patients with different body mass indexes (BMIs).From November 2016 to January 2017, 128 consecutive patients scheduled for head and neck CTA examinations were randomized into the 100-kV group (n = 60) and the 80-kV group (n = 68). Both groups used the automatic tube current modulation technique and the AIDR 3D algorithm. The patients were further grouped as slender (BMI < 22 kg/m2), normal weight (22 kg/m2≤BMI < 25 kg/m2), and overweight (BMI ≥25 kg/m2). The image quality and the radiation dose of each subgroup were analyzed.The images of the head and neck vessels and the brain tissue obtained with 100 kV were all of diagnostic quality. Slender and normal weight patients imaged with 80 kV also produced images of diagnostic quality. However, 80 kV in the overweight patients failed to produce images of diagnostic quality. The radiation dose in the patients imaged with 80 kV was significantly decreased in comparison with those imaged with 100 kV. The effective dose was 0.36 ± 0.06 and 0.41 ± 0.05 mSv in the slender and normal weight patients imaged with 80 kV.Head and neck CTA scanning with 80 kV, automatic tube current modulation, and AIDR 3D algorithm can produce high quality images with reduced radiation dose in slender or normal weight patients.