Fast Adaptive Algorithm Research Articles

Dynamic Simulation of Rail Potential and Stray Current Distribution Based on Fast Adaptive Boundary Element Algorithm

Dynamic Simulation of Rail Potential and Stray Current Distribution Based on Fast Adaptive Boundary Element Algorithm

A novel fast and efficient adaptive shuffled complex evolution algorithm for model parameter calibration

The research and optimization of hydrological forecasting models are among the most crucial components in the scope of water management and flood protection. Optimizing the calibration of hydrological forecasting models is crucial for forecasting performance. A rapid adaptive Shuffled Complex Evolution (SCE) method called Fast Adaptive SCE (FASCE) is proposed for calibrating model parameters. It builds upon the previously established SCE-UA, known for its effectiveness and robustness in the same calibration context. The robustness of the original SCE-UA is expanded upon, introducing a revised adaptive simplex search to bolster efficiency. Additionally, a new strategy for setting up the initial population base enhances explorative capacities. FASCE’s performance has been assessed alongside numerous methods from prior studies, demonstrating its effectiveness. Initial tests were conducted on a set of functions to assess FASCE’s efficacy. Findings revealed that FASCE could curtail the failure rate by a minimum of 80%, whereas the requirement for function evaluations fell between 30% and 60%. Two hydrological models - Support Vector Machine (SVM) and Xinanjiang rainfall-runoff model were employed to estimate the new algorithm’s performance. No failures were reported, and there was a reduction of at least 30% in function evaluations using FASCE. The outcomes from these studies affirm that FASCE can considerably reduce both the number of failures and the count of function evaluations required to reach the global maximum. Hence, FASCE emerges as a viable substitute for model parameter calibration.

Planar ECT Image Reconstruction Based on Solving the Bayesian Model by Combining Fast Iterative Adaptive Shrinkage-Thresholding Algorithm and GMM

Planar ECT Image Reconstruction Based on Solving the Bayesian Model by Combining Fast Iterative Adaptive Shrinkage-Thresholding Algorithm and GMM

Reconfigurable Digital Satellite-Borne Base Station Design and Virtual Function Fast Migration Algorithm.

A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture.

Retraction Note to: A fast adaptive algorithm for nonlinear inverse problems with convex penalty

Retraction Note to: A fast adaptive algorithm for nonlinear inverse problems with convex penalty

On the possibility of detection and classification of noise sources based on analysis of their trajectories at the output of adaptive spatial processing

To detect and classify objects, the source trajectories detected in the process of hydroacoustic observation are used, which contain information about the measured parameters of objects, which are their classification attributes. The analysis of these attributes allows to make a decision about the class of the observed object, for example, a surface or underwater source. As the measured object parameters are used their energy characteristics, parameters of the observed trajectory (bearing, speed of change in bearing and other possible trajectory parameters). In this case, the correctness and speed of the decision on classification depends on the quantity and quality of used classification signs, which are determined by both parameters of the observed object, and the features of sound propagation from the source to the observation device.For signal detection and resolution, we further consider fast projective adaptive algorithms, the use of which, as applied to the problems of in-situ experimental signal detection was considered in [1], [4]. The goal of this class of algorithms is to provide a high probability of detection and accurate measurement of the parameters of the source trajectories under the conditions of a model of multi-beam propagation and scattering in the real ocean environment [5]-[8]. The proposed work is a continuation of the work [1], [4], and aims to ensure the use of experimental field data not only for detection, but also for the classification of the observed sources.The subject of the study are the results of the full-scale experiment of hydroacoustic noise direction finding, given earlier and described in detail in [1], [4]. For the experiment was used antenna of L = 56 vertical daisy chains (of 10 elements each), equidistantly spaced horizontally. The antenna was installed at a depth of 200 meters in the coastal marine zone of the coastal wedge near the shipping lanes. The elements of the flat antenna were affected by signals from surface ships moving ncontrollably in the observation area and one underwater source.A singular decomposition of sampled antenna element data was used to construct adaptive algorithms. Modification of the initial results of singular decomposition allows to create algorithms that provide priority conditions for extraction of separate components of the observed (e. g., weakest) signals when constructing direction finding terrain.In this regard, in addition to the non-adaptive direction finding relief, it is proposed to form three variants of direction finding relief, each of which solves part of the general problem of selection and classification of individual varieties of observed signals:– Initial, corresponding to the energy of the signals of the input sample with amplified components of the weakest signals (overview algorithm);– direction finding relief, which uses an algorithm to detect weak and scattered signals;– direction finding terrain, which highlights the coherent components of signals.Analysis of the trajectories of more than 30 sources in the episode of two hours and forty minutes has been carried out, which increased the reliability of the detection and measurement accuracy of the parameters of the observed objects. Joint analysis of the trajectories of sources based on different variants of the bearing relief allowed to improve the conditions for the detection of weak signals and to make classification decisions using the classification attribute of the width of the area of fluctuations of the surface target trajectory for signals with a strong scattered component.

RETRACTED ARTICLE: A fast adaptive algorithm for nonlinear inverse problems with convex penalty

RETRACTED ARTICLE: A fast adaptive algorithm for nonlinear inverse problems with convex penalty

An improved fast adaptive complex approximation message passing algorithm

To solve the problem of reconstructing complex sparse signals from linear measurements of additive white Gaussian noise (AWGN) under the condition of unknown sparse signal distribution, this paper proposes a fast adaptive complex approximate message passing (CAMP) algorithm with significantly reduced mean square error and adaptive to parameter selection. First, we establish a complex sparse signal distribution model. Secondly, the unknown parameters of the distribution model are estimated in each iteration, and the estimated values are used as prior information to obtain the complex shrinkage function with minimum mean square error (MMSE). Finally, an improved fast adaptive CAMP algorithm is obtained by combining the updated shrinkage function with the CAMP. The algorithm has the advantages of fast convergence, low computational complexity, small mean square error, and good robustness. Theoretical analysis and simulation verify the effectiveness of the proposed algorithm.

Randomized block-coordinate adaptive algorithms for nonconvex optimization problems

Nonconvex optimization problems have always been one focus in deep learning, in which many fast adaptive algorithms based on momentum are applied. However, the full gradient computation of high-dimensional feature vector in the above tasks become prohibitive. To reduce the computation cost for optimizers on nonconvex optimization problems typically seen in deep learning, this work proposes a randomized block-coordinate adaptive optimization algorithm, named RAda, which randomly picks a block from the full coordinates of the parameter vector and then sparsely computes its gradient. We prove that RAda converges to a δ-accurate solution with the stochastic first-order complexity of O(1/δ2), where δ is the upper bound of the gradient’s square, under nonconvex cases. Experiments on public datasets including CIFAR-10, CIFAR-100, and Penn TreeBank, verify that RAda outperforms the other compared algorithms in terms of the computational cost.

Fast adaptive sampling with operation time control

The rational choice of sampling points is crucial for supervised machine learning algorithms. Adaptive sampling ensures optimal distribution of points in the multidimensional space and provides higher approximation accuracy with respect to the random sampling. Most approaches to the adaptive sampling maximize the function evaluating the compromise between local exploitation and global exploration. However, existing accurate methods suffer from long sampling time, while fast methods are not accurate. Only a few reported methods are available as open source implementations. We introduce a fast adaptive sampling algorithm with accuracy comparable to that of the best-known adaptive sampling methods and demonstrate a comparative study of different approaches to minimization of approximation errors. The method scales linearly with number of sampling points and supports batch generation of new points. The optimization function is the integral norm of an approximation error, the Lp norm, where the norm parameter p regulates sampling, inclining it either towards local exploitation or, conversely, global exploration. The small Lp norm is achieved by reducing the function approximation error and the size of the region with large variation after adding a new sampling point. Our solution is similar to kriging in terms of the choice between local exploitation and global exploration. The difference is in error estimation which depends on the values of the function in sampling points in contrast to homoscedastic variance estimate of kriging. Fast and accurate adaptive sampling is of interest to supervised machine learning tasks such as training a model interatomic potential, quantitative analysis of spectra, self-driving chemical laboratories, and many others. The source code for the new adaptive sampling algorithm MinLpE is available on Github and is distributed under a LGPL license.

Review of the monograph by G.S. Malyshkin and V.S. Melkanovich “Classical and fast projection adaptive algorithms in hydroacoustics”

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LOCAL ADAPTIVE PHASE CORRECTION ALGORITHM FOR 3D PROFILOMETRY SYSTEMS BASED ON PHASE SHIFTING METHOD

It is very popular to reflect sinusoidal phase patterns to the surface with technique of demodulation of the phase information in order to get 3D surface depth with 2D images.The phase information that extracted with this method is wrapped; so it must be unwrapped.Even though the phase information is unwrapped; there will be some errors because of possibility of non-sinusoidal characteristics of phase patterns, surface discontinuities, low sample ratesand technical handicaps (poor calibration and hardware malfunctions etc).To deal with those errors resulting from the phase unwrapping process, there are some computationally expensive and complex methods have been presented.In this paper, a fast and low complexlocal adaptive phase correction algorithm forfour-step phase shifting methodis implemented especially for oral applications. The method is firstly validated by using synthetic data. After validation process, an optic test system is realized and a few experiments are performed by using real data. For optic system, a look-up table based calibration techniqueis also implemented for getting accurate surface phase information. Visual and quantitative simulation results are presented to show the performance of the method.

The Impact of Intelligent Accounting Information Management on Corporate Governance Information Transparency

In order to improve the transparency of corporate governance information, this paper combines intelligent accounting information technology to analyze the information transparency in the process of corporate governance, analyzes the fast adaptive neighbor clustering algorithm, and realizes the joint spectral embedding of data by creating block diagonal anchor graphs. Moreover, this paper re-selects anchor points from the spectral embedding data and constructs a new block diagonal anchor graph. In addition, using the principle of adaptive nearest neighbor clustering, this paper performs structured graph learning on the block diagonal anchor graph matrix. After derivation, the Laplace eigenvalue decomposition problem of the whole graph in adaptive nearest neighbor clustering can be transformed into a block matrix singular value decomposition problem. The regression analysis shows that there is a linear correlation between the advanced degree of intelligent accounting information management technology and the transparency of corporate governance information.

The Accelerated 3D Capacitance Extraction Based on Adaptive Low-Rank Matrix Factorization

In order to accelerate the direct boundary element method (DBEM) of capacitance extraction, the proposed method explores the local low-rank character of the coefficient matrix, and performs fixed-precision low-rank matrix factorizations to approximate it to accelerate the iterative solution without lost of accuracy. A fast adaptive low-rank matrix factorization algorithm is proposed aiming at higher efficiency on subsequent matrix-vector multiplication. Experiments on several IC interconnect structures show that the proposed fast adaptive algorithm (called fastQB) achieves a maximum speedup of 1.5X over the existing randQB_EI algorithm. The iterative solution of the DBEM equations is accelerated by at most 16.8X with the proposed technique based on adaptive low-rank matrix factorization.

A fast adaptive algorithm for training deep neural networks

A fast adaptive algorithm for training deep neural networks

A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar.

Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient to guarantee the performance in the heterogeneous clutter environment. Currently, most sparse recovery/representation (SR) techniques to reduce the requirement of training samples still suffer from high computational complexities. To remedy this problem, a fast group sparse Bayesian learning approach is proposed. Instead of employing all the dictionary atoms, the proposed algorithm identifies the support space of the data and then employs the support space in the sparse Bayesian learning (SBL) algorithm. Moreover, to extend the modified hierarchical model, which can only apply to real-valued signals, the real and imaginary components of the complex-valued signals are treated as two independent real-valued variables. The efficiency of the proposed algorithm is demonstrated both with the simulated and measured data.

Fast Active Fault-Tolerant Control for a Quadrotor UAV Against Multiple Actuator Faults

This paper proposes a fast adaptive fault estimator-based active fault-tolerant control strategy for a quadrotor UAV against multiple actuator faults. A fast adaptive fault estimation algorithm is designed to estimate the unknown actuator fault parameters. By synthesizing the fast adaptive fault estimator with the embedded control law, an active fault-tolerant control mechanism is established to compensate the adverse effects of multiple actuator faults. The effectiveness of the proposed strategy is validated through both numerical simulations and experimental tests.

LightAdam: Towards a Fast and Accurate Adaptive Momentum Online Algorithm

LightAdam: Towards a Fast and Accurate Adaptive Momentum Online Algorithm

Small Infrared Target Detection Based on Fast Adaptive Masking and Scaling With Iterative Segmentation

Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.

Stability Analysis of a Fast Convergence Adaptive Algorithm Based on Square Sum of Correlation Function and Application to Adaptive Notch Filter

Stability Analysis of a Fast Convergence Adaptive Algorithm Based on Square Sum of Correlation Function and Application to Adaptive Notch Filter