Adaptive Estimation Algorithm Research Articles

An Adaptive Optimization Algorithm in LSTM for SOC Estimation Based on Improved Borges Derivative

This paper presents an optimization algorithm in the long short-term memory (LSTM) network for state of charge (SOC) estimation based on the fractal derivative. The improved Borges derivative as a fractal derivative is introduced to the parameter optimization in LSTM networks, and the integer-order derivatives in the adaptive momentum estimation (Adam) algorithm are generalized to the improved Borges derivatives. To take advantage of the orders, we analyze the relative speed of the improved Borges derivative to the integer-order derivative. Meanwhile, a tuning method for the orders is presented to flexibly adjust the training speed of parameters. The Adam algorithm with the improved Borges derivative (Adam-IB) is designed to estimate the SOC of lithium-ion batteries, and the training speed for SOC estimation is flexibly adjusted via the Adam-IB algorithm. The experiments are carried out under different working conditions. Compared with the Adam algorithm, the Adam-IB algorithm is obviously satisfactory to estimate the SOC.

Times Square Sampling: An Adaptive Algorithm for Free Energy Estimation

Estimating free energy differences, an important problem in computational drug discovery and in a wide range of other application areas, commonly involves a computationally intensive process of sampling a family of high-dimensional probability distributions and a procedure for computing estimates based on those samples. The variance of the free energy estimate of interest typically depends strongly on how the total computational resources available for sampling are divided among the distributions, but determining an efficient allocation is difficult without sampling the distributions. Here we introduce the Times Square sampling algorithm, a novel on-the-fly estimation method that dynamically allocates resources in such a way as to significantly accelerate the estimation of free energies and other observables, while providing rigorous convergence guarantees for the estimators. We also show that it is possible, surprisingly, for on-the-fly free energy estimation to achieve lower asymptotic variance than the maximum-likelihood estimator MBAR, raising the prospect that on-the-fly estimation could reduce variance in a variety of other statistical applications. Supplementary materials for this article are available online.

Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model

The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. Given the influence of cross-interference between the two states indicated above, this study establishs a co-estimation framework of battery SOC and SOT. This framwork is based on an innovative electrothermal model and adaptive estimation algorithms. The first-order RC electric model and an innovative thermal model are components of the electrothermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body, capable of capturing the detailed thermodynamics of large-format Li-ion batteries. Moreover, the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances. Besides, the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 ​°C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 ​°C.

Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification

Infrared (IR) spectroscopy is a powerful technique for detecting and identifying Microplastics (MPs) in the environment. However, the aging of MPs presents a challenge in accurately identification and classification. To address this challenge, a classification model based on deep convolutional neural networks (CNNs) was developed using infrared spectra results. Particularly, original infrared (IR) spectra were used as the sample dataset, therefore, relevant spectral details were preserved and additional noise or distortions were not introduced. The Adam (Adaptive moment estimation) algorithm was employed to accelerate gradient descent and weight update, the Dropout function was implemented to prevent overfitting and enhance the generalization performance of the network. An activation function ReLu (Rectified Linear Unit) was also utilized to simplify the co-adaptation relationship among neurons and prevent gradient disappearance. The performance of the CNN model in MPs classification was evaluated based on accuracy and robustness, and compared with other machine learning techniques. CNN model demonstrated superior capabilities in feature extraction and recognition, and greatly simplified the pre-processing procedure. The identification results of aged commercial microplastic samples showed accuracies of 40 % for Artificial Neural Network, 60 % for Random Forest, 80 % for Deep Neural Network, and 100 % for CNN, respectively. The CNN architecture developed in this work also demonstrates versatility by being suitable for both limited data cases and potential expansion to include more discrete data in the future.

Selection of an algorithm for classifying data quoted on the Day Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox

The article contains an analysis leading to the selection of an algorithm for classifying data listed on the Day-Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox. In this regard, an introduction to deep learning methods, classification methods, and classification algorithms is provided first. Particular attention was paid to the essence of three important deep learningmethods in the classification, i.e. the methods called: Stochastic Gradient Descent Momentum, Root Mean Square Prop and Adaptive Moment Estimation. Then, three architectures of artificial neural networks used in deep learning were characterized, i.e.: Deep Belief Network, Convolutional Neural Network and Recurrent Neural Network. Attention was paid to the selection parameters of algorithms for learning deep artificial neural networks that can be used in classification, such as: accuracy, information losses and learning time. Practical aspects of research experiments were also shown, including selected results of research conducted on volume and fixing 1 data quoted on the TGE S.A. Day-Ahead Market. After analyzing the obtained test results for the hourly system, it was noted that the least suitable algorithm for classification purposes was the Stochastic Gradient Descent Momentum algorithm, which in each case had worse results than the other two algorithms, i.e. the Adaptive Moment Estimation algorithm and the Root Mean algorithm Square Prop. However, the best algorithm turned out to be the Adaptive Moment Estimation algorithm, which obtained the highest accuracy, which was at a level comparable to the Root Mean Square Prop algorithm, with the latter algorithm having larger losses.

Implementation of a Robust PMAE Algorithm to Limit Large Gain Variation in a Fuel-Cell Integrated EV Charging Architecture

This paper proposes an adaptive error convergence technique to improve the charging dynamics of an electric vehicle (EV) charging system in a grid-integrated proton-exchange membrane fuel cell (PEMFC) architecture. The fuel cell is most often characterized by its slow dynamics during multiple load transients and preferably operated as an auxiliary energy source in EV charging architecture. The delayed response affects the overall charging dynamics with further controller phase lag and non-linear system parameters to induce a significant convergence error during reference command tracking. To effectively control multi-source integrated charging dynamics in an EV charging architecture, a projection-modified adaptive estimation (PMAE) algorithm is proposed in this paper. The control algorithm establishes a variable gain adaptation within a bounded error contour and updates the controller gains based on the tracking error dynamics gradient. The fuel cell, supporting other energy resources, provides a stronger charging resiliency with a duty-modified control technique at the EV charging station. To validate the effectiveness of the controller implementation, a grid-integrated multi-objective EV charging prototype is designed and developed for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) at variable operating conditions.

An adaptive bearing rigid formation control of multi-agent systems with nonlinear dead-zone inputs

In this paper, an adaptive bearing rigid formation control strategy for a class of nonlinear system with unknown dead-zone inputs and external disturbance is proposed. Firstly, the I-Type fuzzy system is used to approximate the unknown nonlinear dynamics of the formation model, and the approximation errors and unknown external disturbance are eliminated by the parameter adaptive estimation. Furthermore, the adaptive dynamic estimation algorithm is utilized to estimate and compensate the unknown dead-zone parameters, effectively suppressing the impact of dead-zone on formation system performance. Finally, the stability of the formation system is proved based on LaSalle’s invariance principle, and the effectiveness of the algorithm is verified by simulation results.

Constructing artificial boundary condition of dispersive wave systems by deep learning neural network

To solve one dimensional dispersive wave systems in an unbounded domain, a uniform way to establish localized artificial boundary conditions is proposed. The idea is replacing the half-infinite interval outside the region of interest with a super element which exhibits the same dynamics response. Instead of designing the detailed mechanical structures of the super element, we directly reconstruct its stiffness, mass, and damping matrices by matching its frequency-domain reaction force with the expected one. An artificial neural network architecture is thus specifically tailored for this purpose. It comprises a deep learning part to predict the response of generalized degrees of freedom under different excitation frequencies, along with a simple linear part for computing the external force vectors. The trainable weight matrices of the linear layers correspond to the stiffness, mass, and damping matrices we need for the artificial boundary condition. The training data consists of input frequencies and the corresponding expected frequency domain external force vectors, which can be readily obtained through theoretical means. In order to achieve a good result, the neural network is initialized based on an optimized spring-damper-mass system. The adaptive moment estimation algorithm is then employed to train the parameters of the network. Different kinds of equations are solved as numerical examples. The results show that deep learning neural networks can find some unexpected optimal stiffness/damper/mass matrices of the super element. By just introducing a few additional degrees of freedom to the original truncated system, the localized artificial boundary condition works surprisingly well.

Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study.

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

Intelligent finite-time formation control for flapping wing micro aerial vehicles

This paper investigates the intelligent finite time formation control for multiple Flapping wing micro aerial vehicles (FWMAVs) system. Firstly, the translational and the rotational attitude motion equations are proposed based on the Lagrangian equation for FWMAVs. The motion system is decouple into an internal and an external dual loop subsystems. An adaptive neural network estimation algorithm is proposed based on the internal and external double loop system of the coupled model to effectively estimate the uncertainties and the external disturbances of the model. In addition, two effective intelligent control protocols are presented for the translational and the rotational attitude motion subsystem, respectively, by utilizing potential energy function, generalized inverse matrix, and finite-time stability. The main contribution of this paper is the case that, four control objectives are achieved for multiple FWMAVs system, including the estimation of uncertainties, collision avoidance, connectivity preservation, and finite time convergence. Finally, a simulation example of formation tracking control is given by using matlab software in the numerical simulation part, and the effectiveness of the obtained results and the superiority of the control protocol are verified.

Parameter training method for convolutional neural networks based on improved Hausdorff-like derivative

In this paper, we propose a parameter training method in convolutional neural networks (CNNs). To introduce the orders and the cost function into the parameter optimization in CNNs, we give a definition of the Hausdorff-like derivative by analyzing the definition of the Hausdorff derivative, and a corresponding improved form is proposed. The improved Hausdorff-like (IHL) derivative is utilized in the parameter training of the back propagation in CNNs, and the orders are introduced in the back propagation for training of parameters. Therefore, we adjust the orders by judging the size of the cost function during the parameter training in CNNs, and the tuning of the orders can flexibly adjust the training speed of the parameters. An adaptive tuning approach for the orders is presented by analyzing the rule of the orders for the IHL derivative. By combining the proposed parameter training method with the adaptive moment estimation (Adam) algorithm, we propose the Adam algorithm with the IHL derivative. The experiments based on residual networks are carried out, and the experiments show that the proposed algorithm outperforms the original algorithm.

A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area

ABSTRACT Floods remain one of the most devastating weather-induced disasters worldwide, resulting in numerous fatalities each year and severely impacting socio-economic development and the environment. Therefore, the ability to predict flood-prone areas in advance is crucial for effective risk management. The objective of this research is to assess and compare three convolutional neural networks, U-Net, WU-Net, and U-Net++, for spatial prediction of pluvial flood with a case study at a tropical area in the north of Vietnam. They are relative new convolution algorithms developed based on U-shaped architectures. For this task, a geospatial database with 796 historical flood locations and 12 flood indicators was prepared. For training the models, the binary cross-entropy was employed as the loss function, while the Adaptive moment estimation (ADAM) algorithm was used for the optimization of the model parameters, whereas, F1-score and classification accuracy (Acc) were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models, achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and information to enhance decision-making processes and facilitate the implementation of effective risk management strategies.

Automatic crater shape retrieval using unsupervised and semi-supervised systems

Impact craters are depressions formed due to impacts on the surface of planetary bodies. Recent deep learning-based crater detection methods assume craters as circular-shaped without much attention to extracting craters’ shape and morphology. Craters’ shape information (i.e., instance segmentation map for craters) can be helpful for many advanced analyses including crater formation and surface characteristics. However, publicly available ground truth catalogs for the lunar surface do not contain crater shape annotations and are also challenged by the missing craters problem. We attempt to solve these challenges by proposing a novel estimation and refinement-based approach using a combination of unsupervised and semi-supervised systems. Our method consists of (a) learning estimated crater segmentation (ECS) maps by a novel adaptive rim estimation algorithm (unsupervised system) using side information, (b) refining ECS by a cascade of Mask region-based convolutional neural networks (R-CNNs) to obtain refined crater segmentation (RCS) maps (semi-supervised system), and (c) combining RCS followed by predicting a highly accurate crater segmentation map. In the absence of any publicly available catalog for crater shape annotations, we conducted a ranking-based user study to compare against the state-of-the-art. The proposed method outperforms by achieving the best ranking for 63.53% crater images as compared to 9.67% for state-of-the-art. Further, the extracted shapes of the craters are utilized to improve the estimate of the craters’ diameter, depth, and other morphological factors to be made publicly available: https://drive.google.com/drive/folders/1ghBf2FXNIJUEQkAM2GjLZNiIXKEhZMEB?usp=sharing.

An Adaptive Sparse Channel Estimation Algorithm for OFDM Systems Based on Distributed Compressive Sensing

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.

An unsupervised neural network for graphical health index construction and residual life prediction

To better characterize the health status and performing remaining useful life prediction, a composite health index is developed through the fusion of multi-channel signals. However, most of the existing literature limits the data fusion to be linear, which implies that the underlying degradation pattern must follow a linear form. This strong prerequisite of these approaches undermines the effectiveness of existing techniques for capturing the potential nonlinear nature of degradation process. In order to overcome this limitation as well as to improve the predictability, this paper proposes a nonlinear health index construction method achieving by an unsupervised neural network. Specifically, a neural network structure is introduced to approximate the highly nonlinear relationship between signals and health status. Furthermore, we consider the remaining useful life prediction as a binary classification problem, and then propose a maximal classification margin constraint, which is integrated with the monotonicity and minimal variability at the failure time to formulate the novel loss function. To estimate the model parameter, we developed a customized adaptive moment estimation algorithm (Adam). The comprehensive case study is performed based on the benchmark C-MAPSS dataset. As reported in the experiment, the constructed health index can better characterize the underlying degradation process.

Adaptive nonlinear group delay mode estimation

The decomposition of non-stationary signals remains a challenge in a wide variety of fields. Especially, the impulse or cross-mode signals are difficult to be reconstructed by recent methods due to their transient characteristic. Moreover, most methods rely heavily on the user-defined settings of the regularized parameter for the convex optimization algorithm. In this work, an adaptive nonlinear group delay mode estimation (ANGDME) algorithm is proposed by exploiting the sparsity of signals formulated as the nonlinear group delay model. The ANGDME introduces a complex Bayesian compressive sensing (CBCS) framework to process the sparse reconstruction. Then, a hierarchical Laplace scale mixture (LSM) prior is utilized to model dependencies among coefficients and provides superior probabilistic predictions. Furthermore, the estimator of amplitudes and group delays (GDs) of signals are obtained from the posterior distribution by Bayesian inference instead of point estimation. Finally, the proposed method updates the dictionary matrix in a traditional data-driven manner, resulting in a high-resolution time-frequency representation. Both simulated and experimental results illustrate the adaptability and effectiveness of the proposed method.

Event-Triggered Adaptive Finite-Time Control for MIMO Nonlinear Systems With Actuator Faults

This paper presents a novel finite-time adaptive control scheme for uncertain multi-input-multi-output (MIMO) nonlinear systems with actuator faults by virtue of event-triggered methods and prescribed performance control techniques. This approach provides superior transient and steady-state performance of the tracking error while guaranteeing stability for reducing the occupation of network resources. The measurable states are directly utilized for controller design, and a priori knowledge of system nonlinear functions is not required. Importantly, no complex approximation structure is utilized on the system dynamics, which increases robustness against uncertainties and actuator faults. The negative influences of unknown nonlinearities on the tracking performance is explicitly compensated by the proposed adaptive estimation algorithm. The closed-loop finite-time stability, including the avoidance of Zeno behavior for event-triggered mechanism, is also analyzed by rigorous theoretical proofs. The application of the controller to practical linear motor systems demonstrates the applicability.

Adaptive Order-Switching Kalman Filter for Orbit Determination Using Deep-Neural-Network-Based Nonlinearity Detection

This paper proposes an adaptive estimation algorithm for orbit determination, which consists of a deep-neural-network (DNN)-based nonlinearity detector combined with an adaptive order-switching procedure, to reduce the computational complexity while still maintaining the estimation accuracy. The DNN is trained to quickly evaluate the nonlinearity degree of the state equation. An adaptive order-switching strategy is designed based on the nonlinearity degree predicted by the DNN. The algorithm switches to a high-order method when the nonlinearity of the state equation is significant and uses a linear method when the nonlinearity degree is low. The proposed method is applied to estimate the orbit of a spacecraft in cislunar space. The sample forms in the inertial frame and rotating frame are investigated and compared to find the optimum one to train the DNN. Simulations show that the proposed method can deliver accurate state estimations comparable with the state estimations obtained by the second-order extended Kalman filter but with only half of the computational cost.

Machine-Learning-Based Classification of Frequency Hopping in Radio Networks for Communication Reconnaissance

Abstract This article presents a customized approach for training a supervised learning neural network with the adaptive moment estimation algorithm, to classify the number of frequency hopping networks in an operational area. The algorithm was constructed based on data experimentally collected from a real-time spectrum analyzer for military very high frequency hopping networks. The impact of some training parameters on classification efficiency is briefly discussed while the obtained accuracy was above 97% for both test and validation data in all training variations. With this promising result, the proposed algorithm has the potential to be utilized in developing operational systems capable of real-time signal reconnaissance for military frequency hopping radio networks.

Adaptive neural decision tree for EEG based emotion recognition

An adaptive neural decision tree is investigated to recognize electroencephalogram (EEG) emotion signal with ability of intelligently selecting network structure. Firstly, to overcome lack of position information of the vectorized input EEG signal, the original one-dimensional EEG vector signal is cast into a two-dimensional matrix signal added with channel position information. Secondly, the depthwise convolution is adopted in transformers to deal with each channel by one convolution kernel, thus decreases the number of parameters. Then, to overcome model interpretability problem, an adaptive neural decision tree (ANT) based emotion recognition method is explored by embedding neural networks into the decision tree to perform feature extracting, path selecting, and label classifying. Further, ANT can automatically search optimized parameters by using the adaptive moment estimation algorithm, and explore tree architectures by using the exploration–exploitation trade-off reinforcement learning method to get the global optimal network structure without manually setting complex hyperparameters. Finally, by 5-fold cross-validation, binary, four-class and eight-class classification experiments are carried out on DEAP datasets, the average accuracy of the ANT algorithm are 99.14 ± 0.456%, 98.95 ± 0.84%, 97.58 ± 2.311%, respectively, which verifies the effectiveness of the proposed method, compared with the traditional decision tree method.