Traditional Estimation Methods Research Articles

Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique

Accurate state of charge (SOC) estimation helps achieve efficient battery management, which is essential for transportation electrification. Significantly different from existing data-driven estimation methods only considering battery electrical information, this study proposes a mechanics-based battery SOC estimation using deep learning techniques. First, an experimental setup for measuring pouch-type battery stress is designed, followed by constructing four typical operating conditions to establish a sophisticated battery dataset and investigate the primary relationship between battery stress and SOC. Then, a data-driven estimation model composed of long short-term memory neural network is investigated to achieve the interlink between battery external measurements and internal states, in which the battery voltage, current, and stress sequences within a sliding window length are fed into the deep learning model. Quantitative experimental results demonstrate that the proposed mechanics-based battery SOC estimation approach can achieve acceptable accuracy and is adaptable to different training and operating conditions, as well as ensure the estimation performance under limited data length. Moreover, the proposed method has also been proven robust to the interference of battery measurement noise compared to the traditional estimation method.

Constrained Least-Squares Parameter Estimation for a Double Layer Capacitor

This paper presents an estimation of the parameters for a Double Layer Super Capacitor (DLC) that is modelled with a two-branch circuit. The estimation is achieved using a constrained minimization technique, which is developed off-line and uses a single constraint to write the matrix equation. The model is algebraically manipulated to obtain a matrix equation, and a signal processing system is developed to prepare the signals for the identification algorithms. The proposed method builds on the results obtained using an unconstrained ordinary least-squares (OLS) technique. The method is tested both in simulation and experimentally, using a specially-designed experimental rig. A current ramp input is used to generate the corresponding output voltage and its derivatives. The results obtained from the constrained off-line minimization algorithm are compared with those obtained using a traditional off-line estimation method. The discussion of the results shows that the proposed method outperforms the traditional estimation technique. In summary, this paper contributes to the field of DLC parameter estimation by introducing a new off-line constrained minimization technique. The results obtained from the simulations and experimental rig demonstrate the effectiveness of the proposed method with two of three parameters showing relative errors less than 5%.

Multistate joint estimation of ultracapacitor based on trans-scale dual extended Kalman filter

Accurate ultracapacitor state of charge (SOC) estimation directly affects safety, durability and reliability of hybrid energy storage system. For that, this paper presents a multistate joint estimation method of ultracapacitor based on trans-scale dual extended Kalman filter to predict SOC and model parameters in real-time. Firstly, an ultracapacitor model is established by using experimental data. Then, the model is combined with two Kalman filters, one of which is used to estimate SOC, and another one is applied to achieve trans-scale update the model parameters. Finally, the effectiveness of the method is verified in the urban dynamometer driving schedule at different temperatures. The validation results show that the mean absolute errors of voltage, SOC and capacity estimation are within 0.49 mV, 0.63% and 0.04 Ah, respectively. Moreover, compared with the other two traditional SOC estimation methods, whose results show that the proposed algorithm has higher estimation accuracy and more stable convergence ability.

Environment-growth nexus and corruption in the MENA region: Novel evidence based on method of moments quantile estimations

This study contributes to the environment-growth nexus literature by examining corruption's effect on environmental quality in 17 Middle East and North Africa (MENA) countries between 1984 and 2018. We use four different indicators of environmental degradation (ecological footprint, carbon dioxide emissions, greenhouse gas emissions, and carbon emission intensity) to gain a comprehensive view of the relationship. This study's methodological value added is the application of the method of moments quantile regression, which enables us to account for different relationships between the independent variables and environmental quality at different levels of environmental degradation. The results indicate that corruption worsens environmental quality based on three of the four indicators of environmental degradation, while the impact appears to become less severe at higher levels of environmental deterioration. The results also indicate that traditional mean-based panel data estimation methods provide an incomplete picture of the factors behind environmental problems: The explanatory variables' impacts tend to vary at different levels of environmental quality, and the impact's sign can even change when moving from the lowest to the highest environmental degradation quantiles. Overall, the results highlight the importance of curbing corruption to enable enforcement of more stringent environmental regulations.

Veracious Interpolated Measure of Angle and Length for Underwater Motion Blurred Images

The quality of the underwater image is impairing with the atmospheric conditions. In this, one of the most significant issues in recent days is due to the motion blur induced by the imaging device or by the movement of the object in underwater image quality degradation. The various parameters of the blurred image must be identified to fix the effect of blurring in post-imaging. Therefore, spectrum-based parameter estimation method is proposed. Initially, to estimate the point spread function (PSF), the angle and the length is measured from image spectrum using radon transform. Then, for the accurate estimation of PSF, Optimized Polynomial Lagrange Interpolation (OPLI) is proposed. The data were collected and analyzed in various natural and structured water bodies in Chennai without affecting the real environment. It is observed that for the underwater images collected, the proposed OPLI approach outperforms compared to few existing traditional estimation methods like cepstral, hough, and radon. Then this veracious interpolated measure of angle and length (VIMAL) is restored using modified Lucy algorithm and is evaluated which results in high performance than the existing classical state-of-the-art methods.

Design-based composite estimation of small proportions in small domains

Traditional direct estimation methods are inefficient for domains of a survey population with small sample sizes. To estimate the domain proportions, we combine the direct estimators and the regression-synthetic estimators based on domain-level auxiliary information. For the case of small true proportions, we propose the design-based linear combination that is a robust alternative to the empirical best linear unbiased predictor (EBLUP) based on the Fay–Herriot model.
 We imitate the Lithuanian Labor Force Survey, where we estimate the proportions of the unemployed and employed in municipalities. We show where the proposed design-based composition and estimator of its mean square error are competitive for EBLUP and its accuracy estimation.

A penalized least product relative error loss function based on wavelet decomposition for non-parametric multiplicative additive models

In this paper, an estimation method based on wavelet decomposition is proposed for non-parametric multiplicative additive models. The proposed method can transform the estimation problem of non-parametric models into the estimation of parametric models without assuming the error distribution. To achieve the sparsity of the model, we apply the smoothly clipped absolute deviation (SCAD) penalty to estimate and select the variables based on the least product relative error loss function. Simulation results support the theoretical characteristics. We show that the proposed method is more effective than the traditional spline estimation method. Applications further show the performance of the proposed method with the actual data.

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.

Reinforcement Learning Based Parameter Lookup Table Generating Method for Optimal Torque Control of Induction Motors

In the optimal control of induction motors, it is a challenging task to maintain the optimal torque over the varying operation conditions. This paper proposes a parameter look-up table generating method, that can achieve an optimal torque over a wide range of currents and speeds, even though the commands of current are not set correctly. Based on the motor’s testing data, this method uses a reinforcement-learning algorithm to generate parameter look-up tables iteratively. Experimental results show that the proposed method can learn appropriate parameters from the running data to output an optimal torque. The comparative studies show that the proposed method can generate 5%-25% more torque than traditional model-based parameter estimation methods, over a wide range of currents and speeds. Furthermore, the proposed method has a faster convergence feature and a higher identification resolution than many conventional search-based methods.

A Reference-Free Method for the Thematic Accuracy Estimation of Global Land Cover Products Based on the Triple Collocation Approach

Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality reference data poses a severe risk to traditional accuracy estimation methods, in which reference data are always required. Thus, meeting the needs of large-scale, fast evaluation for GLC products becomes challenging. The triple collocation approach (TCCA) is originally applied to assess classification accuracy in earthquake damage mapping when ground truth is unavailable. TCCA can provide unbiased accuracy estimation of three classification systems when their errors are conditionally independent. In this study, we extend the idea of TCCA and test its performance in the accuracy estimation of GLC data without ground reference data. Firstly, to generate two additional classification systems besides the original GLC data, a k-order neighbourhood is defined for each assessment unit (i.e., geographic tiles), and a local classification strategy is implemented to train two classifiers based on local samples and features from remote sensing images. Secondly, to reduce the uncertainty from complex classification schemes, the multi-class problem in GLC is transformed into multiple binary-class problems when estimating the accuracy of each land class. Building upon over 15 million sample points with remote sensing features retrieved from Google Earth Engine, we demonstrate the performance of our method on WorldCover 2020, and the experiment shows that screening reliable sample points during training local classifiers can significantly improve the overall estimation with a relative error of less than 4% at the continent level. This study proves the feasibility of estimating GLC accuracy using the existing land information and remote sensing data, reducing the demand for costly reference data in GLC assessment and enriching the assessment approaches for large-scale land cover data.

Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data

This study examines the use of a recurrent neural network for estimating the parameters of a Hawkes model based on high-frequency financial data, and subsequently, for computing volatility. Neural networks have shown promising results in various fields, and interest in finance is also growing. Our approach demonstrates significantly faster computational performance compared to traditional maximum likelihood estimation methods while yielding comparable accuracy in both simulation and empirical studies. Furthermore, we demonstrate the application of this method for real-time volatility measurement, enabling the continuous estimation of financial volatility as new price data keeps coming from the market.

Deep learning-based energy harvesting with intelligent deployment of RIS-assisted UAV-CFmMIMOs

The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we introduce a novel framework called CURe, which combines the advantages of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to provide uninterrupted energy harvesting for IoT devices through RFEH. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further enhance RFEH, we propose DeNCE, a channel estimation technique based on deep learning (DL) that eliminates the need for traditional closed-form equation-based channel estimation methods. Through evaluation, we first validate the performance of CURe by comparing it with the modified bisection search for max–min fairness (MBS-MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.

Composite adaptive online inverse optimal control approach to human behavior learning

There have been broad ranges of human-in-the-loop (HiTL) control systems, like fly-by-wire aircraft, share driving, and energy management. To develop advanced HiTL control systems with augmented intelligence, it is desirable that the machine is empowered to understand how a skilled human operator determines the control actions. It is common to model the human operator as an optimal controller with an unknown weighting matrix which depicts the tradeoff between multiple control objectives. Our aim is thus to learn the weighting matrix of the human objective function using only the system state measurement. Accordingly, for the HiTL system, a composite adaptive inverse optimal control (IOC) approach, which integrates concurrent learning (CL) based composite adaptive estimation and linear matrix inequality (LMI) optimization techniques, is proposed to learn human behavior online. The proposed composite adaptive IOC method consists of two steps: first, a composite adaptive update law is proposed to online estimate the human feedback gain matrix, which removes the persistent excitation condition required by traditional adaptive estimation methods, and then, an LMI optimization problem is solved to retrieve the weighting matrix of the human objective function with the learned feedback gain. The applicability and effectiveness of the proposed method are demonstrated with a steering control simulation.

A Pose Estimation Method Combining Instance Segmentation and Point Pair Features

For the traditional pose estimation method based on point pair features, both the preprocessing of scene point cloud and the construction of point pair features of scene point cloud have serious time-consuming problems, which cannot meet the needs of actual industry. Therefore, this paper proposes a pose estimation method that combines instance segmentation and point pair features. Therefore, this paper proposes a pose estimation method that combines instance segmentation and point pair features. First, use the Mask R-CNN-based instance segmentation network to obtain the location of the target object in the two-dimensional image of the scene; then, obtain the local point cloud data of the space where the target object is located from this position and the depth information of the scene; finally, the local the point cloud data is used as the scene point cloud based on point pair feature pose estimation to perform feature matching with the point cloud of the target object.

A fault diagnosis method for variable speed gearbox bearing based on SET improved multi-source ridge line

Due to the noise interference and frequency ambiguity of gearbox vibration signal under variable speed, the traditional instantaneous frequency estimation method is prone to frequency mutation and low ridge line fusion accuracy. This paper proposes an improved method of extracting multi-source time–frequency ridge lines based on time–frequency analysis by exploring the relationship between the vibration signal of the gearbox without a tachometer and the fluctuation law of the bearing rotation frequency. In the proposed method, the Variable Mode Decomposition (VMD) filter and Synchroextracting Transform (SET) are first used to analyze the time frequency of the gearbox vibration signal. Secondly, the local peak search algorithm obtains multiple time–frequency ridge lines from low-frequency and high-frequency resonance frequency bands. Thirdly, the synchronization process is carried out based on the maximum energy spectrum in the time spectrum. The multi-source ridge line extraction algorithm is improved with reduced frequency mutation and optimized ridge line fusion accuracy. Finally, the optimized speed curve is used to track the vibration signal order of the gearbox. The bearing fault characteristic order is obtained from the angular order spectrum, and the effectiveness of the proposed method is verified by the simulation and the experiment.

Distributed Localization Estimation for Dynamic Multiagent Systems

This article investigates the real-time localization problem of dynamic multiagent systems with repetitive operation characteristics under directed graph. A distributed localization estimation algorithm based on iterative learning is proposed. The barycentric coordinates calculated based on the relative distance are used to estimate the real coordinates of the agent. Different from the traditional estimation methods along the time axis, the proposed method utilizes the information of iteration axis simultaneously. In this method, the current estimation coordinates are updated by using the estimation coordinates of the same sampling time in previous iteration, the estimation accuracy is improved, and the velocity constraint is removed. Additionally, the real-time localization problem of dynamic multiagent systems under arbitrary deployment is concerned. An improved distributed localization estimation algorithm with signed coefficients based on iterative learning is proposed. Meanwhile, the results are also extended to the localization estimation of multiagent systems with arbitrary deployment in 3-D space. By introducing Richardson iteration and infinite norm, the global asymptotic convergence of the proposed methods is guaranteed. Finally, numerical simulations and the Qbot-2e robot experiment are provided to show the effectiveness and validity of the obtained results.

Dynamic Parameter Identification of Serial Robots Using a Hybrid Approach

Model-based control can provide high-accuracy performance over position-based or velocity-based control. Therefore, to employ model-based control in industrial robots, it is important to estimate the dynamic parameters as accurately as possible. However, traditional estimation methods, such as least squares (LS), are not sufficiently accurate, and the feasibility of dynamic parameters cannot be guaranteed. In this article, an iterative hybrid least square (IHLS) algorithm is proposed to estimate the base parameters of industrial robots by dividing the identification processes into two loops. The inner loop integrates a linear matrix inequality with the semidefinite programming technique to guarantee physical feasibility and reuses the torque deviations between the measured torque and predicted torque to estimate the base parameters of the robot, while the outer loop substitutes the Stribeck friction model for the Coulomb-viscous friction model to estimate the joint friction torque. Moreover, backpropagation neural network (BPNN) is introduced to further estimate the joint friction torque based on the Stribeck friction model. Experiments are conducted on two industrial robots, and four methods are compared in dynamic parameter identification. Experimental results show that the hybrid approach of the IHLS algorithm with the BPNN has the best performance among the four methods.

Multi-Stage Voltage Sag State Estimation Using Event-Deduction Model Corresponding to EF, EG, and EP

Knowledge of the disturbance of the concerned bus is essential to make decisions to mitigate voltage sags. The most commonly used method is to estimate the state of voltage sag in the absence of a monitor. However, the traditional state estimation method, which is designed for rectangular voltage sags, is difficult to apply to multi-stage voltage sags (MSVSs). This study proposes an MSVS state-estimation method based on event-deduction models. First, we propose a method to identify the causes of MSVSs. Second, we present a method to calculate the matrix of relay protection trips (MRPT), which corresponds to the fault clearing time of the relay protection and a method to calculate the fault power line set to reduce the computing workload of the MRPT-based estimation method. Third, we propose a method to calculate the impedance matrix of a power system under different disturbance events, resulting in different stages of the MSVS and an event-deduction model based on this impedance matrix to deduce these events. Furthermore, we present a model to estimate the state of voltage sag for MSVS based on the event deduction result. Finally, we use the IEEE-118 system to validate the proposed method.

Software Development Effort Estimation Using Relational Database and Optimized Learning Mechanism

Accurately estimating the cost of software development is crucial for effective project planning and resource allocation. However, traditional cost estimation methods rely heavily on expert judgment and historical data, which can be time consuming and prone to errors. This study suggests a learning-based cost estimation model that leverages relational databases to improve accuracy. The proposed approach estimates project cost based on the effort required to complete software development, which is a key driver of the project cost. The proposed model is designed to address the challenges posed by the variability in open-source development, including variable team sizes, working hours, and expertise. The study collects and pre-processes data from open-source platforms and selects cost drivers and metrics based on logical rules and SQL queries. Moreover, we propose an optimized Artificial Neural Network (ANN) with augmented topology to automate the selection of neuron units, layers, and adjustment of learnable parameters according to the input variables. The proposed model is evaluated on a 100 open-source software repositories dataset and demonstrates its effectiveness in accurately estimating development cost. The system is implemented using Python and evaluated using performance parameters such as MSE, RMSE, MAE, and MMRE. Results indicate that our proposed model offers a more accurate and efficient approach to software cost estimation, especially for freelancers and outsourcing firms. The proposed model has the potential to save time and resources and improve the reliability and accuracy of software cost estimation.

Target location and velocity estimation with the multistatic MU‐MIMO‐OFDM modulation signal

AbstractIn passive radar and joint communication and radar sensing (JCRS), target sensing with the multiuser multiple input multiple output orthogonal frequency division multiplexing (MU‐MIMO‐OFDM) modulation signal is gaining increasing interest. Multiple transmit nodes emitting the MU‐MIMO‐OFDM modulation signals at the same carrier frequency and one receiver collecting the target‐reflected signals for target location and velocity estimation are considered. This is a typical scenario when using the fifth‐generation (5G) communication network signal for target sensing. In this scenario, the echo signals corresponding to different transmit nodes are not resolved in the receiver, and the modulated data symbols cannot be removed from the received signals. Most traditional parameter estimation methods in passive radar and JCRS may not be suitable here. A location and velocity estimation method with the received echo signals is proposed. Specifically, the location parameters are extracted directly from the received echo signals. The location estimation is cast into a block sparse vector reconstruction problem. The variational Bayesian sparsity learning (VBSL) method is exploited for the reconstruction of the block sparse vector. Accelerated VBSL methods are developed for improving the computational efficiency. Simulations verify the effectiveness of the proposed methods.