Traditional Estimation Methods Research Articles

Stochastic Stability of Markovian Neural Networks With Generally Hybrid Transition Rates.

This article studies the problem of the stability for Markovian neural networks (MNNs) with time delay. The transition rate is considered to be generally hybrid, which treats those existing ones as its special cases. The introduced generally hybrid transition rates (GHTRs) make these systems more general and practical. Apropos of the GHTRs, a double-boundary approach rather than the traditional estimation method is introduced to make full use of the error information in GHTRs. In order to fully capture system information, a parameter-type-delay-dependent-matrix (PTDDM) approach is proposed, in which the PTDDM approach removes some zero components on slack matrices in previous works. Thus, the PTDDM approach can fully link the relationship among time delay and state-related vectors. Based on these ingredients, a novel stochastic stability condition is proposed for MNNs with GHTRs. A numerical example is illustrated to demonstrate the effectiveness of the proposed approaches.

Remote estimation of smart meter operation error considering error fluctuation and nonlinear dynamic line loss

Aiming at the problems of high cost, low efficiency, poor accuracy, and incomplete coverage existing in the on-site calibration operation mode of smart electricity meters, a remote estimation method of operation error of smart electricity meters was proposed by analyzing the big data under the advanced measurement system, which took into account the error fluctuation and nonlinear dynamic line loss. Firstly, the fluctuation of smart meter error was analyzed, and the remote estimation model of smart meter operation error was established by screening the electricity quantity data with similar operation states to reduce the influence of error fluctuation in the subsequent model solving. Secondly, the traditional line loss estimation method is improved, and a nonlinear dynamic line loss model is proposed to make the line loss change nonlinearly with the actual power supply so that the error estimate in the model is closer to the actual value. The results show that the proposed method effectively improves the accuracy of the operation error estimation results of smart electricity meters, helps to detect abnormal measurement points timely and accurately, and provides support for the efficient operation and maintenance of smart power grids.

State of charge estimation for lithium-ion batteries under varying temperature conditions based on adaptive dual extended Kalman filter

Accurate estimation of the state-of-charge (SOC) is of extreme importance for the reliability and safety of lithium-ion battery operation, for prevention of overcharge, deep discharge, and irreversible damage to batteries. Traditional SOC estimation methods do not consider the effect of temperature on estimation, which may lead to significant errors in the SOC estimation. Considering the effect of temperature change on SOC estimation for lithium-ion batteries, this paper presents a SOC estimation method based on adaptive dual extended Kalman filter (ADEKF). First, the radial basis function neural network (RBFNN) and the forgetting factor recursive least square (FFRLS) methods are adopted to characterize the relationship between battery temperature and polarization resistance and capacitance (RC) of the dual-polarization model based on experimental lithium-ion battery data, then the relationship between identified RC parameters and temperature is described by the temperature characteristic function. Secondly, an estimation method based on the ADEKF is proposed to update the RC parameters online. This method could reduce the SOC estimation error caused by the mismatch between the set RC parameter value and the actual RC parameter value due to ambient temperature changes. Finally, the experimental data of federal urban driving schedule (FUDS) at -10°C, 25°C, and 50°C are selected to simulate and verify the SOC estimation method proposed in this paper. The results indicate that, compared with the method not considering the effect of ambient temperature, the method developed in this paper is able to achieve accurate SOC estimation with a smaller root mean square error and mean absolute error in a wide range of temperatures.

Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis

Crypto assets succeeded in making their name known to large masses with Bitcoin, which emerged as a result of the creation of the first genesis block in 2008. Until 2010, the aforementioned recognition showed itself mostly in areas such as games, but over time it managed to enter the portfolios of individual investors. Especially as of end of 2017, the rapid increases in monetary value quickly attracted the attention of corporate companies and then the (Central Banks). These assets have created different alternatives (also know as altcoins) by working and have managed to become one of the important financial instruments today. This study has examined in detail the techniques (Chaos theory, Onchain analysis and Sentiment analysis) developed on the price predictions of crypto assets, which are very important in terms of the number and quality of investors. In the study, findings were obtained that new techniques such as onchain and sentiment are more prominent in estimating crypto asset prices compared to traditional asset price estimation methods of crypto assets and that these techniques can make consistent estimations.

6D object position estimation from 2D images: a literature review

The 6D pose estimation of an object from an image is a central problem in many domains of Computer Vision (CV) and researchers have struggled with this issue for several years. Traditional pose estimation methods (1) leveraged on geometrical approaches, exploiting manually annotated local features, or (2) relied on 2D object representations from different points of view and their comparisons with the original image. The two methods mentioned above are also known as Feature-based and Template-based, respectively. With the diffusion of Deep Learning (DL), new Learning-based strategies have been introduced to achieve the 6D pose estimation, improving traditional methods by involving Convolutional Neural Networks (CNN). This review analyzed techniques belonging to different research fields and classified them into three main categories: Template-based methods, Feature-based methods, and Learning-Based methods. In recent years, the research mainly focused on Learning-based methods, which allow the training of a neural network tailored for a specific task. For this reason, most of the analyzed methods belong to this category, and they have been in turn classified into three sub-categories: Bounding box prediction and Perspective-n-Point (PnP) algorithm-based methods, Classification-based methods, and Regression-based methods. This review aims to provide a general overview of the latest 6D pose recovery methods to underline the pros and cons and highlight the best-performing techniques for each group. The main goal is to supply the readers with helpful guidelines for the implementation of performing applications even under challenging circumstances such as auto-occlusions, symmetries, occlusions between multiple objects, and bad lighting conditions.

Time delay estimation method based on generalized logarithmic hyperbolic secant function in impulsive noise

In this paper, a generalized logarithmic hyperbolic secant (GLHS) function is introduced that can effectively suppress impulsive noise while guarding the signal of interest from damage. Also, an analysis of the optimal scaling parameter choices for the GLHS function was studied. Then, in order to address the performance drawbacks of the traditional time delay estimation methods based on correlation under an impulsive noise environment, a novel GLHS-based correlation (GLHSC) is further developed, and the reliable time delay estimation result is obtained by finding the peak of GLHSC. The comprehensive Monte Carlo simulation results demonstrate that the performance of the method based on GLHSC is better than other robust competitive methods based on correlation in terms of probability of resolution and estimation accuracy, especially in a heavy-tailed noise environment.

Quantitative characterization of methane adsorption in shale using low-field NMR

Quantification of methane content in shales is a critical parameter for estimation of their potential gas production capacity. Traditional gravimetric methods for estimation of this quantity are sensitive only to adsorbed methane and are difficult to apply either to intact shale rock cores or via field measurements. Here non-invasive low-field nuclear magnetic resonance (LF-NMR) is applied to quantify excess methane adsorption capacity in two intact shale rock plugs at pressures up to 150 bar; validation is provided against destructive gravimetric methods performed on fragments from the same shale rock plugs. The resultant NMR transverse relaxation time (T2) distributions contain three distinct peaks (referred to as peaks P1 – P3) which are allocated to adsorbed methane in organic pores, methane constrained to inorganic pores and bulk methane located predominately in fractures, respectively. The area of peak P1 is observed to increase with pressure up to 100 bar, after which it reaches a plateau, whilst the area of peaks P2 and P3 both increase linearly with pressure up to 150 bar. The most accurate estimate of excess methane adsorption capacity is obtained via a combination of an overall system mass balance and the methane located in inorganic pores and fractures (peaks P2 and P3, respectively), where excellent agreement is produced with corresponding gravimetric measurements for both shale samples studied.

Application of Bispectrum Dimensionality Reduction Method in Ultrasonic Echo Signal Processing

Processing ultrasonic echo signals to obtain high-precision residual thickness information of the pipeline wall is the key to nondestructive testing of corrosion of a long-distance pipeline. The traditional power spectrum estimation method assumes that an analyzed echo signal is Gaussian, and the useful information is insufficiently extracted, which leads to errors in the processing results. In this paper, to solve this problem, the bispectrum, which requires the least amount of computation in higher-order spectral estimation, is proposed to process an echo signal with a non-minimum phase and non-Gaussian characteristics. The bispectrum is projected onto a one-dimensional frequency space using the dimensionality reduction method, and one-dimensional diagonal slices of the bispectrum are extracted to analyze the characteristics of the echo signal, which significantly improves the intuitiveness of data processing. The experimental results show that the bispectrum dimensionality reduction method has high accuracy in processing ultrasonic echo signals, and the relative error of the residua wall thickness is below 2%. A C-scan image displaying the shape, size, depth, and other characteristics of pipeline corrosion obtained by the proposed method is much better than that using the traditional power spectrum estimation method. Therefore, the proposed method is suitable for nondestructive testing of corrosion of long-distance pipelines.

Multivariate and Global Particle Swarm Algorithm Optimization in mmWave Massive MIMO for Angle Domain Channel Estimation

Exploitation of massive Multiple-Input Multiple-Output gains for downlink transmission in Millimetre Wave Systems comes at the expense of obtaining accurate channel estimation and computing complexity. Moreover, the fundamental task of massive Multiple-Input Multiple-Output channel estimation is to exploit the characteristics of the channel and sparsity of these multi-antennas systems to simplify complicated spatial structures. We tackle the channel estimation problem in constructing angle domain channel model with multivariate optimization. In the proposed global particle swarm optimization for angle domain aided scheme, the channel estimation design is decoupled into two parts The simulation results are provided to demonstrate the superior performance of the proposed algorithm over the traditional CS-based channel estimation methods.

An DL-based approach for Packet Error Compensation using radio mobile network quality parameters in a rainfall scenario

This study proposes a new approach to guarantee the Quality of Service of mobile radio links via Packet Error Rate (PER) compensation at varying levels of rainfall. Compared to traditional radio channel degradation estimation methods, the proposed study uses a different set of parameters mainly related to the cell selection and handover mechanisms, more appropriate for rainfall scenarios. By means of a new model based on a Convolutional Neural Network (CNN), the proposed technique estimates the PER on two different radio links in such a way as to dynamically adapt the weights of a VPN (Virtual Private Network) Bonding algorithm based on a dual Subscriber Identity Module (SIM) system belonging to two different telephone operators. This guarantees a halved adaptation frequency when compared to traditional cellular bonding methods and avoids saturating the channel band during the estimation of the weights. The experimental results carried out in video surveillance applications for smart road scenarios show that it is possible to dynamically improve the radio link with an enhancement in the quality perceived by the user of about 200%.

Motion pose estimation of inshore ships based on point cloud

This paper proposes a new method to obtain the pose information estimation of the ship at multiple points based on point cloud data. Compared with the traditional pose estimation method, which regards the ship as a point to estimate the pose information of the ship, the proposed algorithm can effectively obtain the pose information of the ship from multiple points. This paper uses the spatial location of numerous key points from point cloud data by studying the spatial structure information of the ship. By tracking the spatial trajectory changes of the key points of the ship, the real-time angle, distance, velocity, and other information of the ship’s multiple points are finally estimated. The algorithm is verified by simulating the point cloud data when the inshore is berthing. The results show that the error of the proposed method is much smaller than that of other existing methods, and the proposed method can provide more accurate and abundant ship pose information. The method presented in this paper is competitive with other existing nearshore attitude estimation methods.data is at: https://drive.google.com/file/d/1LYN_tMrqtncnENzC2E5Fer3DTwL8tXH9/view?usp=sharingcode is at: https://github.com/serendipitylxd/Estimation-of-ship-pose

Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

Gravitational-wave parameter estimation for compact binary signals typically relies on sequential estimation of the properties of the detector Gaussian noise and of the binary parameters. This procedure assumes that the noise variance, expressed through its power spectral density, is perfectly known in advance. We assess the impact of this approximation on the estimated parameters by means of an analysis that simultaneously estimates the noise and compact binary parameters, thus allowing us to marginalize over uncertainty in the noise properties. We compare the traditional sequential estimation method and the new full marginalization method using events from the GWTC-3 catalog. We find that the recovered signals and inferred parameters agree to within their statistical measurement uncertainty. At current detector sensitivities, uncertainty about the noise power spectral density is a subdominant effect compared to other sources of uncertainty.

Multifunctional Estimation and Analysis of Lithium-Ion Battery State Based on Data Model Fusion under Multiple Constraints

To overcome the problems of over-idealised estimation results, low efficiency, and insufficient reliability of traditional battery state estimation methods, this study proposes a multifunctional estimation and analysis model of battery state of charge (SOC), battery capacity, and state of power (SOP), based on data model fusion. First, the data-driven multi-scale extended Kalman filter (MDEKF) was used to de-noise the original data with random errors observed by the sensor in each cycle. The de-noised data were input to a temporal convolutional network (TCN) as training samples, and the estimation model was obtained through TCN neural network machine learning. Furthermore, a peak power estimation method based on multiple constraints was used. The accurate SOC estimation results obtained through the TCN network were used to describe and enhance the relationship between the SOC, voltage, and peak power. Therefore, the proposed method avoids the disadvantage of TCN relying excessively on the accuracy of the training data and retains the advantages of MDEKF efficiency and low cost. The experimental results show that this method can obtain accurate estimations of multi-states of battery. The dangers of over-charging and over-discharging are effectively avoided, and the safety and reliability of lithium-ion batteries are improved.

Distributed state estimation method based on WLS-AKF hybrid algorithm for active distribution networks

Given the difficulty of traditional state estimation methods in meeting the computational efficiency and accuracy requirements of active distribution networks, we propose a distributed state estimation (DSE) method for active distribution networks based on the weighted least squares-adaptive Kalman filter (WLS-AKF) hybrid algorithm. A multi-criteria partition model of an active distribution network that is suitable for DSE has been established. The model comprehensively considers the impact of partitioning results on DSE calculation accuracy and efficiency from the three perspectives of structure, measurement, and performance and solves the model using an improved genetic algorithm. A partition decoupling method is proposed herein, which completely decouples the sub-regions without requiring the measurement configuration of the sub-region boundary nodes and effectively reduces the DSE calculation scale. Furthermore, the paper proposes a DSE algorithm based on WLS-AKF that uses AKF to provide accurate pseudo-measurement of boundary nodes for WLS, which improves the calculation efficiency while ensuring the DSE calculation accuracy. The proposed method is analyzed and verified using the improved IEEE118 node system. The results show that the proposed method has high computational accuracy and efficiency, and can obtain high-precision estimation results in the case of missing data.

An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range

Capacity estimation is essential for battery health management during the whole lifecycle. The data-driven technique has shown advanced performance in battery capacity estimation. However, the strict limitations on application scenarios and the long duration for feature determination are still the bottlenecks of existing data-driven estimation methods. This study proposes a data-driven capacity estimation method only using 10-min relaxation voltage data, which is adaptable to the high state of charge (SOC) range. The experiments of commercial batteries are designed to investigate the coupling relationship between relaxation voltage, battery aging, and charging cut-off SOC. Further, a novel dual Gaussian process regression (GPR) framework is put forward, in which one GPR is responsible for the battery open-circuit voltage (OCV) estimation using the sequential relaxation voltage feature, and another GPR estimates battery capacity with the corresponding relaxation voltage feature and the estimated OCV. Quantitative experimental results demonstrate that the proposed approach can achieve accurate OCV estimation with extremely sparse voltage data. When SOC is larger than 90%, the capacity estimation achieves a mean absolute error of 2.493% over the battery lifecycle, showing a noticeable improvement over the traditional estimation method.

Data-Based Flow Rate Prediction Models for Independent Metering Hydraulic Valve

Accurate valve flow rate prediction is essential for the flow control process of independent metering (IM) hydraulic valve. Traditional estimation methods are difficult to meet the high-precision requirements under the restricted space of the valve. Thus data-based flow rate prediction method for IM valve has been proposed in this study. We took the four-spool IM valve as the research object, and carried out the IM valve experiments to generate labeled data. Picking up the post-valve pressure and valve opening as input, we developed and compared eight different data-based estimation models, including machine learning and deep learning. The results indicated that the SVR and DNN with three hidden layers performed better than others on the whole dataset in the trade-off of overfitting and precision. And MAPE of these two models was close to 4%. This study provides further guidelines on high-precision flow rate prediction of hydraulic valves, and has definite application value for development of digital and intelligent hydraulic systems in construction machinery.

PerUnet: Deep Signal Channel Attention in Unet for WiFi-Based Human Pose Estimation

Traditional image-based pose estimation methods tend to perform poorly in occlusion and darkness. Thus, researchers established sensor-based pose estimation methods such as radio frequency and infrared to resolve the challenges. Nevertheless, traditional sensors remain the weakness of high cost and low flexibility. In this article, we propose the multimodal network PerUnet to establish a WiFi-based human pose estimation network. Benefitting from the powerful multihead attention mechanism and Unet-like architecture, PerUnet fuses fine-grained pose features and contextual information of WiFi channel state information (CSI) to achieve precise pose estimation. Moreover, we propose the attention-based denoising (ABD) method to overcome traditional filters’ disadvantages and help PerUnet extract pose features from CSI. To evaluate the performance of PerUnet, we establish the novel multimodal dataset Wi-Pose composed of images, CSI, and pose annotations. The experimental results demonstrate that PerUnet achieves a competitive performance of WiFi-based pose estimation on Wi-Pose. To further promote the research on WiFi-based human pose estimation, Wi-Pose has been publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NjtechCVLab/Wi-PoseDataset</uri> .

Improving insurers’ loss reserve error prediction: Adopting combined unsupervised-supervised machine learning techniques in risk management

Emerging literature focuses on insurers' earnings management using estimated liability for unpaid claims, known as loss reserve. An insurance company generally uses the traditional estimation methods with linear estimation to measure loss reserve error, but those methods are often criticized for several statistical shortcomings, such as estimation technique, correlated contributing variables, ignorance of the interactions, and higher-order terms. To overcome such shortcomings, this paper proposes an unsupervised-supervised machine learning approach, hierarchical clustering, and artificial neural network (ANN) by adopting a combined unsupervised-supervised method, cluster analysis (i.e., unsupervised), and various supervised machine learning algorithms such as Boostings, Support Vector Machine (SVM) and RReliefF. We show evidence that each cluster has its own foundation variables to predict and Boosting and ANN estimation provide a more efficient framework to improve insurers' reserve error. Also, the different value and order of RReliefF between Boosting and OLS show the under-or over-estimated predictor, and each year's influential variables are found to be consistent over time, which indicates that the firm's previous year's loss reserve model can predict the future loss reserve error. This paper contributes to the existing literature by suggesting a more robust, consistent, and efficient prediction method (i.e., unsupervised-supervised combination method) to improve insurers' loss reserve error prediction.

Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system, and its estimation plays an important role in the performance evaluation of the navigation system. Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution, without considering the influence of the pollution introduced by the GNSS signal, which is susceptible to external interference. To address this problem, a high-precision filter estimation method using Gaussian process regression (GPR) is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator (USQUE) to improve the navigation accuracy. Based on the advantage of the GPR machine learning function, the estimation performance of the sliding window for model training is measured. This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter. The combination of GPR and the USQUE algorithm establishes a robust mechanism framework, which enhances the robustness and stability of traditional methods. The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy, which demonstrates the effectiveness of the proposed method.

A New Strategy for Forest Height Estimation Using Airborne X-Band PolInSAR Data

Because the penetration depth of electromagnetic waves in forests is large in the longer wavelength band, most traditional forest height estimation methods are carried out using polarimetric interferometry synthetic aperture radar (PolInSAR) data of the L or P band, and the estimation method is a three-stage method based on the random volume over ground (RVoG) model. For X-band electromagnetic waves, the penetration depth of radar waves in forests is limited, so the traditional forest height estimation method is no longer applicable. In view of the above problems, in this paper we propose a new forest height estimation strategy for airborne X-band PolInSAR data. Firstly, the sub-view interferometric SAR pairs obtained via frequency segmentation (FS) in the Doppler domain are used to extend the polarimetric interferometry coherence coefficient (PolInCC) range of the original SAR image under different polarization states, so as to obtain the accurate ground phase. For the determination of the effective volume coherence coefficient (VCC), part of the fitting line of the extended-range PolInCC distribution that is intercepted by the fixed extinction coherence coefficient curve (FECCC) of the fixed range is averaged to obtain the accurate effective VCC. Finally, the high-precision forest canopy height in the X-band is estimated using the effective VCC with the ground phase removed in the look-up table (LUT). The effectiveness of the proposed method was verified using airborne-measured data obtained in Shaanxi Province, China. The comparison was carried out using different strategies, in which we substituted one step of the process with the conventional method. The results indicated that our new strategy could reduce the root mean square error (RMSE) of the predicted canopy height vastly to 1.02 m, with a lower estimation height error of 12.86%.