State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Combination of Metal Shielding and Distance Estimation for Electromagnetic Compatibility Guarantee

In this paper, a solution for electromagnetic compatibility guarantee based on the combination of metal shielding and circuit components distance estimation methods is presented. The electromagnetic noises generated from a working radio-electronic unit can expand into the space and act on other around radio-electronic units. An EMC guaranteed radio-electronic unit by the suitable technique method will not cause the electromagnetic noise to others. In opposition, it will not be under electromagnetic action from another one. Due to the power of electromagnetic noise, the metal shielding, distance estimation or other technique methods should be used to guarantee EMC. Every method has own advantage as so as weakness for detail radio-electronic unit, so it is necessary to choose a suitable method to guarantee EMC for them, the combination of metal shielding and distance estimation is a choice, for example. The proposed solution has been evaluated by using CST (Computer Simulation Technology) software and EMxpertEHX analyzer in oscillator circuit context. The simulated results on CST show that the proposed solution decreases the electromagnetic radiation about of 39.1 dB at frequency 500 MHz in comparison to results when nothing electromagnetic compatibility methods are not used. The experimental results on the oscillator circuit are presented. The electromagnetic radiation reduction of the oscillator circuit is about of (25 - 30) dB. In comparison to individual metal shielding and distance estimation methods, the effectiveness of the proposed solution for electromagnetic compatibility guarantee is significantly increased.

IN-SITU DISTANCE RESOLVED OPTICALLY TRANSPARENT THIN-LAYER MICROSCOPIC FTIR SPECTROELECTROCEMISTRY.ESTIMATION OF THE ELECTROCHEMICAL CONCENTRATION-DISTANCE PROFILE

An in-situ optically transparent thin-layer microscopic FTIR spectroelectro- chemical cell was constructed.Using this cell,we characterize a concentration-distance profile in the electrochemical diffusion thin-layer by in-situ adjusting the focal point at different distances to the electrode surface.

Distance Estimation and Material Classification of a Compliant Tactile Sensor Using Vibration Modes and Support Vector Machine

Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.

Leading directions and effective distance of larch offspring dispersal at the upper treeline in the Northern and Polar Urals, Russia

Climate has changed sufficiently over the last 150 years and forced out upper treeline advance at the most studied sites around the world.The rate of advance has been extremely variable–from tens to hundreds meters in altitude.This is because the degree at which tree frontal populations respond to climate change depends on the complex interaction of biological and physical factors.The resulting stand pattern is the consequence of the interaction between dispersal and survival functions.A few publications have addressed the question of how this pattern is generated.In order to understand how the spatial structure of tree stands was formed at the upper limit of their distribution in the Ural Mountains,we assessed the distance and direction of dispersal of offspring from maternal individuals.We found that in frontal Larix sibirica Ledeb.populations,‘effective’dispersal of offspring ranges from 3 to 758 m(with a median of 20–33 m in open forest and 219 m in single-tree tundra in the Polar Urals and 107 m in open forest in the Northern Urals).We revealed that most of the offspring effectively dispersed not only in the direction of the prevailing winds,but also in the opposite direction up the slope,and the distance can reach 500–760 m.The data obtained can be used to develop an individual-based model which is capable of simulating in detail the dynamics of tree stands at the upper limit of their growth and reliably predicting the future position and pattern of treeline ecotone as growth conditions continue to improve in the face of observed climate change.

Asymptotic Normality Distribution of Simulated Minimum Hellinger Distance Estimators for Continuous Models

Certain distributions do not have a closed-form density, but it is simple to draw samples from them. For such distributions, simulated minimum Hellinger distance (SMHD) estimation appears to be useful. Since the method is distance-based, it happens to be naturally robust. This paper is a follow-up to a previous paper where the SMHD estimators were only shown to be consistent;this paper establishes their asymptotic normality. For any parametric family of distributions for which all positive integer moments exist, asymptotic properties for the SMHD method indicate that the variance of the SMHD estimators attains the lower bound for simulation-based estimators, which is based on the inverse of the Fisher information matrix, adjusted by a constant that reflects the loss of efficiency due to simulations. All these features suggest that the SMHD method is applicable in many fields such as finance or actuarial science where we often encounter distributions without closed-form density.

RESEARCH OF THE MEAN COOK DISTANCE AND THE ROBUST ESTIMATE OF SCALE PARAMETER

Based on the principle of statistics and the research results by Zhu Jianjun, the paper establishesthe mean Cook distance of scale parameter and studies its properties. The paper still presents the robust estimate of scale parameter with minimum mean Cook distance and theoretically shows that scale parameter isnot "nuisance parameter" and it determined by the plus weight squared sums of the residuals of observations.A simple numerical example is given.

A plotless density estimator with a Norton-Rice distribution for ordered distances

A Norton-Rice distribution(NRD)is a versatile,flexible distribution for k ordered distances from a random location to the k nearest objects.In a context of plotless density estimation(PDE)with n randomly chosen sample locations,and distances measured to the k=6 nearest objects,the NRD provided a good fit to distance data from seven populations with a census of forest tree stem locations.More importantly,the three parameters of a NRD followed a simple trend with the order(1,…,6)of observed distances.The trend is quantified and exploited in a proposed new PDE through a joint maximum likelihood estimation of the NRD parameters expressed as a functions of distance order.In simulated probability sampling from the seven populations,the proposed PDE had the lowest overall bias with a good performance potential when compared to three alternative PDEs.However,absolute bias increased by 0.8 percentage points when sample size decreased from 20 to 10.In terms of root mean squared error(RMSE),the new proposed estimator was at par with an estimator published in Ecology when this study was wrapping up,but otherwise superior to the remaining two investigated PDEs.Coverage of nominal 95%confidence intervals averaged 0.94 for the new proposed estimators and 0.90,0.96,and 0.90 for the comparison PDEs.Despite tangible improvements in PDEs over the last decades,a globally least biased PDE remains elusive.

State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

A Fractal Compression Algorithm Based on Collage Distance Estimating and Compensating

A new fractal image compression scheme based on collage distance is explained, which can provide of the scheme two different compression codes containing their own unique collage distance sets and different types of similar transforms. The analysis and applications of the scheme indicate that it can be adopted as a suitable alternative for balancing between the distortion measure and compression ratio occurring in ordinary fractal compression method.

Diagnostics in generalized nonlinear models based on maximum L_q-likelihood estimation

In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood estimation is considered. Three diagnostic statistics are used to detect whether the outliers exist in the data set. Simulation results show that when the sample size is small, the values of diagnostic statistics based on the maximum Lq-likelihood estimation are greater than the values based on the maximum likelihood estimation. As the sample size increases, the difference between the values of the diagnostic statistics based on two estimation methods diminishes gradually. It means that the outliers can be distinguished easier through the maximum Lq-likelihood method than those through the maximum likelihood estimation method.

Method to reconnoiter pulse amplitude train for phased array radar based on Hausdorff distance

This paper presents a method to estimate beam pointing of phased array radar by the pulse amplitude train, which is significant in radar electronic reconnaissance and electronic support measure. Firstly, the antenna patterns modeling of the phased array system is exploited to build the radar sweeping model and the signal propagation model. Secondly, the relationship between the variation of the radiated power and the antenna beam pointing angles in the given airspace is analyzed. Based on the above two points, the sample with obvious amplitude characteristics of the pulse amplitude train can be screened out after detecting the train peaks. Finally, the sample is matched to the subsequent pulse amplitude train based on the Hausdorff distance. The proposed methods have less prior knowledge and higher efficiency and are easier to process. By cross correlating the sample of the pulse amplitude train with the sample data of the antenna follow-up radiation, the probability of detection of the beam pointing direction becomes larger in case that the subsequent antenna beam returns to the specific position.

Outlier Detection Based on Robust Mahalanobis Distance and Its Application

Classical Mahalanobis distance is used as a method of detecting outliers, and is affected by outliers. Some robust Mahalanobis distance is proposed via the fast MCD estimator. However, the bias of the MCD estimator increases significantly as the dimension increases. In this paper, we propose the improved Mahalanobis distance based on a more robust Rocke estimator under high-dimensional data. The results of numerical simulation and empirical analysis show that our proposed method can better detect the outliers in the data than the above two methods when there are outliers in the data and the dimensions of data are very high.

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

From Nonparametric Density Estimation to Parametric Estimation of Multidimensional Diffusion Processes

The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process. Under this condition, we define an estimator of the density based on kernel function and study his properties (almost sure convergence and asymptotic normality). After, using the estimator of the density, we construct the minimum Hellinger distance estimator of the parameters of the diffusion process and establish the almost sure convergence and the asymptotic normality of this estimator. To illustrate the properties of the estimator of the parameters, we apply the method to two examples of multidimensional diffusion processes.

Hellinger distance based probability distribution approach to performance monitoring of nonlinear control systems

Control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.

Studies on the High Resolution DOA Estimation for UUV Based on Synthetic Aperture Technique

To increase the limited spatial processing gain of physical aperture of UUV(unmanned underwater vehicle) linear array and satisfy the demand of long distance target detection,a flank array based on the synthetic aperture technique is introduced into UUV,and a modified beam domain passive synthetic aperture processing algorithm(BDPSA) suitable for the flank array is proposed concurrently,which sums the beamforming of linear array coherently for successive measurement after phase compensation to make the beam output peak corresponding to the expected target bearing,expand the array aperture effectively and improve the resolution.The simulation of detection probability and distinguishing probability for double targets within 1,1/2,1/3 and 1/4 beam-width shows that the method of BDPSA has lower SNR threshold for target distinguishing,improves the detection probability and distinguishing probability under low SNR,and realizes the long-distance and high resolution bearing estimation because of the obvious improvement of the spatial array gain.

THE MEASURE OF MEAN COOK DISTANCE UNDER THE CONTAMINATED ERROR MODEL

Redefines the mean Cook distance under the contaminated error model. With the help of the mean Cook distance, the paper discusses the relationship between local influence of small perturbations and high leverage case. A new robust method for the adjustment of geodetic networks is proposed. The suggested method, a generalization of the robust method with the minimum mean Cook distance, is more efficient than the others. The basic feature of the method is that the equivalent weight functions of the robust estimates are determined according to the principle of statistics.

Institutional Distance and CrossNational Income Gaps

ihis paper examines the root causes of current cross-national institutional difference from the perspective of national hetereogeneity and provides detailed explanations on the justification and effectiveness of using cross-national genetic distance as the instrumental variable （IV） of institutional difference. We combined 10,585 samples by pairing 146 countries and regions, created a cross-national institutional distance variable composed of 14 indicators from the World Bank and the Heritage Foundation in three aspects including differences of political system, economic system and institutional implementation attributes, and conducted a cross-section IV estimation for the long-term effect of institutional differences on cross-national income gaps using data between 1996 and 2010. Empirical results indicate that institutional difference has a long-term significant positive effect on cross-national income gaps and such an effect has a tendency to increase during sample period. With factors like human capital, geographical factor, language and religion under control, we still arrived at similar conclusions. The empirical results are demonstrated to be robust using different genetic distance measurement indicators and estimation methods.

Combining Self-Organizing Map and Lipschitz Condition for Estimation in Direction of Arrival

There are many DOA estimation methods based on different signal features, and these methods are often evaluated by experimental results, but lack the necessary theoretical basis. Therefore, a direction of arrival (DOA) estimation system based on self-organizing map (SOM) and designed for arbitrarily distributed sensor array is proposed. The essential principle of this method is that the map from distance difference of arrival (DDOA) to DOA is Lipschitz continuity, it indicates the similar topology between them, and thus Kohonen SOM is a suitable network to classify DOA through DDOA. The simulation results show that the DOA estimation errors are less than 1° for most signals between 0° to 180°. Compared to MUSIC, Root-MUSIC, ESPRIT, and RBF, the errors of signals under signal-to-noise ratios (SNR) declines from 20 dB to 2 dB are robust, SOM is better than RBF and almost close to MUSIC. Further, the network can be trained in advance, which makes it possible to be implemented in real-time.