A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Method for determining the position of landslide slip-surface with a typical inclinometric curves

In the actual monitoring of deep hole displacement,the identification of slip surfaces is primarily based on abrupt changes observed in the inclinometric curve.In conventional identification methods,inclinometric curves exhibiting indications of sliding can be categorized into three types:B-type,D-type,and r-type.The position of the slip surface is typically determined by identifying the depth corresponding to the point of maximum displacement mutation.However,this method is sensitive to the interval of measurement points and the observation scale of the coordinate axes and suffers from unclear sliding surfaces and uncertain values.Based on the variation characteristics of these diagonal curves,we classified the landslide into three components:the sliding body,the sliding interval,and the immobile body.Moreover,three different generalization models were established to analyze the relationships between the curve form and the slip surface location based on different physical indicators such as displacement rate,relative displacement,and acceleration.The results show that the displacement rate curves of an r-type slope exhibit a clustering feature in the sliding interval,and by solving for the depth of discrete points within the step phase,it is possible to determine the location of the slip surface.On the other hand,D-type slopes have inflection points in the relative displacement curve located at the slip surface.The acceleration curves of B-type slopes exhibit clustering characteristics during the sliding interval,while the scattered acceleration data demonstrate wandering characteristics.Consequently,the slip surface location can be revealed by solving the depth corresponding to the maximum acceleration with cubic spline interpolation.The approach proposed in this paper was applied to the monitoring data of a landslide in Yunnan Province,China.The results indicate that our approach can accurately identify the slip surface location and enable computability of its position,thereby enhancing applicability and reliability of the deep-hole displacement monitoring data.

Analytical Determination of Size and Location of Roadside Horizontal Sightline Offsets for Compound Curves

AASHTO’s guideline for geometric design of highways and similar guidelines require that roadside areas on the inside of horizontal curves be cleared of high objects to provide stopping sight distance. The guidelines have analytical models for determining the extent of clearance, known as the horizontal sightline offset or clearance offset, for simple curves. Researchers in the past have developed analytical models for clearance offsets for spiraled and reverse curves. Very few researchers developed analytical models for available sight distances for compound curves. Still missing are models for horizontal sightline offsets and locations of the offsets for compound curves. The objective of this paper is to present development of analytical models and charts for determining horizontal sightline offsets and their locations for compound curves. The paper considers curves whose component arcs are individually shorter than stopping sight distance. The resulting models and the charts have been verified with accurate values determined using graphical methods. The models and the charts will find application in geometric design of highway compound curves.

Using Pearson’s System of Curves to Approximate the Distributions of the Difference between Two Correlated Estimates of Signal-to-Noise Ratios: The Cases of Bivariate Normal and Bivariate Lognormal Distributions

Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference between two correlated SNRs when the readings are from bivariate normal and bivariate lognormal distribution. We use the Pearsons system of curves to approximate the difference between the two estimates and use the bootstrap methods to validate the approximate distributions of the statistic of interest. Methods: The paper uses the delta method to find the first four central moments, and hence the skewness and kurtosis which are important in the determination of the parameters of the Pearsons distribution. Results: The approach is illustrated in two examples;one from veterinary microbiology and food safety data and the other on data from clinical medicine. We derived the four central moments of the target statistics, together with the bootstrap method to evaluate the parameters of Pearsons distribution. The fitted Pearsons curves of Types I and II were recommended based on the available data. The R-codes are also provided to be readily used by the readers.

Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy violations.To address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed recently.However,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many attacks.As such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)operations.The formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably secure.The proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and unlinkability.Moreover,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and impersonations.From the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security features.Therefore,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

Sensitive Information Security Based on Elliptic Curves

The elliptic curve cryptography algorithm represents a major advancement in the field of computer security. This innovative algorithm uses elliptic curves to encrypt and secure data, providing an exceptional level of security while optimizing the efficiency of computer resources. This study focuses on how elliptic curves cryptography helps to protect sensitive data. Text is encrypted using the elliptic curve technique because it provides great security with a smaller key on devices with limited resources, such as mobile phones. The elliptic curves cryptography of this study is better than using a 256-bit RSA key. To achieve equivalent protection by using the elliptic curves cryptography, several Python libraries such as cryptography, pycryptodome, pyQt5, secp256k1, etc. were used. These technologies are used to develop a software based on elliptic curves. If built, the software helps to encrypt and decrypt data such as a text messages and it offers the authentication for the communication.

Review on Service Curves of Typical Scheduling Algorithms

In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the quality-of-service(QoS)requirements of different network services.Concurrently,network calculus has found widespread application in network modeling and QoS analysis.Network calculus abstracts the details of how nodes or networks process data packets using the concept of service curves.This paper summarizes the service curves for typical scheduling algorithms,including Strict Priority(SP),Round Robin(RR),Cycling Queuing and Forwarding(CQF),Time Aware Shaper(TAS),Credit Based Shaper(CBS),and Asynchronous Traffic Shaper(ATS).It introduces the theory of network calculus and then provides an overview of various scheduling algorithms and their associated service curves.The delay bound analysis for different scheduling algorithms in specific scenarios is also conducted for more insights.

The Effectiveness of the Havriliak-Negami Model in Predicting the Master Curves of the Asphalt Blends with SBS Triblock Copolymer and Organic-Montmorillonite at Different Temperatures and Frequencies

The dynamic viscoelastic properties of asphalt AC-20 and its composites with Organic-Montmorillonite clay (OMMt) and SBS were modeled using the empirical Havriliak-Negami (HN) model, based on linear viscoelastic theory (LVE). The HN parameters, α, β, G0, G∞and τHN were determined by solving the HN equation across various temperatures and frequencies. The HN model successfully predicted the rheological behavior of the asphalt and its blends within the temperature range of 25˚C - 40˚C. However, deviations occurred between 40˚C - 75˚C, where the glass transition temperature Tg of the asphalt components and the SBS polymer are located, rendering the HN model ineffective for predicting the dynamic viscoelastic properties of composites containing OMMt under these conditions. Yet, the prediction error of the HN model dropped to 2.28% - 2.81% for asphalt and its mixtures at 100˚C, a temperature exceeding the Tg values of both polymer and asphalt, where the mixtures exhibited a liquid-like behavior. The exponent α and the relaxation time increased with temperature across all systems. Incorporating OMMt clay into the asphalt blends significantly enhanced the relaxation dynamics of the resulting composites.

Gaussian Distance Weighted Algorithm for Geometric Characteristics of Three-Dimensional Discrete Curves

Discrete curves are composed of a set of ordered discrete points distributed at the intersection of the scanning plane and the surface of the object. In order to accurately calculate the geometric characteristics of any point on the discrete curve, a distance-based Gaussian weighted algorithm is proposed to estimate the geometric characteristics of three-dimensional space discrete curves. According to the definition of discrete derivatives, the algorithm fully considers the relative position difference between a specific point and its neighboring points, introduces the distance weighting idea, and integrates the smoothing strategy. The experiment uses two spatial discrete curves for uniform and non-uniform sampling, and compares them with two commonly used estimation algorithms. The comparative analysis is carried out in terms of sampling density, neighborhood radius and noise resistance. The experimental results show that the Gaussian distance weighted algorithm is effective and provides an efficient algorithm for underground pipeline safety detection.

Magnetic Field Curves and Magnetic Equipotential Surfaces around Crossing Electrical Wires Replacing Classical Magnetic Field Lines

This article is based on a recent model specifically defining magnetic field values around electrical wires. With this model, calculations of field around parallel wires were obtained. Now, this model is extended with the new concept of magnetic equipotential surface to magnetic field curves around crossing wires. Cases of single, double, and triple wires are described. Subsequent article will be conducted for more general scenarios where wires are neither infinite nor parallel.

The Origin of the Flat Rotation Curves in Spiral Galaxies: The Hidden Roles of Glitching SMDEOs and Emission of Gravitational Waves

Supermassive DEOs (SMDEOs) are cosmologically evolved objects made of irreducible incompressible supranuclear dense superfluids: The state we consider to govern the matter inside the cores of massive neutron stars. These cores are practically trapped in false vacua, rendering their detection by outside observers impossible. Based on massive parallel computations and theoretical investigations, we show that SMDEOs at the centres of spiral galaxies that are surrounded by massive rotating torii of normal matter may serve as powerful sources for gravitational waves carrying away roughly 1042 erg/s. Due to the extensive cooling by GWs, the SMDEO-Torus systems undergo glitching, through which both rotational and gravitational energies are abruptly ejected into the ambient media, during which the topologies of the embedding spacetimes change from curved into flatter ones, thereby triggering a burst gravitational energy of order 1059 erg. Also, the effects of glitches found to alter the force balance of objects in the Lagrangian-L1 region between the central SMDEO-Torus system and the bulge, enforcing the enclosed objects to develop violent motions, that may explain the origin of the rotational curve irregularities observed in the innermost part of spiral galaxies. Our study shows that the generated GWs at the centres of galaxies, which traverse billions of objects during their outward propagations throughout the entire galaxy, lose energy due to repeatedly squeezing and stretching the objects. Here, we find that these interactions may serve as damping processes that give rise to the formation of collective forces f∝m(r)/r, that point outward, endowing the objects with the observed flat rotation curves. Our approach predicts a correlation between the baryonic mass and the rotation velocities in galaxies, which is in line with the Tully-Fisher relation. The here-presented self-consistent approach explains nicely the observed rotation curves without invoking dark matter or modifying Newtonian gravitation in the low-field approximation.

Characterization of the generalized permeability jail in tight reservoirs by analyzing relative-permeability curves and numerical simulation

This study comprehensively characterizes the boundary values of generalized permeability jail in tight reservoirs through relative-permeability curve analysis,numerical simulation,and economic evaluation.A total number of 108 relative-permeability curves of rock samples from tight reservoirs were obtained,and the characteristics of relative-permeability curves were analyzed.The irreducible water saturation(Swi)mainly ranges from 20% to 70%,and the residual gas saturation(Sgr)ranges from 5% to 15% for 55% of the samples.The relative-permeability curves are categorized into six types(Category-Ⅰ to Ⅵ)by analyzing the following characteristics:The relative permeability of gas at Swi,the relative permeability of water at Sgr,and the relative permeability corresponding to the isotonic point.The relative permeability curves were normalized to facilitate numerical simulation and evaluate the impact of different types of curves on production performance.The results of simulation show significant difference in production performance for different types of relative-permeability curves:Category-Ⅰ corresponds to the case with best well performance,whereas Categories-Ⅴ and Ⅵ correspond to the cases with least production volume.The results of economic evaluation show a generalized permeability jail for Categories-Ⅳ,Ⅴ,and Ⅵ,and the permeability jail develops when the relative permeability of gas and water is below 0.06.This study further quantifies the range of micro-pore parameters corresponding to the generalized permeability jail for a tight sandstone reservoir.

A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

Parametric curves such as Bézier and B-splines, originally developedfor the design of automobile bodies, are now also used in image processing andcomputer vision. For example, reconstructing an object shape in an image,including different translations, scales, and orientations, can be performedusing these parametric curves. For this, Bézier and B-spline curves can be generatedusing a point set that belongs to the outer boundary of the object. Theresulting object shape can be used in computer vision fields, such as searchingand segmentation methods and training machine learning algorithms. Theprerequisite for reconstructing the shape with parametric curves is to obtainsequentially the points in the point set. In this study, a novel algorithm hasbeen developed that sequentially obtains the pixel locations constituting theouter boundary of the object. The proposed algorithm, unlike the methods inthe literature, is implemented using a filter containing weights and an outercircle surrounding the object. In a binary format image, the starting point ofthe tracing is determined using the outer circle, and the next tracing movementand the pixel to be labeled as the boundary point is found by the filter weights.Then, control points that define the curve shape are selected by reducing thenumber of sequential points. Thus, the Bézier and B-spline curve equationsdescribing the shape are obtained using these points. In addition, differenttranslations, scales, and rotations of the object shape are easily provided bychanging the positions of the control points. It has also been shown that themissing part of the object can be completed thanks to the parametric curves.

Investigation of magnetization reversal and domain structures in perpendicular synthetic antiferromagnets by first-order reversal curves and magneto-optical Kerr effect

Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)3are fabricated by magnetron sputtering technology. We study the domain structure and switching field distribution in p-SAF by changing the thickness of the infrared space layer. The strongest exchange coupling field(Hex) is observed when the thickness of Ir layer(tIr) is 0.7 nm and becoming weak according to the Ruderman–Kittel–Kasuya–Yosida-type coupling at 1.05 nm,2.1 nm, 4.55 nm, and 4.9 nm in sequence. Furthermore, the domain switching process between the upper Co/Ni stack and the bottom Co/Ni stack is different because of the antiferromagnet coupling. Compared with ferromagnet coupling films, the antiferromagnet samples possess three irreversible reversal regions in the first-order reversal curve distribution.With tIrincreasing, these irreversible reversal regions become denser and smaller. The results from this study will help us understand the details of the magnetization reversal process in the p-SAF.

Quality influencing factors of dispersion curves from short period dense arrays based on a convolutional neural network across the north section of the Xiaojiang fault area

The number of dispersion curves increases significantly when the scale of a short-period dense array increases.Owing to a substantial increase in data volume,it is important to quickly evaluate dispersion curve quality as well as select the available dispersion curve.Accordingly,this study quantitatively evaluated dispersion curve quality by training a convolutional neural network model for ambient noise tomography using a short-period dense array.The model can select high-quality dispersion curves that exhibit a≤10%difference between the results of manual screening and the proposed model.In addition,this study established a dispersion curve loss function by analyzing the quality of the dispersion curve and the corresponding influencing factors,thereby estimating the number of available dispersion curves for the existing observation systems.Furthermore,a Monte Carlo simulation experiment is used to illustrates the station-pair interval distance probability density function,which is independent of station number in the observational system with randomly deployed stations.The results suggested that the straight-line length should exceed 15 km to ensure that loss rate of dispersion curves remains<0.5,while maintaining the threshold ambient noise tomography accuracy within the study area.

考虑碳流约束的电力系统能碳安全域模型与计算方法

针对当前电力系统碳减排任务与安全稳定运行需求,研究电力系统低碳安全运行技术。现有的能量流安全域模型使电力系统安全监视与控制更加科学有效,但忽略了系统运行过程中可能存在的“高碳”风险。文中提出了一种考虑碳流约束的电力系统能碳耦合安全域(简称能碳安全域)模型与计算方法,旨在保证系统安全性的同时减排提效。为完整刻画电力系统的低碳运行能力极限,分别从负荷端和源端角度提出了考虑碳流约束的系统最大供电能力曲线及最大消纳能力曲线模型。基于系统最大供电能力工作点,提出了能碳安全边界仿真拟合计算方法,实现了能碳安全域的降维观测。最后,结合算例验证了所提模型的有效性。

物理化学效应对膨胀土收缩特性的影响机制

膨胀土由于其骨架带有较多的固定负电荷,层间存在与负电荷平衡的可交换阳离子,使得土体呈现较强的胀缩性。研究结果表明,膨胀土的胀缩性会受到孔隙溶液化学成分的影响。选用广西地区的强膨胀土作为研究对象,开展了不同浓度的NaCl溶液对膨胀土土-水特征曲线和收缩曲线影响的试验研究,引入了粒间应力的概念对收缩曲线进行描述,该粒间应力考虑了渗透、毛细和吸附的影响。结果表明:孔隙盐溶液是通过渗透吸力对土-水特征曲线产生影响,对基质吸力的影响较小。土样在脱湿过程中的收缩变形是由粒间应力来控制的,类似于加压固结现象。大部分的收缩都发生在毛细阶段,为弹塑性变形;吸附阶段的收缩较少,为弹性变形。通过识别压缩曲线上的弹塑性分界点可以得出毛细和吸附作用的分界点,该分界点与独立测量的不同密实度下的持水曲线结果一致。结果表明,粒间应力能够更好地描述膨胀土的化学-力学行为,特别是在低含水率条件下。

近断层脉冲型地震动下调谐黏滞质量阻尼器减震结构易损性分析

选择典型脉冲型近断层地震动记录分别对一安装有调谐黏滞质量阻尼器(tuned viscous mass damper,TVMD)、黏滞阻尼器(viscous damper, VD)和无阻尼器的6层3跨钢框架结构进行增量动力分析,进而基于增量动力分析结果得到3种结构不同损伤状态的易损性曲线。易损性分析结果对比表明:随着惯容单元的引入,减震结构可靠性显著提升;相较于相同阻尼的VD,TVMD可以更有效地提升结构的抗震性能,减小结构在不同损伤状态下的失效概率。随着质量比的增加,TVMD减震结构在不同损伤状态下的失效概率减小,TVMD对结构性能状态的提升不会随非线性的增加而减小,且在结构经历弹性、弹塑性直至倒塌的整个过程中减震控制性能发挥稳定。进一步选取3组不同脉冲周期的近断层地震动记录对三种结构进行时程分析,讨论TVMD在不同脉冲周期近断层地震动下的减震控制效果。结果表明,在近断层脉冲型地震动作用下,TVMD并不都会实现阻尼单元“阻尼增效”,阻尼器的性能发挥与地震动的特性有关。相较于VD,当T_1/T_p≥2时,TVMD可以更好地减少结构能量输入但其能量耗散效率低于VD,不能实现“阻尼增效”;当T_1/T_p <2时,TVMD可以减小结构残余位移,具有更优的减少能量输入和增大能量耗散效率的双重效应,体现了惯容的“阻尼增效”。

布鲁氏菌病患者外周血白细胞及血小板参数的变化及临床意义

目的 研究布鲁氏菌病患者外周血白细胞及血小板参数的变化及其特点,探讨其临床应用价值。方法 纳入确诊布鲁氏菌病的患者90例,其中急性期59例,慢性期31例,另外选取同期40例健康人群为健康对照组。收集患者的临床症状等基本资料;采用全自动血液分析仪检测各组外周血白细胞计数、淋巴细胞计数、中性粒细胞计数、单核细胞计数、血小板计数、平均血小板体积(MPV)、血小板分布宽度(PDW);采用流式细胞仪检测外周血CD3^(+)T、CD4^(+)T及CD8^(+)T细胞计数,计算患者的中性粒细胞/淋巴细胞比值(NLR)及血小板/淋巴细胞比值(PLR),分析各细胞参数的变化特点并分析各参数对布鲁氏菌病的临床诊断价值。结果 慢性期组的关节痛和脊柱炎占比高于急性期组(P<0.05);与健康对照组相比,急性期组的白细胞计数、中性粒细胞计数、血小板计数及NLR均降低,慢性期组MPV和PDW降低,而CD3^(+)T细胞计数和单核细胞计数升高(P<0.05)。有关节炎症状的急性期布病患者白细胞计数、淋巴细胞计数、CD3^(+)T细胞计数和CD8^(+)T细胞计数低于无关节炎的急性期布病患者,而PLR则升高(P<0.05)。受试者工作特征(ROC)曲线分析结果显示中性粒细胞计数和NLR水平对于急性期布鲁氏菌病的ROC曲线下面积(AUC)分别为0.780和0.717,而CD3^(+)T细胞计数对于诊断慢性期布鲁氏菌病的AUC为0.748。在急性期患者中,CD3^(+)T、CD8^(+)T细胞计数及PLR水平对诊断关节炎的AUC分别为0.779、0.768、0.722。结论 急性期和慢性期布鲁氏菌病患者外周血白细胞和血小板参数均有不同程度的改变,对于布鲁氏菌病的诊断以及急性期布病患者关节炎的诊断有一定的临床应用价值。

反复荷载下圆钢管型钢再生混凝土组合柱恢复力模型研究

为建立圆钢管型钢再生混凝土组合柱的恢复力模型,对11根圆钢管型钢再生混凝土组合柱试件进行了低周反复荷载试验研究,考虑了再生骨料取代率、配钢率及钢管径厚比等不同设计参数的影响,分析了组合柱的地震破坏形态及滞回性能。基于组合柱的力学特征及曲线形状,提出了圆钢管型钢再生混凝土组合柱骨架曲线的三折线参数模型,采用理论推导与数据拟合的方法确定了组合柱骨架曲线的模型参数。在此基础上,给出了组合柱的滞回规则和卸载规律,构建了组合柱的恢复力模型,计算滞回曲线与试验滞回曲线吻合良好,表明该恢复力模型较好地反映了反复荷载下组合柱的受力特征点及滞回性能,可为此类组合柱的推广提供技术参考。