Aging Characteristics of Lithium-Ion Battery Under Fast Charging Based on Electrochemical-thermalmechanical Coupling Model

The aging characteristics of lithium-ion battery(LIB)under fast charging is investigated based on an electrochemical-thermal-mechanical(ETM)coupling model.Firstly,the ETM coupling model is established by COMSOL Multiphysics.Subsequently,a long cycle test was conducted to explore the aging characteristics of LIB.Specifically,the effects of charging(C)rate and cycle number on battery aging are analyzed in terms of nonuniform distribution of solid electrolyte interface(SEI),SEI formation,thermal stability and stress characteristics.The results indicate that the increases in C rate and cycling led to an increase in the degree of nonuniform distribution of SEI,and thus a consequent increase in the capacity loss due to the SEI formation.Meanwhile,the increases in C rate and cycle number also led to an increase in the heat generation and a decrease in the heat dissipation rate of the battery,respectively,which result in a decrease in the thermal stability of the electrode materials.In addition,the von Mises stress of the positive electrode material is higher than that of the negative electrode material as the cycling proceeds,with the positive electrode material exhibiting tensile deformation and the negative electrode material exhibiting compressive deformation.The available lithium ion concentration of the positive electrode is lower than that of the negative electrode,proving that the tensile-type fracture occurring in the positive material under long cycling dominated the capacity loss process.The aforementioned studies are helpful for researchers to further explore the aging behavior of LIB under fast charging and take corresponding preventive measures.

基于Fast R-CNN的动态分区多轿厢电梯调度研究

为了在有限的空间内有效提高垂直交通系统的运载效率,在原有的一条井道内安装多个电梯轿厢,即"一井多梯"或称多轿厢电梯应运而生,它是提高运行效率、解决垂直交通拥挤的最佳选择,也是全世界垂直交通运输领域研究的前沿问题。针对多轿厢电梯的调度问题,笔者提出了一种基于Fast R-CNN的动态分区多轿厢电梯调度方法,首先通过FastR-CNN模型检测厅前和轿厢内人数;然后运用检测结果进行合理派梯;最后根据派梯任务划分轿厢的运行区域,实现合理调度。通过实验仿真表明,该方法适用于电梯的各种交通模式,具有较高的运行效率和灵活性。

基于改进Faster R-CNN的行人检测算法

针对行人检测中复杂环境,提出一种改进Faster R-CNN的行人检测算法,使用深度卷积网络从图片中提取适合检测目标的特征。基于Faster R-CNN算法,以Soft-NMS算法代替传统NMS算法,加强Faster R-CNN算法对重叠区域的识别能力。同时,算法通过"Hot Anchors"代替均匀采样的锚点避免大量额外计算,提高检测效率。最后,将21分类问题的Faster R-CNN框架,修改成适用于行人检测的2分类检测框架。实验结果表明:改进Faster R-CNN的行人检测算法在VOC 2007行人数据集,检测效率和准确率分别提升33%、2.6%。

Fast R-CNN深度学习和无人机遥感相结合在松材线虫病监测中的初步应用研究

松材线虫病因其破坏性强、传播速度快和防治难度大等特点,严重威胁着我国的松林资源。及时发现、定位和清理病死松树是控制松材线虫病蔓延的有效手段。本研究利用小型无人机获得松材线虫病疫点的可见光和多光谱的航摄影像。根据松树针叶颜色变化,将松材线虫Bursaphelenchus xylophilus侵染的松树分为病树和枯死树两种类型。将无人机遥感正摄影像图切割成瓦片图,根据不同植被指数的特征差异,筛选出含病树和枯死树的瓦片图。训练Fast R-CNN深度学习框架形成最终模型,通过模型运算获得病枯死松树的分布地图及坐标点位置。研究结果显示Fast R-CNN深度学习和无人机遥感相结合能有效识别出病树和枯死树,正确率分别达到90%和82%,漏检率分别为23%和34%,可为大面积监测松材线虫病的发生现状和流行动态、评估防控效果和灾害损失提供技术支撑。

基于Fast R-CNN网络的雾霾天人车防碰撞研究

由于雾霾天气对车辆视线的干扰,容易导致交通事故的发生。基于Fast R-CNN网络的雾霾天人车防碰撞系统,提出了一种基于深度学习的防碰撞方法。首先,采用深度学习算法对人车进行实时目标检测,以便及时发现前方车辆和障碍物。其次,对检测到的障碍物进行分类和跟踪,以便对其进行有效避让。最后,通过实验验证了提出的方法在雾霾天气下的有效性和可行性。实验结果表明,该方法可以有效提高人车在雾霾天气下的行驶安全性和稳定性,避免碰撞事故的发生。

基于Faster R-CNN算法的船舶识别检测

目前,检测卫星图像中船舶的常用方法如合成孔径雷达(synthetic-aperture radar,SAR)对多目标仍难以达到精确检测,而更快速的区域卷积神经网络(faster region-based convolutional neural network,Faster R-CNN)算法是一种深度学习算法,用于物体检测和分类时,可以实现高精度实时监测。文章应用Faster R-CNN算法对卫星图像中的船舶进行识别和检测,并与传统尺度不变特征转换(scale-invariant feature transform,SIFT)算法、快速区域卷积神经网络(fast region-based convolutional neural network,Fast R-CNN)算法进行对比。研究结果表明,Faster R-CNN算法比传统SIFT算法和Fast R-CNN算法有更好的收敛速度和识别精度,该算法在船舶识别方面具有较大潜力。

A Fast Version of LASG/IAP Climate System Model and Its 1000-year Control Integration

A fast version of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASG）/Institute of Atmospheric Physics （IAP） climate system model is briefly documented. The fast coupled model employs a low resolution version of the atmospheric component Grid Atmospheric Model of IAP/LASG （GAMIL）, with the other parts of the model, namely an oceanic component LASG/IAP Climate Ocean Model （LICOM）, land component Common Land Model （CLM）, and sea ice component from National Center for Atmospheric Research Community Climate System Model （NCAR CCSM2）, as the same as in the standard version of LASG/IAP Flexible Global Ocean Atmosphere Land System model （FGOALS_g）. The parameterizations of physical and dynamical processes of the atmospheric component in the fast version are identical to the standard version, although some parameter values are different. However, by virtue of reduced horizontal resolution and increased time-step of the most time-consuming atmospheric component, it runs faster by a factor of 3 and can serve as a useful tool for longterm and large-ensemble integrations. A 1000-year control simulation of the present-day climate has been completed without flux adjustments. The final 600 years of this simulation has virtually no trends in global mean sea surface temperatures and is recommended for internal variability studies. Several aspects of the control simulation＇s mean climate and variability are evaluated against the observational or reanalysis data. The strengths and weaknesses of the control simulation are evaluated. The mean atmospheric circulation is well simulated, except in high latitudes. The Asian-Australian monsoonal meridional cell shows realistic features, however, an artificial rainfall center is located to the eastern periphery of the Tibetan Plateau persists throughout the year. The mean bias of SST resembles that of the standard version, appearing as a ＂double ITCZ＂ （Inter-Tropical Convergence Zone） associated with a westward extension of the equatorial eastern Pacific cold tongue. The sea ice extent is acceptable but has a higher concentration. The strength of Atlantic meridional overturning is 27.5 Sv. Evidence from the 600-year simulation suggests a modulation of internal variability on ENSO frequency, since both regular and irregular oscillations of ENSO are found during the different time periods of the long-term simulation.

基于Kano-FAST的瓦楞纸激光印刷设备设计研究

目的推动印刷行业朝更高效的方向迈进,提高印刷设备的易操作性和视觉识别性。方法将Kano和FAST(Function Analysis System Technique)模型引入瓦楞纸激光打印设备设计的前期应用需求分析中,通过问卷的方法获取用户的基本要求,并划分为几个子类型,进而建立Kano的二维功能属性模型。采用FAST法建立功能树,辅助使用Kano模型,从而更精准地分析用户需求,并更好地根据其需求进行优化设计。结果综合运用设计原理,针对性地挖掘瓦楞纸激光印刷设备在造型识别性、操作易用性、生产安全性上存在的问题,进而输出更优解。结论该设计方法的引入有助于为同类型的印刷设备设计提供参考,并引起更多相关厂家的重视,推动印刷行业向更积极的方向发展。

A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

This study aims to detect and prevent greening disease in citrus trees using a deep neural network.The process of collecting data on citrus greening disease is very difficult because the vector pests are too small.In this paper,since the amount of data collected for deep learning is insufficient,we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm.We want to use the Cascade Region-based Convolutional Neural Networks(Cascade R-CNN)Swin model,which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in citrus.In this paper,we try to improve model safety by establishing a linear relationship between samples using Mixup and Cutmix algorithms,which are image processing-based data augmentation techniques.In addition,by using the ImageNet dataset,transfer learning,and stochastic weight averaging(SWA)methods,more accuracy can be obtained.This study compared the Faster Region-based Convolutional Neural Networks Residual Network101(Faster R-CNN ResNet101)model,Cascade Regionbased Convolutional Neural Networks Residual Network101(Cascade RCNN-ResNet101)model,and Cascade R-CNN Swin Model.As a result,the Faster R-CNN ResNet101 model came out as Average Precision(AP)(Intersection over Union(IoU)=0.5):88.2%,AP(IoU=0.75):62.8%,Recall:68.2%,and the Cascade R-CNN ResNet101 model was AP(IoU=0.5):91.5%,AP(IoU=0.75):67.2%,Recall:73.1%.Alternatively,the Cascade R-CNN Swin Model showed AP(IoU=0.5):94.9%,AP(IoU=0.75):79.8%and Recall:76.5%.Thus,the Cascade R-CNN Swin Model showed the best results for detecting citrus greening disease.

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.

Model-based design method of two-axis four-actuator fast steering mirror system

This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of normal TAFA FSM were investigated. Based on the structure and the commonality, the conditions of single-axis idea, high-frequency resonance and coupling were modeled gradually. Combining these models, a holonomic system model was established to reflect and predict the performance of TAFA FSM. A model-based design method was proposed based on the holonomic system model. The design flow and design concept of the method were described. In accordance with the method, a TAFA FSM was designed. Simulations and experiments of the FSM were done, and the results of them were compared. The compared results indicate that the holonomic system model can well reflect and predict the performance of TAFA FSM. The bandwidth of TAFA FSM is more than 250 Hz; adjust time is less than 15 ms;overshoot is less than 8%; position accuracy is better than 10 μrad; the FSM prototype can satisfy the requirements.

Fast R-CNN人脸检测技术浅析

人脸检测是计算机视觉的重要组成部分,随着人脸检测技术的不断发展,其应用范围越来越广,被广泛的应用于访问控制、监视系统以及其他种类的安全应用中。在当前的人脸检测技术中仍旧存在这不少的问题,严重阻碍了其进行快速的发展,其中尤以面部遮挡、光照、低分辨率以及缩放差异等问题影响较大。本文对Fast R-CNN人脸检测网络进行了比较深入的分析研究,在此基础上,论述了联合人脸检测和对齐的级联卷积神经网络的重要意义,对于从事人脸检测相关工作的技术人员具有一定的借鉴意思。

H_∞ Optimal Model Reduction for Singular Fast Subsystems

In this paper, H∞ optimal model reduction for singular fast subsystems will be inves-tigated. First, error system is established to measure the error magnitude between the original andreduced systems, and it is demonstrated that the new feature for model reduction of singular systemsis to make H∞ norm of the error system finite and minimal. The necessary and su?cient conditionis derived for the existence of the H∞ suboptimal model reduction problem. Next, we give an exactand practicable algorithm to get the parameters of the reduced subsystems by applying the matrixtheory. Meanwhile, the reduced system may be also impulsive. The advantages of the proposedalgorithm are that it is more ?exible in a straight-forward way without much extra computation, andthe order of the reduced systems is as minimal as possible. Finally, one illustrative example is givento illustrate the e?ectiveness of the proposed model reduction approach.

Development of a displacement-reactivity feedback model for dynamic behavior simulation in fast burst reactor

In this study, a displacement-reactivity feedback model, which can directly represent the inherent ‘‘thermal expansion extinction effect'' of fast burst reactors(FBRs),was developed with the aid of the static neutron transport component of the FBR-MPC code. Dynamic behaviors of bursts in the Godiva I reactor were simulated by coupling the simplified multiphysics models consisting of the point kinetic equations for neutronics, adiabatic equation for temperature, and thermoelastic equations for displacement/stress with the developed model. The results were compared with the corresponding experimental data and those obtained using the traditional fission yield(temperature rise)-reactivity feedback models. It was found that the developed model can provide good results for the bursts with no or a small inertia effect. For the bursts with a prominent inertia effect, the smaller burst width and asymmetric distribution of the fission rate curve, noticed in the experiments but not evident using the traditional models, can be reproduced. In addition, the realistic oscillations in reactivity and fission rate caused by the core vibration, as well as the deeper sub-prompt criticality in the plateau following the burst, can be observed. Therefore, the developed displacement-reactivity feedback model can be expected to be an effective tool for calculating the dynamic behaviors of bursts.

Fast forward modeling of muon transmission tomography based on model voxelization ray energy loss projection

To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxelization energy loss projection algorithm.First,the energy loss equation for muon transmission tomography is derived from the Bethe–Bloch formula,and the imaging region is then dissected into several units using the model voxelization method.Thereafter,the three-dimensional(3-D)imaging model is discretized into parallel and equally spaced two-dimensional(2-D)slices using the model layering method to realize a dimensional reduction of the 3-D volume data and accelerate the forward calculation speed.Subsequently,the muon energy loss transmission tomography equation is discretized using the ray energy loss projection method to establish a set of energy loss equations for the muon penetration voxel model.Finally,the muon energy loss values at the outgoing point are obtained by solving the projection coefficient matrix of the ray length-weighted model,achieving a significant reduction in the number of muons and improving the computational efficiency.A comparison of our results with the simulation results based on the Monte Carlo method verifies the accuracy and effectiveness of the algorithm proposed in this paper.The metallic mineral identification tests show that the proposed algorithm can quickly identify high-density metallic minerals.The muon energy loss response can accurately identify the boundary of the anomalies and their spatial distribution characteristics.

Fast Fourier Transform Approximation of Foreign Currency Option Pricing Based on Exponential Lévy Model

To study the approximation of foreign currency option prices when the underlying assets＇ price dynamics are described by exponential Lévy processes, the convolution representations for option pricing formulas were given, and then the fast Fourier transform （FFT） algorithm was used to get the approximate values of option prices. Finally, a numerical example was given to demonstrate the calculate steps to the option price by FFT.

Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

Modeling and Analysis of SO₂Emissions under Fast Fluidized Bed Conditions Using One Dimensional Model

Fluidized bed combustion behavior of coal and biomass is of practical interest due to its significant involvement in heating systems and power plant operations. This combustion behavior has been studied by many experimental techniques along with different kinetic models. In this study, SO2 emissions have been studied out in a pilot scale test facility of Circulating Fluidized Bed combustor (70 KW) under fast fluidized bed conditions burning coal with Pakistani wheat straw. One dimensional Mathematical model is being developed to predict the SO2 emissions under different operating conditions like bed temperature, Ca/S molar ratio, solids circulation rate, excess air ratio and secondary to primary air ratio. These parameters are varied to validate the model and encouraging correlation is found between the experimental values and model predictions.

FAST宁静区生态系统服务时空分异特征及权衡/协同关系分析

基于InVEST与光能利用率模型(CASA)对2000-2020年间500m口径球面射电望远镜(FAST)宁静区植被净初级生产力(NPP),水源涵养,土壤保持,生境质量四项生态系统服务进行计算,分析其在不同尺度上的时空变化特征,识别其生态系统服务热点区域,采用权衡-协同指数和随机森林模型揭示四项生态系统服务间的权衡/协同作用关系和强度及其与驱动因子的关系.结果表明:(1)2000-2020年,FAST宁静区四项生态系统服务整体上呈增加趋势,其中NPP增加730.544gC/m^(2),水源涵养量增加5.925mm,生境质量得分上升0.007,土壤保持量增加0.295t/km^(2).(2)圈层差异明显,其中核心区增加最快,服务功能最优;其次是边远区,生态有明显好转;最后是中间区,生态修复相对较慢.(3)土地利用类型中,林地和草地对研究区生态系统服务量的促进作用最大.(4)生态系统服务热点整体上由Ⅰ类和Ⅱ类热点区向非热点区,Ⅲ类热点区及Ⅳ类热点区转变.(5)区内的四项生态系统服务间关系整体以协同为主,且协同作用逐渐增强,在空间上水源涵养与土壤保持和NPP在部分地区呈权衡作用.(6)FAST宁静区四项生态系统服务间权衡/协同关系作用强度的主要驱动因子是降水,蒸散发,石漠化敏感性,坡度和人类活动强度.

基于动物创伤模型试验培训FAST超声技术在急诊本科实习生中的应用研究

目的评价基于动物创伤模型试验培训创伤超声重点评估法(FAST)超声技术在急诊本科实习生临床技能培训中的应用效果。方法选取我院2022年1月至2024年3月在我院急诊科的本科实习生65名作为研究对象,随机分为对照组和实验组。对照组采用传统教学方法,实验组按照动物创伤模型教学模式实施教学,比较两组本科实习生出科理论及操作成绩、学习满意度、学习能力和学习行为。结果两组实习生的理论及操作成绩均较培训前有所提高,实验组实习生的理论及操作成绩均明显高于对照组,差异均有统计学意义(均P<0.05)。两组实习生培训后的学习满意度均有所提高,实验组实习生的学习满意度得分明显高于对照组,差异均有统计学意义(均P<0.05)。两组实习生培训后的学习能力得分和学习行为得分均有所提高,实验组得分均明显高于对照组,差异均有统计学意义(均P<0.05)。结论基于动物创伤模型试验培训FAST超声技术能有效提高急诊本科实习生的学习成绩和学习满意度,学习能力明显提高,对提高本科实习生的成长与急诊诊疗水平具有积极意义。