Inverse design of nonlinear phononic crystal configurations based on multi-label classification learning neural networks

Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic features exhibit potential applications in acoustic frequency conversion,non-reciprocal wave propagation,and non-destructive testing.Precisely manipulating the harmonic band structure presents a major challenge in the design of nonlinear phononic crystals.Traditional design approaches based on parameter adjustments to meet specific application requirements are inefficient and often yield suboptimal performance.Therefore,this paper develops a design methodology using Softmax logistic regression and multi-label classification learning to inversely design the material distribution of nonlinear phononic crystals by exploiting information from harmonic transmission spectra.The results demonstrate that the neural network-based inverse design method can effectively tailor nonlinear phononic crystals with desired functionalities.This work establishes a mapping relationship between the band structure and the material distribution within phononic crystals,providing valuable insights into the inverse design of metamaterials.

A Non-parametric Gradient-Based Shape Optimization Approach for Solving Inverse Problems in Directed Self-Assemblyof Block Copolymers

We consider the inverse problem of finding guiding pattern shapes that result in desired self-assembly morphologies of block copolymer melts.Specifically,we model polymer selfassembly using the self-consistent field theory and derive,in a non-parametric setting,the sensitivity of the dissimilarity between the desired and the actual morphologies to arbitrary perturbations in the guiding pattern shape.The sensitivity is then used for the optimization of the confining pattern shapes such that the dissimilarity between the desired and the actual morphologies is minimized.The efficiency and robustness of the proposed gradient-based algorithm are demonstrated in a number of examples related to templating vertical interconnect accesses(VIA).

Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic systems.Existing PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and labor-intensive.Recently,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish PEDmodels.This study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 norm.It further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better interpretability.In addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent methods.The results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

Quasi-plastic deformation mechanisms and inverse Hall-Petch relationship in nanocrystalline boron carbide under compression

Grain boundaries(GBs)play a significant role in the deformation behaviors of nanocrystalline ceramics.Here,we investigate the compression behaviors of nanocrystalline boron carbide(nB_(4)C)with varying grain sizes using molecular dynamics simulations with a machine-learning force field.The results reveal quasi-plastic deformation mechanisms in nB_(4)C:GB sliding,intergranular amorphization and intragranular amorphization.GB sliding arises from the presence of soft GBs,leading to intergranular amorphization.Intragranular amorphization arises from the interaction between grains with unfavorable orientations and the softened amorphous GBs,and finally causes structural failure.Furthermore,nB_(4)C models with varying grain sizes from 4.07 nm to 10.86 nm display an inverse Hall-Petch relationship due to the GB sliding mechanism.A higher strain rate in nB_(4)C often leads to a higher yield strength,following a 2/3 power relationship.These deformation mechanisms are critical for the design of ceramics with superior mechanical properties.

An Effective Meshless Approach for Inverse Cauchy Problems in 2D and 3D Electroelastic Piezoelectric Structures

In the past decade,notable progress has been achieved in the development of the generalized finite difference method(GFDM).The underlying principle of GFDM involves dividing the domain into multiple sub-domains.Within each sub-domain,explicit formulas for the necessary partial derivatives of the partial differential equations(PDEs)can be obtained through the application of Taylor series expansion and moving-least square approximation methods.Consequently,the method generates a sparse coefficient matrix,exhibiting a banded structure,making it highly advantageous for large-scale engineering computations.In this study,we present the application of the GFDM to numerically solve inverse Cauchy problems in two-and three-dimensional piezoelectric structures.Through our preliminary numerical experiments,we demonstrate that the proposed GFDMapproach shows great promise for accurately simulating coupled electroelastic equations in inverse problems,even with 3%errors added to the input data.

Parallel Technologies with Image Processing Using Inverse Filter

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

美标碳素钢SS钢轨成分及轧制工艺优化

通过60E1和115RE断面钢轨对比,优化钢轨化学成分,采取窄成分控制,提高C含量,控制Mn、Cr元素含量,优化轧制工艺,小批量生产了60E1断面SS钢轨,钢轨性能达到R_(p0.2)≥512 MPa,R_(m)≥982 MPa,A≥10%,踏面硬度(HB)≥310。小批量工业生产实践证明,60E1断面SS钢轨生产工艺顺行,性能稳定,满足用户需要,可以扩量生产。

基于轻量化SSD的交通标志检测算法

实时精确的交通标志检测是自动驾驶和智能交通的关键技术。针对现有智能检测算法检测复杂真实道路场景下的交通标志速度慢、无法较好地适用于嵌入式终端设备的问题,提出了一种基于轻量化SSD的交通标识检测算法。该算法采用MobileNetV3_large网络替代VGG16网络,可减少模型参数,提高检测实时性;利用添加SE模块的逆残差结构B-neck替换对应的标准卷积增强低层特征层的语义信息;设计改进RFB网络提升小交通标志的检测能力,重新设置预设先验框的尺寸,提升模型对特定数据集的检测能力。实验结果表明,改进SSD算法在中国交通标志检测数据集上的mAP值可达89.04%,比MobileNet-SSD算法提高了5.26%;帧率可达60 frames/s,比SSD算法提高了23 frames/s。所提算法具有较高的实时性和检测精度,对复杂交通环境具有更好的鲁棒性。

基于ICEEMDAN和分布熵的SS-Y伸缩仪信号随机噪声压制方法

结合改进的自适应噪声完备集合经验模态分解(ICEEMDAN)与分布熵(DistEn),提出一种无需自定义算法参数、去噪效果较好的伸缩仪信号随机噪声压制方法。首先将伸缩仪信号进行ICEEMDAN处理,得到若干个本征模态函数(IMF);然后计算各IMF分量的分布熵值,根据不同分布熵值的大小和表征的分量信号混乱程度,有针对性地对各IMF进行取舍;最后进行线性重构。设计仿真信号去噪实验和SS-Y伸缩仪信号去噪实验,结果表明,基于ICEEMDAN-DistEn去噪模型的伸缩仪信号重构还原度较好,去噪效果显著,明显优于CEEMDAN-DistEn、小波去噪和卡尔曼滤波等去噪模型。

基于 SSA-GRNN 的非接触式目标表面红外激光物性反演方法

在目标物性反演时,接触式测量方法在复杂环境下进行存在困难,而非接触式测量方法,由于测量数据相比接触式测量存在一定的误差,导致反演准确率受到影响。针对以上问题,提出了一种基于红外激光回波的非接触式目标表面物性反演方法。首先,测量不同目标表面的激光回波强度信息,采用麻雀搜索算法,优化并训练广义回归神经网络,建立红外激光回波强度预测模型;其次,分析测量距离、测量角度对激光回波强度的影响,建立材料表面激光回波强度数据库;最后,采集未知目标在四种距离下的表面激光回波强度信息,赋予材料种类编号,输入到回波强度预测模型中,计算预测值与实际值的相对误差,反演未知目标表面材料物性。实验结果表明:在反演目标相同的情况下,回波强度预测结果的均方根误差从传统网络的11.337降低到了优化后的2.482。优化后的神经网络模型的相对反演准确率可达88.89%以上,与传统方法相比,平均反演准确率提高了45.83%,文中所提方法具有较高的准确性和推广性,为武器系统非合作目标的探测、材料反演提供方法,提高了目标识别能力。

Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method

In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years.How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future.To evaluate the changes in major air pollutant emissions over this region,this paper employs ensemble Kalman filtering(EnKF)for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System(NAQPMS)simulation data to inversely estimate the emission rates of SO_(2),NOX,CO,and primary PM_(2.5)over BTH region in February from 2014 to 2019.The results show that SO_(2),NOX,CO,and primary PM_(2.5)emissions in the BTH region decreased in February from 2014 to 2019 by 83%,37%,41%,and 42%,while decreases in Beijing during this period were 86%,67%,59%,and 65%,respectively.Compared with the prior emission inventory,the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region,with simulated root mean square errors of the monthly average concentrations of SO_(2),NOX,PM_(2.5),and CO reduced by 41%,30%,31%,and 22%,respectively.The average uncertainties of SO_(2),NOX,PM_(2.5),and CO inversion emissions in2014-19 are±14.03%yr^(-1),±28.91%yr^(-1),±126.15%yr^(-1),and±43.58%yr^(-1).Compared with the uncertainty of MEIC emission,the uncertainties of all species changed by+2%yr^(-1),-2%yr^(-1),-26%yr^(-1),and-4%yr^(-1),respectively.The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions.The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014.The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.

一种双天线GNSS接收机混合干扰抑制方法

相对于单天线GNSS接收机,阵列GNSS接收机具有空间分辨能力,当干扰信号与卫星信号在时域频域上产生混叠时,其能够从空域上对干扰信号进行抑制,具有更强的干扰抑制能力。但阵列GNSS接收机相对于单天线GNSS接收机需要更多的阵元,随着阵元数目的增加,系统成本也相应的增加,限制了阵列GNSS接收机的应用范围。双天线GNSS接收机既具有空域抗干扰能力,同时又具有价格低廉的特点,是一种较好的折中选择。对于单一的连续波干扰,其能够产生很好的抑制效果,但是当连续波干扰中混有脉冲干扰时,由于受到自由度的限制,双天线GNSS接收机无法对混合干扰进行有效抑制,进而影响接收机的正常工作。针对于上述问题,本文提出一种新的混合干扰抑制算法。首先利用脉冲的时域特征,对待处理信号进行分块处理,确保至少有一个数据块中不含有脉冲干扰,随后对不含脉冲干扰的数据块,使用空时最小功率(Space-Time Adaptive Processing Power Inversion,STAP-PI)算法得到最优权值,然后利用该权值抑制原信号中的连续波干扰。最后,对处理之后信号中残余的脉冲干扰进行时域置零处理,从而达到抑制混合干扰的目的。仿真实验和实采实验结果均证明了所提算法的有效性。

GNSS/SINS组合导航系统的改进变分贝叶斯自适应滤波算法

针对系统噪声及测量噪声统计量不准确的组合导航系统线性高斯状态模型,提出一种组合导航系统的改进变分贝叶斯自适应滤波算法(modified variational Bayesian adaptive filter,MVBAKF)。首先,选择Wishart分布作为已知均值的高斯分布协方差矩阵的共轭先验,并给出测量噪声方差、状态向量及其预测误差协方差矩阵的联合概率分布函数;然后,利用变分贝叶斯方法给出测量噪声方差及状态向量预测误差协方差矩阵的计算公式,进而提出具有迭代性质的MVBAKF算法;最后,进行基于MVBAKF算法的GNSS/SINS组合导航系统仿真实验。结果表明,相对于传统VBAKF算法,MVBAKF算法可较准确地估计测量噪声方差,有效克服系统噪声统计量不准确对滤波精度的影响,进而提高组合导航系统的滤波精度。

SSA-MLP模型在岩质边坡稳定性预测中的应用

岩质边坡的力学参数量化及稳定性分析对岩质边坡灾害的防治具有重要意义。Hoek-Brown(H B)准则是一种用于确定岩体力学参数的经典方法,能反映出边坡岩体变形和位移的非线性破坏特征。在此基础上,首先,提出一种麻雀搜索算法(Sparrow Search Algorithm,SSA)改进多层感知器(Multi-Layer Perceptron,MLP)的神经网络模型,并用于边坡稳定性预测、指标敏感性分析及参数反演。其次,将收集的1085组岩质边坡的几何参数和H B准则参数等作为输入变量,极限平衡理论Bishop法求解的安全系数作为输出变量,对SSA MLP模型进行训练学习和性能评估。最后,将该模型运用于25个边坡实例,验证模型的有效性。结果显示,该模型收敛速度快、精度高,为边坡稳定性分析和参数量化提供了一种新思路。

基于PSO-LSSVM-BP模型的高边坡力学参数反分析及稳定性评价

基于粒子群优化(PSO)算法和最小二乘支持向量机(LSSVM)算法构建非线性映射关系,结合反向传播(BP)神经网络对非线性映射关系生成的数据库进行机器学习,构建了PSO-LSSVM-BP模型确定最优岩体力学参数。PSO-LSSVM-BP模型以高边坡监测位移数据作为输入信息,通过反分析获得高边坡岩体力学参数,将反分析参数用于FLAC3D位移数值计算,结果表明模拟结果与监测数据吻合较好,验证了该模型的可行性和有效性。基于PSO-LSSVM-BP模型,对不同蓄水位下两河口水电站进水口高边坡稳定性进行了评价,发现水位是影响边坡稳定性的主要因素,随着水位上升,边坡位移逐渐增大,其表面和断层处损伤程度加深,边坡局部点安全系数有所下降,但整体点安全系数均大于1.30,有一定安全裕度。

T6SS阳性CRKP临床感染特征及毒力基因分析

目的分析T6SS阳性耐碳青霉烯类肺炎克雷伯菌(CRKP)临床感染特征,以及其耐药、毒力基因检出率和生物膜形成能力,为临床防控CRKP感染提供参考数据。方法收集2019年1月—2022年12月安徽某三甲医院临床分离的CRKP菌株及患者资料,PCR法检测T6SS基因、毒力基因、耐药基因和分子分型,96孔板结晶紫染色法检测生物膜形成能力。结果共纳入160株CRKP。标本来源以痰(46.9%)和血(26.3%)为主。CRKP菌株呈现多重耐药表型,以携带bla KPC(80.6%)为主,其次为bla NDM(17.5%)。根据是否携带T6SS将CRKP分为T6SS阳性组(129株,80.6%)和T6SS阴性组(31株,19.4%)。T6SS阳性组患者患慢性肺部疾病和心脏疾病比例高于T6SS阴性组(P<0.05),且预后较阴性组差(P<0.05)。T6SS阳性组中,iuc A、mrk D、rmp A2、peg 344、wab G、fim H检出率均高于T6SS阴性组(均P<0.05)。CRKP中以ST11型(68.8%)为主,其中K64-ST11型占比70.9%,K47-ST11型占比25.5%。T6SS阳性组ST11型和K64-ST11型CRKP占比均高于T6SS阴性组(均P<0.05)。T6SS阳性组CRKP生物膜形成能力强于T6SS阴性组(P<0.001)。两组除bla OXA-48基因外,在携带其他碳青霉烯类耐药基因和抗菌药物耐药率方面差异无统计学意义。结论该地区CRKP呈现多重耐药,CRKP菌株T6SS检出率高,T6SS阳性CRKP毒力基因检出率更高,且生物膜形成能力更强。

Inverse design of mechanical metamaterial achieving a prescribedconstitutive curve

Besides exhibiting excellent capabilities such as energy absorption,phase-transforming metamaterials offer a vast design space for achieving nonlinear constitutive relations.This is facilitated by switching between different patterns under deformation.However,the related inverse design problem is quite challenging,due to the lack of appropriate mathematical formulation and the convergence issue in the post-buckling analysis of intermediate designs.In this work,periodic unit cells are explicitly described by the moving morphable voids method and effectively analyzed by eliminating the degrees of freedom in void regions.Furthermore,by exploring the Pareto frontiers between error and cost,an inverse design formulation is proposed for unit cells.This formulation aims to achieve a prescribed constitutive curve and is validated through numerical examples and experimental results.The design approach presented here can be extended to the inverse design of other types of mechanical metamaterials with prescribed nonlinear effective properties.

SS-OCT在角膜成像中的应用进展

成像技术的进步彻底改变了眼科领域,改变了我们对眼科疾病的理解、诊断和治疗。扫频源光学相干断层扫描(SS-OCT)是一种最新的非接触式高分辨率成像技术。它进一步提高了成像深度和扫描速度,增加了新的算法和功能。SS-OCT允许对角膜结构进行三维评估,提供角膜前、后表面的曲率和高度图以及角膜厚度图。这可以帮助眼科医生筛查和诊断角膜病变,如圆锥角膜、角膜营养不良和变性。SS-OCT更快、更精准和更灵敏的角膜测量有助于对即将进行角膜和屈光手术的患者做好术前计划和术后监测。文章总结SS-OCT技术的发展及其在角膜疾病中潜在的临床效用与在手术中的应用进展,以支持更多未来可能的研究和临床治疗。

OptoGPT: A foundation model for inverse design in optical multilayer thin film structures

Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each layer.These methods also cannot accommodate versatile design situations under different angles and polarizations.In addition,how to benefit practical fabrications and manufacturing has not been extensively considered yet.In this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.

UNIQUENESS OF INVERSE TRANSMISSION SCATTERING WITH A CONDUCTIVE BOUNDARY CONDITION BY PHASELESS FAR FIELD PATTERN

In this paper,we establish the unique determination result for inverse acoustic scattering of a penetrable obstacle with a general conductive boundary condition by using phaseless far field data at a fixed frequency.It is well-known that the modulus of the far field pattern is invariant under translations of the scattering obstacle if only one plane wave is used as the incident field,so it is impossible to reconstruct the location of the underlying scatterers.Based on some new research results on the impenetrable obstacle and inhomogeneous isotropic medium,we consider different types of superpositions of incident waves to break the translation invariance property.