BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification

Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.

Direct scaling of residual displacements for bilinear and pinching oscillators

The estimation of residual displacements in a structure due to an anticipated earthquake event has increasingly become an important component of performance-based earthquake engineering because controlling these displacements plays an important role in ensuring cost-feasible or cost-effective repairs in a damaged structure after the event.An attempt is made in this study to obtain statistical estimates of constant-ductility residual displacement spectra for bilinear and pinching oscillators with 5%initial damping,directly in terms of easily available seismological,site,and model parameters.None of the available models for the bilinear and pinching oscillators are useful when design spectra for a seismic hazard at a site are not available.The statistical estimates of a residual displacement spectrum are proposed in terms of earthquake magnitude,epicentral distance,site geology parameter,and three model parameters for a given set of ductility demand and a hysteretic energy capacity coefficient in the case of bilinear and pinching models,as well as for a given set of pinching parameters for displacement and strength at the breakpoint in the case of pinching model alone.The proposed scaling model is applicable to horizontal ground motions in the western U.S.for earthquake magnitudes less than 7 or epicentral distances greater than 20 km.

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.

Quantum color image scaling based on bilinear interpolation

As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the 2^(n_(1)) × 2^(n_(2)) quantum color image scaling. Firstly, the improved novel quantum representation of color digital images(INCQI) is employed to represent a 2^(n_(1)) × 2^(n_(2)) quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is analyzed.Finally, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.

Stabilization of fractional bilinear systems with multiple inputs

In this paper,we study in a constructive way the stabilization problem of fractional bilinear systems with multiple inputs.Using the quadratic Lyapunov functions and some additional hypotheses on the unit sphere,we construct stabilizing feedback laws for the considered fractional bilinear system.A numerical example is given to illustrate the efficiency of the obtained result.

Nonlinear wave propagation in acoustic metamaterials with bilinear nonlinearity

Nonlinear phononic crystals have attracted great interest because of their unique properties absent in linear phononic crystals.However,few researches have considered the bilinear nonlinearity as well as its consequences in acoustic metamaterials.Hence,we introduce bilinear nonlinearity into acoustic metamaterials,and investigate the propagation behaviors of the fundamental and the second harmonic waves in the nonlinear acoustic metamaterials by discretization method,revealing the influence of the system parameters.Furthermore,we investigate the influence of partially periodic nonlinear acoustic metamaterials on the second harmonic wave propagation,and the results suggest that pass-band and band-gap can be transformed into each other under certain conditions.Our findings could be beneficial to the band gap control in nonlinear acoustic metamaterials.

The Influence of Saturated and Bilinear Incidence Functions on the Dynamical Behavior of HIV Model Using Galerkin Scheme Having a Polynomial of Order Two

Mathematical modelling has been extensively used to measure intervention strategies for the control of contagious conditions.Alignment between different models is pivotal for furnishing strong substantiation for policymakers because the differences in model features can impact their prognostications.Mathematical modelling has been widely used in order to better understand the transmission,treatment,and prevention of infectious diseases.Herein,we study the dynamics of a human immunodeficiency virus(HIV)infection model with four variables:S(t),I(t),C(t),and A(t)the susceptible individuals;HIV infected individuals(with no clinical symptoms of AIDS);HIV infected individuals(under ART with a viral load remaining low),and HIV infected individuals with two different incidence functions(bilinear and saturated incidence functions).A novel numerical scheme called the continuous Galerkin-Petrov method is implemented for the solution of themodel.The influence of different clinical parameters on the dynamical behavior of S(t),I(t),C(t)and A(t)is described and analyzed.All the results are depicted graphically.On the other hand,we explore the time-dependent movement of nanofluid in porous media on an extending sheet under the influence of thermal radiation,heat flux,hall impact,variable heat source,and nanomaterial.The flow is considered to be 2D,boundary layer,viscous,incompressible,laminar,and unsteady.Sufficient transformations turn governing connected PDEs intoODEs,which are solved using the proposed scheme.To justify the envisaged problem,a comparison of the current work with previous literature is presented.

State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances.A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data.Based on the bilinear state observer,a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function.The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance.Furthermore,to improve the performance of the proposed algorithm,a dynamicmoving window is designed which can update the dynamical data by removing the oldest data and adding the newestmeasurement data.A numerical example of identification of bilinear systems is presented to validate the theoretical analysis.

基于双线性RepVGG注意力网络的花卉分类

为进一步提高花卉分类的准确率,在对双线性卷积神经网络、RepVGG及注意力机制进行研究的基础上,提出一种基于双线性RepVGG注意力机制的网络模型。首先利用RepVGG网络替换原始的特征提取网络VGG,以提高对花卉主要特征的提取能力;然后在两个RepVGG网络中分别引入通道注意力及空间注意力机制,并利用两个RepVGG网络外积后生成的高维双线性特征,来提取花卉的细粒度特征;最后通过结构重参数化,将RepVGG的各层转换为单路结构,以提高模型推理的速度。实验结果表明,在增强的Oxford-102数据集上,新模型与原始模型及常见模型相比,其推理速度及分类准确率均有较大的提升,与未引入注意力前相比,分类准确率也有一定的提升。

结合双线性网络和注意力的蓝藻图像识别方法

针对实际工程环境中采集的蓝藻图像数量不均衡、复杂光照条件影响、蓝藻图像局部区域特征捕捉不全面等问题,提出一种结合双线性网络和注意力的蓝藻图像识别方法。首先构建蓝藻图像数据集,并使用图像增强算法对数据集进行优化,再使用双线性网络充分提取蓝藻图像特征信息,同时结合卷积块注意力机制,聚焦重要的局部特征,忽视无用信息,进一步提升对无蓝藻、颗粒状蓝藻、带状蓝藻和片状蓝藻等4类不同形态蓝藻图像的分类效果。在构建的Algea-ultimate蓝藻数据集上进行实验,结果表明:相比识别效果最好的经典网络模型ResNet18,所提方法在识别准确率上提升7.29%,识别精度有明显提升。识别方法可用于太湖流域水质监测和预警平台中,提供蓝藻图像自动识别功能,为实时监测水体蓝藻形态提供智能化解决方案。

基于自适应纹理特征融合的纹理图像分类方法

现有基于深度学习的图像分类方法普遍缺少纹理特征的针对性,分类精度较低,难以同时适用于简单纹理和复杂纹理分类。提出一种基于自适应纹理特征融合的深度学习模型,能够结合类间差异性纹理特征做出分类决策。首先,根据纹理特征的最大类间差异性,构建图像的纹理特征图像;然后,采用原始图像与特征鲜明的纹理特征图像并行训练改进的双线性模型,获取双通道特征;最后,基于决策融合构建自适应分类模块,连接原图与纹理集的平均池化特征图进行通道权重提取,根据通道权重融合2个并行神经网络模型的分类向量,得到最优融合分类结果。在KTH-TIPS,KTH-TIPS-2b, UIUC和DTD 4个公共纹理数据集上对模型的分类性能进行评估,分别得到了99.98%、99.95%、99.99%和67.09%的准确率,表明所提模型具有普遍高效的识别性能。

基于双线性插值的单目标检测算法

针对已有算法对于空中小目标检测效果差、精度低、漏检等问题,提出一种基于双线性插值的单目标检测算法。对主干网络进行设计,再根据特征图的特点,使用不同的方法进行特征融合。在特征融合的过程中使用双线性插值算法进行上采样。对算法的先验框进行设计,使算法的先验框尺寸与数据集目标框尺寸更加契合。实验结果表明,提出算法的平均检测精度对比SSD提升了10.29%,算法计算量对比Faster-RCNN减少了82.18%。

双层铁磁薄膜中层间耦合和面外各向异性对系统共振频率的影响

本文采用能量极小原理,重点讨论了层间双线性耦合、双二次型耦合和面外单轴各向异性对双层铁磁薄膜中铁磁共振频率的影响。分别从三个方面讨论光学模和声学模共振频率对层间双线性耦合、双二次型耦合以及面外单轴各向异性的依赖。结果表明,层间双线性耦合和双二次型耦合可以提高光学模的共振频率。然而,面外单轴各向异性会降低光学模和声学模的共振频率,尤其是对声学模的影响更加明显。此外,对于光学模,双二次型耦合比双线性耦合更为重要。这些结果有助于新型微波器件的设计。

一种基于四平面的图像插值算法

针对现有图像插值算法存在的不足,提出了四平面插值算法,根据待插入点周围的4个像素点结合周边的像素点,从4个平面中选择一个作为插值平面,对于不符合平面插值的情况,则利用双线性插值,规避了用同一个插值公式对所有像素进行插值存在的不足,实验结果和数据分析表明了本方法的有效性。

基于匿名批量认证和会话密钥交互的安全协议

由于以无线方式完成车间通信和车与路边设施通信,车联网易遭受多类安全攻击。为此,提出基于批量匿名认证和会话密钥交互的安全协议(ABKS)。ABKS协议采用匿名相互认证策略,提高车间通信的安全性。同时,ABKS协议采用批量认证策略,降低路边设施的认证负担。此外,ABKS协议维护消息的完整性,避免了消息在传输过程中被篡改。性能分析表明,提出的ABKS协议能够防御潜在的攻击。相比于同类协议,ABKS协议降低了算法的复杂度。

基于粒子群算法的钢丝网垫隔振器参数识别

目的 证明粒子群优化算法对钢丝网垫隔振器参数识别的有效性和准确性。探究不同的加载条件对钢丝网垫元件力学性能的影响。方法 对钢丝网垫元件进行正弦加载试验,获取试验响应数据。以双折线模型对钢丝网垫的力学性能进行描述,建立隔振器的参数识别方程,应用粒子群算法和试验响应数据识别出模型参数,并研究不同加载条件下识别参数的变化规律。结果 用识别出的参数计算绘制的迟滞回线和试验结果吻合较好。改变激励幅值和频率,钢丝网垫的一次刚度、三次刚度、滑移刚度、等效阻尼均发生规律性变化,而改变钢丝网垫所受静载,曲线变化极为微小。结论 研究证明了粒子群优化算法对钢丝网垫隔振器参数识别的有效性和准确性,是一种有工程应用价值的参数识别方法。

双线性参数系统的极大似然递推参数估计

研究具有自回归滑动平均噪声的双线性参数系统,该系统结构复杂,噪声项的研究更具普遍意义。为实现系统参数的在线辨识,采用梯度搜索方法,推导了双线性参数系统的随机梯度算法。对于出现的未知项,基于递阶辨识原理使用其估计值进行替代。极大似然估计方法基于概率论,具有良好的一致性、渐近正态性和可用性,在引入极大似然估计方法后得到了相应的极大似然随机梯度算法,为进一步减小有色噪声对参数估计精度的影响,结合多新息辨识理论,将标量单新息扩展为多新息向量,研究了具有自回归滑动平均噪声的双线性参数系统的极大似然多新息随机梯度参数估计方法,并进行了仿真验证。

时滞戒毒模型的稳定性和Hopf分岔

研究了一类具有双线性接触率的时滞SLHMTQ戒毒模型.以吸毒者毒瘾复发的时滞为分岔参数,利用特征值法探究了模型的局部渐近稳定性和局部Hopf分岔存在性,推导出模型产生局部Hopf分岔的时滞临界值,并利用中心流形定理讨论了分岔周期解的性质.

基于稳态响应规律的双线性滞回模型等效参数研究

为了研究等效线性化方法的位移估算精度,以双线性滞回模型为分析模型,分析了非线性单自由度体系的自由振动过程,指出采用割线刚度法(R-H法)计算的等效参数与实际结果偏差较大,进而提出了一种简谐力作用下的等效参数计算方法。考虑了简谐激励的激励周期以及结构振动的位移峰值对计算结果的影响,通过数值分析对不同参数对应下的非线性体系进行等效线性化分析,验证了所提出的等效参数计算方法的有效性与准确性。结果表明,采用R-H法计算的等效刚度偏大,而改进方法能够合理估算非线性时程位移结果,其计算精度优于R-H法。

任意大小图像的量子描述及双线性插值方法

为解决任意大小图像的量子描述及缩放问题,提出一种改进的量子图像描述方法。采用量子基态描述像素值,采用量子叠加态描述像素位置,该叠加态可包含任意数量的基态,描述任意大小的图像。提出一种基于量子傅里叶变换的图像双线性插值方法。基于量子傅里叶变换基本原理,设计实现加法和乘法的量子线路,利于这两个模块及其它辅助模块,研究实现图像双线性插值的量子线路设计方法,设计双线性插值放大和缩小两种具体的量子线路。基于线路中采用的量子基本门的数量,分析线路的复杂度,其结果表明,该方法可以实现对经典方法的指数加速。经典计算机上的仿真结果验证了提出方法的有效性。