基于SSA-RF的SAR图像变化检测方法

为了提高合成孔径雷达(Synthetic Aperture Radar, SAR)图像变化检测精度,提出一种基于麻雀搜索算法优化随机森林(Sparrow Search Algorithm-Random Forest, SSA-RF)的SAR图像变化检测方法。该方法首先通过对数比值法得到两时相SAR图像的差异图,然后在差异图预处理后进行层次模糊C均值(Fuzzy C-means, FCM)聚类预检测,生成有效标签。最后,利用标签训练随机森林二次检测模型,并采用麻雀搜索算法对随机森林进行参数优化构建性能稳定的分类器,得到差异图变化类与不变化类的分类结果,从而实现SAR图像的变化检测。实验结果表明,该方法较好地保留了变化区域信息,有效提高了SAR图像的变化检测精度。

Damage detection of 3D structures using nearest neighbor search method

An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented.The frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the structures.Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data.The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios.The accuracy of the approach was verified using measured data from an experimental test;moreover,two asymmetric 3D numerical examples were considered as the numerical study.The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network.Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data.The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise,but the nearest neighbor search method is more robust against noisy data than the artificial neural network.

改进麻雀搜索算法优化SVM的异常点检测

支持向量机是检测异常点的常用方法,但其仍然存在难以高效获取最优参数,导致检测效率低、稳定性差的问题。鉴于此,提出一种改进的麻雀搜索算法ISSA,并将其用于优化支持向量机参数。首先,采用改进折射反向学习和可变对数螺线改进传统麻雀搜索算法;然后,利用改进麻雀搜索算法ISSA对支持向量机参数进行优化;最后,将优化后的支持向量机用于异常点检测。仿真实验结果表明,在G-mean和F-measure 2个评价指标上,利用ISSA优化后的支持向量机检测效果明显优于其它3种分类算法,具有更优秀的检测效率、稳定性和泛化能力。

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.

基于RSSI的WSN二维对数搜索定位算法

定位技术是无线传感器网络(WSN)应用的关键技术之一。针对WSN中的定位问题,提出一种基于接收信号强度指示(RSSI)测距的二维对数分布式搜索定位算法。采用改进的RSSI测距模型测量节点之间的距离,利用质心定位算法结果作为搜索起点,设计一种基于最小加权距离误差和的目标函数,对于每个节点通过二维对数搜索的方法,搜索具有最小加权距离误差和的点作为定位位置。仿真实验比较了质心定位算法、不带权值的二维对数搜索定位算法、基于RSSI的二维对数搜索定位算法在不同条件下的定位性能,结果表明基于RSSI的二维对数搜索定位算法的定位精度远优于质心定位算法,相比不带权值的二维对数搜索定位算法约提高了0.02R。

对数曲线轮廓度误差几何遍历搜索评定算法

结合平面曲线轮廓度误差评定的最小条件原则及对数曲线的几何特性,提出了基于几何遍历搜索的对数曲线轮廓度误差评定算法。首先,采用最小二乘法得到最小二乘对数曲线和最小二乘误差;其次,在最小二乘对数曲线上选取两个特征点作为参考点,并在并在参考点周围按一定规则布置一系列的辅助点;然后,以两个特征点周围的辅助点两两结合构造出一系列的辅助对数曲线,并计算所有测量点到辅助对数曲线的距离极差值;通过比较和判断,最终实现对数曲线轮廓度的最小区域评定。列出了该评定技术的详细原理和步骤,实例证明,与最小二乘法相比,该算法具有极高的评定精度,适用于一些误差精度要求较高的零件或设备的轮廓度误差评定。

适用于多指标的空气质量评价对数型幂函数指数公式

为建立科学合理、普适通用且计算简便的空气环境质量评价模型,在适当设定空气污染指标"参照值"和规范变换式的基础上,提出了对通常采用的7项空气污染指标的"规范值"都适用的空气质量评价指数公式。采用智能解域搜索算法优化公式中的参数,得到优化后对多指标皆适用的空气质量评价的对数型幂函数指数公式。将该公式用于多个地区的空气环境质量评价,其评价结果与其他多种评价方法一致。研究表明,该对数型幂函数指数公式能为空气环境质量评价提供一种简便、实用的新方法。

具有对数递减权重的混沌正弦余弦算法

对于基本正余弦算法存在的收敛精度低、易陷入局部最优解的问题,提出一种具有对数递减权重的混沌正弦余弦算法。采用对数递减权重描述的非线性权重对当前个体位置进行修正,提高算法收敛精度、加快收敛速度;引入改进Tent混沌搜索策略以有效保持种群多样性,避免算法陷入局部最优。对10个基准测试函数仿真实验,结果表明,与PSO算法、GWO算法、WOA以及几种改进正余弦算法相比,该算法求解精度、收敛速度得到明显改进。

基于LASSA算法的多无人机协同航迹规划方法

针对基本麻雀搜索算法(sparrow search algorithm, SSA)在求解多无人机(unmanned aerial vehicle, UAV)协同航迹规划问题时收敛精度不高,易于陷入局部最优等问题,提出了一种使用对数螺旋策略和自适应步长策略的SSA(logarithmic spiral strategy and adaptive step size strategy of the SSA, LASSA)多UAV协同航迹规划方法。首先利用分层规划思想,先建立单UAV航迹规划模型,然后再建立时间协同多UAV航迹规划模型,将航迹规划问题转化为函数优化问题。其次,采用对数螺旋策略,扩大对周围空间的搜索,增强种群个体多样,并利用自适应步长策略,更好地控制开发和探索行为。最后,利用LASSA对提出的航迹规划模型进行求解。仿真结果表明,本文提出的LASSA能够在满足时间协同的条件下,规划出代价近似最优、符合约束的协同飞行航迹,证明了本文提出的基于LASSA的多UAV协同航迹规划方法的有效性和改进算法的优越性。

动态调整概率的双重布谷鸟搜索算法

布谷鸟搜索算法是一种新兴的仿生智能算法,存在着求解精度低、易陷入局部最优及收敛速度慢等缺陷,提出了动态调整概率的双重布谷鸟搜索算法(DECS)。首先,在自适应发现概率P中引入了种群分布熵,通过算法的所处迭代阶数和种群分布情况,动态改变发现概率P的大小,有利于平衡布谷鸟算法局部寻优和全局寻优的能力,加快收敛速度;其次,在布谷鸟寻窝的路径位置更新公式中,采用了一种新型步长因子更新寻优方式,形成Levy飞行双重搜索模式,充分搜索空间;最后,在随机偏好游走的更新公式引入非线性对数递减的惯性权重策略,使得算法有效克服易陷入局部最优的缺陷,提高寻优搜索能力。与4种算法相比和19个测试函数的仿真结果表明:改进布谷鸟算法的寻优性能明显提高,收敛速度更快,求解精度更高,具有更强的全局搜索能力和跳出局部最优能力。

求解函数优化问题的改进布谷鸟搜索算法

在工程优化中,大多问题是连续优化问题,即函数优化问题。针对布谷鸟算法求解函数优化问题时存在的收敛速度慢、求解精度不高和易陷入局部最优等问题,文中提出非线性惯性权重对数递减和随机调整发现概率的布谷鸟搜索算法(Cuc-koo Search Algorithm with Logarithmic Decline of Nonlinear Inertial Weights and Random Adjustment Discovery Probability,DWCS)。首先,在布谷鸟寻窝的路径和位置更新公式中,设计一种随进化迭代次数非线性递减的惯性权重来改进鸟巢位置的更新方式,以协调布谷鸟算法的探索和开发能力;其次,引入随机调整发现概率代替固定值发现概率,使较大和较小的发现概率随机出现,从而有利于平衡算法的全局探索和局部开发能力,加快算法收敛速度,增加种群多样性;最后,分析对数递减参数和随机调整发现概率,选取对数递减最佳参数组合和随机调整发现概率的最佳取值范围,此时,函数的优化效果最好。与BA,CS,PSO,ICS算法相比,所提算法极大地提高了寻优精度,显著地减少了迭代次数,有效地提高了收敛速度和鲁棒性。在16个测试函数中,DWCS均能收敛到全局最优解,证明了DWCS在求解连续复杂函数优化问题上具有较强的竞争力。

DV-Hop测距修正的定位算法研究

针对DV-Hop测距定位精度不准问题,提出基于DV-Hop测距修正的对数搜索(improved DV-Hop Rangingbased Logarithmic Search,DH-RLS)定位算法。DH-RLS算法利用锚节点间的真实距离信息估计跳距误差,修正跳距值,提高测距精度。利用质心定位算法估计未知节点的位置,并将此位置作为搜索起点,再利用搜索目标函数进行搜索,直至搜索到具有最小距离误差和的点。仿真数据表明,DH-RLS定位算法的归一化平均误差低于同类算法。通过对测距修正,降低了测距误差,同时利用搜索目标算法提高了定位精度。

Upper bound solution for active thrust of nonhomogeneous cohesive soil considering effects of intermediate principal stress

Considering the variation of cohesion along the depth,the upper bound solution of active earth pressure for a rough inclined wall with sloped backfill is formulated based on a log-spiral failure mechanism.For a more accurate prediction,the influence of intermediate principal stress is taken into consideration using the unified strength theory.Converting the search for the active pressure to an optimization problem,the most critical failure surface can be located by a natural selection-based gravitational search algorithm(GSA).The proposed method is validated compared with existing methods for noncohesive and cohesive cases and proved to be more accordance with the limit equilibrium solution.The influences of the variation of soil cohesion and intermediate principal stress on active earth pressure coefficient are then fully studied.It can be concluded that both the variations of soil cohesion and intermediate principal stress have a significant influence on the active earth pressure coefficient.

Novel two-dimensional PdSe phase:A puckered material with excellent electronic and optical properties

By combining structural search and first-principles calculations,we predict a new stable twodimensional PdSe monolayer,and systematically investigate its structural,electronic and optical properties.The calculated formation enthalpy,phonon spectra and molecular dynamic simulations confirm that PdSe monolayer possesses excellent thermodynamic and dynamic stability.PdSe monolayer is a semiconductor with an indirect band gap of∼1.10 eV.The carrier transport of PdSe monolayer is dominated by hole and exhibits remarkable anisotropy due to the intrinsic structure anisotropy.The optical properties also show obvious anisotropic characteristic with considerable absorption coefficient and broad absorption from the visible to ultraviolet regions.Benefiting from these excellent physical properties,PdSe monolayer is expected to be a promising candidate as electronic and optoelectronic devices.

一种基于相关性分析与对数搜索聚类的跨媒体检索方法

为了在不需人工标记的多媒体数据集上进行跨媒体检索,避免不同媒体之间的语义提取与关联时具有的局限性等问题,首先提出了一种基于相关性分析的音频与图像间的关系数据集的收集机制,然后提出了一种基于对数搜索思想的聚类方法用于跨媒体检索模型的训练,最后提出了一种基于关联性分析的跨媒体检索方法。实验结果表明,所提方案的音频检索图像的第一结果的平均相关度为82.92%,而图像检索音频的第一结果的平均相关度为61.25%,能够较好地解决当前环境下的图像、音频之间的跨媒体搜索需求。

基于C8051F020的超声波测距系统

工业领域对液位的高精度测量,是保证产品质量的关键。国内超声测量仪表大都采用硬件电路,对回波信号进行寻峰,精度难以提高。本设计电路,独创性地采用软件寻峰的方法,能精准地确定回波到达时刻,极大提高了仪表的精度,减小了盲区。同时硬件电路设计上创新性地采用对数转换电路,扩大了回波信号的处理范围,保证了仪表精度在全量程范围内的一致性。

自适应对数双搜索人工蜂群策略异源图像配准

人工蜂群算法存在收敛精度不足、容易陷入局部最优解的缺点。为此,提出一种基于自适应对数收敛双搜索策略的人工蜂群算法。该算法引入自适应系数和对数收敛系数改进雇佣蜂阶段和观察蜂阶段的搜索策略,防止算法陷入局部极值;同时引入双搜索策略,加快收敛速度并提高算法精度。然后在该改进人工蜂群算法的基础上提出一种自然场景下果园中苹果异源图像配准算法,采用SURF算法的非旋转因子与黑塞矩阵响应阈值两个参数作为改进后人工蜂群算法的初始蜂群二维向量,采用均方根误差函数作为适应度函数进行寻优搜索。在实验阶段引入樽海鞘群算法、蝙蝠算法和另一种改进人工蜂群算法与所提算法进行性能比较,实验结果表明,在全局进化次数固定的条件下,所提算法使人工蜂群算法的性能有了50%~90%的提升,且异源图像配准结果较传统SURF算法能够得到更多正确的特征点配对。

Two-dimensional Pt_(2)P_(3)monolayer:A promising bifunctional electrocatalyst with different active sites for hydrogen evolution and CO_(2)reduction

Green hydrogen production and CO_(2) fixation have been identified as the fundamental techniques for sustainable economy.The open challenge is to develop high performance catalysts for hydrogen evolution reaction(HER)and CO_(2) electroreduction(CO_(2)ER)to valuable chemicals.Under such context,this work reported computational efforts to design promising electrocatalyst for HER and CO_(2)ER based on the swarm-intelligence algorithm.Among the family of transition-metal phosphides(TMPs),Pt_(2)P_(3) monolayer has been identified as excellent bifunctional catalysts due to high stability,excellent conductivity and superior catalytic performance.Different from typical d-block catalysts,p-band center presented by P atoms within Pt_(2)P_(3) monolayer plays the essential role for its reactivity towards HER and CO_(2)ER,underlining the key value of p-electrons in advanced catalyst design and thus providing a promising strategy to further develop novel catalysts made of p-block elements for various energy applications.

Solving the Multi-discrete Logarithm Problems over a Group of Elliptic Curves with Prime Order

In this paper, we discuss the expected number of steps in solving multi-discrete logarithm problems over a group of elliptic curves with prime order by using Pollard＇s rho method and parallel collision search algorithm. We prove that when using these algorithms to compute discrete logarithms, the knowledge gained through computing many logarithms does not make it easier for finding other logarithms. Hence in an elliptic cryptosystem, it is safe for many users to share the same curve, with different private keys.

椭圆曲线素阶群上的离散对数求解

该文对解椭圆曲线上离散对数的Pollard ρ算法和并行碰撞搜索算法分别建立了它 们的图论模型和分析了碰撞技巧,比较了两个算法,进而提出了设计迭代函数的准则并 给出一个改进的并行碰撞算法.