振动噪声信号分离优化算法研究

机械振动信号中混杂的背景噪声对信号的分析处理以及振动结构的建模有很大影响,在独立分量分析（ICA）理论研究的基础上,以信息论中的最小化互信息准则作为ICA的判据,提出了一种基于负熵的FastICA共轭梯度快速算法。该算法计算效率高,收敛性能好;适合对初始点不敏感和对鲁棒性有要求的信号处理。实验结果表明了对振动信号的背景噪声分离具有较好的效果。     

基于独立分量分析的图像特征提取及泊松噪声去除

针对泊松噪声具有与信号相关和不一致性分布的特性,提出了一种基于独立成分分析(ICA)的软阈值滤波算法。泊松噪声对基于高阶统计量的ICA变换不敏感,其能量均匀分布在ICA域。首先将包含泊松噪声的图像变换到ICA域,然后再对图像独立成分进行软阈值滤波。该算法对图像和噪声具有自适应能力,在噪声去除和图像细节保留方面达到一个平衡。实验结果表明:该算法不仅可以有效去除图像中的泊松噪声和提高图像质量,而且具有良好的鲁棒性。     

支持服务关联的全局最优服务选择算法

针对面向服务的军事综合电子信息系统候选服务的服务质量往往依赖于其他候选服务的问题,提出了一种支持服务关联的全局最优服务选择方法.该方法将基于QoS的军事信息服务选择问题建模为带QoS约束的多目标组合优化问题,采用基于独立成分分析(ICA)的多目标分布估计算法同时优化多个目标函数,最终产生一组满足约束条件的Pareto最优解服务组合集.对比实验结果表明,基于ICA的多目标分布估计算法通过概率图模型可以描述服务间的相互关系,可以更好地解决存在服务关联的组合服务选择问题,具有良好的有效性和可行性.     

基于独立成分分析的掌纹识别

本文研究了独立成分分析(ICA)两种不同的结构ICA I和ICAII在掌纹识别中的应用.为了提高识别准确性和可靠性,该方法首先对掌纹图像进行预处理,提取掌纹感兴趣(ROI)区域进行特征提取和匹配.为了减少计算量,运用ICA算法之前,先采用主成分分析(PCA)算法去除掌纹图像的二阶统计特征相关性,其余的高阶统计特征由ICA分离.对于PolyU掌纹图像库,基于ICA模型的预测误差平方和(SPE)小于PCA,而且重构的原始图像优于PCA.为了比较两种算法识别性能,本丈分别用PCA、ICA I、ICAII提取特征掌纹子空间,然后将待识别图像投影到低维子空间上,最后用余弦距离进行掌纹匹配.实验结果表明,ICA算法两种结构的识别率均高于PCA,ICAII在性能上优于ICA I.     

一类修正Zhang H.C.非单调共轭梯度算法

为有效求解大规模无约束优化问题,本文基于RMFI共轭梯度法,结合Zhang H.C.非单调线搜索步长规则,提出了一类新的共轭梯度算法.在适当的条件下,证明了新算法的全局收敛性.数值算例表明,新算法比Zhang H.C.非单调规则下的标准RMFI方法收敛速度更快,更有效.同时,本文进一步研究了Zhang H.C.非单调线搜索步长规则的一个基于强迫函数的拓展模型,并从理论上证明了基于此拓展模型的新算法的全局收敛性.     

基于粒子群优化算法的多聚焦图像融合

现有融合方法的融合规则不能根据图像的后续使用目的进行自适应调整,不同融合方法的优点也不易综合,为解决这些缺点,提出一种基于粒子群优化算法的融合方法.该方法首先把利用DBSS(2,2)离散小波变换和梯度金字塔融合方法分别产生的图像一起作为初始粒子,然后根据后续处理的要求来构造由多个图像评价指标的加权和所组成的目标函数,再利用粒子群优化算法来优化目标函数从而获取最终的结果图像.两组实验从主观视觉和定量评价指标(标准方差、平均梯度、熵、空间频率,相关系数、均方交叉熵等)两方面证明了该方法的有效性.     

基于H(o)lder指数和ICA的海域SAR图点目标检测

本文将分形H(o)lder指数和信号分离相结合,利用独立成分分析技术(ICA,Independent ComponentAnalysis),实现了海杂波SAR图的散斑抑制和点目标检测.首先,计算点态H(o)lder指数图,并提出二值模糊方法对其处理;接着使用ICA技术得到该图的基图像和独立成分;提出空间分离法,对独立成分进行分离,同时对基图进行对应分类,获得非噪声和噪声两个空间.最后在非噪声空间上重构图像.实验部分,将该算法与传统算法进行对比,证实了该算法的有效性和优越性.     

一种自适应阈值的Canny边缘检测算法

针对传统Canny算法阁值选择困难的问题,本文提出一种基于最大类间方差法的自适应同步搜索高低阈值的方法.该方法首先根据梯度直方图信息将对应的像素分为三类;其次基于改进的Otsu算法定义评价函数,该评价函数描述了类间方差;最后依次搜索评价函数的一个最大值,自动获取Canny算子的高低阈值.该方法不需要人为设定任何参数.与...     

任意信号源盲分离算法性能研究

1.引言 常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化(Infomax)以及独立成分分析(ICA)等.这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关,一旦假设的概率密度与实际信号的密度函数相差甚远,分离性能将大大降低.本文提出采用核函数密度估计的方法进行任意信号源的盲分离,并通过典型算例与几种盲分离算法进行了性能比较,验证了方法的可行性.     

基于ITD与ICA的滚动轴承故障特征提取方法

针对滚动轴承故障信号因受背景噪声、信号传递途径、轴承各部件间相互作用及其它能量较大振源信号干扰,限制传统方法提取故障特征信息的准确性问题,提出结合固有时间尺度分解(ITD)及独立分量分析(ICA)的信号分析方法,将单通道振动信号进行ITD分解,得到若干固有旋转分量及一个趋势项,基于互相关准则对分解信号进行重组作为ICA的输入矩阵,采用Fast ICA算法解混,实现故障特征信号与噪声信号分离,从而提取故障特征信息。通过滚动轴承故障诊断实验结果分析表明该方法有效可行,具有一定工程应用价值。     

Source extraction from functional magnetic resonance imaging data using ICA based on fourth-order and exponential contrast functions

Independent component analysis (ICA) is one of the well-known statistical techniques used for blind source separation. It is also used for the extraction of sources from functional magnetic resonance imaging (fMRI) data. Benchmark for different ICA algorithms is speed and accuracy. In this article, we will be focusing on two simple contrast functions along with matrix-based updating rules. Fixed-point iteration is used for optimization of the contrast functions. Application of matrix-based weight updating makes the process converge rapidly. Validity of the algorithms is tested by comparing the speed and accuracy on simulated and actual fMRI data with other conventional ICA approaches.     

State-space independent component analysis for nonlinear dynamic process monitoring

The cost effective benefits of process monitoring will never be over emphasised. Amongst monitoring techniques, the Independent Component Analysis (ICA) is an efficient tool to reveal hidden factors from process measurements, which follow non-Gaussian distributions. Conventionally, most ICA algorithms adopt the Principal Component Analysis (PCA) as a pre-processing tool for dimension reduction and de-correlation before extracting the independent components (ICs). However, due to the static nature of the PCA, such algorithms are not suitable for dynamic process monitoring. The dynamic extension of the ICA (DICA), similar to the dynamic PCA, is able to deal with dynamic processes, however unsatisfactorily. On the other hand, the Canonical Variate Analysis(CVA) is an ideal tool for dynamic process monitoring, however is not sufficient for nonlinear systems where most measurements follow non-Gaussian distributions. To improve the performance of nonlinear dynamic process monitoring, a state space based ICA (SSICA) approach is proposed in this work. Unlike the conventional ICA, the proposed algorithm employs the CVA as a dimension reduction tool to construct a state space, from where statistically independent components are extracted for process monitoring. The proposed SSICA is applied to the Tennessee Eastman Process Plant as a case study. It shows that the new SSICA provides better monitoring performance and detect some faults earlier than other approaches, such as the DICA and the CVA.     

Multiscale ICA for fundus image analysis

Independent component analysis is a technique used for separation of statistically independent sources. It can estimate unknown sources from a mixture of sources without any prior knowledge about them. The sources should be non‐Gaussian and independent with each other. In this work, multiscale ICA is proposed for medical images (fundus images, MRI Images). The data matrix is formed by considering the higher sub‐bands of multiscale decompositions. Performance of multiscale ICA is evaluated and compared with the ICA algorithms using simulated signals and different medical images using Amari performance index and Comon test values. Results show that API and Comon test values are less for multiscale ICA for simulated signals. In case of pathological images, the features are separated correctly by multiscale ICA. Multiscale ICA performs better than simple ICA for separation and detection of independent components from medical images (fundus images), such as blood vessels and artifacts. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 327–337, 2013     

An evolutionary optimization-based approach for simulation of endurance time load functions

A novel optimization method based on Imperialist Competitive Algorithm (ICA) for simulating endurance time (ET) excitations was proposed. The ET excitations are monotonically intensifying acceleration time histories that are used as dynamic loading. Simulation of ET excitations by using evolutionary algorithms has been challenging due to the presence of a large number of decision variables that are highly correlated due to the dynamic nature of the problem. Optimal parameter values of the ICA algorithm for simulating ETEFs were evaluated and were used to simulate ET excitations. In order to increase the capability of the ICA and provide further search in the optimization space, this algorithm was combined with simulated annealing (SA). The new excitation results were compared with the current practice for simulation of ET excitations. It was shown that the proposed ICA-SA method leads to more accurate ET excitations than the classical optimization methods.     

An improved imperialist competitive algorithm based photolithography machines scheduling

Abstract: Photolithography machine is one of the most expensive equipment in semiconductor manufacturing system, and as such is often the bottleneck for processing wafers. This paper focuses on photolithography machines scheduling with the objective of total completion time minimisation. In contrast to classic parallel machines scheduling, it is characterised by dynamical arrival wafers, re-entrant process flows, dedicated machine constraints and auxiliary resources constraints. We propose an improved imperialist competitive algorithm (ICA) within the framework of a rolling horizon strategy for the problem. We develop a variable time interval-based rolling horizon strategy to decide the scheduling point. We address the global optimisation in every local scheduling by proposing a mixed cost function. Moreover, an adaptive assimilation operator and a sociopolitical competition operator are used to prevent premature convergence of ICA to local optima. A chaotic sequence-based local search method is presented to accelerate the rate of convergence. Computational experiments are carried out comparing the proposed algorithm with ILOG CPLEX, dispatching rules and meta-heuristic algorithms in the literature. It is observed that the algorithm proposed shows an excellent behaviour on cycle time minimisation while with a good on time delivery rate and machine utilisation rate.     

Region-Based Multimodal Image Fusion Using ICA Bases

In this paper, we present a novel multimodal image fusion algorithm in the independent component analysis (ICA) domain. Region-based fusion of ICA coefficients is implemented, where segmentation is performed in the spatial domain and ICA coefficients from separate regions are fused separately. The ICA coefficients from given regions are consequently weighted using the Piella fusion metric in order to maximize the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and also shows improvement over other state-of-the-art algorithms     

Integrated process planning and scheduling using an imperialist competitive algorithm

Effective performance of modern manufacturing systems requires integrating process planning and scheduling more tightly, which is consistently challenged by the intrinsic interrelation and intractability of these two problems. Traditionally, these two problems are treated sequentially or separately. Integration of process planning and scheduling (IPPS) provides a valuable approach to improve system performance. However, IPPS is more complex than job shop scheduling or process planning. IPPS is strongly NP-hard in that, compared to an NP-hard job shop scheduling problem with a determined process plan, the process plan for each job in IPPS is also to be optimised. So, an imperialist competitive algorithm (ICA) is proposed to address the IPPS problem with an objective of makespan minimisation. An extended operation-based representation scheme is presented to include information on various flexibilities of process planning with respect to determined job shop scheduling. The main steps of the proposed ICA, including empires construction, assimilation, imperialistic competition, revolution and elimination, are elaborated using an illustrative example. Performance of the proposed ICA was evaluated on four sets of experiments taken from the literature. Computational results of the ICA were compared with that of some existing algorithms developed for IPPS, which validates the efficiency and effectiveness of the ICA in solving the IPPS problem.     

基于独立分量分析的管道异常振动事件定位方法

针对传统互相关时延估计法定位管道故障点的局限性,提出了基于独立分量分析(ICA)的互相关时延估计方法用于管道异常振动事件定位.利用ICA滤波后的管道沿线振动信号进行互相关运算,抑制了相关高斯噪声引起的时延估计误差,为管道安全监测系统提供了一种新的高精度时延估计方法.仿真结果和现场实验数据表明,该方法可以快速精确地定位管道周围异常事件的发生位置,改善了相关运算的时延估计性能,相对于传统互相关时延估计法,可进一步降低时延估计的平均误差和均方差,具有更高的定位精度和定位一致性.     

Factorial coding of natural images: how effective are linear models in removing higher-order dependencies?

The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform coding framework, where it can be characterized by the constraint of a triangular filter matrix. A randomly sampled whitening basis and the Haar wavelet are included in the comparison as well. The comparison of all these methods is carried out for different patch sizes, ranging from 2x2 to 16x16 pixels. In spite of large differences in the shape of the basis functions, we find only small differences in the multi-information between all decorrelation transforms (5% or less) for all patch sizes. Among the second-order methods, PCA is optimal for small patch sizes and predictive coding performs best for large patch sizes. The extra gain achieved with ICA is always less than 2%. In conclusion, the edge filters found with ICA lead to only a surprisingly small improvement in terms of its actual objective.     

Group scheduling in flexible flow shops: a hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism

This paper applied a novel evolutionary algorithm, imperialist competitive algorithm (ICA), for a group scheduling problem in a hybrid flexible flow shop with sequence-dependent setup times by minimising maximum completion time. This algorithm simulates a social-economical procedure, imperialistic competition. Initial population is generated randomly and evolution is carried out during the algorithm. Firstly individuals, countries, are divided into two categories: imperialists and colonies. Imperialist competition will occur among these empires. This competition will increase some empires authority by ruining a weak empire and dividing its colonies among others. Electromagnetic-like mechanism concepts are employed here to model the influence of the imperialist on their colonies. The algorithm will continue until one imperialist exists and possesses all countries. In order to prevent carrying out extensive experiments to find optimum parameters of the algorithm, we apply the Taguchi approach. The computational results are compared with the outstanding benchmark on the flow shop scheduling problem, random key genetic algorithms (RKGA), and it shows superiority of the ICA.