The BYY annealing learning algorithm for Gaussian mixture with automated model selection

Bayesian Ying-Yang (BYY) learning has provided a new mechanism that makes parameter learning with automated model selection via maximizing a harmony function on a backward architecture of the BYY system for the Gaussian mixture. However, since there are a large number of local maxima for the harmony function, any local searching algorithm, such as the hard-cut EM algorithm, does not work well. In order to overcome this difficulty, we propose a simulated annealing learning algorithm to search the global maximum of the harmony function, being expressed as a kind of deterministic annealing EM procedure. It is demonstrated by the simulation experiments that this BYY annealing learning algorithm can efficiently and automatically determine the number of clusters or Gaussians during the learning process. Moreover, the BYY annealing learning algorithm is successfully applied to two real-life data sets, including Iris data classification and unsupervised color image segmentation.     

基于马尔可夫混合模型的电子商务搜索引擎用户行为聚类

对搜索引擎用户行为进行聚类分析有利于为用户提供个性化的服务。为了能准确地刻画用户行为的动态性,提出利用马尔可夫混合模型,对电子商务搜索引擎的用户行为模式聚类。模型假设每一类用户行为可表示为一个马尔可夫模型,当用户使用搜索引擎时,每个用户以一定的概率属于某一聚类;该用户的行为序列,由对应的马尔可夫模型产生。同时,为了解决参数估计和模型自动选择的问题,将贝叶斯阴阳和谐学习理论应用于该混合模型,提出针对该模型的和谐度函数及自适应梯度算法。仿真实验结果表明,与传统的最大期望（EM）算法相比,基于贝叶斯阴阳机的自适应梯度算法能更高效和准确地同时进行参数学习和模型选择。最后,将所提出的聚类方法应用于真实的电子商务搜索引擎点击日志,初步验证了本模型的有效性。     

面向大规模数据集的贝叶斯网络参数学习算法

贝叶斯网络的学习可以分为结构学习和参数学习。期望最大化（EM）算法通常用于不完整数据的参数学习,但是由于EM算法计算相对复杂,存在收敛速度慢和容易局部最大化等问题,传统的EM算法难于处理大规模数据集。研究了EM算法的主要问题,采用划分数据块的方法将大规模数据集划分为小的样本集来处理,降低了EM算法的计算量,同时也提高了计算精度。实验证明,该改进的EM算法具有较高的性能。     

Simultaneous feature selection and Gaussian mixture model estimation for supervised classification problems

A new expectation maximization (EM) algorithm for time-critical supervised classification tasks in remote sensing is proposed. Compared to standard EM and other approaches, it has the following advantages: (1) No knowledge about the class distributions is needed. (2) The number of components is estimated. (3) It does not require careful initialization. (4) Singular estimates are avoided due to the ability of pruning components. (5) The best discriminating features are identified simultaneously. (6) The features are identified by incorporating Mahalanobis distances.     

Proportional data modeling via selection and estimation of a finite mixture of scaled Dirichlet distributions

This paper proposes an unsupervised algorithm for learning a finite mixture of scaled Dirichlet distributions. Parameters estimation is based on the maximum likelihood approach, and the minimum message length (MML) criterion is proposed for selecting the optimal number of components. This research work is motivated by the flexibility issues of the Dirichlet distribution, the widely used model for multivariate proportional data, which has prompted a number of scholars to search for generalizations of the Dirichlet. By introducing the extra parameters of the scaled Dirichlet, several useful statistical models could be obtained. Experimental results are presented using both synthetic and real datasets. Moreover, challenging real-world applications are empirically investigated to evaluate the efficiency of our proposed statistical framework.     

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.     

基于免疫克隆选择算法的马斯京根模型参数估计

针对马斯京根河道洪水演算模型参数估计中所存在的线性化、求解复杂、精度差等问题,提出了一种基于免疫克隆选择算法（ICSA）的马斯京根模型参数估计新方法。实验和应用结果表明,基于免疫克隆选择的马斯京根模型参数估计算法具有求解速度快,计算精度高,算法控制参数设置简便、通用性强等特点,与现有的马斯京根模型参数估计方法相比,该算法显示出更好的优化性能,能够很好地解决马斯京根模型的参数最优估计问题,从而为马斯京根模型参数的估计提供了一种新的更为有效的方法。该算法也可广泛应用于其他洪水预报模型的优化问题。     

基于[t]分布混合模型的半监督网络流分类方法

针对传统高斯分布容易受到数据样本边缘值和离群点噪声的影响,改用t分布替代原有的高斯混合模型,并使用期望最大化（Expectation Maximization,EM）算法对网络流数据样本进行t分布混合模型的建模。为降低EM算法的迭代次数,对t分布混合模型进行了改进,用理论和实验验证了算法的有效性,并对网络多媒体业务流进行了分类研究。实验表明,提出的算法有较高的分类准确率,拟合的模型要优于传统的K-Means算法和传统的高斯混合模型的EM算法。     

一种基于 Bernoulli混合模型的不完整数据文本分类方法

在Bernoulli混合模型和期望最大化(EM)算法的基础上给出了一种基于不完整数据的改进方法。首先在已标记数据的基础上通过Bernoulli混合模型和朴素贝叶斯算法得到似然函数参数估计初始值, 然后利用含有权值的EM算法对分类器的先验概率模型进行参数估计,得到最终的分类器。实验结果表明,该方法在准确率和查全率方面要优于朴素贝叶斯文本分类。     

基于多目标EDA的特征基因选择*

基因（特征）数远大于条件（样本）数,基因表达数据中往往存在大量噪声,并且生物学或医学工作者期 望能从大量的基因中挑选出与疾病诊断有关的标志基因,因此,应用基因表达数据进行疾病分类预测的关键环 节是基因选择。目前常用的方法有过滤法和缠绕法。结合过滤法和缠绕法的优点,提出基因选择的多目标分布 估计算法（MOEDA）。首先通过打分函数确定MOEDA的候选基因集合,在确定候选基因后,MOEDA通过对 KNN分类器的多个性能指标及基因数目等多个目标进行优化,从候选基因中选取综合区分能力最强的特征基 因子集     

A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving

On one hand, multiple object detection approaches of Hough transform (HT) type and randomized HT type have been extended into an evidence accumulation featured general framework for problem solving, with five key mechanisms elaborated and several extensions of HT and RHT presented. On the other hand, another framework is proposed to integrate typical multi-learner based approaches for problem solving, particularly on Gaussian mixture based data clustering and local subspace learning, multi-sets mixture based object detection and motion estimation, and multi-agent coordinated problem solving. Typical learning algorithms, especially those that base on rival penalized competitive learning (RPCL) and Bayesian Ying-Yang (BYY) learning, are summarized from a unified perspective with new extensions. Furthermore, the two different frameworks are not only examined with one viewed crossly from a perspective of the other, with new insights and extensions, but also further unified into a general problem solving paradigm that consists of five basic mechanisms in terms of acquisition, allocation, amalgamation, admission, and affirmation, or shortly A5 paradigm.     

基于粒子群优化算法的Richards模型参数估计和算法有效性

针对Richards模型参数估计较为困难的实际问题,提出将Richards模型的参数估计问题转化为一个多维无约束函数优化问题。结合谷氨酸菌体的实际生长浓度数据,在Matlab 2012b环境中,利用粒子群优化（PSO）算法建立适应度函数,在最小线性二乘意义下估计Richards模型中的4个参数,并建立了拟合的生长曲线和最优值变化曲线。为进一步验证算法有效性,将PSO算法与该模型传统参数估计法中的四点法和遗传算法(GA)进行了比较,以相关指数和剩余标准差作为评价指标。结果表明,PSO算法对Richards模型的拟合效果良好,对模型的参数估计有着很好的适用性。     

空间约束层次加权Gamma混合模型的SAR图像分割

目的 合成孔径雷达（SAR）图像中像素强度统计分布呈现出复杂的特性,而传统混合模型难以建模非对称、重尾或多峰等特性的分布。为了准确建模SAR图像统计分布并得到高精度分割结果,本文提出一种利用空间约束层次加权Gamma混合模型（HWGaMM）的SAR图像分割算法。方法 采用Gamma分布的加权和定义混合组份;考虑到同质区域内像素强度的差异性和异质区域间像素强度的相似性,采用混合组份加权和定义HWGaMM结构。采用马尔可夫随机场（MRF）建模像素空间位置关系,利用中心像素及其邻域像素的后验概率定义混合权重以将像素邻域关系引入HWGaMM,构建空间约束HWGaMM,以降低SAR图像内固有斑点噪声的影响。提出算法结合M-H（Metropolis-Hastings）和期望最大化算法（EM）求解模型参数,以实现快速SAR图像分割。该求解方法避免了M-H算法效率低的缺陷,同时克服了EM算法难以求解Gamma分布中形状参数的问题。结果 采用3种传统混合模型分割算法作为对比算法进行分割实验。拟合直方图结果表明本文算法具有准确建模复杂统计分布的能力。在分割精度上,本文算法比基于高斯混合模型（GMM）、Gamma分布和Gamma混合模型（GaMM）分割算法分别提高33%,29%和9%。在分割时间上,本文算法虽然比GMM算法多64 s,但与基于Gamma分布和GaMM算法相比较分别快600 s和420 s。因此,本文算法比传统M-H算法的分割效率有很大的提高。结论 提出一种空间约束HWGaMM的SAR图像分割算法,实验结果表明提出的HWGaMM算法具有准确建模复杂统计分布的能力,且具有较高的精度和效率。     

自动化码头双场桥调度与集装箱存储选位建模

针对不可相互穿越的双轨道式龙门起重机（RMG）调度和集装箱存储选位问题,考虑双RMG间的安全距离、缓冲区容量等约束,以双RMG调度为主线、集装箱存储选位为辅线,设置决策变量描述执行任务之间的关系,以最小化总任务的完工时间为目标,建立双RMG调度和存储选位耦合模型。设计遗传与蚁群融合算法（GAAA）对模型进行求解,分析接力模式和混合模式下的效率问题,并将GAAA与CPLEX求得的解进行对比分析。实验结果表明,集装箱任务量在8~150时,接力模式的效率优于混合模式;在小、中大规模实验中,GAAA的最小完工时间比CPLEX的结果分别平均减少2.65%、18.50%,算法的运行时间分别平均减少88.6%、99.19%,验证了模型的有效性。     

基于IGA与GMM的图像多阈值分割方法*

为了实现图像的有效分割,提出了一种自适应多阈值图像分割方法,能够自动获得最佳分割阈值数目和阈值。该方法对灰度直方图进行合适尺度的连续小波变换,将小波变换曲线中幅值为负的波谷点构成阈值候选集;再应用免疫遗传算法从阈值候选集中选取准阈值,准阈值的个数对应为最佳分割类数;根据准阈值构建灰度直方图的高斯混合模型,由最小误差准则求得分割阈值。仿真实验表明,该方法能够实现图像的自动多阈值分割,能够得到很好的分割结果且分割效率高,在多目标图像分割中能够得到很好的应用。     

利用模型选择确定视觉词袋模型中词汇数目

视觉词袋（Visual Bag-of-Words）模型在图像分类、检索和识别等计算机视觉领域有了广泛的应用,但是视觉词袋模型中词汇数目往往是根据经验确定或者采用有监督的交叉学习选取。提出一种确定视觉词袋模型中词汇数目的无监督方法,利用模型选择的思想来解决问题。使用高斯混合模型描述具有不同词汇数目的视觉词袋,计算各模型贝叶斯信息准则的值,选取贝叶斯信息准则最小值对应的词汇数目。与交叉验证的监督学习在图像分类实验的对比结果说明该方法准确有效。     

Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling

Mathematical modeling of plant growth has gained increasing interest in recent years due to its potential applications. A general family of models, known as functional–structural plant models (FSPMs) and formalized as dynamic systems, serves as the basis for the current study. Modeling, parameterization and estimation are very challenging problems due to the complicated mechanisms involved in plant evolution. A specific type of a non-homogeneous hidden Markov model has been proposed as an extension of the GreenLab FSPM to study a certain class of plants with known organogenesis. In such a model, the maximum likelihood estimator cannot be derived explicitly. Thus, a stochastic version of an expectation conditional maximization (ECM) algorithm was adopted, where the E-step was approximated by sequential importance sampling with resampling (SISR). The complexity of the E-step creates the need for the design and the comparison of different simulation methods for its approximation. In this direction, three variants of SISR and a Markov Chain Monte Carlo (MCMC) approach are compared for their efficiency in parameter estimation on simulated and real sugar beet data, where observations are taken by censoring plant’s evolution (destructive measurements). The MCMC approach seems to be more efficient for this particular application context and also for a large variety of crop plants. Moreover, a data-driven automated MCMC–ECM algorithm for finding an appropriate sample size in each ECM step and also an appropriate number of ECM steps is proposed. Based on the available real dataset, some competing models are compared via model selection techniques.     

整合供应商选择的多层级选址-库存模型

针对多级供应链网络设计中选址和库存一体化决策问题,基于梯级库存策略,建立了整合供应商选择的多层级选址-库存模型。模型以网络中供应商的选择成本、工厂和配送中心的打开成本、层级之间的运输成本、库存成本、采购成本和生产成本之和最小为目标,同时对供应商的选择、工厂和配送中心的选址、配送中心对顾客的分配、层级之间的运输量、工厂和配送中心的订货批量进行决策。为了求解所建立的模型,设计了基于部分编码的粒子群优化算法。20个不同规模的算例测试表明：所建立的模型是有效的,能用于多层级供应链网络的设计;所设计的算法无论是在求解精度,还是在运算速度上都明显优于数学优化软件Lingo 9.0,尤其是当供应链网络中总节点数较大时。     

DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

Objective.Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, limiting the development of accurate and reliable object distinction.Method.To this end, we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone to improve in preserving and reusing critical feature information. Additionally, convolutional block attention modules (CBAM) have been implemented to improve attention performance mechanisms for strong and discriminating deep spatial feature extraction that results in superior detection under various challenging environments. Moreover, an additional feature fusion layers and a Swin-Transformer Prediction Head (SPH) have been added leveraging advanced self-attention mechanism for more efficient detection of multiscale object sizes and simultaneously reducing the computational complexity.Results.Evaluating the model performance in large-scale Road Damage Dataset (RDD-2018), at a detection rate of 62.4 FPS, DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25%, F1-score of 81.18%, and precision (P) value of 89.51% outperforming current state-of-the-art models.Significance.The present research provides an effective and efficient damage localization model addressing the shortcoming of existing DL-based damage detection models by providing highly accurate localized bounding box prediction. Current work constitutes a step towards an accurate and robust automated damage detection system in real-time in-field applications.     

基于机器学习的PM2.5短期浓度动态预报模型

针对目前现有的PM2.5模式预报系统的预报值偏离实际浓度较大的问题,从上海市浦东气象局获得2015年2月至7月的PM2.5实况观测浓度、PM2.5模式预报（WRF-Chem）浓度和5个主要气象因子的模式预报数据资料,联合应用支持向量机（SVM）和粒子群优化（PSO）算法建立滚动预报模型,对PM2.5未来24小时浓度进行预报,同时对未来一天的昼、夜均值及日均值浓度进行预报,并与径向基函数神经网络（RBFNN）、多元线性回归法（MLR）、模式预报（WRF-Chem）作对比。实验结果表明,相比其他预报方法,所提出的SVM模型较大提高了PM2.5未来1小时浓度预报精度,这与此前的研究结论相符;所提模型能对PM2.5未来24小时浓度进行较好的预报,能对未来一天的昼均值、夜均值及日均值进行有效预报,并且对未来12小时的逐时浓度及未来一天的夜均值浓度的预报准确度较高。