Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.

Fully Automated Density-Based Clustering Method

Cluster analysis is a crucial technique in unsupervised machine learning,pattern recognition,and data analysis.However,current clustering algorithms suffer from the need for manual determination of parameter values,low accuracy,and inconsistent performance concerning data size and structure.To address these challenges,a novel clustering algorithm called the fully automated density-based clustering method(FADBC)is proposed.The FADBC method consists of two stages:parameter selection and cluster extraction.In the first stage,a proposed method extracts optimal parameters for the dataset,including the epsilon size and a minimum number of points thresholds.These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters.The proposed method was evaluated on different benchmark datasets andmetrics,and the experimental results demonstrate its competitive performance without requiring manual inputs.The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method,k-means,spectral clustering,DBSCAN,FCDCSD,Gaussian mixtures,and density-based spatial clustering methods.It can handle any kind of data set well and perform excellently.

基于EMD-PSO-HMM刀具磨损监控系统

为解决在机械加工过程中刀具的磨损及崩刃对加工质量和效率的影响,通过机器人学习技术,设计一套基于EMD-PSO-HMM刀具磨损监控系统。首先提取不同刀具磨损状态下主轴的电流信号,由于传统小波分析及傅里叶分析在信号分析过程存在一定局限性,文章采用EMD算法对加工过程中主轴电流信号进行不同尺度信号分解并提取特征参数,将提取的特征值输入HMM模型进行训练迭代。为解决HMM模型在模型训练的过程中存在局部最小值的问题,文章引入粒子群算法对HMM模型的输入参数进行全局搜索以达到最优值。基于以上形成的EMD-PSO-HMM刀具磨损监控系统在实际刀具磨损状态评估过程中具有较高的准确性。

Density-based trajectory outlier detection algorithm

With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.

Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

基于GASVM-HMM算法的飞行员操控意图识别

针对传统意图识别方法的识别单一性和实时性差的问题,提出基于GASVM-HMM算法的飞行员操控意图识别方法。20名被试者在A320飞行模拟器上进行测试,采集飞行过程中飞行员与显示屏和控制装置的交互动作数据,并建立操控意图数据集。方法将GA与SVM算法结合进行优化,提高识别的精度,并将GASVM层的输出转化为概率作为HMM层的输入值,进一步提高整体意图识别模型的准确性。与传统的算法进行对比后发现,GASVM-HMM算法的准确率较高,达到92.92%。最后进行实时验证,证明了算法的有效性。

LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

基于GMM训练与HMM变换的波纹管振动信号分析

在分析了波纹补偿器异常与正常振动信号后,提出了一种基于GMM训练与HMM变换的振动信号分析方法。首先进行波纹管振动采集实验,保存振动数据,并进行初步时域分析;其次将在自然语言处理领域广泛应用的隐马尔可夫模型应用于波纹补偿器的振动数据分析;最后对提到的故障特征进行基于GMM-HMM算法的波纹补偿器的故障诊断模型构建。通过对实验数据进行测试验证,故障识别率高达96.7%,而传统的算法分析波纹管振动故障,其识别率最高仅为86.7%。此结果表明该算法实现了对波纹补偿器运行状态的准确识别,保证了故障诊断的合理性与高效性。

基于多时窗共空间模式的HMM运动想象脑电识别

运动想象脑电具有识别效果不佳及复杂时序信号建模困难的问题,因此提出一种基于多时窗共空间模式的隐马尔可夫模型运动想象脑电识别方法;首先将运动想象脑电划分为多个短时窗信号,然后使用共空间模式提取特征序列,以滤除脑电通道间的冗余信息,最后采用前向-后相算法与Viterbi算法求解隐马尔可夫模型并完成分类识别;将该方法在公开运动想象脑电数据集上进行实验,得到77.17%的分类正确率,相较隐马尔可夫模型算法提升了5.74%,验证了所提方法的有效性。

基于HMM多维特征融合的电弧故障检测

近些年,伴随着电力系统工作的优化与改革,电力系统中引入和应用了更多先进性的科学技术,极大地提升了电力系统的运行效率和质量,但是也使得电力系统的工作环境越发复杂多变,对电弧故障检测可靠性要求也越来越高。基于此,文章概述了电弧故障的定义、类型、检测方式和检测意义,介绍了HMM统计模型及其适用于电弧故障检测的原因,并探讨了基于HMM多维特征融合的电弧故障检测步骤和相关案例,以供参考。

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social robotics.It enhances systems’ability to interpret and respond to human behavior precisely.This research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse actions.The overall purpose of this study is to develop a robust and accurate system for human interaction recognition.This research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out scenes.Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method.Feature extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)techniques.The application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification process.It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities.The impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed technique.The proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM classification.This enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

基于HMM的远动联调语音多层次背景噪音滤除模型

构建基于HMM的远动联调语音多层次背景噪音滤除模型,多层次滤除远动联调语音背景噪音,确保远动联调工作顺利完成。在远动联调语音识别阶段,输入提取到远动联调语音信号MFCC特征,使用改进Viterbi递推解码算法,获取远动联调语音MFCC特征与参数库中HMM参数相对应的概率密度,将概率密度最大值在远动联调语音字库中对应的字当作有效远动联调语音识别结果,完成背景噪音与无效语音数据初步滤除;在此基础上,使用平移不变小波去噪方法深度滤除有效远动联调语音背景噪音,完成远动联调语音多层次背景噪音滤除。实验结果表明:该方法应用后,背景噪音滤除较为彻底,保证远动联调语音语义准确性较高。

基于DNN-HMM的蒙古语声学模型结构实验研究

DNN-HMM作为语音识别中的一种混合建模技术,由深度神经网络和隐马尔可夫模型组成。在使用蒙古语语料库构建DNN-HMM声学模型的过程中,为了研究DNN-HMM结构对蒙古语声学建模的影响以及蒙古语语料库规模与DNN-HMM声学模型结构的关系,通过设计DNN-HMM声学模型中DNN的结构,该文提出Rectangle DNN-HMM、Trapezoid DNN-HMM、Polygon DNN-HMM和Hourglass DNN-HMM四种结构的DNNHMM声学模型,并以Kaldi实验平台为基础进行实验,选取音素作为建模单元,使用三种规模的蒙古语语料库分别构建四种结构的DNN-HMM声学模型。深度结构和宽度结构实验结果表明,深度为6层的Polygon DNNHMM结构适合蒙古语声学模型建模;随着语料库规模的增大,通过适当增加声学模型的宽度,可以使声学模型的每一层都能学习到更丰富的语音特征,提高语音识别的准确率。

基于HMM的交通运行态势评估及预测方法

文章以西安绕城高速公路为例,应用构建的预测模型,预测路网交通运行态势,并评估预测结果,对比HMM、自回归移动平均模型和灰色马尔可夫模型三种预测方法的准确率和误差,所提出的HMM预测模型不仅能从整体上预测路网交通运行态势的态势值,且准确率更高、误差更小。

基于HMM的低空目标航迹威胁识别

稳健的低空目标威胁识别是低空域安全防护的重要任务。传统的多属性决策方法对目标运动参数的量测精度要求较高,忽略了目标运动的时序关联信息,在实际应用中缺乏噪声稳健性和动态分析能力。因此,在多属性决策方法的基础上引入了隐马尔可夫模型,提出了一种动态稳健的低空目标威胁等级识别方法。通过建立隐状态与威胁等级、威胁数值之间的内在联系,将威胁识别问题转化为隐马尔可夫模型的状态解码问题。相比于常规算法,所提方法能够有效地抑制量测噪声干扰并具有一定的威胁预测能力。仿真实验验证了所提方法的有效性和稳健性。

智慧城市时空大数据平台建设与实践——以宜兴市为例

在新一代信息技术与城市现代化的深度融合下,智慧城市的建设显得尤为重要。城市时空大数据平台是智慧城市的基础支撑。目前的智慧城市普遍存在时空数据单一、数据更新周期长、信息孤岛现象严重等问题。智慧宜兴时空大数据平台建设项目构建统一的时空基准,将各类信息资源集中于一体,引入基于HMM模型的中文地名地址引擎构建技术,并提出一种基于深度学习的电子地图快速更新方法。该平台可为用户提供大量的时空数据和分析服务,具有明显的社会效益和推广价值,为创建“智慧宜兴”奠定了坚实的基础。

基于HMM-FNN模型的复杂动态手势识别

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低维子特征序列,降低了模型的复杂度.此外,它还可以充分利用人的经验辅助模型结构的创建与优化.实验表明,该方法是一种有效的复杂动态手势识别方法,并且优于传统的HMM模型方法.

基于小波域HMM模型的稳健多比特图像水印算法

稳健性是多比特图像水印的关键问题之一,提出了一种基于小波域隐马尔可夫模型(hiddenMarkovmodel,简称HMM)的多比特图像水印算法,该算法的主要特点为:(1)利用向量HMM模型精确描述图像小波系数间的统计特性,基于此统计模型的水印盲检测系统较之传统的相关检测器,在性能上有显著的提升;(2)结合视觉掩盖特性,自适应地调整水印嵌入强度,使之在一定的嵌入强度下,视觉主观失真较小;(3)提出了一种适合隐马尔可夫模型树型结构的多比特数据优化嵌入策略和最大似然检测.数值仿真结果表明,该算法可以较好地利用图像小波域的低频子带以实现较大容量图像水印的嵌入,并在抵抗Stirmark平台攻击,如JPEG压缩、加噪、中值滤波和线性滤波等方面具有很强的稳健性.

基于HMM与神经网络的声学模型研究

神经网络能依靠权值进行长时间记忆和知识存储,但是对输入模式的瞬时相应的记忆能力比较差;而隐马尔科夫模型的短时记忆的能力比较强,但是假定的前提又与实际情况不符.因此,采用HMM和ANN的混合模型来取双方之长,并在这种混合模型的基础上,对神经网络从结构设计、训练、到训练后期的结构调整进行了全程的优化;应用隐节点剪枝算法,并利用广义的Hebb规则重新确定网络的参数.实验表明,这种混合模型在语音识别中取得了良好的效果.