A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Threshold-Based Adaptive Gaussian Mixture Model Integration(TA-GMMI)Algorithm for Mapping Snow Cover in Mountainous Terrain

Snow cover is an important parameter in the fields of computer modeling,engineering technology and energy development.With the extensive growth of novel hardware and software compositions creating smart,cyber physical systems’(CPS)efficient end-to-end workflows.In order to provide accurate snow detection results for the CPS’s terminal,this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model(GMM)for the FY-4A satellite data.At present,most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum,which is based on the normalized difference snow index(NDSI)with thresholds in different wavebands.These algorithms require a large amount of manually labeled data for statistical analysis to obtain the appropriate thresholds for the study area.Consideration must be given to both the high and low elevations in the study area.It is difficult to extract all snow by a fixed threshold in mountainous and rugged terrains.In this research,we avoid relying on a manual analysis for different elevations.Therefore,an algorithm based on the GMM is proposed,integrating the threshold-based algorithm and the GMM.First,the threshold-based algorithm with transferred thresholds from other satellites’analysis results are used to coarsely classify the surface objects.These results are then used to initialize the parameters of the GMM.Finally,the parameters of that model are updated by an expectation-maximum(EM)iteration algorithm,and the final results are outputted when the iterative conditions end.The results show that this algorithm can adjust itself to mountainous terrain with different elevations,and exhibits a better performance than the threshold-based algorithm.Compared with orbit satellites’snow products,the accuracy of the algorithm used for FY-4A is improved by nearly 2%,and the snow detection rate is increased by nearly 6%.Moreover,compared with microwave sensors’snow products,the accuracy is increased by nearly 3%.The validation results show that the proposed algorithm can be adapted to a complex terrain environment in mountainous areas and exhibits good performance under a transferred threshold without manually assigned labels.

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

Cascaded projection of Gaussian mixture model for emotion recognition in speech and ECG signals

A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.

Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management

Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.

Adaptive moving target detection algorithm based on Gaussian mixture model

In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

基于改进YOLOv8和GMM图像点集匹配的双目测距方法

针对无人塔吊系统的研究需求,提出一种基于改进YOLOv8和GMM图像点集匹配的双目测距方法,对驾驶室外环境中的塔吊吊钩进行检测识别并测距。通过双目摄像头进行图像采集,引入FasterNet骨干网络和Slim-neck颈部连接层,对YOLOv8目标检测算法进行改进,有效检测画面中的塔吊吊钩并获取检测框的二维坐标信息;采用局部敏感哈希方法,并融合分阶段匹配策略,提升GMM图像点集匹配模型的匹配效率,针对检测框中的塔吊吊钩,进行特征点匹配;最后通过双目相机三角测量原理计算得出塔吊吊钩的深度信息。实验结果表明,改进后的YOLOv8算法与原算法相比,精确率P提高了2.9%,平均精度AP50提高了2.2%,模型复杂度降低了10.01 GFLops,参数量减少了3.37 M,在提升检测精度的同时实现了模型的轻量化。改进后的图像点集匹配算法与原算法相比,各个指标表现出更加良好的鲁棒性。最后在工程现场对塔吊吊钩进行识别与测距,误差可接受范围内有效完成了塔吊吊钩的检测识别与测距任务,验证了本方法的可行性。

基于模态理论和改进GMM的声发射源识别研究

基于模态声发射信号理论,提出了一种利用声学对数倒谱统计参数作为声发射信号特征参数的分析与提取方法。从声发射信号多模态特性出发,提出了一个基于改进高斯混合模型的声发射源信号识别系统。理论分析和实验结果表明,该方法能准确地判断声发射信号源,不仅能够应用于突发型声发射信号的识别,而且可以应用于连续型声发射信号的识别。

基于MFCC和GMM的瓷砖空鼓率识别系统及方法

针对瓷砖因内部空鼓而引起的松动、脱落等质量问题或其他安全隐患问题,研制了一套用于瓷砖空鼓率识别的试验系统。该系统采用梅尔倒谱系数(MFCC)法提取瓷砖敲击声的特征参数,再用高斯混合模型(GMM)法对MFCC特征参数进行分类和识别。试验结果表明,采用MFCC和GMM相结合的方法,可以对瓷砖空鼓情况进行有效识别,该方法具有良好的应用前景。

基于GMM的流体旋转设备运行可靠性在线评价方法

针对流体旋转设备运行工况多变且难以区分,导致运行可靠性评价准确率低的问题,提出了一种基于高斯混合模型(GMM)的流体旋转设备运行可靠性在线性评价方法。首先,根据设备历史运行数据,基于快速搜索和发现密度峰值的聚类算法(DPC),进行工况划分,构建不同工况条件下的基于GMM的运行可靠性基准模型;其次,使用XGBoost算法对设备实时运行状态进行工况识别,约减冗余指标,构建设备运行可靠性的评价指标体系;然后,计算度量评价指标与对应工况下基准模型指标的偏离程度,以马氏距离作为度量标准,进一步计算得到设备运行可靠性评价指数;最后,以矿用离心机设备为例,进行了多工况下的运行可靠性实例分析和模型验证。研究结果表明,该方法能够在线实时反应设备当前的运行可靠性水平,当离心机设备运行可靠性低于0.857时,认为设备进入劣化状态,且评价准确率达到98%以上。

基于GMM的湿筛混凝土轴拉损伤演化机制研究

为研究不同加载阶段下的二级配湿筛混凝土开裂模式与损伤演化过程,将声发射技术(AE)与高斯混合模型(GMM)进行结合作为损伤识别手段,以3种加载速率(1×10^(-6)、5×10^(-6)、25×10^(-6)s^(-1))作为试验变量,对二级配湿筛混凝土开展单轴拉伸损伤时空演化机制试验研究。结果表明:随着加载速率增大,湿筛混凝土试件内部裂缝开展更加密集,并且裂缝种类随机性更高;利用GMM法对声发射数据进行处理分类结果显示,拉伸裂缝为试验加载过程的主要开裂模式,加载速率升高会导致剪切裂缝占比增大;随着加载速率增大,拉伸裂缝频率分布明显扩大,而剪切裂缝与混合裂缝频率分布基本不变;随着加载进行,拉伸裂缝与剪切裂缝概率密度区域均向AF轴趋近;GMM法所得裂缝开裂模式有拉伸裂缝、剪切裂缝与混合裂缝3种类别,并且随着加载进行,混合断裂区所处位置也会发生变化;相较于常规裂缝模式分类方法,GMM法提供了更好的裂缝分类近似值分析,对裂缝开裂模式表述更加可靠。

基于GMM的纳米制造刀具磨损状态在线识别

为满足纳米制造刀具磨损状态在线诊断对时间和精度的要求,采用跨物理数据融合建模方案,建立具有物理一致性的高斯混合模型(GMM),以动态识别原子力显微镜(AFM)尖端状态。随机抽取历史加工数据,提取特征参数并进行训练,获得3维GMM模型并预存;以加窗分帧的形式,截取连续过程中短时段纳米加工力时变信号,构成瞬时稳态数据空间;以尖端旋转周期为时间单位,计算横向加工力的特征参数:极大值、峰-峰值和方差;采用马氏距离检测并去除异常值。使用预存的GMM模型,对每帧特征参数聚类,识别尖端磨损状态;根据连续分析帧的尖端失效点数据变化曲线,探测跟踪尖端状态。实验证明该算法平均识别精度为0.8917,平均召回率为0.963;每2000个点的最长识别时间为31ms,平均识别时间为23.97ms,适用于大规模纳米制造的刀具磨损在线自动诊断。

A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of positioning.Non-line-of sight(NLOS)is a primary challenge in indoor complex environment.In this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error.Firstly,fitting polynomials are used to predict the measured values.The residuals of predicted and measured values are clustered by Gaussian mixture model(GMM).The LOS probability and NLOS probability are calculated according to the clustering centers.The measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in turn.The distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to probability.Finally,the maximum likelihood method is used to calculate the position coordinate estimation.Through simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper.And it shows strong robustness in strong NLOS environment.

A multi-target tracking algorithm based on Gaussian mixture model

Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.

基于DTW-GMM的光纤传感系统声纹识别方法

为了满足易燃易爆环境的声纹识别需求,设计了直线型萨格奈克干涉光纤声音传感系统,利用维纳滤波算法对语音数据进行了降噪,通过三电平削波法获取了基音周期特征,采用动态时间规整算法筛选了说话人样本,并提取了梅尔频率倒谱系数特征,运用高斯混合模型-期望最大化算法开展了声纹识别实验研究,同时探究了光纤声音传感系统的频率响应特性与声纹特征,研究了采集语音幅值对声纹识别结果的影响。实验结果表明,系统可实现300~3500 Hz频率段的声音信号感知,声音幅值从0.9 V降至0.15 V时最大与次大对数似然值之差由35.5降至10.9,识别结果从成功变为失败。重复性实验表明,在10 km的传感光纤上,距声源2 m位置处,传感系统可对400段时长为3~5 s之间的文本无关语音段实现准确检测,且综合识别准确率为94.75%。本系统有望为易燃易爆环境中的设备故障、应急救援、渗漏监测等领域提供声纹识别的解决方案。

RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.

Color-texture segmentation using JSEG based on Gaussian mixture modeling

An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.