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A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model 被引量:2
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作者 Long Cheng Peng Zhao +1 位作者 Dacheng Wei Yan Wang 《China Communications》 SCIE CSCD 2023年第2期179-197,共19页
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 enviro... 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. 展开更多
关键词 wireless sensor network indoor localization NLOS environment gaussian mixture model(gmm) fitting polynomial
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Marginalized cubature Kalman filtering algorithm based on linear/nonlinear mixed-Gaussian model
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作者 Hu Yumei Hu Zhentao Jin Yong 《High Technology Letters》 EI CAS 2018年第4期362-368,共7页
Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the ma... Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system. 展开更多
关键词 state estimation marginalized modeling mixed-gaussian model CUBATURE KALMAN FILTER
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EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION 被引量:3
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作者 蔡维玲 丁军娣 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期266-274,共9页
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-... 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. 展开更多
关键词 pattern recognition image processing image segmentation gaussian mixture model gmm expectation maximization (EM)
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A multi-target tracking algorithm based on Gaussian mixture model 被引量:3
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作者 SUN Lili CAO Yunhe +1 位作者 WU Wenhua LIU Yutao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第3期482-487,共6页
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 ... 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. 展开更多
关键词 multiple-target tracking gaussian mixture model(gmm) data association expectation maximization(EM)algorithm
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基于GMM-Ada-LASSO模型的高维过程统计质量监控方法
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作者 张帅 杨剑锋 薛丽 《统计与决策》 北大核心 2024年第17期47-52,共6页
针对高维数据往往不服从正态分布导致统计监控模型识别精度低、监控效率差的问题,文章提出一种基于高斯混合模型的变量选择控制图方法。首先,利用高斯混合模型将高维过程分解成若干个服从正态分布的子分布;然后,运用Adaptive LASSO算法... 针对高维数据往往不服从正态分布导致统计监控模型识别精度低、监控效率差的问题,文章提出一种基于高斯混合模型的变量选择控制图方法。首先,利用高斯混合模型将高维过程分解成若干个服从正态分布的子分布;然后,运用Adaptive LASSO算法识别潜在异常变量;最后,构建多元EWMA控制图实现高维过程统计质量监控。通过仿真实验,在六种不同情形下对所提方法的监控性能进行测试。结果表明,与传统MEW⁃MA和VS-MEWMA控制图相比,所提监控方法对非正态数据具有较强的稳健性,对高维过程具有良好的监控性能。 展开更多
关键词 高维数据 非正态过程 高斯混合模型 变量选择控制图
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ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL
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作者 Jian Zhihua Yang Zhen 《Journal of Electronics(China)》 2010年第1期1-7,共7页
Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters fo... Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation. 展开更多
关键词 Speech processing Voice conversion Non-Linear Canonical Correlation Analysis(NLCCA) gaussian mixture model(gmm)
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A GAUSSIAN MIXTURE MODEL-BASED REGULARIZATION METHOD IN ADAPTIVE IMAGE RESTORATION
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作者 Liu Peng Zhang Yan Mao Zhigang 《Journal of Electronics(China)》 2007年第1期83-89,共7页
A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region accor... A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images. 展开更多
关键词 Image processing gaussian mixture model gmm Hopfield Neural Network (Hopfield-NN) REGULARIZATION Adaptive image restoration
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基于GMM和GA-LSTM的稀土熔盐电解过程原料含量状态识别模型
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作者 张震 朱尚琳 +3 位作者 伍昕宇 刘飞飞 何鑫凤 王家超 《中国有色金属学报》 EI CAS CSCD 北大核心 2024年第5期1727-1742,共16页
在高温高风险的稀土熔盐电解工艺中,为了实现稀土熔盐电解过程原料含量状态的智能识别,提出了一种基于混合高斯背景建模(GMM)和遗传算法优化的长短期记忆神经网络(GA-LSTM)的分类模型。模型通过GMM算法、R通道自适应滤波和中值滤波准确... 在高温高风险的稀土熔盐电解工艺中,为了实现稀土熔盐电解过程原料含量状态的智能识别,提出了一种基于混合高斯背景建模(GMM)和遗传算法优化的长短期记忆神经网络(GA-LSTM)的分类模型。模型通过GMM算法、R通道自适应滤波和中值滤波准确提取图像的火焰前景和特征,以量化熔盐电解反应的剧烈程度,进而判断稀土熔盐电解处于原料含量过多或含量正常状态;然后利用GA-LSTM神经网络建立熔盐表面火焰特征和稀土熔盐电解过程原料含量状态的非线性映射关系。结果表明:模型的识别精度高达99.79%,具有较好的泛化性,为实现稀土熔盐电解工艺自动化提供了一定的参考价值。 展开更多
关键词 稀土熔盐 火焰 特征 混合高斯模型 长短期记忆神经网络 遗传算法
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Online split-and-merge expec tation-maximization training of Gaussian mixture model and its optimization
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作者 Ran Xin Zhang Yongxin 《High Technology Letters》 EI CAS 2012年第3期302-307,共6页
This paper presents a new online incremental training algorithm of Gaussian mixture model (GMM), which aims to perform the expectation-maximization(EM) training incrementally to update GMM model parameters online ... This paper presents a new online incremental training algorithm of Gaussian mixture model (GMM), which aims to perform the expectation-maximization(EM) training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance. 展开更多
关键词 gaussian mixture model gmm online training split-and-merge expectation-maximization(SMEM) speech processing
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基于BFOA-PSO-GMM的轨道电路故障诊断研究
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作者 孙波 赵梦莹 何晖 《铁道学报》 EI CAS CSCD 北大核心 2024年第5期85-91,共7页
针对轨道电路系统庞大、故障种类繁多等问题,提出一种融合细菌觅食优化算法和粒子群优化算法的高斯混合模型,对轨道电路的多种故障类型进行诊断。该模型通过融合细菌觅食优化算法与粒子群优化算法,找寻适合EM算法的初始值,利用合适的初... 针对轨道电路系统庞大、故障种类繁多等问题,提出一种融合细菌觅食优化算法和粒子群优化算法的高斯混合模型,对轨道电路的多种故障类型进行诊断。该模型通过融合细菌觅食优化算法与粒子群优化算法,找寻适合EM算法的初始值,利用合适的初始值有效避免EM算法陷入局部最优,提高模型的故障诊断能力。通过对实测数据的训练和测试实验表明,本模型比传统高斯混合模型的故障诊断准确率提高了31.85%,比采用粒子群优化算法改进模型的故障诊断准确率提高了9.4%,即本模型对轨道电路的故障诊断更加有效。 展开更多
关键词 轨道电路 故障诊断 高斯混合模型 粒子群优化算法 细菌觅食优化算法
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基于改进GMM的智能变电站视频目标检测方法研究
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作者 李聪 《微型电脑应用》 2024年第5期88-91,共4页
研究基于改进GMM的智能变电站视频目标检测方法,该方法精准检测变电站视频目标。构建智能变电站视频序列各像素的高斯分布模型,通过判断像素点与高斯分布模型匹配情况,确定该像素点是否为背景,利用四邻域法调整学习率,实现首个匹配高斯... 研究基于改进GMM的智能变电站视频目标检测方法,该方法精准检测变电站视频目标。构建智能变电站视频序列各像素的高斯分布模型,通过判断像素点与高斯分布模型匹配情况,确定该像素点是否为背景,利用四邻域法调整学习率,实现首个匹配高斯分布模型权值调整,根据权值与方差的比值大小实现背景高斯分布模型的降序排列,获取智能变电站视频背景图像,当前帧图像除去背景图像即可得到前景图像,通过去除前景图像的离散点噪声、视频目标内部噪声和阴影,检测智能变电站视频目标。实验结果表明,像素高斯分布为5时,该方法检测到的智能变电站前景图像中的视频目标效果更好,错检像素、漏检前景像素平均值均较低。 展开更多
关键词 改进gmm 智能变电站 视频目标检测 高斯分布模型
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Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection
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作者 Loka Raj Ghimire Roshan Chitrakar 《Journal of Computer Science Research》 2021年第2期1-10,共10页
Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique ... Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category. 展开更多
关键词 Anomaly detection Clustering EM classification Expectation maximization(EM) gaussian mixture model(gmm) gmm classification Intrusion detection Naïve Bayes classification
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基于GMM与JA的轨迹学习与泛化方法研究
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作者 杜闯 《建模与仿真》 2024年第5期5558-5565,共8页
本文针对机器人拆解任务中路径起点或终点发生变化时重定位时间成本高、编程效率低的问题,提出一种基于高斯混合模型(GMM)与Jerk Accuracy模型(JA)的轨迹学习与泛化方法。首先,通过高斯混合模型和高斯混合回归获得最优示教轨迹,然后引... 本文针对机器人拆解任务中路径起点或终点发生变化时重定位时间成本高、编程效率低的问题,提出一种基于高斯混合模型(GMM)与Jerk Accuracy模型(JA)的轨迹学习与泛化方法。首先,通过高斯混合模型和高斯混合回归获得最优示教轨迹,然后引入JA模型,从优化角度生成具有泛化能力的复现轨迹,实现任务位置约束下起点或终点轨迹的泛化。最后,设计仿真实验对所提出方法进行验证。结果表明:该方法有效解决了上述问题,相较于传统的GMM-DMP方法,实验结果显示泛化轨迹与示教轨迹的相似性有了明显提高,验证了所提方法的有效性。 展开更多
关键词 机器人拆解 轨迹学习 轨迹泛化 高斯混合模型 JERK Accuracy模型
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基于PCA和GMM的宽带网络流量异常检测方法
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作者 周永博 《通信电源技术》 2024年第15期192-194,共3页
随着网络规模和复杂度的不断提升,宽带网络流量异常检测成为保障网络稳定运行的关键。文章研究一种基于主成分分析(Principal Component Analysis,PCA)和高斯混合模型(Gaussian Mixture Model,GMM)的宽带网络流量异常检测方法。首先,利... 随着网络规模和复杂度的不断提升,宽带网络流量异常检测成为保障网络稳定运行的关键。文章研究一种基于主成分分析(Principal Component Analysis,PCA)和高斯混合模型(Gaussian Mixture Model,GMM)的宽带网络流量异常检测方法。首先,利用PCA技术对网络流量数据进行特征提取与降维处理,以降低数据的维度和复杂性;其次,采用GMM对降维后的数据进行分类;最后,使用KDD 99数据集对所提方法进行测试。实验表明,该方法能够有效检测宽带网络中的异常流量,具有较高的适应性和稳定性。 展开更多
关键词 主成分分析(PCA) 高斯混合模型(gmm) 网络流量 异常检测
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Semantic image annotation based on GMM and random walk model 被引量:1
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作者 田东平 《High Technology Letters》 EI CAS 2017年第2期221-228,共8页
Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk... Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation. 展开更多
关键词 semantic image annotation gaussian mixture model gmm random walk rival penalized expectation maximization (RPEM) image retrieval
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基于GMM聚类的铁路网络数据风险等级分类方法 被引量:1
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作者 商婧 王佳宁 +2 位作者 刘旭 李琪 王健 《铁路计算机应用》 2023年第11期39-44,共6页
铁路行业信息基础设施及重要信息系统产生的数据种类繁多、数量庞大且价值密度高,而不同类型或等级的铁路网络数据存在不同级别的安全风险。为了完善铁路网络数据风险评估机制,设计一种基于高斯混合模型(GMM,Gaussian Mixture Model)聚... 铁路行业信息基础设施及重要信息系统产生的数据种类繁多、数量庞大且价值密度高,而不同类型或等级的铁路网络数据存在不同级别的安全风险。为了完善铁路网络数据风险评估机制,设计一种基于高斯混合模型(GMM,Gaussian Mixture Model)聚类的铁路网络数据风险等级分类方法。从数据和风险角度提取关键信息,构建风险信息数据集;通过K-means聚类获得初始聚类中心;基于混合距离计算进行GMM聚类,实现数据风险等级划分。经实验验证,与传统K-means聚类、谱聚类算法相比,GMM聚类算法对铁路网络数据的聚类效果更优,能够更加准确地对铁路网络数据进行风险等级分类,从而为进一步落实铁路网络数据安全管理要求提供重要的技术支撑。 展开更多
关键词 高斯混合模型(gmm)聚类 K-MEANS聚类 最大期望(EM)算法 铁路网络 数据风险 风险等级分类
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MODELING INTRAPERSONAL DEFORMATION SUBSPACE USING GMM FOR PALMPRINT IDENTIFICATION
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作者 Li Qiang Qiu Zhengding Sun Dongmei 《Journal of Electronics(China)》 2006年第4期543-548,共6页
In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use in... In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy. 展开更多
关键词 Palmprint identification Density estimation gaussian mixture model gmm Principle Component Analysis (PCA) Intrapersonal Deformation Subspace (IDS)
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一种基于改进的GMM算法的数据丢失预测模型 被引量:1
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作者 王晖 姜春茂 《长江信息通信》 2023年第3期28-34,共7页
随着云平台上运行任务的数量急剧增加,任务失败的概率也随之增加,数据的丢失是任务失败的主要原因。如果在任务运行前判断出是否可能发生丢失以及其丢失类型,那么就可以提前采取措施避免或减少损失。该模型基于谷歌在2019年发布的最新... 随着云平台上运行任务的数量急剧增加,任务失败的概率也随之增加,数据的丢失是任务失败的主要原因。如果在任务运行前判断出是否可能发生丢失以及其丢失类型,那么就可以提前采取措施避免或减少损失。该模型基于谷歌在2019年发布的最新云集群数据,对任务的数据丢失问题进行了深入的研究,针对不同任务属性探究其与数据丢失的相关性,并选用了GMM(Gaussian Mixed Model)算法并将其改进来建立数据丢失预测模型。经过多种聚类算法的实验比较,改进后的GMM模型表现出极好的适应性和准确性,能够精准且迅速地在任务运行前判断其发生数据丢失的可能性以及判断其丢失类型。最后根据预测出的不同数据丢失类型,给出了一定的建议。 展开更多
关键词 谷歌云集群 任务失败 数据丢失预测 gaussian mixed model
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基于GMM-HMM的话题生命周期状态识别及趋势预测方法 被引量:1
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作者 朱恒民 蔡婷婷 魏静 《现代情报》 2023年第3期26-32,41,共8页
[目的/意义]本研究对正处于演化过程中的话题进行状态识别及趋势预测,为相关部门了解话题现状,对话题进行有效监管提供科学依据。[方法/过程]首先,考虑网民情感,结合话题的新颖度和关注度,构建话题生命周期状态观测指标;其次,基于隐马... [目的/意义]本研究对正处于演化过程中的话题进行状态识别及趋势预测,为相关部门了解话题现状,对话题进行有效监管提供科学依据。[方法/过程]首先,考虑网民情感,结合话题的新颖度和关注度,构建话题生命周期状态观测指标;其次,基于隐马尔可夫模型(HMM)和高斯混合模型(GMM)的原理,提出话题生命周期状态识别及趋势预测方法;最后,选用微博话题构建数据集,设计对比实验,验证方法的有效性。[结果/结论]基于GMM-HMM的话题状态识别及趋势预测方法的F1值和准确率均高于87%,MAPE低于3.5%,相较于GaussianHMM和BP神经网络具有较大优势。 展开更多
关键词 话题生命周期状态 话题状态识别 话题趋势预测 高斯混合隐马尔可夫模型
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基于改进JRD及误差修正的轴承剩余寿命预测方法 被引量:1
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作者 刘玉山 张旭帮 +2 位作者 王灵梅 孟恩隆 郭东杰 《机电工程》 北大核心 2024年第1期72-80,共9页
目前,风电机组齿轮箱性能发生初始退化时难以识别,现有退化指标易出现剧烈波动、单调性较差,且无法准确预测齿轮箱关键部件如轴承的剩余使用寿命(RUL),针对该问题,提出了一种基于改进杰森-瑞丽散度(JRD)及误差修正的双指数模型轴承RUL... 目前,风电机组齿轮箱性能发生初始退化时难以识别,现有退化指标易出现剧烈波动、单调性较差,且无法准确预测齿轮箱关键部件如轴承的剩余使用寿命(RUL),针对该问题,提出了一种基于改进杰森-瑞丽散度(JRD)及误差修正的双指数模型轴承RUL预测方法。首先,提取了振动信号样本的多域特征指标,利用高斯混合模型(GMM)与指数型权重JRD,得到了样本的后验概率分布向量,再经归一化处理得到置信值(CV);然后,对轴承从初始健康状态退化至当前检查时刻的CV值进行了相空间重构,提取了CV序列的动力学特征,并将其作为相关向量机(RVM)的训练集,获得了支撑整个退化轨迹的相关向量;最后,利用双指数模型拟合了相关向量,外推趋势至失效门限以计算RUL,并引入了差分整合移动平均自回归模型(ARIMA),对拟合相关向量产生的拟合误差进行了预测,以修正预测的结果。实验结果表明:改进后的退化指标单调性指标提高14.3%;且在不同工况、不同时刻下,经误差修正后的轴承的RUL预测结果较未修正之前有明显提高。研究结果表明:该预测方法可为风电机组齿轮箱重要部件的预测性维护提供参考。 展开更多
关键词 滚动轴承 剩余使用寿命预测 高斯混合模型 杰森-瑞丽散度 误差修正 双指数模型 置信值 差分整合移动平均自回归模型
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