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

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Chinese Journal of Chemical Engineering 2023年第5期57卷 151-161页

作者： Xiongzhuo Zhu Dali Gao Chong Yang Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

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 ***, 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 *** with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

关键词： Chemical processes Principal component analysis gaussian mixture model Process monitoring Ensemble Process control

Multipath mitigation method based on gaussian mixture model in RF relative measurement

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Chinese Journal of Aeronautics 2018年第12期31卷 2270-2279页

作者： Weiqing MU Rongke LIU Zijie WANG Xinxin YANG School of Electronic and Information Engineering Beihang University

Radio Frequency(RF) technology represents a high-precision relative navigation solution that has significant potential for application to earth-orbiting satellites. In precision applications, multipath errors dominate the total error because observables, which are used to estimate carrier-phase integer ambiguity, are not always subject to a gaussian distribution when dualfrequency ambiguity estimation methods are used in the presence of multipath. As it has been shown that ranging observables obey a gaussian mixture distribution, this study proposes improvements to the accuracy of estimation based on multipath mitigation founded on the gaussian mixture model. To this end, such a model is created for integer ambiguity resolution in the presence of multipath, using which the theoretical error in dual-frequency ambiguity estimation is *** Maximization(EM), which aids dual-frequency ambiguity estimation, is subsequently proposed to reduce the effect of multipath errors. Finally, two experimental scenarios are implemented to test the performance of the proposed method. The results show that EM-aided dualfrequency ambiguity estimation reduces the range error to approximately 20% in comparison with simple dual-frequency ambiguity estimation. Therefore the proposed technique is effective for multipath mitigation in RF relative measurement.

关键词： Dual-frequency ambiguity estimation gaussian mixture model Multipath mitigation RF relative measurement Satellite navigation

Application of a Novel Method for Machine Performance Degradation Assessment Based on gaussian mixture model and Logistic Regression

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Chinese Journal of Mechanical Engineering 2011年第5期24卷 879-884页

作者： LIU Wenbin ZHONG Xin LEE Jay LIAO Linxia ZHOU Min Chemical Safety Engineering Research Center of Ministry of Education Beijing University of Chemical Technology Beijing 100029 China The First Research Institute of Ministry of Public Security Beijing 100048 China National Science Foundation Industry/University Cooperative Research Center of Intelligent Maintenance Systems University of Cincinnati Cincinnati OH 45221 USA China National Petroleum Corporation Beijing 100007 China

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 ***,failure data are always hard to acquire,thus making those techniques hard to be *** this paper,a novel method which does not need failure history data is *** 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 ***-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 ***,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 *** SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine *** 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 *** proposed method has engineering significance for machine performance degradation assessment.

关键词： performance degradation assessment gaussian mixture model logistic regression proactive maintenance sensor fusion

Multimodal process monitoring based on transition-constrained gaussian mixture model

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Chinese Journal of Chemical Engineering 2020年第12期28卷 3070-3078页

作者： Shutian Chen Qingchao Jiang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is *** multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical *** study proposes a transition-constrained gaussian mixture model method for efficient multimodal process *** technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online *** process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode *** studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

关键词： Multimodal process monitoring gaussian mixture model State transition matrix Process control Process systems Systems engineering

Multimodal Biometric Score Fusion Using gaussian mixture model and Monte Carlo Method

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Journal of Computer Science & Technology 2010年第4期25卷 771-782页

作者： R Raghavendra Rao Ashok G Hemantha Kumar Department of Studies in Computer Science University of Mysore Department of E&C Channabasaveshwara Institute of Technology

Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on gaussian mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.

关键词： multimodal biometric system match score level fusion gaussian mixture model Monte Carlo method.

Short-term traffic safety forecasting using gaussian mixture model and Kalman filter

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第4期14卷 231-243页

作者： Sheng JIN Dian-hai WANG Cheng XU Dong-fang MA College of Civil Engineering and Architecture Zhejiang University Zhejiang Police College

In this paper,a prediction model is developed that combines a gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on *** time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe ***,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) *** propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance *** test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different *** results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

关键词： Forecasting Traffic safety gaussian mixture model Kalman filter

RBMDO Using gaussian mixture model-Based Second-Order Mean-Value Saddlepoint Approximation

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Computer modeling in Engineering & Sciences 2022年第8期132卷 553-568页

作者： Debiao Meng Shiyuan Yang Tao Lin Jiapeng Wang Hengfei Yang Zhiyuan Lv School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu611731China Failure Mechanics&Engineering Disaster Prevention and Mitigation Key Laboratory of Sichuan ProvinceSichuan UniversityChengdu610065China Yangzhou Yangjie Electronic Technology Co. Ltd.Yangzhou225008China Sichuan Special Equipment Inspection and Research Institute Chengdu610100China Food Safety Inspection Technology Center of Administration for Market Regulation of Sichuan Province Chengdu610017China

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

关键词： Uncertain factors reliability-based multidisciplinary design optimization saddlepoint approximate gaussian mixture model collaborative optimization

Automated segmentation of intraretinal cystoid macular edema based on gaussian mixture model

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Journal of Innovative Optical Health Sciences 2020年第1期3卷 35-47页

作者： Jinghong Wu Sijie Niu Qiang Chen Wen Fan Songtao Yuan Dengwang Li Shandong Key Laboratory of Medical Physics and Image Processing&Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Shandong Normal University Jinan 250358P.R.China School of Information Science and Engineering University of Jinan Jinan 250022P.R.China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P.R.China Department of Ophthalmology the First Affiliated Hospital with Nanjing Medical University Nanjing 210094P.R.China

We introduce a method based on gaussian mixture model(GMM)clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy(DR)from spectral domain optical coherence tomography(SD-OCT)images in this ***,each B-scan is segmented using GMM *** original chustering results are refined using location and thickness ***,the spatial information among every consecutive five B-scans is used to search potential ***,the improved level-set method is used to obtain the accurate *** high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

关键词： gaussian mixture model level-set spectral domain optical coherence tomography(SD-OCT) segmentation

Threshold-Based Adaptive gaussian mixture model Integration(TA-GMMI)Algorithm for Mapping Snow Cover in Mountainous Terrain

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Computer modeling in Engineering & Sciences 2020年第9期124卷 1149-1165页

作者： Yonghong Zhang Guangyi Ma Wei Tian Jiangeng Wang Shiwei Chen School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Atmospheric Physics Nanjing University of Information Science&TechnologyNanjing210044China

Snow cover is an important parameter in the fields of computer modeling,engineering technology and energy *** the extensive growth of novel hardware and software compositions creating smart,cyber physical systems’(CPS)efficient end-to-end *** 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 *** 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 *** algorithms require a large amount of manually labeled data for statistical analysis to obtain the appropriate thresholds for the study *** must be given to both the high and low elevations in the study *** is difficult to extract all snow by a fixed threshold in mountainous and rugged *** this research,we avoid relying on a manual analysis for different ***,an algorithm based on the GMM is proposed,integrating the threshold-based algorithm and the ***,the threshold-based algorithm with transferred thresholds from other satellites’analysis results are used to coarsely classify the surface *** results are then used to initialize the parameters of the ***,the parameters of that model are updated by an expectation-maximum(EM)iteration algorithm,and the final results are outputted when the iterative conditions *** results show that this algorithm can adjust itself to mountainous terrain with different elevations,and exhibits a better performance than the threshold-based *** 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 n

关键词： Cyber physical systems FY-4A snow cover gaussian mixture model