检索结果-内蒙古大学图书馆

Functional state-space model for multi-channel autoregressive profiles with application in advanced manufacturing

JOURNAL OF MANUFACTURING SYSTemS 2022年 64卷 356-371页

作者： Zhou, Peng Liu, Peiyao Wang, Shilong Zhang, Chen Zhang, Junxing Li, Shaobo Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China

Multi-channel profile data analysis is an important research topic in modern quality management for advanced manufacturing. The most crucial part is how to model the profile data. Usually, the profiles of sequential manufactured products have auto-correlations and formulate a profile data time series. However, most of the existing studies ignore this auto-correlation and hence set up models with less accurate predictions. This study aims to develop a novel functional state-space model for multi-channel autoregressive profiles. In particular, we expand multi-channel profiles on a set of basis functions. The expansion coefficients are further modeled as evolving according to an autoregressive model. The expectation-maximization algorithm and Kalman filter are used to estimate the expansion coefficients together with the evolving mechanics. Numerical studies and real cases from the manufacturing system show the superiority and generality of our model in describing different multi-channel autoregressive profile data compared to state-of-the-art methods.

关键词： Functional state -space model Autoregressive multi -channel profiles Basis expansion em algorithm Kalman filtering

来源：评论

学校读者我要写书评

暂无评论

A gamma-frailty model for interval-censored data with dependent examination times: a computationally efficient approach

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2022年第10期51卷 6071-6082页

作者： Chen, Chyong-Mei Shen, Pao-sheng Lee, Ting-Hsuan Natl Yang Ming Univ Inst Publ Hlth Taipei Taiwan Tunghai Univ Dept Stat Taichung Taiwan

Interval-censored survival data arise naturally in many fields such as medical follow-up studies, in which the event or failure is not observed exactly but only known to occur within a time interval. Most existing approaches for analyzing interval-censored failure time data assume that the examination times and the failure time are independent or conditionally independent given covariates. While this assumption offers considerable simplification, it is not plausible in some situations, e.g., the visiting rate can be positively or negatively correlated with the risk of failure due to unobservable health status even after adjusting for observable covariates. In this article, we consider dependent interval-censored data, where there exists dependence between the failure time and the entire visiting process. A shared frailty is used to characterize the dependence of hazard function of failure time and intensity function of visiting process. Moreover, the joint model could describe the possible none, positive or negative association between failure time and visiting process. We propose the semiparametric maximum likelihood estimators and develop an em algorithm based on a Poisson data augmentation. The performance of the proposed method is examined through extensive simulation studies and an application to a bladder cancer dataset is presented.

关键词： Data augmentation Dependent censoring em algorithm Interval censored survival data Recurrent event data

来源：评论

学校读者我要写书评

暂无评论

Robust identification for input non-uniformly sampled Wiener model by the expectation-maximisation algorithm

引用

IET SIGNAL PROCESSING 2022年第3期16卷 281-298页

作者： Jin, Qibing Wang, Zeyu Beijing Univ Chem Technol Inst Automat Beisanhuan East Rd 15 Beijing 10029 Peoples R China

The problems of inconsistent data sampling frequency, outliers, and coloured noise often exist in system identification, resulting in unsatisfactory identification results. In this study, a novel identification method of input non-uniform sampling Wiener model with a coloured heavy-tailed noise is proposed. The lifted Wiener model with coloured noise and outlier value disturbed is constructed. Under the expectation-maximisation (em) algorithm framework, the student's t-distribution is introduced to model the contaminated output data. The variance scale is regarded as a unique latent variable, and the iterative parameter estimation formula of the non-uniform sampling Wiener model is derived. The idea of the auxiliary model is applied to acquire the unmeasured middle variable and handle the coloured noise variable in the non-uniformly sampled Wiener model. The Differential Evolution algorithm is used to calculate the intractable part of the Q-function. The convergence analysis of the proposed algorithm is given. Two numerical examples and one water tank simulation are employed to indicate the effectiveness of the proposed method.

关键词： coloured heavy-tailed noise DE algorithm em algorithm non-linear system non-uniformly sampled parameter estimation

来源：评论

学校读者我要写书评

暂无评论

An Image Reconstruction algorithm for Electrical Impedance Tomography Using Measurement Estimation of Virtual Electrodes

引用

IEEE SENSORS JOURNAL 2022年第13期22卷 13012-13022页

作者： Yang, Lu Wu, Hongtao Liu, Kai Chen, Bai Yang, Yunjie Zhu, Chengjun Yao, Jiafeng Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Univ Edinburgh Sch Engn Inst Digital Commun Agile Tomog Grp Edinburgh EH9 3JL Midlothian Scotland Jiangsu Prov Hosp Dept Oncol Nanjing 210008 Peoples R China

For biomedical imaging in mini-scale, the inverse problem of electrical impedance tomography (EIT) is severely ill-conditioned due to the number of electrodes is very limited. In this paper, a novel difference imaging algorithm for 2D-EIT using measurement estimation of virtual electrodes is proposed. The proposed reconstruction algorithm breaks though the limitation of micro EIT sensor's structure and tackles the problem of low spatial resolution in mini-scale by introducing virtual electrodes. The inverse problem of EIT is decomposed into two separately tasks: estimation potentials of virtual electrodes with proper priors, determination of the conductivity distribution using both virtual and real electrodes. It is formulated as a possibility model using virtual electrodes' potentials as latent variables. Real electrodes' potentials are regarded as the observable variables. Conductivity distribution is predicted by a maximum likelihood of the possibility model by using em algorithm. The proposed algorithm is verified by both simulation and experiment. In experiment, medaka fish embryo which is about 1mm in diameter is reconstructed. Comparing with Tikhonov and NOSER regularization method, average ICC is improved 11.54% from 0.7620 to 0.8499 in simulation and improved 5.79% from 0.6978 to 0.7382 in experiment. The algorithm not only performs well in conductivity reconstruction, but also in shape reconstruction. Average position error is decreased 47.57%, average shape error is decreased 24.44% in experiment. It is very suitable for image reconstruction in mini-scale.

关键词： Electrodes Image reconstruction Electric potential Electrical impedance tomography Conductivity Sensors Biomedical measurement Electrical impedance tomography (EIT) image reconstruction measurement estimation virtual electrode em algorithm

来源：评论

学校读者我要写书评

暂无评论

Dealing with Markov-switching parameters in quantile regression models

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2022年第11期51卷 6773-6791页

作者： Kim, Yunmi Huo, Lijuan Kim, Tae-Hwan Univ Seoul Dept Econ Seoul South Korea Beijing Inst Technol Sch Humanities & Social Sci Beijing Peoples R China Yonsei Univ Sch Econ 134 Shinchon Dong Seoul 120749 South Korea

Quantile regression has become a standard modern econometric method because of its capability to investigate the relationship between economic variables at various quantiles. The econometric method of Markov-switching regression is also considered important because it can deal with structural models or time-varying parameter models flexibly. A combination of these two methods, known as "Markov-switching quantile regression (MSQR)," has recently been proposed. Liu and, Liu and Luger propose MSQR models using the Bayesian approach whereas Ye et al.'s proposal for MSQR models is based on the classical approach. In our study, we extend the results of Ye et al. First, we propose an efficient estimation method based on the expectation-maximization algorithm. In our second extension, we adopt the quasi-maximum likelihood approach to estimate the proposed MSQR models unlike the maximum likelihood approach that Ye et al. use. Our simulation results confirm that the proposed expectation-maximization (em) estimation method for MSQR models works quite well.

关键词： em algorithm Markov-switching Quantile regression Quasi-maximum likelihood estimation Structural breaks

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Stationary Subspace Analysis for Monitoring Nonstationary Industrial Processes With Uncertainty

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第5期18卷 3114-3125页

作者： Wu, Dehao Zhou, Donghua Chen, Maoyin Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

Actual industrial processes often show nonstationary characteristics, so nonstationary process monitoring is significant to ensure the safety and reliability of industrial processes. However, existing monitoring methods for nonstationary processes usually ignore process uncertainties, caused by random noises and unknown disturbances. It is worth noting that process uncertainties may degrade the monitoring performance for incipient faults, and result in over-fitting of model parameters. To address the problem of monitoring nonstationary industrial processes with uncertainty, a novel algorithm called probabilistic stationary subspace analysis (PSSA) is proposed in this article. PSSA explicitly models process uncertainties, and distinguishes actual process variations from the uncertainty. In view of the coupling between model parameters, the expectation maximization algorithm is used to estimate the parameters of PSSA, and the closed-form updates are derived in detail. Based on PSSA, two detection statistics are designed for process monitoring. Finally, the effective performance of the proposed method is demonstrated by three case studies, including a numerical example, a closed-loop continuous stirred tank reactor, and a real power plant at Zhejiang Provincial Energy Group of China.

关键词： Uncertainty Probabilistic logic Market research Process monitoring Informatics Principal component analysis Heuristic algorithms Anomaly detection em algorithm nonstationary processes probabilistic stationary subspace analysis (PSSA) process monitoring thermal power plant uncertainty

来源：评论

学校读者我要写书评

暂无评论

Power of Error Correcting Codes for SFBC-OFDM Classification Over Unknown Channels

引用

IEEE ACCESS 2022年 10卷 35643-35652页

作者： Marey, Mohamed Mostafa, Hala Prince Sultan Univ Coll Engn Smart Syst Engn Lab Riyadh 11586 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia

The development of intelligent radios in wireless applications is mainly driven by the growing need for higher data rates, along with constrained spectrum resources. An intelligent radio is one that can autonomously assess the communication environment and automatically update the communication parameters to achieve optimal performance. The problem of determining the type of space-frequency block coding (SFBC) for orthogonal frequency division multiplexing (OFDM) transmissions is one of the main tasks of an intelligent receiver. Previous approaches to this problem are restricted to uncoded communications;nevertheless, existing systems typically utilize error-correcting codes. This study develops a maximum-likelihood (ML) classifier that discriminates among SFBC-OFDM signals using the soft outputs of a channel decoder. The mathematical analysis shows that the maximization of the likelihood function can be carried out by employing an iterative expectation-maximization (em) procedure. A channel estimator is also included in the proposed classifier as a vital step. The findings show that the classification performance of the proposed algorithm is considerably better than the classical classifiers reported in the literature, at the cost of an acceptable increase in computing complexity.

关键词： Error correction codes Codes Classification algorithms OFDM Transmitting antennas Block codes Modulation Signal classification SFBCs em algorithm

来源：评论

学校读者我要写书评

暂无评论

Nonparametric K-means algorithm with applications in economic and functional data

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第2期51卷 537-551页

作者： Feng, Zhangmei Zhang, Jiamin Shanghai Univ Finance & Econ Shanghai Peoples R China

Inspired by the well-known relationship between K-means algorithm and Expectation-Maximization (em) algorithm for mixture models, we propose nonparametric K-means algorithm for estimation of nonparametric mixture of regressions and mixture of Gaussian processes. The proposed methods are illustrated by extensive numerical simulations, comparisons, and analysis of two real datasets. Simulation studies and applications demonstrate that our method is an effective and competitive procedure for modified em algorithm in nonparametric mixture settings.

关键词： K-means algorithm mixture models nonparametric regression Gaussian processes em algorithm

来源：评论

学校读者我要写书评

暂无评论

Generative Model of Autoencoders Self-Learning on Images Represented by Count Samples

引用

AUTOMATION AND RemOTE CONTROL 2022年第12期83卷 1959-1983页

作者： Antsiperov, V. E. Russian Acad Sci Kotelnikov Inst Radioengn & Elect Moscow 125009 Russia

The paper substantiates the concept of autoencoders focused on automatic generation ofcompressed images. We propose a solution to the problem of synthesizing such autoencoders inthe context of machine learning methods, understood here as learning based on the input imagesthemselves (in the bootstrap spirit). For these purposes, a special representation of images hasbeen developed using samples of counts of a controlled size (sampling representations). Based onthe specifics of this representation, a generative model of autoencoders is formalized, which is thenspecified to a probabilistic parametric sampling model in the form of a mixture of *** on the concept of receptive fields, a reduction of the general model of a mixture ofcomponents to a grid model of finite components of an exponential family is discussed. Thisallows the synthesis of computationally realistic coding algorithms.

关键词： machine learning autoencoder generative model mixture of distributions em algorithm receptive field

来源：评论

学校读者我要写书评

暂无评论

A Truncated Mixture Transition Model for Interval-Valued Time Series

引用

JOURNAL OF FINANCIAL ECONOMETRICS 2023年第4期22卷 1130-1169页

作者： Luo, Yun Gonzalez-Rivera, Gloria Monmouth Univ Dept Econ Finance & Real Estate W Long Branch NJ 07764 USA Univ Calif Dept Econ Riverside CA 92521 USA

We propose a model for interval-valued time series that specifies the conditional joint distribution of the upper and lower bounds as a mixture of truncated bivariate normal distributions. It preserves the interval natural order and provides great flexibility on capturing potential conditional heteroscedasticity and non-Gaussian features. The standard expectation maximization (em) algorithm applied to truncated mixtures does not provide a closed-form solution in the M step. A new em algorithm solves this problem. The model applied to the interval-valued IBM daily stock returns exhibits superior performance over competing models in-sample and out-of-sample evaluation. A trading strategy showcases the usefulness of our approach.

关键词： em algorithm interval-valued data mixture transition model truncated normal distribution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：