Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

Asymptotic normality of error density estimator in stationary and explosive autoregressive models

In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.

NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

Exploration of the Impact Mechanism of Government Credibility Based on Variable Screening Method

Government credibility is an important asset of contemporary national governance, an important criterion for evaluating government legitimacy, and a key factor in measuring the effectiveness of government governance. In recent years, researchers’ research on government credibility has mostly focused on exploring theories and mechanisms, with little empirical research on this topic. This article intends to apply variable selection models in the field of social statistics to the issue of government credibility, in order to achieve empirical research on government credibility and explore its core influencing factors from a statistical perspective. Specifically, this article intends to use four regression-analysis-based methods and three random-forest-based methods to study the influencing factors of government credibility in various provinces in China, and compare the performance of these seven variable selection methods in different dimensions. The research results show that there are certain differences in simplicity, accuracy, and variable importance ranking among different variable selection methods, which present different importance in the study of government credibility issues. This study provides a methodological reference for variable selection models in the field of social science research, and also offers a multidimensional comparative perspective for analyzing the influencing factors of government credibility.

The Credibility Estimators under MLINEX Loss Function

In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric quadratic loss function. A credibility model with multiple contracts was established and the corresponding credibility estimator was derived under MLINEX loss function. For this model the estimations of the structure parameters and a numerical example were also given.

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

The growth of the internet and technology has had a significant effect on social interactions.False information has become an important research topic due to the massive amount of misinformed content on social networks.It is very easy for any user to spread misinformation through the media.Therefore,misinformation is a problem for professionals,organizers,and societies.Hence,it is essential to observe the credibility and validity of the News articles being shared on social media.The core challenge is to distinguish the difference between accurate and false information.Recent studies focus on News article content,such as News titles and descriptions,which has limited their achievements.However,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user engagement.In the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and shares.We calculate user credibility and combine it with article content with the user’s context.After combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or fake.Therefore,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required features.The dataset contains 23000+articles with millions of user engagements.The highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest classifier.Our discoveries confirmed that the proposed classifier would effectively classify misinformation in social networks.

Robust Estimators for Poisson Regression

The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.

Joint polarization and DOA estimation based on improved maximum likelihood estimator and performance analysis for conformal array

The conformal array can make full use of the aperture,save space,meet the requirements of aerodynamics,and is sensitive to polarization information.It has broad application prospects in military,aerospace,and communication fields.The joint polarization and direction-of-arrival(DOA)estimation based on the conformal array and the theoretical analysis of its parameter estimation performance are the key factors to promote the engineering application of the conformal array.To solve these problems,this paper establishes the wave field signal model of the conformal array.Then,for the case of a single target,the cost function of the maximum likelihood(ML)estimator is rewritten with Rayleigh quotient from a problem of maximizing the ratio of quadratic forms into those of minimizing quadratic forms.On this basis,rapid parameter estimation is achieved with the idea of manifold separation technology(MST).Compared with the modified variable projection(MVP)algorithm,it reduces the computational complexity and improves the parameter estimation performance.Meanwhile,the MST is used to solve the partial derivative of the steering vector.Then,the theoretical performance of ML,the multiple signal classification(MUSIC)estimator and Cramer-Rao bound(CRB)based on the conformal array are derived respectively,which provides theoretical foundation for the engineering application of the conformal array.Finally,the simulation experiment verifies the effectiveness of the proposed method.

Improved population mean estimator with exponential function under non-response

In this article,we consider a new family of exponential type estimators for estimating the unknown population mean of the study variable.We propose estimators taking advantage of the auxiliary variable information under the first and second non-response cases separately.The required theoretical comparisons are obtained and the numerical studies are conducted.In conclusion,the results show that the proposed family of estimators is the most efficient estimator with respect to the estimators in literature under the obtained conditions for both cases.

Distributed Trimmed Hill Estimator

Proceeded from trimmed Hill estimators and distributed inference, a new distributed version of trimmed Hill estimator for heavy tail index is proposed. Considering the case where the number of observations involved in each machine can be either the same or different and either fixed or varying to the total sample size, its consistency and asymptotic normality are discussed. Simulation studies are particularized to show the new estimator performs almost in line with the trimmed Hill estimator.

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information.Also,it utilizes pilots to offer more helpful information about the communication channel.The proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based CSEs.The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators.Using three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based CSEs.The BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)CSEs.In addition,the computational and learning time complexities for DNN-CSEs are provided.These estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

基于轻型自限制注意力的结构光相位及深度估计混合网络

相位提取与深度估计是结构光三维测量中的重点环节,目前传统方法在结构光相位提取与深度估计方面存在效率不高、结果不够鲁棒等问题。为了提高深度学习结构光的重建效果,本文提出了一种基于轻型自限制注意力(Light Self-Limited-Attention,LSLA)的结构光相位及深度估计混合网络,即构建一种CNN-Transformer的混合模块,并将构建的混合模块放入U型架构中,实现CNN与Transformer的优势互补。将所提出的网络在结构光相位估计和结构光深度估计两个任务上进行实验,并和其他网络进行对比。实验结果表明:相比其他网络,本文所提出的网络在相位估计和深度估计的细节处理上更加精细,在结构光相位估计实验中,精度最高提升31%;在结构光深度估计实验中,精度最高提升26%。该方法提高了深度神经网络在结构光相位估计及深度估计的准确性。

文化遗产数字叙事信任模型:概念与框架

信任是数字环境下文化遗产数字叙事的基础。文化遗产数字叙事是一种具有重构和演绎特质的叙事方式,在数字化、数据化和艺术化阶段会产生不同的信任问题。作为一种特殊的信息系统,文化遗产数字叙事的人机交互特征体现出系统、信息和用户之间的相互连通和协同影响,系统叙事的可信度和用户体验的信任感构成了信任模型的双向动态互动,涉及文化遗产信息资源、叙事性架构、数字化呈现、系统本身的功能建设和知识服务,以及用户的意图和行动。文化遗产数字叙事信任模型不仅有助于提升文化遗产的数智化活化利用水平,促进对文化遗产信息资源的深度挖掘、叙事内容的创意演绎、文化内涵的有效阐释与呈现,而且有望为可信的文化遗产数字叙事提供理论和实践指导,助力文化遗产数字化转型发展。

Encoderless Five-phase PMa-SynRM Drive System Based on Robust Torque-speed Estimator with Super-twisting Sliding Mode Control

In this paper,a robust torque speed estimator(RTSE)for linear parameter changing(LPC)system is proposed and designed for an encoderless five-phase permanent magnet assisted synchronous reluctance motor(5-phase PMa-SynRM).This estimator is utilized for estimating the rotor speed and the load torque as well as can solve the speed sensor fault problem,as the feedback speed information is obtained directly from the virtual sensor.In addition,this technique is able to enhance the 5-phase PMa-SynRM performance by estimating the load torque for the real time compensation.The stability analysis of the proposed estimator is performed via Schur complement along with Lyapunov analysis.Furthermore,for improving the 5-phase PMa-SynRM performance,five super-twisting sliding mode controllers(ST-SMCs)are employed with providing a robust response without the impacts of high chattering problem.A super-twisting sliding mode speed controller(ST-SMSC)is employed for controlling the PMa-SynRM rotor speed,and four super-twisting sliding mode current controllers(ST-SMCCs)are employed for controlling the 5-phase PMa-SynRM currents.The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed RTSE and the ST-SMSC with ST-SMCCs approach for a 750-W 5-phase PMa-SynRM under load disturbance,parameters variations,single open-phase fault,and adjacent two-phase open circuit fault conditions.

带多级免赔额的风险保费及其贝叶斯估计

在汽车保险中,保单设计常常会提供多个级别的免赔额以供被保险人选择.因此,在汽车保险的聚合风险模型中,将索赔额按大小分为若干级别.进而,建立了在均值-方差保费原理下风险保费的贝叶斯模型,并获得了风险保费和贝叶斯估计的显式解.研究结论显示:在一定条件下,贝叶斯估计能表达为样本估计和聚合估计的加权平均.同时,研究了风险保费的线性贝叶斯估计,获得了在任意分布下风险保费的最优信度估计,证明了贝叶斯估计和信度估计的强相合性和渐近正态性.最后,利用数值模拟方法验证了估计的大样本性质.

基于机器学习的成本法在专利价值评估中的应用研究--以“新能源汽车”为例

[目的/意义]构建基于机器学习的成本法专利价值评估方法,快速识别海量专利的实际成本,并预测其价值区间,在为专利价值评估提供新研究思路的同时,也为专利转移转化定价提供了参考借鉴。[方法/过程]通过Innography数据库与Incopat数据库下载“新能源汽车”领域多指标专利数据,提取专利成本影响因素与专利价值影响因素,并形成专利数据训练集与专利数据预测集;构建AutoGluon机器学习分类算法,将包含成本数据的Innography专利数据训练集导入模型进行训练,并将训练好的模型对Incopat专利数据预测集进行成本预测;最后使用成本法并结合本研究提出的专利价值指数对预测结果进行计算,估算其价格区间。[结果/结论]通过实证分析与结果验证可知,本研究构建的基于机器学习的成本法专利价值评估方法在预测专利价值区间中具备一定有效性,为促进专利价值评估研究深化及专利转移转化定价实践发展提供了参考。

锂离子电池健康状态估计及寿命预测研究进展综述

随着锂离子电池的应用越来越广泛,锂电池健康状态的精确估计和剩余寿命的实时预测对于锂电池系统的安全运行和降低运维成本具有重要意义。锂电池内部复杂的物理化学反应和外部复杂工作条件,使得实现精准的健康状态估计和寿命预测具有挑战性。该文综述近年来锂电池健康状态估计和剩余使用寿命预测方法的研究现状,分析基于物理/数学模型、数据驱动、模型法和数据驱动融合,以及多种数据驱动融合的锂电池健康状态估计方法的优缺点及适用条件,并对比分析不同数据驱动类型的锂电池寿命预测方法。指出锂电池健康状态估计及寿命预测尚存在的问题,并对未来研究方向进行展望,对完善锂电池健康状态估计和寿命预测算法理论体系、指导实际应用技术具有重要意义。

基于深度学习的二维人体姿态估计研究进展

人体姿态估计在人体行为识别、人机交互、体育运动分析等方面有着广泛的应用前景,是计算机视觉领域的一个研究热点。在最近的十年中,得益于深度学习技术,大量的研究工作极大地推动了人体姿态估计技术的发展,但由于受训练样本不足、人体姿态的多变性、遮挡、环境的复杂性等因素影响,人体姿态估计仍然面临着诸多的挑战。文中对近年来基于深度学习的2D人体姿态估计方法进行归纳和总结,着重分析一些有代表性的人体姿态估计方法的思路及工作原理,以便研究人员了解当前的研究现状、面临的挑战以及今后的研究方向,拓展研究思路。