Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

Saliency detection and edge feature matching approach for crater extraction

Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.

共同所有权与会计信息可比性——来自中国资本市场的经验证据

作为中国股票市场重要参与者,机构投资者共同投资多家上市公司的现象越来越普遍,但学术界对这一现象的研究仍相当匮乏。基于中国沪深A股市场机构投资者共同持有上市公司股份数据,文章考察了共同所有权与会计信息可比性之间的关系。实证结果表明:(1)共同所有权具有显著的公司治理作用和信息传播效应,有助于提升会计信息可比性;(2)相比于未拥有共同机构投资者的企业,拥有共同机构投资者企业的会计信息可比性更高;(3)当共同机构投资者为稳定型机构投资者时,共同所有权与会计信息可比性的正向关系更为显著;(4)相比于国有企业,共同所有权主要提高了民营企业的会计信息可比性。

管理会计视域下的钢铁企业“双碳”转型:困境与路径

“双碳”目标的提出对钢铁产业的碳减排进程具有推动作用,低碳绿色转型已成为钢铁企业不可逆的趋势。本文基于钢铁行业调研,研究分析企业实现“双碳”目标的路径和困境,以及管理会计如何更好地助力钢铁企业进行“双碳”转型。本文研究发现,钢铁行业具有碳排放量基数高、冶炼工艺转型难的特点,这导致钢铁企业“双碳”转型面临着地方政府、政治、技术等方面的压力。在此背景下,企业可以综合运用碳金融、产业碳预算、转移定价、标杆管理等管理会计工具助力钢铁企业“双碳”目标的实现。本文的研究为钢铁企业的“双碳”转型提供了新的视角。

交互门控循环单元及其在到达时间估计中的应用

门控循环单元(gated recurrent unit, GRU)是一种有代表性的深度神经网络,它在众多序列学习任务中达到了国际领先的水平.然而,在门控循环单元的每个时间步之间,输入信息与隐含状态信息缺乏交互,这对更好地挖掘上下文语义信息带来了挑战.针对这个问题,本文提出了一个新颖的序列学习通用的语义特征提取模型:交互门控循环单元(interactive gated recurrent unit, InterGRU),可以让输入与隐含状态向量在各时间步间进行多轮充分的交互.并且,在到达时间估计(estimated time of arrival, ETA)这个有代表性、有挑战的时空序列预测任务上,本文提出了一套基于交互门控循环单元的深度学习框架(InterGRU-ETA).本文在来自滴滴出行平台真实场景下的海量数据集上充分地实验验证了InterGRU-ETA.结果表明,我们的框架在预测准确率上优于目前国际上最先进的方法.这反映了交互门控循环单元在捕获序列语义信息上的性能优势和广阔前景.

数据驱动的多工况过程异常监测方法:综述与展望

由于原材料、设定点、外界环境等因素变化,工业过程通常具有多个运行工况.多工况过程异常监测技术是保证工业系统安全运行的有效方式,也是过程监测技术的研究热点与难点.首先,对多工况过程异常监测方法进行概述,并将多工况过程的常见处理方式归纳为整体建模、自适应建模、混合建模和多模型建模方法;其次,分别回顾了多工况平稳过程和多工况非平稳过程的研究现状,并分析了各类方法的优缺点;最后,结合实际工业系统特点,指出该领域亟须解决的问题和未来发展方向.

A Human Active Lower Limb Model for Chinese Pedestrian Safety Evaluation

A subsystem impactor test for pedestrian lower limb injury evaluation has been brought in China New Car Assessment Protocol(CNCAP).Concerning large anthropometric differences of the people from different countries,the present study aims to establish and validate a finite element lower limb model representing 50th Chinese male size for pedestrian safety research,then compare its biomechanical responses with the general models currently in wide use in the world for pedestrian safety evaluation.Concerning the vehicle-pedestrian impact loading environment,the previously developed lower limb model with three-dimensional muscles was adjusted and validated through the related experiments.Then,the biomechanical responses of the validated model were compared with the Total Human Model for Safety(THUMS)and Advanced Pedestrian Legform Impactor(aPLI)models by combing with four typical vehicles.The results showed that both consistency and significant differences of biomechanical responses existed between the present model and the other two models.The injury measurements of the thigh region of the present model showed extremely large differences with the other two models,while the tibia and Medial Collateral Ligament(MCL)injury measurements show similar values.Thus,it can be concluded that directly using the aPLI or THUMS models for Chinese pedestrian safety evaluation is not robust concerning both kinematic responses and injury measurements.

Condition-based switching and replacement policies for a two-unit warm standby redundant system subject to non-instantaneous switchover

This study investigates condition-based switching and replacement policies for a two-unit warm standby redundant system subject to non-instantaneous switchover. The system consists of two identical units: one is operating unit, while the other is standby unit. Two units are under continuous monitoring and degradation described by Gamma processes. Both switching and replacement policies are based on the degradation level of the operating unit. The switching and replacement thresholds are decision variables decided by minimizing the long-run expected cost rate. We first setup the mathematical optimization model and then analyze the global optimal solution of replacement threshold, as well as the existence and uniqueness condition for the local optimal solution of switching threshold(STH). Finally, we find that the optimal replacement threshold is ‘‘the bigger the better'', but the optimal STH depends on some optimal conditions which can be easily computed. Numerical examples are provided to verify the policies, and the effects of noninstantaneous switchover and cost ratio on optimal STHs are numerically discussed.

Examining export trade and corporate innovation:A multiphase difference-in-differences method

Using a multiphase difference-in-differences model, this study investigates the relationship between export trade and the corporate technological innovation of listed companies. It reveals that engaging in export trade increases corporate innovation input and output. In terms of patent output, export trade greatly promotes the output of invention patents and utility model patents with a high technological content. These conclusions remain valid after a series of robustness and endogeneity tests. Regarding the mechanisms of the observed relationships, export trade stimulates corporate technological innovation mainly by realizing economies of scale and increasing risk-taking. The positive correlation between export trade and corporate technological innovation is strongest among state-owned enterprises, non-high-tech enterprises, enterprises based in central and eastern China, enterprises engaged in general trade, and enterprises exporting to developed economies. Given the growing trade frictions ongoing at the time of writing, the conclusions of this study provide vital practical guidance and empirical evidence for a national strategy of innovation-driven development.