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 information. 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%.

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.

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.

一种高速列车制动系统故障检测方法

高速列车制动系统是确保列车安全平稳运行的关键系统之一,对其中发生的微小故障进行及时有效的检测可避免事故的发生并提高系统的可靠性.本文首先对高速列车制动系统中的EP阀漏气故障进行了简要分析;其次,提出了一种面向该系统的故障诊断架构;再次,针对故障特点提出了一种基于全局边际判别分析的故障检测方法;最后,在中车青岛四方车辆研究所有限公司的制动试验平台上对本文所提架构和方法进行了有效验证.

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.

Visual Information Based Social Force Model for Crowd Evacuation

With the increase in large-scale incidents in real life, crowd evacuation plays a pivotal role in ensuring the safety of human crowds during emergency situations. The behavior patterns of crowds are well rendered by existing crowd dynamics models. However, most related studies ignore the information perception of pedestrians.To overcome this issue, we develop a visual information based social force model to simulate the interpretable evacuation process from the perspective of visual perception. Numerical experiments indicate that the evacuation efficiency and decision-making ability promote rapidly within a small range with the increase in unbalanced prior knowledge. The propagation of acceleration behavior caused by emergencies is asymmetric due to the anisotropy of visual information. Therefore, this model effectively characterizes the effect of visual information on crowd evacuation and provides new insights into the information perception of individuals in complex scenarios.