针对交互式多模型(IMM)算法计算量大、模型切换时性能不佳的特点,提出了一种新的机动目标跟踪算法——方差模型概率(Variance Model Probability,VMP)算法。该算法结合多模型思想,利用当前量测残差在线推导模型方差,自适应调整模型概率...针对交互式多模型(IMM)算法计算量大、模型切换时性能不佳的特点,提出了一种新的机动目标跟踪算法——方差模型概率(Variance Model Probability,VMP)算法。该算法结合多模型思想,利用当前量测残差在线推导模型方差,自适应调整模型概率。模型概率大小与方差成反比,滤波输出为各模型加权和。为减小量测噪声引起的误差影响,在设定的时间窗内求方差平均值。仿真结果表明,VMP算法不仅性能优于交互式多模型算法,同时也减少了计算量,提高了费效比。展开更多
Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficie...Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.展开更多
Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical al...Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.展开更多
In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inve...In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.展开更多
Gossiping is a popular technique for probabilistic reliable multicast （or broadcast）. However, it is often difficult to understand the behavior of gossiping algorithms in an analytic fashion. Indeed, existing analys...Gossiping is a popular technique for probabilistic reliable multicast （or broadcast）. However, it is often difficult to understand the behavior of gossiping algorithms in an analytic fashion. Indeed, existing analyses of gossip algorithms are either based on simulation or based on ideas borrowed from epidemic models while inheriting some features that do not seem to be appropriate for the setting of gossiping. On one hand, in epidemic spreading, an infected node typically intends to spread the infection an unbounded number of times （or rounds）; whereas in gossiping, an infected node （i.e., a node having received the message in question） may prefer to gossip the message a bounded number of times. On the other hand, the often assumed homogeneity in epidemic spreading models （especially that every node has equal contact to everyone else in the population） has been silently inherited in the gossiping literature, meaning that an expensive mcnlbership protocol is often needed for maintaining nodes＇ views. Motivated by these observations, the authors present a characterization of a popular class of fault-tolerant gossip schemes （known as ＂push-based gossiping＂） based on a novel probabilistic model, while taking the afore-mentioned factors into consideration.展开更多
文摘针对交互式多模型(IMM)算法计算量大、模型切换时性能不佳的特点,提出了一种新的机动目标跟踪算法——方差模型概率(Variance Model Probability,VMP)算法。该算法结合多模型思想,利用当前量测残差在线推导模型方差,自适应调整模型概率。模型概率大小与方差成反比,滤波输出为各模型加权和。为减小量测噪声引起的误差影响,在设定的时间窗内求方差平均值。仿真结果表明,VMP算法不仅性能优于交互式多模型算法,同时也减少了计算量,提高了费效比。
基金Supported by the National Natural Science Foundation of China(61374140)Shanghai Postdoctoral Sustentation Fund(12R21412600)+1 种基金the Fundamental Research Funds for the Central Universities(WH1214039)Shanghai Pujiang Program(12PJ1402200)
文摘Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.
基金Supported by the National High Technology Research and Development Program of China(2013AA040701)
文摘Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.
基金provided by the National Science and Technology Major Project(No.2011ZX05004-004)China National Petroleum Corporation Key Projects(No.2014E2105)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.
基金supported in part by the US National Science Foundation
文摘Gossiping is a popular technique for probabilistic reliable multicast （or broadcast）. However, it is often difficult to understand the behavior of gossiping algorithms in an analytic fashion. Indeed, existing analyses of gossip algorithms are either based on simulation or based on ideas borrowed from epidemic models while inheriting some features that do not seem to be appropriate for the setting of gossiping. On one hand, in epidemic spreading, an infected node typically intends to spread the infection an unbounded number of times （or rounds）; whereas in gossiping, an infected node （i.e., a node having received the message in question） may prefer to gossip the message a bounded number of times. On the other hand, the often assumed homogeneity in epidemic spreading models （especially that every node has equal contact to everyone else in the population） has been silently inherited in the gossiping literature, meaning that an expensive mcnlbership protocol is often needed for maintaining nodes＇ views. Motivated by these observations, the authors present a characterization of a popular class of fault-tolerant gossip schemes （known as ＂push-based gossiping＂） based on a novel probabilistic model, while taking the afore-mentioned factors into consideration.
