In order to model effectively hybrid systems,a new modeling method of extended Petri nets,which is called extended object-orient hybrid Petri net （EOHPN）,is proposed.To deal with the complexity of hybrid systems, ob...In order to model effectively hybrid systems,a new modeling method of extended Petri nets,which is called extended object-orient hybrid Petri net （EOHPN）,is proposed.To deal with the complexity of hybrid systems, object-oriented abstraction mechanisms such as encapsulation and classifications are merged into EOHPN models.To combine the continuous part and discrete part of hybrid systems and to reduce the complexity of hybrid systems,a hybrid Petri net is introduced and extended with object-oriented modeling technology.Development of object models is suggested on the basis of the defined EOHPN.Finally, an application-oriented case is presented to illustrate that how the proposed EOHPN is used to model hybrid systems.The resulting model validates that the EOHPNs can deal with the modeling complexity of hybrid systems.展开更多
Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffe...Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to misjudgment.To tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the proposed method outperformed popular deep CNNs and existing prevalent attention models for smoke and fire detection problems.展开更多
Hybrid Petri nets(HPNs) are widely used to describe and analyze various industrial hybrid systems that have both discrete-event and continuous discrete-time behaviors. Recently,many researchers attempt to utilize them...Hybrid Petri nets(HPNs) are widely used to describe and analyze various industrial hybrid systems that have both discrete-event and continuous discrete-time behaviors. Recently,many researchers attempt to utilize them to characterize power and energy systems. This work proposes to adopt an HPN to model and analyze a microgrid that consists of green energy sources. A reachability graph for such a model is generated and used to analyze the system properties.展开更多
To describe a semiconductor wafer fabrication flow availably, a new modeling method of extended hybrid Petri nets （EHPNs） was proposed. To model the discrete part and continuous part of a complex photolithography pr...To describe a semiconductor wafer fabrication flow availably, a new modeling method of extended hybrid Petri nets （EHPNs） was proposed. To model the discrete part and continuous part of a complex photolithography process, hybrid Petri nets （HPNs） were introduced. To cope with the complexity of a photolithography process, object-oriented methods such as encapsulation and classifications were integrated with HPN models. EHPN definitions were presented on the basis of HPN models and object-oriented methods. Object-oriented hybrid Petri subnet models were developed for each typical physical object and an EHPN modeling procedure steps were structured. To demonstrate the feasibility and validity of the proposed modeling method, a real wafer photolithography case was used to illustrate the modeling procedure. dynamic modeling of a complex photolithography process effectively The modeling results indicate that the EHPNs can deal with the dynamic modeling of a complex photolithography process effectively.展开更多
为了借用一阶混杂Petri网(First-Order Hybrid Petri Nets)的建模原语和分析方法来分析流体随机Petri网(Fluid Stochastic Petri Nets)以克服流体随机Petri网数值分析方法的局限性,本文提出了一种流体随机Petri网转换成一阶混杂Petri网...为了借用一阶混杂Petri网(First-Order Hybrid Petri Nets)的建模原语和分析方法来分析流体随机Petri网(Fluid Stochastic Petri Nets)以克服流体随机Petri网数值分析方法的局限性,本文提出了一种流体随机Petri网转换成一阶混杂Petri网的形式化描述方法,并对其转换的正确性进行了证明,最后通过实例分析了流体随机Petri网转换成一阶混杂Petri网的必要性。展开更多
应用混合Petri网建立故障诊断模型,应用广义随机Petri网建立Boe ing 777非相似余度飞控计算机故障行为模型.描述了非相似余度系统的结构以及故障的产生和传播的动态过程,分析了该系统的可靠度和容错度,并有效地消除了瞬态故障对分析系...应用混合Petri网建立故障诊断模型,应用广义随机Petri网建立Boe ing 777非相似余度飞控计算机故障行为模型.描述了非相似余度系统的结构以及故障的产生和传播的动态过程,分析了该系统的可靠度和容错度,并有效地消除了瞬态故障对分析系统可靠性的影响.展开更多
针对目前协同设计中业务过程描述能力不足和柔性差等问题,从并发性、可伸缩性和协同性等五方面进行分析,提出了一种在空间上三维分布的Petri网建模方法.将组合、颜色和定时约束Petri网引入协同设计的流程建模之中,设计了协同设计下云工...针对目前协同设计中业务过程描述能力不足和柔性差等问题,从并发性、可伸缩性和协同性等五方面进行分析,提出了一种在空间上三维分布的Petri网建模方法.将组合、颜色和定时约束Petri网引入协同设计的流程建模之中,设计了协同设计下云工作流业务流程的HPN(hybrid Petri net)模型;运用随机Petri网的思想对协同设计各流程流转、工作效率等进行分析,详细分析了其性能指标,验证了模型的可达性,并从业务冲突及模型分解等方面提出了优化.展开更多
基金The National Key Laboratory Program ( No.51458060104JW0316)the National High Technology Research and De-velopment Program of China (863 Program) (No.2003AA414120).
文摘In order to model effectively hybrid systems,a new modeling method of extended Petri nets,which is called extended object-orient hybrid Petri net （EOHPN）,is proposed.To deal with the complexity of hybrid systems, object-oriented abstraction mechanisms such as encapsulation and classifications are merged into EOHPN models.To combine the continuous part and discrete part of hybrid systems and to reduce the complexity of hybrid systems,a hybrid Petri net is introduced and extended with object-oriented modeling technology.Development of object models is suggested on the basis of the defined EOHPN.Finally, an application-oriented case is presented to illustrate that how the proposed EOHPN is used to model hybrid systems.The resulting model validates that the EOHPNs can deal with the modeling complexity of hybrid systems.
基金supported by the National Key Research and Development Program of China(Grant No. 2021ZD0112302)the National Natural Science Foundation of China(Grant Nos. 62076013, 62021003, 61890935)CAAI-Huawei MindSpore Open Fund(Grant No. CAAIXSJLJJ-2021-016A)。
文摘Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to misjudgment.To tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the proposed method outperformed popular deep CNNs and existing prevalent attention models for smoke and fire detection problems.
基金supported by the Deanship of Scientific Research(DSR)King Abdulaziz University,Jeddah(23-135-35-HiCi)
文摘Hybrid Petri nets(HPNs) are widely used to describe and analyze various industrial hybrid systems that have both discrete-event and continuous discrete-time behaviors. Recently,many researchers attempt to utilize them to characterize power and energy systems. This work proposes to adopt an HPN to model and analyze a microgrid that consists of green energy sources. A reachability graph for such a model is generated and used to analyze the system properties.
基金Project(60574054) supported by the National Natural Science Foundation of China
文摘To describe a semiconductor wafer fabrication flow availably, a new modeling method of extended hybrid Petri nets （EHPNs） was proposed. To model the discrete part and continuous part of a complex photolithography process, hybrid Petri nets （HPNs） were introduced. To cope with the complexity of a photolithography process, object-oriented methods such as encapsulation and classifications were integrated with HPN models. EHPN definitions were presented on the basis of HPN models and object-oriented methods. Object-oriented hybrid Petri subnet models were developed for each typical physical object and an EHPN modeling procedure steps were structured. To demonstrate the feasibility and validity of the proposed modeling method, a real wafer photolithography case was used to illustrate the modeling procedure. dynamic modeling of a complex photolithography process effectively The modeling results indicate that the EHPNs can deal with the dynamic modeling of a complex photolithography process effectively.
文摘为了借用一阶混杂Petri网(First-Order Hybrid Petri Nets)的建模原语和分析方法来分析流体随机Petri网(Fluid Stochastic Petri Nets)以克服流体随机Petri网数值分析方法的局限性,本文提出了一种流体随机Petri网转换成一阶混杂Petri网的形式化描述方法,并对其转换的正确性进行了证明,最后通过实例分析了流体随机Petri网转换成一阶混杂Petri网的必要性。
文摘针对目前协同设计中业务过程描述能力不足和柔性差等问题,从并发性、可伸缩性和协同性等五方面进行分析,提出了一种在空间上三维分布的Petri网建模方法.将组合、颜色和定时约束Petri网引入协同设计的流程建模之中,设计了协同设计下云工作流业务流程的HPN(hybrid Petri net)模型;运用随机Petri网的思想对协同设计各流程流转、工作效率等进行分析,详细分析了其性能指标,验证了模型的可达性,并从业务冲突及模型分解等方面提出了优化.
