Along with the extensive use of workflow, analysis methods to verify the correctness of the workflow are becoming more and more important. In the paper, we exploit the verification method based on Petri net for workfl...Along with the extensive use of workflow, analysis methods to verify the correctness of the workflow are becoming more and more important. In the paper, we exploit the verification method based on Petri net for workflow process models which deals with the verification of workflow and finds the potential errors in the process design. Additionally, an efficient verification algorithm is given.展开更多
Classical management accounting(MA)Focusing on the facilitating perspective,focuses on decision facilitating and influencing(Demski&Feltham,1976).MA has to provide information to managers and depending on the prob...Classical management accounting(MA)Focusing on the facilitating perspective,focuses on decision facilitating and influencing(Demski&Feltham,1976).MA has to provide information to managers and depending on the problem complexity,they have to solve problems in a dyadic way.A dual process model,the heuristic systematic model(HSM),expands this so-called manager-accountant-dyad and shows different cases of actual human information processing.Managers and accountants either process systematically or heuristically.So far,many concepts have been designed in relation to the normative concept of the economical rational principle.Consequently,recent research only uses systematic information processing,based on the principle of the economic man.In this paper,a decision-behavior oriented approach tries to describe actual decision makers such as managers and accountants and shows new possibilities within MA.Therefore,the potential of heuristic information processing is analyzed,based on the phenomenon of ecological rationality as one shape of bounded rationality.Thus,different cognitive heuristics in business economics are identified and analyzed.Furthermore,the outstanding performance of heuristics compared with more complex calculations is shown.Unfortunately,these findings have been limited to marketing and investments so far.Significant research is needed,regarding conditions for applications and success factors of heuristics in business economics.New empirical findings have to be explicitly transferred to MA.展开更多
The reservoir volumetric approach represents a widely accepted, but flawed method of petroleum play resource calculation. In this paper, we propose a combination of techniques that can improve the applicability and qu...The reservoir volumetric approach represents a widely accepted, but flawed method of petroleum play resource calculation. In this paper, we propose a combination of techniques that can improve the applicability and quality of the resource estimation. These techniques include： 1） the use of the Multivariate Discovery Process model （MDP） to derive unbiased distribution parameters of reservoir volumetric variables and to reveal correlations among the variables; 2） the use of the Geo-anchored method to estimate simultaneously the number of oil and gas pools in the same play; and 3） the crossvalidation of assessment results from different methods. These techniques are illustrated by using an example of crude oil and natural gas resource assessment of the Sverdrup Basin, Canadian Archipelago. The example shows that when direct volumetric measurements of the untested prospects are not available, the MDP model can help derive unbiased estimates of the distribution parameters by using information from the discovered oil and gas accumulations. It also shows that an estimation of the number of oil and gas accumulations and associated size ranges from a discovery process model can provide an alternative and efficient approach when inadequate geological data hinder the estimation. Cross-examination of assessment results derived using different methods allows one to focus on and analyze the causes for the major differences, thus providing a more reliable assessment outcome.展开更多
Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard ...Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.展开更多
Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and requir...Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Building owners,designers and constructors are seeing a rapid increase in the number of sustainably designed high performance buildings.These buildings provide numerous benefits to the owners and occupants to include ...Building owners,designers and constructors are seeing a rapid increase in the number of sustainably designed high performance buildings.These buildings provide numerous benefits to the owners and occupants to include improved indoor air quality,energy efficiency,and environmental site standards;and ultimately enhance productivity for the building occupants.As the demand increases for higher building energy efficiency and environmental standards,application of a set of process models will support consistency and optimization during the design process.Systems engineering process models have proven effective in taking an integrated and comprehensive view of a system while allowing for clear stakeholder engagement,requirements definition,life cycle analysis,technology insertion,validation and verification.This paper overlays systems engineering on the sustainable design process by providing a framework for application of the Waterfall,Vee,and Spiral process models to high performance buildings.Each process model is mapped to the sustainable design process and is evaluated for its applicability to projects and building types.Adaptations of the models are provided as Green Building Process Models.展开更多
Aims Recent mechanistic explanations for community assembly focus on the debates surrounding niche-based deterministic and dispersalbased stochastic models.This body of work has emphasized the importance of both habit...Aims Recent mechanistic explanations for community assembly focus on the debates surrounding niche-based deterministic and dispersalbased stochastic models.This body of work has emphasized the importance of both habitat filtering and dispersal limitation,and many of these works have utilized the assumption of species spatial independence to simplify the complexity of the spatial modeling in natural communities when given dispersal limitation and/or habitat filtering.One potential drawback of this simplification is that it does not consider species interactions and how they may influence the spatial distribution of species,phylogenetic and functional diversity.Here,we assess the validity of the assumption of species spatial independence using data from a subtropical forest plot in southeastern China.Methods We use the four most commonly employed spatial statistical models—the homogeneous Poisson process representing pure random effect,the heterogeneous Poisson process for the effect of habitat heterogeneity,the homogenous Thomas process for sole dispersal limitation and the heterogeneous Thomas process for joint effect of habitat heterogeneity and dispersal limitation—to investigate the contribution of different mechanisms in shaping the species,phylogenetic and functional structures of communities.Important Findings Our evidence from species,phylogenetic and functional diversity demonstrates that the habitat filtering and/or dispersal-based models perform well and the assumption of species spatial independence is relatively valid at larger scales(50×50 m).Conversely,at local scales(10×10 and 20×20 m),the models often fail to predict the species,phylogenetic and functional diversity,suggesting that the assumption of species spatial independence is invalid and that biotic interactions are increasingly important at these spatial scales.展开更多
With regards to the assembly line of cost control of Dechang(HK)company,the motor housing’s cost control of process will be necessarily respected.Because the supply quantity is big in a machine the price of motor hou...With regards to the assembly line of cost control of Dechang(HK)company,the motor housing’s cost control of process will be necessarily respected.Because the supply quantity is big in a machine the price of motor housing is small,so that the cost control of automatic production line is significant with modeling.It is found that the control of equipment includes in shaft and crank linkage for benefit which also needs to be controlled in detail.For the sake of benefits can we fundamentally resolve the main problem of high cost process.展开更多
With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking f...With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.展开更多
Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications compli...Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.展开更多
With the raising complexity of modern civil aircraft,both academy and industry have shown strong interests on MBSE(Model-Based System Engineering).However,following the application of MBSE,the duration of the design p...With the raising complexity of modern civil aircraft,both academy and industry have shown strong interests on MBSE(Model-Based System Engineering).However,following the application of MBSE,the duration of the design phase exceeded expectations.This paper conducted a survey to the relevant participants involved in the design,revealed that a lack of proper process management is a critical issue.The current MBSE methodology does not provide clear guidelines for monitoring,controlling,and managing processes,which are crucial for both efficiency and effectiveness.To address this,the present paper introduced an improved Process Model(PM)within the MBSE framework for civil aircraft design.This improved model incorporates three new Management Blocks(MB):Progress Management Block(PMB),Review Management Block(RMB),and Configuration Management Block(CMB),developed based on the Capability Maturity Model Integration(CMMI).These additions aim to streamline the design process and better align it with engineering practices.The upgraded MBSE method with the improved PM offers a more structured approach to manage complex aircraft design projects,and a case study is conducted to validate its potential to reduce timelines and enhance overall project outcomes.展开更多
The textile industry,while creating material wealth,also exerts a significant impact on the environment.Particularly in the textile manufacturing phase,which is the most energy-intensive phase throughout the product l...The textile industry,while creating material wealth,also exerts a significant impact on the environment.Particularly in the textile manufacturing phase,which is the most energy-intensive phase throughout the product lifecycle,the problem of high energy usage is increasingly notable.Nevertheless,current analyses of carbon emissions in textile manufacturing emphasize the dynamic temporal characteristics while failing to adequately consider critical information such as material flows and energy consumption.A carbon emission analysis method based on a holographic process model(HPM)is proposed to address these issues.First,the system boundary in the textile manufacturing is defined,and the characteristics of carbon emissions are analyzed.Next,an HPM based on the object-centric Petri net(OCPN)is constructed,and simulation experiments are conducted on three different scenarios in the textile manufacturing.Subsequently,the constructed HPM is utilized to achieve a multi-perspective analysis of carbon emissions.Finally,the feasibility of the method is verified by using the production data of pure cotton products from a certain textile manufacturing enterprise.The results indicate that this method can analyze the impact of various factors on the carbon emissions of pure cotton product production,and by applying targeted optimization strategies,carbon emissions have been reduced by nearly 20%.This contributes to propelling the textile manufacturing industry toward sustainable development.展开更多
To investigate the process of information technology （IT） impacts on firm competitiveness, an integrated process model of IT impacts on firm competitiveness is brought forward based on the process-oriented view, the...To investigate the process of information technology （IT） impacts on firm competitiveness, an integrated process model of IT impacts on firm competitiveness is brought forward based on the process-oriented view, the resource-based view and the complementary resource view, which is comprised of an IT conversion process, an information system （IS） adoption process, an IS use process and a competition process. The application capability of IT plays the critical role, which determines the efficiency and effectiveness of the aforementioned four processes. The process model of IT impacts on firm competitiveness can also be used to explain why, under what situations and how IT can generate positive organizational outcomes, as well as theoretical bases for further empirical study.展开更多
Workflow management is an important aspect in CSCW at present. The elementary knowledge of workflow process is introduced, the Petri nets based process modeling methodology and basic definitions are provided, and the ...Workflow management is an important aspect in CSCW at present. The elementary knowledge of workflow process is introduced, the Petri nets based process modeling methodology and basic definitions are provided, and the analysis and verification of structural and behavioral correctness of workflow process are discussed. Finally, the algorithm of verification of process definitions is proposed.展开更多
To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new busi...To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new business process model which is multi-role, multi-dimensional, integrated and dynamic is proposed relying on inter-organizational collaboration. Compatible with the traditional linear sequence model, the new model is an M x N multi-dimensional mesh, and provides horizontal and vertical formal descriptions for the collaboration business process model. Finally, the pi-calculus theory is utilized to verify the deadlocks, livelocks and synchronization of the example models. The result shows that the proposed approach is efficient and applicable in inter-organizational business process modeling.展开更多
There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities be...There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.展开更多
With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning te...With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.展开更多
Modeling and attitude control methods for a satellite with a large deployable antenna are studied in the present paper. Firstly, for reducing the model dimension, three dynamic models for the deploying process are dev...Modeling and attitude control methods for a satellite with a large deployable antenna are studied in the present paper. Firstly, for reducing the model dimension, three dynamic models for the deploying process are developed, which are built with the methods of multi-rigid-body dynam- ics, hybrid coordinate and substructure. Then an attitude control method suitable for the deploying process is proposed, which can keep stability under any dynamical parameter variation. Subse- quently, this attitude control is optimized to minimize attitude disturbance during the deploying process. The simulation results show that this attitude control method can keep stability and main- tain proper attitude variation during the deploying process, which indicates that this attitude con- trol method is suitable for practical applications.展开更多
Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input ...Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.展开更多
文摘Along with the extensive use of workflow, analysis methods to verify the correctness of the workflow are becoming more and more important. In the paper, we exploit the verification method based on Petri net for workflow process models which deals with the verification of workflow and finds the potential errors in the process design. Additionally, an efficient verification algorithm is given.
文摘Classical management accounting(MA)Focusing on the facilitating perspective,focuses on decision facilitating and influencing(Demski&Feltham,1976).MA has to provide information to managers and depending on the problem complexity,they have to solve problems in a dyadic way.A dual process model,the heuristic systematic model(HSM),expands this so-called manager-accountant-dyad and shows different cases of actual human information processing.Managers and accountants either process systematically or heuristically.So far,many concepts have been designed in relation to the normative concept of the economical rational principle.Consequently,recent research only uses systematic information processing,based on the principle of the economic man.In this paper,a decision-behavior oriented approach tries to describe actual decision makers such as managers and accountants and shows new possibilities within MA.Therefore,the potential of heuristic information processing is analyzed,based on the phenomenon of ecological rationality as one shape of bounded rationality.Thus,different cognitive heuristics in business economics are identified and analyzed.Furthermore,the outstanding performance of heuristics compared with more complex calculations is shown.Unfortunately,these findings have been limited to marketing and investments so far.Significant research is needed,regarding conditions for applications and success factors of heuristics in business economics.New empirical findings have to be explicitly transferred to MA.
文摘The reservoir volumetric approach represents a widely accepted, but flawed method of petroleum play resource calculation. In this paper, we propose a combination of techniques that can improve the applicability and quality of the resource estimation. These techniques include： 1） the use of the Multivariate Discovery Process model （MDP） to derive unbiased distribution parameters of reservoir volumetric variables and to reveal correlations among the variables; 2） the use of the Geo-anchored method to estimate simultaneously the number of oil and gas pools in the same play; and 3） the crossvalidation of assessment results from different methods. These techniques are illustrated by using an example of crude oil and natural gas resource assessment of the Sverdrup Basin, Canadian Archipelago. The example shows that when direct volumetric measurements of the untested prospects are not available, the MDP model can help derive unbiased estimates of the distribution parameters by using information from the discovered oil and gas accumulations. It also shows that an estimation of the number of oil and gas accumulations and associated size ranges from a discovery process model can provide an alternative and efficient approach when inadequate geological data hinder the estimation. Cross-examination of assessment results derived using different methods allows one to focus on and analyze the causes for the major differences, thus providing a more reliable assessment outcome.
基金Supported by the National High Technology Research and Development Program of China(2014AA041803)the National Natural Science Foundation of China(61320106009)
文摘Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.
基金funded by Fundación CajaCanarias and Fundación Bancaria“la Caixa”,grant number 2023DIG11.
文摘Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘Building owners,designers and constructors are seeing a rapid increase in the number of sustainably designed high performance buildings.These buildings provide numerous benefits to the owners and occupants to include improved indoor air quality,energy efficiency,and environmental site standards;and ultimately enhance productivity for the building occupants.As the demand increases for higher building energy efficiency and environmental standards,application of a set of process models will support consistency and optimization during the design process.Systems engineering process models have proven effective in taking an integrated and comprehensive view of a system while allowing for clear stakeholder engagement,requirements definition,life cycle analysis,technology insertion,validation and verification.This paper overlays systems engineering on the sustainable design process by providing a framework for application of the Waterfall,Vee,and Spiral process models to high performance buildings.Each process model is mapped to the sustainable design process and is evaluated for its applicability to projects and building types.Adaptations of the models are provided as Green Building Process Models.
基金NSFC grant of National Natural Science Foundation of China(31170401)Dimensions of biodiversity grant of Natural Science Fundation(NSF 1046113)Natural Science Foundation of Zhejiang Province(Y5100361).
文摘Aims Recent mechanistic explanations for community assembly focus on the debates surrounding niche-based deterministic and dispersalbased stochastic models.This body of work has emphasized the importance of both habitat filtering and dispersal limitation,and many of these works have utilized the assumption of species spatial independence to simplify the complexity of the spatial modeling in natural communities when given dispersal limitation and/or habitat filtering.One potential drawback of this simplification is that it does not consider species interactions and how they may influence the spatial distribution of species,phylogenetic and functional diversity.Here,we assess the validity of the assumption of species spatial independence using data from a subtropical forest plot in southeastern China.Methods We use the four most commonly employed spatial statistical models—the homogeneous Poisson process representing pure random effect,the heterogeneous Poisson process for the effect of habitat heterogeneity,the homogenous Thomas process for sole dispersal limitation and the heterogeneous Thomas process for joint effect of habitat heterogeneity and dispersal limitation—to investigate the contribution of different mechanisms in shaping the species,phylogenetic and functional structures of communities.Important Findings Our evidence from species,phylogenetic and functional diversity demonstrates that the habitat filtering and/or dispersal-based models perform well and the assumption of species spatial independence is relatively valid at larger scales(50×50 m).Conversely,at local scales(10×10 and 20×20 m),the models often fail to predict the species,phylogenetic and functional diversity,suggesting that the assumption of species spatial independence is invalid and that biotic interactions are increasingly important at these spatial scales.
文摘With regards to the assembly line of cost control of Dechang(HK)company,the motor housing’s cost control of process will be necessarily respected.Because the supply quantity is big in a machine the price of motor housing is small,so that the cost control of automatic production line is significant with modeling.It is found that the control of equipment includes in shaft and crank linkage for benefit which also needs to be controlled in detail.For the sake of benefits can we fundamentally resolve the main problem of high cost process.
基金the financial support of the National Key Research and Development Program of China(2021YFE0112800)EU RISE project OPTIMAL(101007963).
文摘With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.
基金supported by the National Natural Science Foundation of China(62402415)in part by the Natural Science Foundation of Shandong Province of China(ZR2024MF129)in part by State Key Laboratory of Massive Personalized Customization System and Technology(No.H&C-MPC-2023-02-03).
文摘Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.
基金supported by the National Natural Science Foundation of China(No.62073267)。
文摘With the raising complexity of modern civil aircraft,both academy and industry have shown strong interests on MBSE(Model-Based System Engineering).However,following the application of MBSE,the duration of the design phase exceeded expectations.This paper conducted a survey to the relevant participants involved in the design,revealed that a lack of proper process management is a critical issue.The current MBSE methodology does not provide clear guidelines for monitoring,controlling,and managing processes,which are crucial for both efficiency and effectiveness.To address this,the present paper introduced an improved Process Model(PM)within the MBSE framework for civil aircraft design.This improved model incorporates three new Management Blocks(MB):Progress Management Block(PMB),Review Management Block(RMB),and Configuration Management Block(CMB),developed based on the Capability Maturity Model Integration(CMMI).These additions aim to streamline the design process and better align it with engineering practices.The upgraded MBSE method with the improved PM offers a more structured approach to manage complex aircraft design projects,and a case study is conducted to validate its potential to reduce timelines and enhance overall project outcomes.
基金National Key R&D Program of China(No.2019YFB1706300)。
文摘The textile industry,while creating material wealth,also exerts a significant impact on the environment.Particularly in the textile manufacturing phase,which is the most energy-intensive phase throughout the product lifecycle,the problem of high energy usage is increasingly notable.Nevertheless,current analyses of carbon emissions in textile manufacturing emphasize the dynamic temporal characteristics while failing to adequately consider critical information such as material flows and energy consumption.A carbon emission analysis method based on a holographic process model(HPM)is proposed to address these issues.First,the system boundary in the textile manufacturing is defined,and the characteristics of carbon emissions are analyzed.Next,an HPM based on the object-centric Petri net(OCPN)is constructed,and simulation experiments are conducted on three different scenarios in the textile manufacturing.Subsequently,the constructed HPM is utilized to achieve a multi-perspective analysis of carbon emissions.Finally,the feasibility of the method is verified by using the production data of pure cotton products from a certain textile manufacturing enterprise.The results indicate that this method can analyze the impact of various factors on the carbon emissions of pure cotton product production,and by applying targeted optimization strategies,carbon emissions have been reduced by nearly 20%.This contributes to propelling the textile manufacturing industry toward sustainable development.
基金The National Natural Science Foundation of China(No.70671024).
文摘To investigate the process of information technology （IT） impacts on firm competitiveness, an integrated process model of IT impacts on firm competitiveness is brought forward based on the process-oriented view, the resource-based view and the complementary resource view, which is comprised of an IT conversion process, an information system （IS） adoption process, an IS use process and a competition process. The application capability of IT plays the critical role, which determines the efficiency and effectiveness of the aforementioned four processes. The process model of IT impacts on firm competitiveness can also be used to explain why, under what situations and how IT can generate positive organizational outcomes, as well as theoretical bases for further empirical study.
文摘Workflow management is an important aspect in CSCW at present. The elementary knowledge of workflow process is introduced, the Petri nets based process modeling methodology and basic definitions are provided, and the analysis and verification of structural and behavioral correctness of workflow process are discussed. Finally, the algorithm of verification of process definitions is proposed.
基金The National Natural Science Foundation of China(No60473078)
文摘To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new business process model which is multi-role, multi-dimensional, integrated and dynamic is proposed relying on inter-organizational collaboration. Compatible with the traditional linear sequence model, the new model is an M x N multi-dimensional mesh, and provides horizontal and vertical formal descriptions for the collaboration business process model. Finally, the pi-calculus theory is utilized to verify the deadlocks, livelocks and synchronization of the example models. The result shows that the proposed approach is efficient and applicable in inter-organizational business process modeling.
文摘There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.
基金supported by the National Natural Science Foundation of China(No.U1960202)。
文摘With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.
基金sponsored by the National Natural Science Foundation of China (No. 11272172)
文摘Modeling and attitude control methods for a satellite with a large deployable antenna are studied in the present paper. Firstly, for reducing the model dimension, three dynamic models for the deploying process are developed, which are built with the methods of multi-rigid-body dynam- ics, hybrid coordinate and substructure. Then an attitude control method suitable for the deploying process is proposed, which can keep stability under any dynamical parameter variation. Subse- quently, this attitude control is optimized to minimize attitude disturbance during the deploying process. The simulation results show that this attitude control method can keep stability and main- tain proper attitude variation during the deploying process, which indicates that this attitude con- trol method is suitable for practical applications.
基金Supported by Beijing Municipal Education Commission (No.xk100100435) and the Key Research Project of Science andTechnology from Sinopec (No.E03007).
文摘Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
