Accurately assessing the carbon sequestration capacity of forests is crucial for mitigating climate change.Traditional methods for estimating Gross Primary Productivity(GPP)of vegetation involve significant uncertaint...Accurately assessing the carbon sequestration capacity of forests is crucial for mitigating climate change.Traditional methods for estimating Gross Primary Productivity(GPP)of vegetation involve significant uncertainties.As a novel remote sensing approach,Solar-Induced chlorophyll Fluorescence(SIF)is directly related to photosynthesis and has demonstrated strong correlations with GPP across various ecosystems,climate zones,and spatial scales.Current GPP estimation methods based on SIF include Light Use Efficiency(LUE)models,the SCOPE process models,and the latest mechanistic light response(MLR)models.Future research should focus on improving the mechanistic understanding of SIF-related processes and promoting the integration of multi-source remote sensing data with SIF-based modeling to enhance the accuracy and universality of GPP estimation.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
Accurate quantification of life-cycle greenhouse gas(GHG)footprints(GHG_(fp))for a crop cultivation system is urgently needed to address the conflict between food security and global warming mitigation.In this study,t...Accurate quantification of life-cycle greenhouse gas(GHG)footprints(GHG_(fp))for a crop cultivation system is urgently needed to address the conflict between food security and global warming mitigation.In this study,the hydrobiogeochemical model,CNMM-DNDC,was validated with in situ observations from maize-based cultivation systems at the sites of Yongji(YJ,China),Yanting(YT,China),and Madeya(MA,Kenya),subject to temperate,subtropical,and tropical climates,respectively,and updated to enable life-cycle GHG_(fp)estimation.The model validation provided satisfactory simulations on multiple soil variables,crop growth,and emissions of GHGs and reactive nitrogen gases.The locally conventional management practices resulted in GHG_(fp)values of 0.35(0.09–0.53 at the 95%confidence interval),0.21(0.01–0.73),0.46(0.27–0.60),and 0.54(0.21–0.77)kg CO_(2)e kg~(-1)d.m.(d.m.for dry matter in short)for maize–wheat rotation at YJ and YT,and for maize–maize and maize–Tephrosia rotations at MA,respectively.YT's smallest GHG_(fp)was attributed to its lower off-farm GHG emissions than YJ,though the soil organic carbon(SOC)storage and maize yield were slightly lower than those of YJ.MA's highest SOC loss and low yield in shifting cultivation for maize–Tephrosia rotation contributed to its highest GHG_(fp).Management practices of maize cultivation at these sites could be optimized by combination of synthetic and organic fertilizer(s)while incorporating 50%–100%crop residues.Further evaluation of the updated CNMM-DNDC is needed for different crops at site and regional scales to confirm its worldwide applicability in quantifying GHG_(fp)and optimizing management practices for achieving multiple sustainability goals.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
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
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
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
The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculati...The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.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
Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increase...Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.展开更多
As a variant of process algebra, π calculus can describe the interactions between evolving processes. By modeling activity as a process interacting with other processes through ports, this paper presents a new appro...As a variant of process algebra, π calculus can describe the interactions between evolving processes. By modeling activity as a process interacting with other processes through ports, this paper presents a new approach: representing workflow models using π calculus. As a result, the model can characterize the dynamic behaviors of the workflow process in terms of the LTS (Labeled Transition Semantics) semantics of π calculus. The main advantage of the workflow model's formal semantic is that it allows for verification of the model's properties, such as deadlock free and normal termination. Moreover, the equivalence of workflow models can be checked through weak bisimulation theorem in the π calculus, thus facilitating the optimization of business processes.展开更多
A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and ...A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.展开更多
Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principle...Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.展开更多
To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new lig...To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.展开更多
The selection of phase change material(PCM)plays an important role in developing high-efficient thermal energy storage(TES)processes.Ionic liquids(ILs)or organic salts are thermally stable,non-volatile,and non-flammab...The selection of phase change material(PCM)plays an important role in developing high-efficient thermal energy storage(TES)processes.Ionic liquids(ILs)or organic salts are thermally stable,non-volatile,and non-flammable.Importantly,researchers have proved that some ILs possess higher latent heat of fusion than conventional PCMs.Despite these attractive characteristics,yet surprisingly,little research has been performed to the systematic selection or structural design of ILs for TES.Besides,most of the existing work is only focused on the latent heat when selecting PCMs.However,one should note that other properties such as heat capacity and thermal conductivity could affect the TES performance as well.In this work,we propose a computer-aided molecular design(CAMD)based method to systematically design IL PCMs for a practical TES process.The effects of different IL properties are simultaneously captured in the IL property models and TES process models.Optimal ILs holding a best compromise of all the properties are identified through the solution of a formulated CAMD problem where the TES performance of the process is maximized.[MPyEtOH][TfO]is found to be the best material and excitingly,the identified top nine ILs all show a higher TES performance than the traditional PCM paraffin wax at 10 h thermal charging time.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant number 42250205,42471510)the Open Found of Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks,MNR(CUG-SRCS-0002)the Open Fund of Hubei Key Laboratory of Regional Ecological Process and Environmental Evolution(REEC-OF-202405).
文摘Accurately assessing the carbon sequestration capacity of forests is crucial for mitigating climate change.Traditional methods for estimating Gross Primary Productivity(GPP)of vegetation involve significant uncertainties.As a novel remote sensing approach,Solar-Induced chlorophyll Fluorescence(SIF)is directly related to photosynthesis and has demonstrated strong correlations with GPP across various ecosystems,climate zones,and spatial scales.Current GPP estimation methods based on SIF include Light Use Efficiency(LUE)models,the SCOPE process models,and the latest mechanistic light response(MLR)models.Future research should focus on improving the mechanistic understanding of SIF-related processes and promoting the integration of multi-source remote sensing data with SIF-based modeling to enhance the accuracy and universality of GPP estimation.
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.
基金jointly supported by the National Key R&D Program of China(Grant No.2022YFE0209200)the National Natural Science Foundation of China(Grant Nos.U22A20562,42330607 and 41761144054)the National Large Scientific and Technological Infrastructure“Earth System Science Numerical Simulator Facility”(Earth-Lab)(https://cstr.cn/31134.02.EL)。
文摘Accurate quantification of life-cycle greenhouse gas(GHG)footprints(GHG_(fp))for a crop cultivation system is urgently needed to address the conflict between food security and global warming mitigation.In this study,the hydrobiogeochemical model,CNMM-DNDC,was validated with in situ observations from maize-based cultivation systems at the sites of Yongji(YJ,China),Yanting(YT,China),and Madeya(MA,Kenya),subject to temperate,subtropical,and tropical climates,respectively,and updated to enable life-cycle GHG_(fp)estimation.The model validation provided satisfactory simulations on multiple soil variables,crop growth,and emissions of GHGs and reactive nitrogen gases.The locally conventional management practices resulted in GHG_(fp)values of 0.35(0.09–0.53 at the 95%confidence interval),0.21(0.01–0.73),0.46(0.27–0.60),and 0.54(0.21–0.77)kg CO_(2)e kg~(-1)d.m.(d.m.for dry matter in short)for maize–wheat rotation at YJ and YT,and for maize–maize and maize–Tephrosia rotations at MA,respectively.YT's smallest GHG_(fp)was attributed to its lower off-farm GHG emissions than YJ,though the soil organic carbon(SOC)storage and maize yield were slightly lower than those of YJ.MA's highest SOC loss and low yield in shifting cultivation for maize–Tephrosia rotation contributed to its highest GHG_(fp).Management practices of maize cultivation at these sites could be optimized by combination of synthetic and organic fertilizer(s)while incorporating 50%–100%crop residues.Further evaluation of the updated CNMM-DNDC is needed for different crops at site and regional scales to confirm its worldwide applicability in quantifying GHG_(fp)and optimizing management practices for achieving multiple sustainability goals.
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.
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Item Sponsored by National Natural Science Foundation of China(50474016)
文摘The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.
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.
基金supported by the National Natural Science Foundation of China(No.61627810)。
文摘Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.
文摘As a variant of process algebra, π calculus can describe the interactions between evolving processes. By modeling activity as a process interacting with other processes through ports, this paper presents a new approach: representing workflow models using π calculus. As a result, the model can characterize the dynamic behaviors of the workflow process in terms of the LTS (Labeled Transition Semantics) semantics of π calculus. The main advantage of the workflow model's formal semantic is that it allows for verification of the model's properties, such as deadlock free and normal termination. Moreover, the equivalence of workflow models can be checked through weak bisimulation theorem in the π calculus, thus facilitating the optimization of business processes.
基金Item Sponsored by National Natural Science Foundation of China(50074026)
文摘A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.
文摘Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.
基金support provided by the National Natural Science Foundation of China(22122802,22278044,and 21878028)the Chongqing Science Fund for Distinguished Young Scholars(CSTB2022NSCQ-JQX0021)the Fundamental Research Funds for the Central Universities(2022CDJXY-003).
文摘To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.
基金the financial support from Max Planck Society,Germany,for the Computer-Aided Material and Process Design(CAMPD)project
文摘The selection of phase change material(PCM)plays an important role in developing high-efficient thermal energy storage(TES)processes.Ionic liquids(ILs)or organic salts are thermally stable,non-volatile,and non-flammable.Importantly,researchers have proved that some ILs possess higher latent heat of fusion than conventional PCMs.Despite these attractive characteristics,yet surprisingly,little research has been performed to the systematic selection or structural design of ILs for TES.Besides,most of the existing work is only focused on the latent heat when selecting PCMs.However,one should note that other properties such as heat capacity and thermal conductivity could affect the TES performance as well.In this work,we propose a computer-aided molecular design(CAMD)based method to systematically design IL PCMs for a practical TES process.The effects of different IL properties are simultaneously captured in the IL property models and TES process models.Optimal ILs holding a best compromise of all the properties are identified through the solution of a formulated CAMD problem where the TES performance of the process is maximized.[MPyEtOH][TfO]is found to be the best material and excitingly,the identified top nine ILs all show a higher TES performance than the traditional PCM paraffin wax at 10 h thermal charging time.
