Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint mode...Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.展开更多
An identification approach of dynamic system is put forward in this paperwhich can provide the fuzzy models with fairly high accuracy.This method consists ofpremise structure identification,premise parameters identifi...An identification approach of dynamic system is put forward in this paperwhich can provide the fuzzy models with fairly high accuracy.This method consists ofpremise structure identification,premise parameters identification,consequent structureand parameters identification.It has been applied to some industrial processes modeling.The simulation study shows its effectiveness.展开更多
A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristi...A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm.展开更多
Fluid catalytic cracking(FCC)is a vitally important refinery process.The fractionation,absorption,and stabilization system in the FCC process is a significant way to obtain key products,and its parameters will directl...Fluid catalytic cracking(FCC)is a vitally important refinery process.The fractionation,absorption,and stabilization system in the FCC process is a significant way to obtain key products,and its parameters will directly affect the quality of the products.In this work,using industrial data from an actual FCC process,a model of the FCC fractionation,absorption,and stabilization system was developed using process simulation software.The sequence quadratic program algorithm was then used to identify the parameters of each tower,increasing the accuracy of the simulation results.Next,using this improved model,a sensitivity analysis was performed to examine the effects of different operating conditions.The pattern-search method was then used to optimize the operating parameters of the system.The results showed that the optimized model has good prediction accuracy,and using the model,it was found that changing the operation parameters could result in a 1.84%improvement in economic benefits.As such,the developed model was demonstrated to be usefully applicable to the optimization of the process operation of an FCC fractionation,absorption,and stabilization system.展开更多
This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected ...This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.展开更多
In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener mode...In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks.Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process.The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is solved.Furthermore,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model.Finally,convergence analysis of the suggested identification scheme is derived using stochastic process theory.The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.展开更多
A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic response...A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.展开更多
Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and contro...Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.展开更多
The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality ...The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.展开更多
In recent years, the process orientation requirements engineering field has received significant interest. However, dealing with ordinary users within an enterprise for developing as-is business process is very comple...In recent years, the process orientation requirements engineering field has received significant interest. However, dealing with ordinary users within an enterprise for developing as-is business process is very complex because this requires skills, responsibility, knowledge, and expertise of business processes. This research answers the following questions: 1) how to systematically gather information regarding as-is business process requirements in an informal environment and by non-expert users? and 2) how can an enterprise refine the identified business process with a semantic process model? The primary purpose of this research is to develop a supporting framework that is suitable for the definition of an as-is business process to be deployed within the enterprise environment. Hence, the focus is on gathering requirements to define the as-is business process. This framework concentrates on three significant aspects of this process: development, refinement, and serialization the semantics of the process model. To accomplish this objective, the LORS framework has been proposed, which consists of four phases (List, Order, Refinement, and Serialization). The framework presents a new unique technique to identify the business process, refine the process, and generate the model semantics. This study assumes that a simple and complete framework will help non-expert users define a high-quality as-is process, such that enhance and facilitate the matching process with existing reference models. We present a case study, evaluate the case study relative to specified criteria, and research the limitations and implications discovered from our research. This research concludes that the LORS framework is simple, flexible, visible, interactive, dynamic, and effective.展开更多
为了实现机器人焊接的免示教路径规划,结合深度学习与点云处理技术,开发了一种高效、稳定的焊缝智能识别算法.首先,采用ETH(Eye-to-hand)构型的工业级3D相机获取焊件周围的二维图像和3D点云模型,利用预先训练的YOLOv8目标检测模型识别...为了实现机器人焊接的免示教路径规划,结合深度学习与点云处理技术,开发了一种高效、稳定的焊缝智能识别算法.首先,采用ETH(Eye-to-hand)构型的工业级3D相机获取焊件周围的二维图像和3D点云模型,利用预先训练的YOLOv8目标检测模型识别焊件所在的ROI区域(region of interest,ROI),模型识别精度为99.5%,从而实现快速剔除背景点云,并基于RANSAC平面拟合、欧式聚类等点云处理算法,对ROI区域的三维点云进行焊缝空间位置的精细识别;最后根据手眼标定结果转化为机器人用户坐标系下的焊接轨迹.结果表明,文中所开发的算法可实现随机摆放的焊缝自动识别和焊接机器人路径规划,生成的轨迹与人工示教轨迹效果相当,偏差在0.5 mm以内.展开更多
Based on the continuous time AR model,this paper presents a new time-domain modal identification method of LTI system driven by the uniformly modulated lévy random excitation.The structural dynamic equation is fi...Based on the continuous time AR model,this paper presents a new time-domain modal identification method of LTI system driven by the uniformly modulated lévy random excitation.The structural dynamic equation is first transformed into the observation equation and the state equation(namely,stochastic differential equation).Based on the property of the strong solution of the stochastic differential equation,the uniformly modulated function is identified piecewise.Then by virtue of the Girsanov theorem,we present the exact maximum likelihood estimators of parameters.Finally,the modal parameters are identified by eigen analysis.Numerical results show that the method not only has high precision and robustness but also has very high computing efficiency.展开更多
Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to contr...Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.展开更多
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
基金This work was supported by the National Natural Science Foundation of China(Nos.51678101,52078093)Liaoning Revitalization Talents Program(No.XLYC1905015).
文摘Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.
文摘An identification approach of dynamic system is put forward in this paperwhich can provide the fuzzy models with fairly high accuracy.This method consists ofpremise structure identification,premise parameters identification,consequent structureand parameters identification.It has been applied to some industrial processes modeling.The simulation study shows its effectiveness.
基金国家高技术研究发展计划(863计划)，the National Natural Science Foundation of China，教育部新世纪高校优秀人才计划
文摘A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm.
基金supported by the National Key Research&Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (Grant No.2021YFE0112800)National Natural Science Foundation of China (Grant Nos.61973124+2 种基金61873093)the SINOPEC Research Program (Grant No.119030-2)Shanghai AI Lab
文摘Fluid catalytic cracking(FCC)is a vitally important refinery process.The fractionation,absorption,and stabilization system in the FCC process is a significant way to obtain key products,and its parameters will directly affect the quality of the products.In this work,using industrial data from an actual FCC process,a model of the FCC fractionation,absorption,and stabilization system was developed using process simulation software.The sequence quadratic program algorithm was then used to identify the parameters of each tower,increasing the accuracy of the simulation results.Next,using this improved model,a sensitivity analysis was performed to examine the effects of different operating conditions.The pattern-search method was then used to optimize the operating parameters of the system.The results showed that the optimized model has good prediction accuracy,and using the model,it was found that changing the operation parameters could result in a 1.84%improvement in economic benefits.As such,the developed model was demonstrated to be usefully applicable to the optimization of the process operation of an FCC fractionation,absorption,and stabilization system.
文摘This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.
基金supported by the National Natural Science Foundation of China(Grant No.62003151)the Natural Science Foundation of Jiangsu Province(Grant No.BK20191035)+1 种基金the Changzhou Sci&Tech Program(Grant No.CJ20220065)the“Blue Project”of Universities in Jiangsu Province,and Zhongwu Youth Innovative Talents Support Program in Jiangsu Institute of Technology.
文摘In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks.Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process.The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is solved.Furthermore,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model.Finally,convergence analysis of the suggested identification scheme is derived using stochastic process theory.The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.
基金Supported by the National Natural Science Foundation of China（51079027）
文摘A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.
基金supported by the Qatar National Research Fund(NPRP5-364-2-142NPRP7-1040-2-293)
文摘Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(61988101,62073340,and 61860206014)+2 种基金in part by the Major Key Project of Peng Cheng Laboratory(PCL)(PCL2021A09)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083,2021RC3018,and 2021RC4054)in part by the Innovation-Driven Project of Central South University,China(2019CX020)。
文摘The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.
文摘In recent years, the process orientation requirements engineering field has received significant interest. However, dealing with ordinary users within an enterprise for developing as-is business process is very complex because this requires skills, responsibility, knowledge, and expertise of business processes. This research answers the following questions: 1) how to systematically gather information regarding as-is business process requirements in an informal environment and by non-expert users? and 2) how can an enterprise refine the identified business process with a semantic process model? The primary purpose of this research is to develop a supporting framework that is suitable for the definition of an as-is business process to be deployed within the enterprise environment. Hence, the focus is on gathering requirements to define the as-is business process. This framework concentrates on three significant aspects of this process: development, refinement, and serialization the semantics of the process model. To accomplish this objective, the LORS framework has been proposed, which consists of four phases (List, Order, Refinement, and Serialization). The framework presents a new unique technique to identify the business process, refine the process, and generate the model semantics. This study assumes that a simple and complete framework will help non-expert users define a high-quality as-is process, such that enhance and facilitate the matching process with existing reference models. We present a case study, evaluate the case study relative to specified criteria, and research the limitations and implications discovered from our research. This research concludes that the LORS framework is simple, flexible, visible, interactive, dynamic, and effective.
文摘为了实现机器人焊接的免示教路径规划,结合深度学习与点云处理技术,开发了一种高效、稳定的焊缝智能识别算法.首先,采用ETH(Eye-to-hand)构型的工业级3D相机获取焊件周围的二维图像和3D点云模型,利用预先训练的YOLOv8目标检测模型识别焊件所在的ROI区域(region of interest,ROI),模型识别精度为99.5%,从而实现快速剔除背景点云,并基于RANSAC平面拟合、欧式聚类等点云处理算法,对ROI区域的三维点云进行焊缝空间位置的精细识别;最后根据手眼标定结果转化为机器人用户坐标系下的焊接轨迹.结果表明,文中所开发的算法可实现随机摆放的焊缝自动识别和焊接机器人路径规划,生成的轨迹与人工示教轨迹效果相当,偏差在0.5 mm以内.
文摘Based on the continuous time AR model,this paper presents a new time-domain modal identification method of LTI system driven by the uniformly modulated lévy random excitation.The structural dynamic equation is first transformed into the observation equation and the state equation(namely,stochastic differential equation).Based on the property of the strong solution of the stochastic differential equation,the uniformly modulated function is identified piecewise.Then by virtue of the Girsanov theorem,we present the exact maximum likelihood estimators of parameters.Finally,the modal parameters are identified by eigen analysis.Numerical results show that the method not only has high precision and robustness but also has very high computing efficiency.
基金The authors thank the MOE AcRF Grant in Singapore for financial support of the projects on Precision Healthcare Development,Manufacturing and Supply Chain Optimization(Grant No.R-279-000-513-133)Advanced Process Control and Machine Learning Methods for Enhanced Continuous Manufacturing of Pharmaceutical Products(Grant No.R-279-000-541-114).
文摘Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.
