Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of CPS.Simulation-oriented model reuse can be used to build a whole CPS model by reusi...Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of CPS.Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of submodels.Model composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems.In this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel nodes.Specifically,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching.Submodel properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are quantified.Based on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are retrieved.Results show that the effectiveness of the model integration method proposed in this paper is verified.展开更多
For modeling and simulation of distillation process, there are lots of special purpose simulators along with their model libraries, such as Aspen Plus and HYSYS. However, the models in these tools lack of flexibility ...For modeling and simulation of distillation process, there are lots of special purpose simulators along with their model libraries, such as Aspen Plus and HYSYS. However, the models in these tools lack of flexibility and are not open to the end-user. Models developed in one tool can not be conveniently used in others because of the barriers among these simulators. In order to solve those problems, a flexible and extensible distillation system model library is constructed in this study, based on the Modelica and Modelica-supported platform MWorks, by the object-oriented technology and level progressive modeling strategy. It supports the reuse of knowledge on different granularities： physical phenomenon, unit model and system model. It is also an interface-friendly, accurate, fast PC-based and easily reusable simulation tool, which enables end-user to customize and extend the framework to add new functionality or adapt the simulation behavior as required. It also allows new models to be composed programmatically or graphically to form more complex models by invoking the existing components. A conventional air distillation column model is built and calculated using the library, and the results agree well with that simulated in Anen Plus.展开更多
The learnware paradigm has been proposed as a new manner for reusing models from a market of various well-trained models,which can relieve users’burden of training a new model from scratch.A learnware consists of a w...The learnware paradigm has been proposed as a new manner for reusing models from a market of various well-trained models,which can relieve users’burden of training a new model from scratch.A learnware consists of a well-trained model and a specification which explains the purpose or specialty of the model without revealing data.By specification matching,the market can identify the most useful learnwares for users’tasks.Prior art attempted to generate the specification by a reduced kernel mean embedding approach.However,such kind of specification is defined by some pre-designed kernel function,which lacks flexibility.In this paper,we advance a methodology for direct specification learning from data,introducing a novel neural network named SpecNet for this purpose.Our approach accepts unordered datasets as input and subsequently produces specification vectors in a latent space.Notably,the flexibility and efficiency of our learned specifications are underscored by their derivation from diverse tasks,rendering them particularly adept for learnware identification.Empirical studies provide validation for the efficacy of our proposed approach.展开更多
Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network models.Yet,for these neural network models,it is necessary to label a tremendous amou...Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network models.Yet,for these neural network models,it is necessary to label a tremendous amount of training data,which is prohibitively expensive in reality.In this paper,we propose OnLine Machine Learning(OLML)database which stores trained models and reuses these models in a new training task to achieve a better training effect with a small amount of training data.An efficient model reuse algorithm AdaReuse is developed in the OLML database.Specifically,AdaReuse firstly estimates the reuse potential of trained models from domain relatedness and model quality,through which a group of trained models with high reuse potential for the training task could be selected efficiently.Then,multi selected models will be trained iteratively to encourage diverse models,with which a better training effect could be achieved by ensemble.We evaluate AdaReuse on two types of natural language processing(NLP)tasks,and the results show AdaReuse could improve the training effect significantly compared with models training from scratch when the training data is limited.Based on AdaReuse,we implement an OLML database prototype system which could accept a training task as an SQL-like query and automatically generate a training plan by selecting and reusing trained models.Usability studies are conducted to illustrate the OLML database could properly store the trained models,and reuse the trained models efficiently in new training tasks.展开更多
Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings.The combined effects of building expansions on energy performanc...Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings.The combined effects of building expansions on energy performance,and daylighting availability remain unexplored.This paper developed a novel simulation model by applying multi-building data and neural-networks framework to examine the impact of adaptive reuse through variables including number of floors,energy generation,façade glazing,and building expansions in various directions.The developed model was validated by comparing simulated and actual energy use of several buildings,yielding an average error of 7.88%.This error represents the deviation between the simulated and actual energy use intensity values.Energy demand reduced by expansion along the East-West axis was 41%greater than that from expansion in the South direction.This was confirmed by sensitivity analysis,with R values of approximately 0.68 for East and West expansions,and 0.16 for the South.Overall,this study demonstrates that expanding buildings in the East-West direction tends to be the most energy-efficient approach for increasing occupied spaces,with its effectiveness potentially influenced by factors such as site location,building orientation,and climatic conditions.展开更多
The Data Market Management Strategy project proposes a comprehensive framework to harness AI technologies for optimizing data-driven decision-making processes.This framework,illustrated as an integrated ecosystem,unde...The Data Market Management Strategy project proposes a comprehensive framework to harness AI technologies for optimizing data-driven decision-making processes.This framework,illustrated as an integrated ecosystem,underscores the importance of data and model reuse through a structured marketplace environment.However,challenges such as data standardization,interoperability,and privacy concerns remain prevalent in current data markets.For instance,many data platforms still suffer from data silos and inconsistent metadata standards,making it difficult for researchers to efficiently access and reuse data across sectors.Addressing these issues,the proposed system integrates a data market and a model marketplace,facilitating seamless information exchange through Computing Cloud in Taiwan,China.Within this ecosystem,users can generate new models,upload,and share data,contributing to a dynamic and continuously evolving repository.The system enables users to access diverse datasets via standardized APIs and develop advanced models within modular containers such as Jupyter Notebooks.The model marketplace serves as a critical hub,supporting AI model sharing,refinement,and lifecycle management,fostering an environment where data and models are continuously reused.By emphasizing interdisciplinary collaboration,the framework enhances resource utilization,mitigates redundant efforts,and accelerates the development of novel AI solutions.The proposed approach aligns with global trends in federated learning,data privacy-preserving techniques,and open AI model hubs(e.g.,Hugging Face,TensorFlow Hub),ensuring ethical and secure data practices.Ultimately,the framework promotes scalable AI-powered applications,contributing to a more sustainable future in data management.展开更多
In the current work,we explored a new knowledge amalgama-tion problem,termed Federated Selective Aggregation for on-device knowledge amalgamation(FedSA).FedSA aims to train an on-device student model for a new task wi...In the current work,we explored a new knowledge amalgama-tion problem,termed Federated Selective Aggregation for on-device knowledge amalgamation(FedSA).FedSA aims to train an on-device student model for a new task with the help of several decentralized teachers whose pre-training tasks and data are different and agnos-tic.The motivation to investigate such a problem setup stems from a recent dilemma of model sharing.Due to privacy,security or in-tellectual property issues,the pre-trained models are,however,not able to be shared,and the resources of devices are usually limited.The proposed FedSA offers a solution to this dilemma and makes it one step further,again,the method can be employed on low-power and resource-limited devices.To this end,a dedicated strategy was proposed to handle the knowledge amalgamation.Specifically,the student-training process in the current work was driven by a novel saliency-based approach which adaptively selects teachers as the par-ticipants and integrated their representative capabilities into the stu-dent.To evaluate the effectiveness of FedSA,experiments on both single-task and multi-task settings were conducted.The experimental results demonstrate that FedSA could effectively amalgamate knowl-edge from decentralized models and achieve competitive performance to centralized baselines.展开更多
基金This work was supported by the National Key R&D Program of China(No.2018YFB1701600).
文摘Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of CPS.Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of submodels.Model composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems.In this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel nodes.Specifically,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching.Submodel properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are quantified.Based on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are retrieved.Results show that the effectiveness of the model integration method proposed in this paper is verified.
基金Supported by the Major State Basic Research Development Program of China (2011CB706502)
文摘For modeling and simulation of distillation process, there are lots of special purpose simulators along with their model libraries, such as Aspen Plus and HYSYS. However, the models in these tools lack of flexibility and are not open to the end-user. Models developed in one tool can not be conveniently used in others because of the barriers among these simulators. In order to solve those problems, a flexible and extensible distillation system model library is constructed in this study, based on the Modelica and Modelica-supported platform MWorks, by the object-oriented technology and level progressive modeling strategy. It supports the reuse of knowledge on different granularities： physical phenomenon, unit model and system model. It is also an interface-friendly, accurate, fast PC-based and easily reusable simulation tool, which enables end-user to customize and extend the framework to add new functionality or adapt the simulation behavior as required. It also allows new models to be composed programmatically or graphically to form more complex models by invoking the existing components. A conventional air distillation column model is built and calculated using the library, and the results agree well with that simulated in Anen Plus.
基金supported by the National Natural Science Foundation of China(Grant Nos.62076121,61921006)the Major Program(JD)of Hubei Province(2023BAA024).
文摘The learnware paradigm has been proposed as a new manner for reusing models from a market of various well-trained models,which can relieve users’burden of training a new model from scratch.A learnware consists of a well-trained model and a specification which explains the purpose or specialty of the model without revealing data.By specification matching,the market can identify the most useful learnwares for users’tasks.Prior art attempted to generate the specification by a reduced kernel mean embedding approach.However,such kind of specification is defined by some pre-designed kernel function,which lacks flexibility.In this paper,we advance a methodology for direct specification learning from data,introducing a novel neural network named SpecNet for this purpose.Our approach accepts unordered datasets as input and subsequently produces specification vectors in a latent space.Notably,the flexibility and efficiency of our learned specifications are underscored by their derivation from diverse tasks,rendering them particularly adept for learnware identification.Empirical studies provide validation for the efficacy of our proposed approach.
基金the National Natural Science Foundation of China under Grant No.62072458.
文摘Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network models.Yet,for these neural network models,it is necessary to label a tremendous amount of training data,which is prohibitively expensive in reality.In this paper,we propose OnLine Machine Learning(OLML)database which stores trained models and reuses these models in a new training task to achieve a better training effect with a small amount of training data.An efficient model reuse algorithm AdaReuse is developed in the OLML database.Specifically,AdaReuse firstly estimates the reuse potential of trained models from domain relatedness and model quality,through which a group of trained models with high reuse potential for the training task could be selected efficiently.Then,multi selected models will be trained iteratively to encourage diverse models,with which a better training effect could be achieved by ensemble.We evaluate AdaReuse on two types of natural language processing(NLP)tasks,and the results show AdaReuse could improve the training effect significantly compared with models training from scratch when the training data is limited.Based on AdaReuse,we implement an OLML database prototype system which could accept a training task as an SQL-like query and automatically generate a training plan by selecting and reusing trained models.Usability studies are conducted to illustrate the OLML database could properly store the trained models,and reuse the trained models efficiently in new training tasks.
基金supported by the Government of Canada’s New Frontiers in Research Fund,through the three federal research funding agencies(CIHR,NSERC,and SSHRC).
文摘Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings.The combined effects of building expansions on energy performance,and daylighting availability remain unexplored.This paper developed a novel simulation model by applying multi-building data and neural-networks framework to examine the impact of adaptive reuse through variables including number of floors,energy generation,façade glazing,and building expansions in various directions.The developed model was validated by comparing simulated and actual energy use of several buildings,yielding an average error of 7.88%.This error represents the deviation between the simulated and actual energy use intensity values.Energy demand reduced by expansion along the East-West axis was 41%greater than that from expansion in the South direction.This was confirmed by sensitivity analysis,with R values of approximately 0.68 for East and West expansions,and 0.16 for the South.Overall,this study demonstrates that expanding buildings in the East-West direction tends to be the most energy-efficient approach for increasing occupied spaces,with its effectiveness potentially influenced by factors such as site location,building orientation,and climatic conditions.
基金the Science Council in Taiwan, China for their support and funding of this project (Grant number: 113-2221-E-492-016)
文摘The Data Market Management Strategy project proposes a comprehensive framework to harness AI technologies for optimizing data-driven decision-making processes.This framework,illustrated as an integrated ecosystem,underscores the importance of data and model reuse through a structured marketplace environment.However,challenges such as data standardization,interoperability,and privacy concerns remain prevalent in current data markets.For instance,many data platforms still suffer from data silos and inconsistent metadata standards,making it difficult for researchers to efficiently access and reuse data across sectors.Addressing these issues,the proposed system integrates a data market and a model marketplace,facilitating seamless information exchange through Computing Cloud in Taiwan,China.Within this ecosystem,users can generate new models,upload,and share data,contributing to a dynamic and continuously evolving repository.The system enables users to access diverse datasets via standardized APIs and develop advanced models within modular containers such as Jupyter Notebooks.The model marketplace serves as a critical hub,supporting AI model sharing,refinement,and lifecycle management,fostering an environment where data and models are continuously reused.By emphasizing interdisciplinary collaboration,the framework enhances resource utilization,mitigates redundant efforts,and accelerates the development of novel AI solutions.The proposed approach aligns with global trends in federated learning,data privacy-preserving techniques,and open AI model hubs(e.g.,Hugging Face,TensorFlow Hub),ensuring ethical and secure data practices.Ultimately,the framework promotes scalable AI-powered applications,contributing to a more sustainable future in data management.
基金supported by National Natural Science Foundation of China (61976186,U20B2066)the Fundamental Research Funds for the Central Universities (2021FZZX001-23,226-2023-00048).
文摘In the current work,we explored a new knowledge amalgama-tion problem,termed Federated Selective Aggregation for on-device knowledge amalgamation(FedSA).FedSA aims to train an on-device student model for a new task with the help of several decentralized teachers whose pre-training tasks and data are different and agnos-tic.The motivation to investigate such a problem setup stems from a recent dilemma of model sharing.Due to privacy,security or in-tellectual property issues,the pre-trained models are,however,not able to be shared,and the resources of devices are usually limited.The proposed FedSA offers a solution to this dilemma and makes it one step further,again,the method can be employed on low-power and resource-limited devices.To this end,a dedicated strategy was proposed to handle the knowledge amalgamation.Specifically,the student-training process in the current work was driven by a novel saliency-based approach which adaptively selects teachers as the par-ticipants and integrated their representative capabilities into the stu-dent.To evaluate the effectiveness of FedSA,experiments on both single-task and multi-task settings were conducted.The experimental results demonstrate that FedSA could effectively amalgamate knowl-edge from decentralized models and achieve competitive performance to centralized baselines.
