The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to de...The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.展开更多
The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to de...The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.展开更多
Artificial cognitive models and computational neuroscience methods have garnered great interest from both neurologist and leading analysts in recent years. Among the cognitive models, HMAX has been widely used in comp...Artificial cognitive models and computational neuroscience methods have garnered great interest from both neurologist and leading analysts in recent years. Among the cognitive models, HMAX has been widely used in computer vision systems for its robustness shape and texture features inspired by the ventral stream of the human brain. This work presents a Color-HMAX （C-HMAX） model based on the HMAX model which imitates the color vision mechanism of the human brain that the HMAX model does not include. C-HMAX is then applied to the German Traffic Sign Recognition Benchmark （GTSRB） which has 43 categories and 51 840 sample traffic signs with an accuracy of 98.41%, higher than most other models including linear discriminant analysis and multi-scale convoiutional neural network.展开更多
Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Sm...Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.展开更多
基金This work was funded by the EPSRC Grant"Improving the product development process through integrated revision control and twinning of digital-physical models during prototyping",reference:EP/R032696/1.
文摘The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.
文摘The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.
基金supported in part by the National Natural Science Foundation of China(Nos.90820305,60775040,and61005085)Aeronautical Science Foundation of China(No.2010ZD01003)
文摘Artificial cognitive models and computational neuroscience methods have garnered great interest from both neurologist and leading analysts in recent years. Among the cognitive models, HMAX has been widely used in computer vision systems for its robustness shape and texture features inspired by the ventral stream of the human brain. This work presents a Color-HMAX （C-HMAX） model based on the HMAX model which imitates the color vision mechanism of the human brain that the HMAX model does not include. C-HMAX is then applied to the German Traffic Sign Recognition Benchmark （GTSRB） which has 43 categories and 51 840 sample traffic signs with an accuracy of 98.41%, higher than most other models including linear discriminant analysis and multi-scale convoiutional neural network.
基金This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1A09082919)this research was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2018-0-01456,AutoMaTa:Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT).Any correspondence related to this paper should be addressed to Dohyeun Kim.
文摘Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.
