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
The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems an...The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems and making decisions in many different fields.These advanced technologies have enabled significant breakthroughs in sectors including entertainment,finance,transportation,and healthcare.AI systems,which can analyze vast volumes of data,have significantly driven efficiency and innovation.With remarkable accuracy,patterns can be identified and predictions generated,improving decision-making processes and facilitating the development of more intelligent solutions.The increasing adoption of these technologies by organizations has expanded the potential for AI to change processes and improve results.展开更多
The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using h...The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using hybrid quantum-classical frameworks.Initially proposed to estimate the ground state energies of quantum systems,VQE combines the quantum circuits with the classical optimization approaches,harnessing the strengths of both computational paradigms [1].展开更多
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.
基金funded by the Research,Development,and Innovation Authority(RDIA),Kingdom of Saudi Arabia,with grant number 13382-PSU-2023-PSNU-R-3-1-EIsupported by the Automated Systems and Computing Lab(ASCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems and making decisions in many different fields.These advanced technologies have enabled significant breakthroughs in sectors including entertainment,finance,transportation,and healthcare.AI systems,which can analyze vast volumes of data,have significantly driven efficiency and innovation.With remarkable accuracy,patterns can be identified and predictions generated,improving decision-making processes and facilitating the development of more intelligent solutions.The increasing adoption of these technologies by organizations has expanded the potential for AI to change processes and improve results.
文摘The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using hybrid quantum-classical frameworks.Initially proposed to estimate the ground state energies of quantum systems,VQE combines the quantum circuits with the classical optimization approaches,harnessing the strengths of both computational paradigms [1].
