The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocatio...The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.展开更多
Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce th...Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.展开更多
For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sec...For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.展开更多
文摘The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.
基金jointly supported by the National Science and Technology Major Project(Nos.2016ZX05002-005-07HZ,2016ZX05014-001-008HZ,and 2016ZX05026-002-002HZ)National Natural Science Foundation of China(Nos.41720104006 and 41274124)+2 种基金Chinese Academy of Sciences Strategic Pilot Technology Special Project(A)(No.XDA14010303)Shandong Province Innovation Project(No.2017CXGC1602)Independent Innovation(No.17CX05011)。
文摘Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.
基金Project(52108101)supported by the National Natural Science Foundation of ChinaProjects(2020GK4057,2021JJ40759)supported by the Hunan Provincial Science and Technology Department,China。
文摘For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.
