Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and ...Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.展开更多
Formation and scheduling are the most important decisions in the virtual modular manufacturing system;however,the global performance optimization of the system may be sacrificed via the superposition of two independen...Formation and scheduling are the most important decisions in the virtual modular manufacturing system;however,the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results.The joint decision of formation and scheduling is very important for system design.Complex and discrete manufacturing enterprises such as shipbuilding and aerospace often comprise multiple tasks,processes,and parallel machines,resulting in complex routes.The queuing time of parts in front of machines may account for 90%of the production cycle time.This study established a weighted allocation model of a formation-scheduling joint decision problem considering queuing time in system.To solve this nondeterministic polynomial(NP)problem,an adaptive differential evolution-simulated annealing(ADE-SA)algorithm is proposed.Compared with the standard differential evolution(DE)algorithm,the adaptive mutation factor overcomes the disadvantage that the scale of DE’s differential vector is difficult to control.The selection strategy of the SA algorithm compensates for the deficiency that DE’s greedy strategy may fall into a local optimal solution.The comparison results of four algorithms of a series of random examples demonstrate that the overall performance of ADE-SA is superior to the genetic algorithm,and average iteration,maximum completion time,and move time are 24%,11%,and 7%lower than the average of other three algorithms,respectively.The method can generate the joint decision-making scheme with better overall performance,and effectively identify production bottlenecks through quantitative analysis of queuing time.展开更多
Making use of microsoft visual studio. net platform, the assistant decision-making system of tunnel boring machine in tunnelling has been built to predict the time and cost. Computation methods of the performance para...Making use of microsoft visual studio. net platform, the assistant decision-making system of tunnel boring machine in tunnelling has been built to predict the time and cost. Computation methods of the performance parameters have been discussed. New time and cost prediction models have been depicted. The multivariate linear regression has been used to make the parameters more precise, which are the key factor to affect the prediction near to the reality.展开更多
基金supported by grants from the National Aeronautics and Space Administration Applied Science Program,USA (NNX12AQ31G,NNX14AP91G,PI:Dr.Liping Di)
文摘Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.
基金supported by the National Natural Science Foundation of China(Grant No.:71972090).
文摘Formation and scheduling are the most important decisions in the virtual modular manufacturing system;however,the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results.The joint decision of formation and scheduling is very important for system design.Complex and discrete manufacturing enterprises such as shipbuilding and aerospace often comprise multiple tasks,processes,and parallel machines,resulting in complex routes.The queuing time of parts in front of machines may account for 90%of the production cycle time.This study established a weighted allocation model of a formation-scheduling joint decision problem considering queuing time in system.To solve this nondeterministic polynomial(NP)problem,an adaptive differential evolution-simulated annealing(ADE-SA)algorithm is proposed.Compared with the standard differential evolution(DE)algorithm,the adaptive mutation factor overcomes the disadvantage that the scale of DE’s differential vector is difficult to control.The selection strategy of the SA algorithm compensates for the deficiency that DE’s greedy strategy may fall into a local optimal solution.The comparison results of four algorithms of a series of random examples demonstrate that the overall performance of ADE-SA is superior to the genetic algorithm,and average iteration,maximum completion time,and move time are 24%,11%,and 7%lower than the average of other three algorithms,respectively.The method can generate the joint decision-making scheme with better overall performance,and effectively identify production bottlenecks through quantitative analysis of queuing time.
文摘Making use of microsoft visual studio. net platform, the assistant decision-making system of tunnel boring machine in tunnelling has been built to predict the time and cost. Computation methods of the performance parameters have been discussed. New time and cost prediction models have been depicted. The multivariate linear regression has been used to make the parameters more precise, which are the key factor to affect the prediction near to the reality.
