Sensor scheduling is used to improve the sensing performance in the estimation of targets’states.However,few papers are on the sensor scheduling for target detection with guiding information.This letter can remedy th...Sensor scheduling is used to improve the sensing performance in the estimation of targets’states.However,few papers are on the sensor scheduling for target detection with guiding information.This letter can remedy this deficiency.A risk-based target detection method with guiding information is provided firstly,based on which,the sensor scheduling approach is aiming at reducing the risk and uncertainty in target detection,namely risk-based sensor scheduling method.What should be stressed is that the Prediction Formula in sensor scheduling is proposed.Lastly,some examples are conducted to stress the effectiveness of this proposed method.展开更多
Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal s...Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.展开更多
Vietnam has become a major market for construction enterprises from East Asian countries, especially from China, to participate in international project contracting, but serious schedule delays have important adverse ...Vietnam has become a major market for construction enterprises from East Asian countries, especially from China, to participate in international project contracting, but serious schedule delays have important adverse effects on local government and foreign investment companies. Based on international engineering contracting mode of Vietnam highway BOT construction projects, we discussed the drive financial factors of schedule delays, using the methods of exploratory factor analysis and questionnaire survey, and evaluated the effects of various factors which are through regression analysis. The empirical results show that the five categories of financial factors, including the policy change, slow payment, financial mismanagement, financial market changes and lack of fiscal, have significant effects on schedule delay. Furtherly, we suggested that strengthening policy research and improving financial management ability should be used to reduce the influence of relevant financial factors on schedule delay, to improve the profitability of international businesses and the motivation of foreign enterprises to participate in Vietnam highway project.展开更多
Construction project scheduling is one of the most critical factors for project success measurement.Not only for the project planning but for construction process management,the scheduling is the basic tool for commun...Construction project scheduling is one of the most critical factors for project success measurement.Not only for the project planning but for construction process management,the scheduling is the basic tool for communication between the owner and the project manager.By developing the schedule before the project starts,the owner knows in advance that the expected timeline of the project.By preparing construction process scheduling,the owner and general contractor can better manage the subcontractors,sub-trades progress,materials storage and deliveries,labors schedule and equipment set up which will eventually save time,money and hassle.Basically,Critical Path Method(CPM) is commonly used in the construction industry.CPM is a deterministic method that assumes that through the network,there is at least one path that determines the project duration and that the path is the critical path.CPM does not consider the uncertainty in the activities;rather it assumes that each activity can be finished in the given situation.Program Evaluation and Review Technique(PERT) is a stochastic technique which is based on the assumption that the duration of a single activity can be described by a probability density function.PERT takes into account the uncertainty during the construction process and has been created out of the need to plan,schedule and control complex projects with many uncertainties.The PERT approach is stated in some books and papers,but there is no deep investigation on the application in the schedule risk assessment.This paper investigates the PERT work process and takes a valuable try on the construction schedule risk assessment by using case studies.The utilization in the estimate the construction liquidated damage with the uncertainties is performed,which also can be used in the insurance company to calculate the insurance premium.展开更多
A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set ...A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.展开更多
基金supported by National Natural Science Foundation(grant 61573374)。
文摘Sensor scheduling is used to improve the sensing performance in the estimation of targets’states.However,few papers are on the sensor scheduling for target detection with guiding information.This letter can remedy this deficiency.A risk-based target detection method with guiding information is provided firstly,based on which,the sensor scheduling approach is aiming at reducing the risk and uncertainty in target detection,namely risk-based sensor scheduling method.What should be stressed is that the Prediction Formula in sensor scheduling is proposed.Lastly,some examples are conducted to stress the effectiveness of this proposed method.
基金This work was supported by the National Key R&D Program of China(2018AAA0101400)the National Natural Science Foundation of China(62173251,61921004,U1713209)the Natural Science Foundation of Jiangsu Province of China(BK20202006).
文摘Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.
文摘Vietnam has become a major market for construction enterprises from East Asian countries, especially from China, to participate in international project contracting, but serious schedule delays have important adverse effects on local government and foreign investment companies. Based on international engineering contracting mode of Vietnam highway BOT construction projects, we discussed the drive financial factors of schedule delays, using the methods of exploratory factor analysis and questionnaire survey, and evaluated the effects of various factors which are through regression analysis. The empirical results show that the five categories of financial factors, including the policy change, slow payment, financial mismanagement, financial market changes and lack of fiscal, have significant effects on schedule delay. Furtherly, we suggested that strengthening policy research and improving financial management ability should be used to reduce the influence of relevant financial factors on schedule delay, to improve the profitability of international businesses and the motivation of foreign enterprises to participate in Vietnam highway project.
文摘Construction project scheduling is one of the most critical factors for project success measurement.Not only for the project planning but for construction process management,the scheduling is the basic tool for communication between the owner and the project manager.By developing the schedule before the project starts,the owner knows in advance that the expected timeline of the project.By preparing construction process scheduling,the owner and general contractor can better manage the subcontractors,sub-trades progress,materials storage and deliveries,labors schedule and equipment set up which will eventually save time,money and hassle.Basically,Critical Path Method(CPM) is commonly used in the construction industry.CPM is a deterministic method that assumes that through the network,there is at least one path that determines the project duration and that the path is the critical path.CPM does not consider the uncertainty in the activities;rather it assumes that each activity can be finished in the given situation.Program Evaluation and Review Technique(PERT) is a stochastic technique which is based on the assumption that the duration of a single activity can be described by a probability density function.PERT takes into account the uncertainty during the construction process and has been created out of the need to plan,schedule and control complex projects with many uncertainties.The PERT approach is stated in some books and papers,but there is no deep investigation on the application in the schedule risk assessment.This paper investigates the PERT work process and takes a valuable try on the construction schedule risk assessment by using case studies.The utilization in the estimate the construction liquidated damage with the uncertainties is performed,which also can be used in the insurance company to calculate the insurance premium.
基金Foundation item： Project（2012AA051603） supported by the National High Technology Research and Development Program 863 Plan of China
文摘A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.
