This methodological investigation deals with measurement and valuation of ecological service functions for urban green space. Social, economic and ecological dimensions for such types of function were analyzed and a ...This methodological investigation deals with measurement and valuation of ecological service functions for urban green space. Social, economic and ecological dimensions for such types of function were analyzed and a concept “integrated ecological service functions” (IESF) was put forward for evaluation. Based upon this conceptual approach, an index system for measuring IESF for urban green space was established. With a methodological integration of fuzzy mathematics, decision making analysis and Delphi method, an AHP fuzzy evaluation techniques for IESF for urban green space, called AFIFUG method, was developed. Such a method has been directly applied to the land use strategic planning of Tianjin out ring green belt(TOGB), and its analysis results have been successfully put into operation.展开更多
The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure o...The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.展开更多
The Beijing-Tianjin-Hebei region boasts rich geothermal resources and new achievements have been made in the exploration and development of geothermal resources in this region based on previous regional investigation....The Beijing-Tianjin-Hebei region boasts rich geothermal resources and new achievements have been made in the exploration and development of geothermal resources in this region based on previous regional investigation.In detail,geothermal reservoirs of Gaoyuzhuang Formation of Jixian System and Changcheng System in Xiongan New Area have been recently discovered,opening up the second space of geothermal resources;the calculation method of the recoverable resources of geothermal fluid with reinjection being considered has been improved in Beijing-Tianjin-Hebei region,and uniform comprehensive assessment of shallow geothermal energy,hydrothermal geothermal resources,and hot dry rocks(HDR)geothermal resources in the whole Beijing-Tianjin-Shijiazhuang region has been completed.The scientific research base for cascade development and utilization of geothermal resources in Beijing-Tianjin-Hebei region has applied hydraulic fracturing technology to the geothermal reservoirs in Gaoyuzhuang Formation.As a result,the production capacity doubled and two-stage cascade utilization composed of geothermal power generation and geothermal heating were realized,with the first-phase installed capacity of 280 kW and the geothermal heating is 30000 m2.In this way,a model of the exploration,development,and utilization of geothermal resources formed.Large-scale utilization has become the future trend of geothermal resource development in Beijing-Tianjin-Hebei region,and great efforts shall be made to achieve breakthroughs in reinjection technology,geothermal reservoir reconstruction technology,thermoelectric technology and underground heat exchange technology.展开更多
A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on s...A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study,we aim to incorporate reinforcement algorithms(Q-learning)into a context-aware system to provide relevant services based on a user’s dynamic context.To accelerate the convergence of reinforcement learning(RL)algorithms and provide the correct services in real situations,we propose a combination of the Q-learning and case-based reasoning(CBR)algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more effi-cient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.展开更多
文摘This methodological investigation deals with measurement and valuation of ecological service functions for urban green space. Social, economic and ecological dimensions for such types of function were analyzed and a concept “integrated ecological service functions” (IESF) was put forward for evaluation. Based upon this conceptual approach, an index system for measuring IESF for urban green space was established. With a methodological integration of fuzzy mathematics, decision making analysis and Delphi method, an AHP fuzzy evaluation techniques for IESF for urban green space, called AFIFUG method, was developed. Such a method has been directly applied to the land use strategic planning of Tianjin out ring green belt(TOGB), and its analysis results have been successfully put into operation.
基金supported by the National Natural Science Foundation of China(No.62111530051)the Fundamental Research Funds for the Central Universities(No.3102017JC06002)the Shaanxi Science and Technology Program,China(No.2017KW-ZD-04).
文摘The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.
基金This work is financially supported by the Special Fund for National Key Research and Development Program of China(2018YFC0604306)China Geological Survey project Survey and Assessment of Geothermal Energy in Xiongan New Area(DD20189112)Technology Innovation Center of Geothermal and Hot Dry Rock Exploration and Development,Ministry of Natural Resources.
文摘The Beijing-Tianjin-Hebei region boasts rich geothermal resources and new achievements have been made in the exploration and development of geothermal resources in this region based on previous regional investigation.In detail,geothermal reservoirs of Gaoyuzhuang Formation of Jixian System and Changcheng System in Xiongan New Area have been recently discovered,opening up the second space of geothermal resources;the calculation method of the recoverable resources of geothermal fluid with reinjection being considered has been improved in Beijing-Tianjin-Hebei region,and uniform comprehensive assessment of shallow geothermal energy,hydrothermal geothermal resources,and hot dry rocks(HDR)geothermal resources in the whole Beijing-Tianjin-Shijiazhuang region has been completed.The scientific research base for cascade development and utilization of geothermal resources in Beijing-Tianjin-Hebei region has applied hydraulic fracturing technology to the geothermal reservoirs in Gaoyuzhuang Formation.As a result,the production capacity doubled and two-stage cascade utilization composed of geothermal power generation and geothermal heating were realized,with the first-phase installed capacity of 280 kW and the geothermal heating is 30000 m2.In this way,a model of the exploration,development,and utilization of geothermal resources formed.Large-scale utilization has become the future trend of geothermal resource development in Beijing-Tianjin-Hebei region,and great efforts shall be made to achieve breakthroughs in reinjection technology,geothermal reservoir reconstruction technology,thermoelectric technology and underground heat exchange technology.
文摘A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study,we aim to incorporate reinforcement algorithms(Q-learning)into a context-aware system to provide relevant services based on a user’s dynamic context.To accelerate the convergence of reinforcement learning(RL)algorithms and provide the correct services in real situations,we propose a combination of the Q-learning and case-based reasoning(CBR)algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more effi-cient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.
