In this paper both processes of landslide and subsidence are considered to be limited systems. Each of these systems in nature might be regarded as an organism. Generally their lifespan must develop with common ecolog...In this paper both processes of landslide and subsidence are considered to be limited systems. Each of these systems in nature might be regarded as an organism. Generally their lifespan must develop with common ecological characteristics, including several evolutional stages, such as initiation, growth, maturation, decline and death. Among these stages, maturation is emphasized so as to find the occurring or thriving date of both systems. An once-through cycle of both landslide and subsidence is established and is accurately predicted by a developed, mathematic model of the Poisson cycle. The Weibull distribution is cited for a landslide example. Both fundamentals are discussed. Stage predictions of landslide and subsidence are performed for several examples. Back analysis of landslides that have already happened are studied with the same model. And when compared with results from the biological mathematic model and with practical results, it is found that they correspond. Stage prediction of subsidences is also researched by the principle of the Poisson cycle.展开更多
It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the sol...It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-shortterm memory(LSTM) and neural network autoregression(NNAR) deep learning methods to predict the upcoming 25 th solar cycle using the sunspot area(SSA) data during the period of May 1874 to December2020. Our results show that the 25 th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.展开更多
With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging mo...With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).展开更多
This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy manageme...This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy.The impacts of different prediction time lengths on driving cycle generation were explored.The results indicate that the original driving cycle is compressed by 50%,which significantly reduces the computational burden while having only a slight effect on the prediction performance.The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model,and the model was verified by simulation using two different testing scenarios.The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks,and it can be successfully used for the energy management of hybrid electric vehicles.展开更多
基金The paper is one part of a project supported by National Education Committee Funds for Doctoral Faculty
文摘In this paper both processes of landslide and subsidence are considered to be limited systems. Each of these systems in nature might be regarded as an organism. Generally their lifespan must develop with common ecological characteristics, including several evolutional stages, such as initiation, growth, maturation, decline and death. Among these stages, maturation is emphasized so as to find the occurring or thriving date of both systems. An once-through cycle of both landslide and subsidence is established and is accurately predicted by a developed, mathematic model of the Poisson cycle. The Weibull distribution is cited for a landslide example. Both fundamentals are discussed. Stage predictions of landslide and subsidence are performed for several examples. Back analysis of landslides that have already happened are studied with the same model. And when compared with results from the biological mathematic model and with practical results, it is found that they correspond. Stage prediction of subsidences is also researched by the principle of the Poisson cycle.
基金supported by the National Natural Science Foundation of China under Grant numbers U2031202,U1731124 and U1531247the special foundation work of the Ministry of Science and Technology of the People’s Republic of China under Grant number 2014FY120300the 13th Five-year Informatization Plan of Chinese Academy of Sciences under Grant number XXH13505-04。
文摘It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-shortterm memory(LSTM) and neural network autoregression(NNAR) deep learning methods to predict the upcoming 25 th solar cycle using the sunspot area(SSA) data during the period of May 1874 to December2020. Our results show that the 25 th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.
基金the National Natural Science Foundation of China(No.52172400).
文摘With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).
基金This research was supported in part by the Young Elite Scientist Sponsorship Program(No.2017QNRC001)the China Association for Science and Technology and a Start-Up Grant(No.M4082268.050)from Nanyang Technological University,Singapore.
文摘This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy.The impacts of different prediction time lengths on driving cycle generation were explored.The results indicate that the original driving cycle is compressed by 50%,which significantly reduces the computational burden while having only a slight effect on the prediction performance.The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model,and the model was verified by simulation using two different testing scenarios.The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks,and it can be successfully used for the energy management of hybrid electric vehicles.