现有基于深度学习的锂电池剩余寿命(Remaining Useful Life,RUL)预测方法中,锂电池多个内部状态所蕴含的寿命信息未得到充分考虑.鉴于此,提出了一种融合电池容量、阻抗与温度三个内部状态的RUL预测模型.首先,引入双向长短时记忆(Bi‑dire...现有基于深度学习的锂电池剩余寿命(Remaining Useful Life,RUL)预测方法中,锂电池多个内部状态所蕴含的寿命信息未得到充分考虑.鉴于此,提出了一种融合电池容量、阻抗与温度三个内部状态的RUL预测模型.首先,引入双向长短时记忆(Bi‑directional Long Short‑Term Memory,Bi‑LSTM)网络学习三种状态数据的时间相关性.其次,利用dropout技术与Bayesian变分推断技术间的等价性实现了RUL预测结果的不确定性量化,得到了预测结果的95%置信区间与概率密度分布(Probability Density Function,PDF),并分析了不同dropout率对预测不确定性的影响.最后,通过四种不同的深度学习模型框架与两种内部状态输入方案的对比实验,验证了本文方法的有效性.展开更多
A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavi...A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.展开更多
Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell t...Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learn展开更多
全球气候变暖、海平面上升背景下,沿海城市极端洪涝事件的发生频率和强度将显著增大,洪涝灾害风险剧增,成为沿海城市安全与发展的严峻挑战。基于深度不确定性的稳健决策(Decision Making under Deep Uncertainty,DMDU)思路,旨在提供长...全球气候变暖、海平面上升背景下,沿海城市极端洪涝事件的发生频率和强度将显著增大,洪涝灾害风险剧增,成为沿海城市安全与发展的严峻挑战。基于深度不确定性的稳健决策(Decision Making under Deep Uncertainty,DMDU)思路,旨在提供长期稳健的决策方案,成为全球沿海城市洪涝风险管理研究的新趋势。该文对比分析了稳健决策、适应路径和期权估值三类主要DMDU方法,基于不确定性、稳健性和适应性剖析了DMDU方法基本原理,提出了DMDU稳健决策的一般性框架。最后,从稳健性与决策目标、政策环境与决策参与以及方法的融合与创新三个方面对DMDU在洪涝风险领域的实践应用进行展望,以期为沿海城市适应气候变化稳健决策提供参考。展开更多
文摘现有基于深度学习的锂电池剩余寿命(Remaining Useful Life,RUL)预测方法中,锂电池多个内部状态所蕴含的寿命信息未得到充分考虑.鉴于此,提出了一种融合电池容量、阻抗与温度三个内部状态的RUL预测模型.首先,引入双向长短时记忆(Bi‑directional Long Short‑Term Memory,Bi‑LSTM)网络学习三种状态数据的时间相关性.其次,利用dropout技术与Bayesian变分推断技术间的等价性实现了RUL预测结果的不确定性量化,得到了预测结果的95%置信区间与概率密度分布(Probability Density Function,PDF),并分析了不同dropout率对预测不确定性的影响.最后,通过四种不同的深度学习模型框架与两种内部状态输入方案的对比实验,验证了本文方法的有效性.
基金supported by the Sichuan Science and Technology Program(No.2020JDJQ0037)。
文摘A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number 223202.
文摘Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learn
文摘全球气候变暖、海平面上升背景下,沿海城市极端洪涝事件的发生频率和强度将显著增大,洪涝灾害风险剧增,成为沿海城市安全与发展的严峻挑战。基于深度不确定性的稳健决策(Decision Making under Deep Uncertainty,DMDU)思路,旨在提供长期稳健的决策方案,成为全球沿海城市洪涝风险管理研究的新趋势。该文对比分析了稳健决策、适应路径和期权估值三类主要DMDU方法,基于不确定性、稳健性和适应性剖析了DMDU方法基本原理,提出了DMDU稳健决策的一般性框架。最后,从稳健性与决策目标、政策环境与决策参与以及方法的融合与创新三个方面对DMDU在洪涝风险领域的实践应用进行展望,以期为沿海城市适应气候变化稳健决策提供参考。
