Recently,many data anonymization methods have been proposed to protect privacy in the applications of data mining.But few of them have considered the threats from user's priori knowledge of data patterns.To solve ...Recently,many data anonymization methods have been proposed to protect privacy in the applications of data mining.But few of them have considered the threats from user's priori knowledge of data patterns.To solve this problem,a flexible method was proposed to randomize the dataset,so that the user could hardly obtain the sensitive data even knowing data relationships in advance.The method also achieves a high level of accuracy in the mining process as demonstrated in the experiments.展开更多
Accurate tropical cyclone(TC)intensity estimation is crucial for preventing and mitigating TC-related disasters.Despite recent advances in TC intensity estimation using convolutional neural networks(CNNs),existing tec...Accurate tropical cyclone(TC)intensity estimation is crucial for preventing and mitigating TC-related disasters.Despite recent advances in TC intensity estimation using convolutional neural networks(CNNs),existing techniques fail to adequately incorporate the priori knowledge of TCs.Therefore,information strongly correlated with TC intensity can be obscured by irrelevant data,limiting model performance.To address this challenge,we introduce the Convective-Stratiform Separation Technique,which acts as a physical constraint on the model,to extract pivotal features from the convective core in satellite infrared imagery.Concurrently,we propose a new dual-branch TC intensity estimation model,comprising a“Satellite Imagery Analysis Branch”to extract overall features from satellite imagery and a“Physics-Guided Branch”to analyze the identified convective cores.We further improve the estimation accuracy by incorporating key physical and environmental factors that are often overlooked by the model.We train the model on 1285 TC cases globally during 2003-2016 and evaluate the performance of best-optimized model using an independent test dataset of 95 TC cases globally from 2017.The results show that the root mean square error(RMSE)of TC intensity estimation is 8.13 kt,demonstrating superior performance compared to existing deep learning models.展开更多
It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model...It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.展开更多
A children’s book as Alice’s Adventures in Wonderland,it is incredibly popular among adults.Finding out why the book arouses adults’interest will enhance people’s understanding of it.This paper draws on the elemen...A children’s book as Alice’s Adventures in Wonderland,it is incredibly popular among adults.Finding out why the book arouses adults’interest will enhance people’s understanding of it.This paper draws on the elements in the book which only adults could appreciate,and finds out that it requires A Priori knowledge,from mathematical analogies to logic,as well as A Posteriori knowledge,including the awareness of social hierarchy,to understand them,which leads to the book’s popularity among adults.展开更多
Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch ...Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.展开更多
文摘Recently,many data anonymization methods have been proposed to protect privacy in the applications of data mining.But few of them have considered the threats from user's priori knowledge of data patterns.To solve this problem,a flexible method was proposed to randomize the dataset,so that the user could hardly obtain the sensitive data even knowing data relationships in advance.The method also achieves a high level of accuracy in the mining process as demonstrated in the experiments.
基金National Natural Science Foundation of China(42075138,42375147)。
文摘Accurate tropical cyclone(TC)intensity estimation is crucial for preventing and mitigating TC-related disasters.Despite recent advances in TC intensity estimation using convolutional neural networks(CNNs),existing techniques fail to adequately incorporate the priori knowledge of TCs.Therefore,information strongly correlated with TC intensity can be obscured by irrelevant data,limiting model performance.To address this challenge,we introduce the Convective-Stratiform Separation Technique,which acts as a physical constraint on the model,to extract pivotal features from the convective core in satellite infrared imagery.Concurrently,we propose a new dual-branch TC intensity estimation model,comprising a“Satellite Imagery Analysis Branch”to extract overall features from satellite imagery and a“Physics-Guided Branch”to analyze the identified convective cores.We further improve the estimation accuracy by incorporating key physical and environmental factors that are often overlooked by the model.We train the model on 1285 TC cases globally during 2003-2016 and evaluate the performance of best-optimized model using an independent test dataset of 95 TC cases globally from 2017.The results show that the root mean square error(RMSE)of TC intensity estimation is 8.13 kt,demonstrating superior performance compared to existing deep learning models.
基金Supported by the National Natural Science Foundation of China(51339004,71171151)
文摘It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.
文摘A children’s book as Alice’s Adventures in Wonderland,it is incredibly popular among adults.Finding out why the book arouses adults’interest will enhance people’s understanding of it.This paper draws on the elements in the book which only adults could appreciate,and finds out that it requires A Priori knowledge,from mathematical analogies to logic,as well as A Posteriori knowledge,including the awareness of social hierarchy,to understand them,which leads to the book’s popularity among adults.
基金the National Natural Science Foundation of China(No.62103009)。
文摘Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.
