Afamous German art critic oncepraised Chinese artist Li Hongtao as China’s Van.Gogh In March,the critic,also the director of the Ludwigs Germany International Artistic Forum,said:"In a European’s eyes,these pai...Afamous German art critic oncepraised Chinese artist Li Hongtao as China’s Van.Gogh In March,the critic,also the director of the Ludwigs Germany International Artistic Forum,said:"In a European’s eyes,these paintings are best symbol of complicated art in the world.This kind of art not only implies deep understanding of traditional Chinese art and culture,but also a strong sense of Eurlopean and American art.Such artistic spirit has been expressed athrough the artist’s strong feeling and active desire."展开更多
We present an approach for generating paintings on photographic images with the style encoded by the example paintings and adopt representative brushes extracted from the example paintings as the painting primitives. ...We present an approach for generating paintings on photographic images with the style encoded by the example paintings and adopt representative brushes extracted from the example paintings as the painting primitives. Our system first divides the given photographic image into several regions on which we synthesize a grounding layer with texture patches extracted from the example paintings. Then, we paint those regions using brushes stochastically chosen from the brush library, with further brush color and shape perturbations. The brush direction is determined by a direction field either constructed by a convenient user interactive manner or synthesized from the examples. Our approach offers flexible and intuitive user control over the painting process and style.展开更多
As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance le...As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.展开更多
文摘Afamous German art critic oncepraised Chinese artist Li Hongtao as China’s Van.Gogh In March,the critic,also the director of the Ludwigs Germany International Artistic Forum,said:"In a European’s eyes,these paintings are best symbol of complicated art in the world.This kind of art not only implies deep understanding of traditional Chinese art and culture,but also a strong sense of Eurlopean and American art.Such artistic spirit has been expressed athrough the artist’s strong feeling and active desire."
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312101) and the National Natural Science Foun-dation of China (Nos. 60403038 and 60373037)
文摘We present an approach for generating paintings on photographic images with the style encoded by the example paintings and adopt representative brushes extracted from the example paintings as the painting primitives. Our system first divides the given photographic image into several regions on which we synthesize a grounding layer with texture patches extracted from the example paintings. Then, we paint those regions using brushes stochastically chosen from the brush library, with further brush color and shape perturbations. The brush direction is determined by a direction field either constructed by a convenient user interactive manner or synthesized from the examples. Our approach offers flexible and intuitive user control over the painting process and style.
基金the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.
