Analogizing with the definition of thermal efficiency of a heat exchanger,the entransy dissipation efficiency of a heat exchanger is defined as the ratio of dimensionless entransy dissipation rate to dimensionless pum...Analogizing with the definition of thermal efficiency of a heat exchanger,the entransy dissipation efficiency of a heat exchanger is defined as the ratio of dimensionless entransy dissipation rate to dimensionless pumping power of the heat exchanger.For the constraints of the total tube volume and total tube surface area of the heat exchanger,the constructal optimization of an H-shaped multi-scale heat exchanger is carried out by taking entransy dissipation efficiency maximization as optimization objective,and the optimal construct of the H-shaped multi-scale heat exchanger with maximum entransy dissipation efficiency is obtained.The results show that for the specified total tube volume of the heat exchanger,the optimal constructs of the first order T-shaped heat exchanger based on the maximizations of the thermal efficiency and entransy dissipation efficiency are obviously different with the lower mass flow rates of the cold and hot fluids.For the H-shaped multi-scale heat exchanger,the entransy dissipation efficiency decreases with the increase in mass flow rate when the heat exchanger order is fixed;for the specified dimensionless mass flow rate M(M<32.9),the entransy dissipation efficiency decreases with the increase in the heat exchanger order.The performance of the multi-scale heat exchanger is obviously improved compared with that of the single-scale heat exchanger.Moreover,the heat exchanger subjected to the total tube surface area constraint is also discussed in the paper.The optimization results obtained in this paper can provide a great compromise between the heat transfer and flow performances of the heat exchanger,provide some guidelines for the optimal designs of heat exchangers,and also enrich the connotation of entransy theory.展开更多
Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current...Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual demand.Besides,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in datasets.To address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance video.Then,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth modulation.The former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with class-imbalance.Finally,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods.And extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail classes.Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 51176203)the Natural Science Foundation for Youngsters of Naval University of Engineering (Grant No. HGDQNJJ11008)
文摘Analogizing with the definition of thermal efficiency of a heat exchanger,the entransy dissipation efficiency of a heat exchanger is defined as the ratio of dimensionless entransy dissipation rate to dimensionless pumping power of the heat exchanger.For the constraints of the total tube volume and total tube surface area of the heat exchanger,the constructal optimization of an H-shaped multi-scale heat exchanger is carried out by taking entransy dissipation efficiency maximization as optimization objective,and the optimal construct of the H-shaped multi-scale heat exchanger with maximum entransy dissipation efficiency is obtained.The results show that for the specified total tube volume of the heat exchanger,the optimal constructs of the first order T-shaped heat exchanger based on the maximizations of the thermal efficiency and entransy dissipation efficiency are obviously different with the lower mass flow rates of the cold and hot fluids.For the H-shaped multi-scale heat exchanger,the entransy dissipation efficiency decreases with the increase in mass flow rate when the heat exchanger order is fixed;for the specified dimensionless mass flow rate M(M<32.9),the entransy dissipation efficiency decreases with the increase in the heat exchanger order.The performance of the multi-scale heat exchanger is obviously improved compared with that of the single-scale heat exchanger.Moreover,the heat exchanger subjected to the total tube surface area constraint is also discussed in the paper.The optimization results obtained in this paper can provide a great compromise between the heat transfer and flow performances of the heat exchanger,provide some guidelines for the optimal designs of heat exchangers,and also enrich the connotation of entransy theory.
基金This work was supported by the National Natural Science Foundation of China(Grant No.62071378)the Shaanxi Province International Science and Technology Cooperation Program(2022KW-04)the Xi’an Science and Technology Plan Project(21XJZZ0072).
文摘Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual demand.Besides,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in datasets.To address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance video.Then,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth modulation.The former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with class-imbalance.Finally,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods.And extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail classes.Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.
