Nowadays,the limits on greenhouse gas emissions are becoming increasingly stringent.In present research,a two-dimensional numerical model was established to simulate the deep removal of 1,1,1,2-tetrafluoroethane(R134a...Nowadays,the limits on greenhouse gas emissions are becoming increasingly stringent.In present research,a two-dimensional numerical model was established to simulate the deep removal of 1,1,1,2-tetrafluoroethane(R134a)from the non-condensable gas(NCG)mixture by cryogenic condensation and de-sublimation.The wall condensation method was compiled into the Fluent software to calculate the condensation of R134a from the gas mixture.Besides,the saturated thermodynamic properties of R134a under its triple point were extrapolated by the equation of state.The simulation of the steam condensation with NCG was conducted to verify the validity of the model,the results matched well with the experimental data.Subsequently,the condensation characteristics of R134a with NCG and the thermodynamic parameters affecting condensation were studied.The results show that the section with relatively higher removal efficiency is usually near the inlet.The cold wall temperature has a great influence on the R134a removal performance,e.g.,a 15 K reduction of the wall temperature brings a reduction in the outlet R134a molar fraction by 85.43%.The effect of changing mass flow rate on R134a removal is mainly reflected at the outlet,where an increase in mass flow rate of 12.6% can aggravate the outlet molar fraction to 210.3% of the original.The research can provide a valuable reference for the simulation of the deep removal of various low-concentration gas using condensation and de-sublimation methods.展开更多
Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,whi...Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.展开更多
This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specif...This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.展开更多
基金funded by the National Natural Science Foundation of China(52076159)。
文摘Nowadays,the limits on greenhouse gas emissions are becoming increasingly stringent.In present research,a two-dimensional numerical model was established to simulate the deep removal of 1,1,1,2-tetrafluoroethane(R134a)from the non-condensable gas(NCG)mixture by cryogenic condensation and de-sublimation.The wall condensation method was compiled into the Fluent software to calculate the condensation of R134a from the gas mixture.Besides,the saturated thermodynamic properties of R134a under its triple point were extrapolated by the equation of state.The simulation of the steam condensation with NCG was conducted to verify the validity of the model,the results matched well with the experimental data.Subsequently,the condensation characteristics of R134a with NCG and the thermodynamic parameters affecting condensation were studied.The results show that the section with relatively higher removal efficiency is usually near the inlet.The cold wall temperature has a great influence on the R134a removal performance,e.g.,a 15 K reduction of the wall temperature brings a reduction in the outlet R134a molar fraction by 85.43%.The effect of changing mass flow rate on R134a removal is mainly reflected at the outlet,where an increase in mass flow rate of 12.6% can aggravate the outlet molar fraction to 210.3% of the original.The research can provide a valuable reference for the simulation of the deep removal of various low-concentration gas using condensation and de-sublimation methods.
基金supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China(2021YFD2000304)Fundamental Research Funds for the Central Universities(531118010509)Natural Science Foundation of Hunan Province,China(2021JJ40114)。
文摘Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.
文摘This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.
