In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from...In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.展开更多
A sensitive and selective liquid chromatography-electrospray ionization tandem mass spectrometry（LC- ES1-MS/MS） was used for the simultaneous determination of metformin and glimepiride in beagle dog plasma with glip...A sensitive and selective liquid chromatography-electrospray ionization tandem mass spectrometry（LC- ES1-MS/MS） was used for the simultaneous determination of metformin and glimepiride in beagle dog plasma with glipizide as internal standard（IS）. After simplified protein precipitation with methanol, both the analytes and IS were chromatographed on a Zorbax CN column via gradient elution with methanol（containing 5 mmol/L ammonium ace- tate） and 5 mmol/L aqueous ammonium acetate as the mobile phase. Detection was performed by multiple reaction monitoring（MRM） scanning via ESI source operated in positive ionization mode. Specificity, linearity, accuracy, pre- cision, recovery, matrix effect and stability were validated for metformin and glimepiride in beagle dog plasma. The calibration curves were linear in a concentration range of 10-10000 ng/mL for metformin and 4-4000 ng/mL for glimepiride with both correlation coefficients higher than 0.99. The recoveries obtained for the analytes and IS were all between 82.7% and 101.2%. The method exhibited excellent performance in terms of selectivity, robustness, short analytical time and simplicity of sample preparation. Finally, the proposed method was applied to a bioequivalence study of self-made bilayer tablet and commercial formulation containing 500 mg of metformin and 1 mg of glimepi- ride in beagle dogs.展开更多
Diesel hydrotreatment removes heteroatoms and polycyclic aromatics in diesel in the presence of highpressure hydrogen gas.The hydrogen solubility is the basis for hydrogen consumption prediction and process design/opt...Diesel hydrotreatment removes heteroatoms and polycyclic aromatics in diesel in the presence of highpressure hydrogen gas.The hydrogen solubility is the basis for hydrogen consumption prediction and process design/optimization.In the presented study,we established a method to predict the hydrogen solubility of diesel molecules and mixture.A modified Henry equation was proposed to illustrate the hydrogen solubility variation among the temperature and pressure.The parameters of the modified Henry equation for typical molecules were regressed from literature data.Then we established an empirical correlation between the parameter and the structure and property of molecules.The method was then combined with a molecular-level compositional model to accurately predict the hydrogen solubility in diesel,illustrating the validity of the method.展开更多
To promote the commercialization of yellow pea flour(YPF)due to its nutritional benefits.Four biscuits with different YPF ratio(10%–50%)were conducted to explore the optimal addition percentage.The effects of YPF on ...To promote the commercialization of yellow pea flour(YPF)due to its nutritional benefits.Four biscuits with different YPF ratio(10%–50%)were conducted to explore the optimal addition percentage.The effects of YPF on the rheological and baking performance of biscuits were performed.The results showed that the substitution ratio of YPF and milling methods had a critical impact on the rheological properties of dough.The dough stability decreased graduallywhile a softening degree increased with YPF ratio increased.In a term of biscuits,the dimensions of length(L),width(W),thickness(T)and color(L*)of biscuits reduced as YPF addition ratio increased,while colors(a*and b*)and hardness apparently increased.In addition,milling methods had a great influence on the texture and sensory evaluation of four biscuits.The dimensions and color parameters of biscuits from fine flours were larger than that from coarse flours,whereas hardness from fine flours was relatively softer,indicating flour with fine particle size could accelerate the extension and expansion of dough network,and improve Maillard reaction during baking.The highest sensory score for short and tough biscuits was obtained given at YPF ratio of 30%without compromising the qualities of biscuits.展开更多
Correction to:YOLOP:You Only Look Once for Panoptic Driving Perception DOI:10.1007/s11633-022-1339-y Authors:Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu The article YOLOP:You...Correction to:YOLOP:You Only Look Once for Panoptic Driving Perception DOI:10.1007/s11633-022-1339-y Authors:Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu The article YOLOP:You Only Look Once for Panoptic Driving Perception,written by Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu,was originally published without Open Access.展开更多
Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of...Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of vision-based ap- plications, therefore text detection and recognition in natu- ral scenes have become important and active research topics in computer vision and document analysis. Especially in re- cent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges （e.g. noise, blur, distortion, occlusion and varia- tion） still remain. The purposes of this survey are three-fold： 1） introduce up-to-date works, 2） identify state-of-the-art al- gorithms, and 3） predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.展开更多
A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panopti...A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panoptic driving perception network(you only look once for panoptic(YOLOP))to perform traffic object detection,drivable area segmentation,and lane detection simultaneously.It is composed of one encoder for feature extraction and three decoders to handle the specific tasks.Our model performs extremely well on the challenging BDD100K dataset,achieving state-of-the-art on all three tasks in terms of accuracy and speed.Besides,we verify the effectiveness of our multi-task learning model for joint training via ablative studies.To our best knowledge,this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS),and maintain excellent accuracy.To facilitate further research,the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.展开更多
Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture...Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.展开更多
Edges are important cues for localizing object proposals. The recent progresses to this problem are mostly driven by defining effective objectness measures based on edge cues. In this paper, we develop a new represent...Edges are important cues for localizing object proposals. The recent progresses to this problem are mostly driven by defining effective objectness measures based on edge cues. In this paper, we develop a new representation named directional edges on which each edge pixel is assigned with a direction toward object center, through learning a direction prediction model with convolutional neural networks in a holistic manner. Based on directional edges, two new objectness measures are designed for ranking object proposals. Experiments show that the proposed method achieves 97.1% object recall at an overlap threshold of 0.5 and 81.9% object recall at an overlap threshold of 0.7 at 1 000 proposals on the PASCAL VOC 2007 test dataset, which is superior to the state-of-the-art methods.展开更多
基金This work was supported in part by the Zhejiang Lab under Grant 20210AB02in part by the Sichuan International Science and Technology Innovation Cooperation/Hong Kong,Macao and Taiwan Science and Technology Innovation Cooperation Project under Grant 2019YFH0163in part by the Key Research and Development Project of Sichuan Provincial Department of Science and Technology under Grant 2018JZ0071.
文摘In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.
文摘A sensitive and selective liquid chromatography-electrospray ionization tandem mass spectrometry（LC- ES1-MS/MS） was used for the simultaneous determination of metformin and glimepiride in beagle dog plasma with glipizide as internal standard（IS）. After simplified protein precipitation with methanol, both the analytes and IS were chromatographed on a Zorbax CN column via gradient elution with methanol（containing 5 mmol/L ammonium ace- tate） and 5 mmol/L aqueous ammonium acetate as the mobile phase. Detection was performed by multiple reaction monitoring（MRM） scanning via ESI source operated in positive ionization mode. Specificity, linearity, accuracy, pre- cision, recovery, matrix effect and stability were validated for metformin and glimepiride in beagle dog plasma. The calibration curves were linear in a concentration range of 10-10000 ng/mL for metformin and 4-4000 ng/mL for glimepiride with both correlation coefficients higher than 0.99. The recoveries obtained for the analytes and IS were all between 82.7% and 101.2%. The method exhibited excellent performance in terms of selectivity, robustness, short analytical time and simplicity of sample preparation. Finally, the proposed method was applied to a bioequivalence study of self-made bilayer tablet and commercial formulation containing 500 mg of metformin and 1 mg of glimepi- ride in beagle dogs.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702400)the Science Foundation of China University of Petroleum,Beijing(Nos.2462018BJC003 and 2462018QZDX04)。
文摘Diesel hydrotreatment removes heteroatoms and polycyclic aromatics in diesel in the presence of highpressure hydrogen gas.The hydrogen solubility is the basis for hydrogen consumption prediction and process design/optimization.In the presented study,we established a method to predict the hydrogen solubility of diesel molecules and mixture.A modified Henry equation was proposed to illustrate the hydrogen solubility variation among the temperature and pressure.The parameters of the modified Henry equation for typical molecules were regressed from literature data.Then we established an empirical correlation between the parameter and the structure and property of molecules.The method was then combined with a molecular-level compositional model to accurately predict the hydrogen solubility in diesel,illustrating the validity of the method.
文摘To promote the commercialization of yellow pea flour(YPF)due to its nutritional benefits.Four biscuits with different YPF ratio(10%–50%)were conducted to explore the optimal addition percentage.The effects of YPF on the rheological and baking performance of biscuits were performed.The results showed that the substitution ratio of YPF and milling methods had a critical impact on the rheological properties of dough.The dough stability decreased graduallywhile a softening degree increased with YPF ratio increased.In a term of biscuits,the dimensions of length(L),width(W),thickness(T)and color(L*)of biscuits reduced as YPF addition ratio increased,while colors(a*and b*)and hardness apparently increased.In addition,milling methods had a great influence on the texture and sensory evaluation of four biscuits.The dimensions and color parameters of biscuits from fine flours were larger than that from coarse flours,whereas hardness from fine flours was relatively softer,indicating flour with fine particle size could accelerate the extension and expansion of dough network,and improve Maillard reaction during baking.The highest sensory score for short and tough biscuits was obtained given at YPF ratio of 30%without compromising the qualities of biscuits.
文摘Correction to:YOLOP:You Only Look Once for Panoptic Driving Perception DOI:10.1007/s11633-022-1339-y Authors:Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu The article YOLOP:You Only Look Once for Panoptic Driving Perception,written by Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu,was originally published without Open Access.
文摘Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of vision-based ap- plications, therefore text detection and recognition in natu- ral scenes have become important and active research topics in computer vision and document analysis. Especially in re- cent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges （e.g. noise, blur, distortion, occlusion and varia- tion） still remain. The purposes of this survey are three-fold： 1） introduce up-to-date works, 2） identify state-of-the-art al- gorithms, and 3） predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.
基金supported by National Natural Science Foundation of China(Nos.61876212 and 1733007)Zhejiang Laboratory,China(No.2019NB0AB02)Hubei Province College Students Innovation and Entrepreneurship Training Program,China(No.S202010487058).
文摘A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panoptic driving perception network(you only look once for panoptic(YOLOP))to perform traffic object detection,drivable area segmentation,and lane detection simultaneously.It is composed of one encoder for feature extraction and three decoders to handle the specific tasks.Our model performs extremely well on the challenging BDD100K dataset,achieving state-of-the-art on all three tasks in terms of accuracy and speed.Besides,we verify the effectiveness of our multi-task learning model for joint training via ablative studies.To our best knowledge,this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS),and maintain excellent accuracy.To facilitate further research,the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.
文摘Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.
文摘Edges are important cues for localizing object proposals. The recent progresses to this problem are mostly driven by defining effective objectness measures based on edge cues. In this paper, we develop a new representation named directional edges on which each edge pixel is assigned with a direction toward object center, through learning a direction prediction model with convolutional neural networks in a holistic manner. Based on directional edges, two new objectness measures are designed for ranking object proposals. Experiments show that the proposed method achieves 97.1% object recall at an overlap threshold of 0.5 and 81.9% object recall at an overlap threshold of 0.7 at 1 000 proposals on the PASCAL VOC 2007 test dataset, which is superior to the state-of-the-art methods.
