The long-path differential optical absorption spectroscopy （LP-DOAS） technique was developed to mea- sure nighttime atmospheric nitrate radical （NO3） concentrations. An optimized retrieval method, resulting in a s...The long-path differential optical absorption spectroscopy （LP-DOAS） technique was developed to mea- sure nighttime atmospheric nitrate radical （NO3） concentrations. An optimized retrieval method, resulting in a small residual structure and low detection limits, was developed to retrieve NO3. The time series of the NO3 concentration were collected from 17 to 24 March, 2006, where a nighttime average value of 15.8 ppt was observed. The interfering factors and errors are also discussed. These results indicate that the DOAS technique provides an essential tool for the quantification of NO3 concentration and in the study of its effects upon nighttime chemistry.展开更多
Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be rela...Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be related to how the real system evolves or how individuals interact with each other in social networks.Although the evolution of the real system may seem to be found regularly,capturing patterns on the whole process of evolution is not trivial.Link prediction is one of the most important technologies in network information mining,which can help us understand the evolution mechanism of real-life network.Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures.Currently,widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures.However,these algorithms on highly sparse or longpath networks have poor performance.Here,we propose a new index that is associated with the principles of structural equivalence and shortest path length(SESPL)to estimate the likelihood of link existence in long-path networks.Through a test of 548 real networks,we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks.Meanwhile,we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques.The results show that the performance of SESPL can achieve a gain of 44.09%over GraphWave and 7.93%over Node2vec.Finally,according to the matrix of maximal information coefficient(MIC)between all the similarity-based predictors,SESPL is a new independent feature in the space of traditional similarity features.展开更多
Nitro MAC(French acronym for continuous atmospheric measurements of nitrogenous compounds)is an instrument which has been developed for the semi-continuous measurement of atmospheric nitrous acid(HONO).This instrument...Nitro MAC(French acronym for continuous atmospheric measurements of nitrogenous compounds)is an instrument which has been developed for the semi-continuous measurement of atmospheric nitrous acid(HONO).This instrument relies on wet chemical sampling and detection using high performance liquid chromatography(HPLC)-visible absorption at540 nm.Sampling proceeds by dissolution of gaseous HONO in a phosphate buffer solution followed by derivatization with sulfanilamide/N-(1-naphthyl)-ethylenediamine.The performance of this instrument was found to be as follows:a detection limit of around 3 ppt with measurement uncertainty of 10%over an analysis time of 10 min.Intercomparison was made between the instrument and a long-path absorption photometer(LOPAP)during two experiments in different environments.First,air was sampled in a smog chamber with concentrations up to 18 ppb of nitrous acid.Nitro MAC and LOPAP measurements showed very good agreement.Then,in a second experiment,ambient air with HONO concentrations below250 ppt was sampled.While Nitro MAC showed its capability of measuring HONO in moderate and highly polluted environments,the intercomparison results in ambient air highlighted that corrections must be made for minor interferences when low concentrations are measured.展开更多
为解决复杂动态环境下全自主移动机器人路径规划存在的效率低、适应性差及多目标权衡难题,提出全自主移动机器人全局最优路径智能规划方法。利用图注意力网络与空间重构单元提取环境的全局最优路径特征,采用改进基于密度的带噪声应用空...为解决复杂动态环境下全自主移动机器人路径规划存在的效率低、适应性差及多目标权衡难题,提出全自主移动机器人全局最优路径智能规划方法。利用图注意力网络与空间重构单元提取环境的全局最优路径特征,采用改进基于密度的带噪声应用空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法实现静态/动态障碍物与通行区的精准划分,构建长短期记忆(Long Short-Term Memory,LSTM)运动预测模型与多传感器融合的障碍物轨迹预测模型。在改进蚁群算法框架下进行全局路径搜索,引入路径长度、时间、安全及能耗的复合权重。采用3次B样条曲线平滑优化路径,引入曲率约束以抑制龙格现象。实验显示,该方法在低负载时段平均路径偏差降低约33%,重规划次数减少约66%,动态避障成功率提高至98%;高负载时段平均路径偏差降低40%,重规划次数减少约52%,成功率达95%。展开更多
文摘The long-path differential optical absorption spectroscopy （LP-DOAS） technique was developed to mea- sure nighttime atmospheric nitrate radical （NO3） concentrations. An optimized retrieval method, resulting in a small residual structure and low detection limits, was developed to retrieve NO3. The time series of the NO3 concentration were collected from 17 to 24 March, 2006, where a nighttime average value of 15.8 ppt was observed. The interfering factors and errors are also discussed. These results indicate that the DOAS technique provides an essential tool for the quantification of NO3 concentration and in the study of its effects upon nighttime chemistry.
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62173065)the Industry-University-Research Innovation Fund for Chinese Universities(Grant No.2021ALA03016)+2 种基金the Fund for University Innovation Research Group of Chongqing(Grant No.CXQT21005)the National Social Science Foundation of China(Grant No.20CTQ029)the Fundamental Research Funds for the Central Universities(Grant No.SWU119062).
文摘Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be related to how the real system evolves or how individuals interact with each other in social networks.Although the evolution of the real system may seem to be found regularly,capturing patterns on the whole process of evolution is not trivial.Link prediction is one of the most important technologies in network information mining,which can help us understand the evolution mechanism of real-life network.Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures.Currently,widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures.However,these algorithms on highly sparse or longpath networks have poor performance.Here,we propose a new index that is associated with the principles of structural equivalence and shortest path length(SESPL)to estimate the likelihood of link existence in long-path networks.Through a test of 548 real networks,we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks.Meanwhile,we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques.The results show that the performance of SESPL can achieve a gain of 44.09%over GraphWave and 7.93%over Node2vec.Finally,according to the matrix of maximal information coefficient(MIC)between all the similarity-based predictors,SESPL is a new independent feature in the space of traditional similarity features.
基金supported by EU Sixth Framework Programme(FP6)Eurochamp program(grant number 505968)EU Seventh Framework Programme(FP7)Eurochamp-2 program(grant number 228335)+2 种基金the NeoRad program from the French National Agency for Research(ANR-07-2/21-8908)the PhotoBat project from the Primequal program of the French Ministry of Environment(Primequal-project number 19599)the PhotoPaq LIFE+program(LIFE 08/ENV/F/000487 PHOTOPAQ)
文摘Nitro MAC(French acronym for continuous atmospheric measurements of nitrogenous compounds)is an instrument which has been developed for the semi-continuous measurement of atmospheric nitrous acid(HONO).This instrument relies on wet chemical sampling and detection using high performance liquid chromatography(HPLC)-visible absorption at540 nm.Sampling proceeds by dissolution of gaseous HONO in a phosphate buffer solution followed by derivatization with sulfanilamide/N-(1-naphthyl)-ethylenediamine.The performance of this instrument was found to be as follows:a detection limit of around 3 ppt with measurement uncertainty of 10%over an analysis time of 10 min.Intercomparison was made between the instrument and a long-path absorption photometer(LOPAP)during two experiments in different environments.First,air was sampled in a smog chamber with concentrations up to 18 ppb of nitrous acid.Nitro MAC and LOPAP measurements showed very good agreement.Then,in a second experiment,ambient air with HONO concentrations below250 ppt was sampled.While Nitro MAC showed its capability of measuring HONO in moderate and highly polluted environments,the intercomparison results in ambient air highlighted that corrections must be made for minor interferences when low concentrations are measured.
文摘为解决复杂动态环境下全自主移动机器人路径规划存在的效率低、适应性差及多目标权衡难题,提出全自主移动机器人全局最优路径智能规划方法。利用图注意力网络与空间重构单元提取环境的全局最优路径特征,采用改进基于密度的带噪声应用空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法实现静态/动态障碍物与通行区的精准划分,构建长短期记忆(Long Short-Term Memory,LSTM)运动预测模型与多传感器融合的障碍物轨迹预测模型。在改进蚁群算法框架下进行全局路径搜索,引入路径长度、时间、安全及能耗的复合权重。采用3次B样条曲线平滑优化路径,引入曲率约束以抑制龙格现象。实验显示,该方法在低负载时段平均路径偏差降低约33%,重规划次数减少约66%,动态避障成功率提高至98%;高负载时段平均路径偏差降低40%,重规划次数减少约52%,成功率达95%。
