Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are sim...Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.展开更多
＂Snow＇s proposition＂ points out the contradictions and conflicts between ＂scientific culture＂ and ＂literary culture＂ and the dilemma of the development of the ＂two cultures.＂ In the positive sense, Snow＇s pr...＂Snow＇s proposition＂ points out the contradictions and conflicts between ＂scientific culture＂ and ＂literary culture＂ and the dilemma of the development of the ＂two cultures.＂ In the positive sense, Snow＇s proposition concerns the contradictions between the integrity of the research subject and the particularity of established disciplines, as well as the trend toward division and isolation in subdivisions of the humanities and social sciences. The key to solving Snow＇s proposition is to take an inter-disciplinary path that pays particular attention to combining discipline-centered with issue-centered research and individual work with teamwork, giving full play to individual endowment, social orientation and environmental orientation and to the optimization of evaluation systems and mechanisms.展开更多
文摘Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.
文摘＂Snow＇s proposition＂ points out the contradictions and conflicts between ＂scientific culture＂ and ＂literary culture＂ and the dilemma of the development of the ＂two cultures.＂ In the positive sense, Snow＇s proposition concerns the contradictions between the integrity of the research subject and the particularity of established disciplines, as well as the trend toward division and isolation in subdivisions of the humanities and social sciences. The key to solving Snow＇s proposition is to take an inter-disciplinary path that pays particular attention to combining discipline-centered with issue-centered research and individual work with teamwork, giving full play to individual endowment, social orientation and environmental orientation and to the optimization of evaluation systems and mechanisms.
