Connected vehicles enabled by communication technologies have the potential to improve trafc mobility and enhance roadway safety such that trafc information can be shared among vehicles and infrastructure.Fruitful spe...Connected vehicles enabled by communication technologies have the potential to improve trafc mobility and enhance roadway safety such that trafc information can be shared among vehicles and infrastructure.Fruitful speed advisory strategies have been proposed to smooth connected vehicle trajectories for better system performance with the help of diferent carfollowing models.Yet,there has been no such comparison about the impacts of various car-following models on the advisory strategies.Further,most of the existing studies consider a deterministic vehicle arriving pattern.The resulting model is easy to approach yet not realistic in representing realistic trafc patterns.This study proposes an Individual Variable Speed Limit(IVSL)trajectory planning problem at a signalized intersection and investigates the impacts of three popular car-following models on the IVSL.Both deterministic and stochastic IVSL models are formulated,and their performance is tested with numerical experiments.The results show that,compared to the benchmark(i.e.,without speed control),the proposed IVSL strategy with a deterministic arriving pattern achieves signifcant improvements in both mobility and fuel efciency across diferent trafc levels with all three car-following models.The improvement of the IVSL with the Gipps’model is the most remarkable.When the vehicle arriving patterns are stochastic,the IVSL improves travel time,fuel consumption,and system cost by 8.95%,19.11%,and 11.37%,respectively,compared to the benchmark without speed control.展开更多
Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfiel...Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfieldmodeling for the efficient prediction of stochastic crack patterns and stress-strain responses.Atotal dataset of 500 samples,including crack propagation images and corresponding stress-strain curves,is generated via random Finite Element Method(FEM)simulations.This dataset is then partitioned into 400 training and 100 testing samples.Themodel demonstrates robust performance with Intersection overUnion(IoU)scores of 0.8438 and 0.8155 on training and testing datasets,and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions.By using these results,the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo(MC)Simulations to capture the key statistical characteristics such as crack density and spatial distribution.Compared to conventional FEM-based methods,this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy.The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials,balancing computational efficiency with physical fidelity in capturing material stochasticity.展开更多
It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the major...It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.展开更多
文摘Connected vehicles enabled by communication technologies have the potential to improve trafc mobility and enhance roadway safety such that trafc information can be shared among vehicles and infrastructure.Fruitful speed advisory strategies have been proposed to smooth connected vehicle trajectories for better system performance with the help of diferent carfollowing models.Yet,there has been no such comparison about the impacts of various car-following models on the advisory strategies.Further,most of the existing studies consider a deterministic vehicle arriving pattern.The resulting model is easy to approach yet not realistic in representing realistic trafc patterns.This study proposes an Individual Variable Speed Limit(IVSL)trajectory planning problem at a signalized intersection and investigates the impacts of three popular car-following models on the IVSL.Both deterministic and stochastic IVSL models are formulated,and their performance is tested with numerical experiments.The results show that,compared to the benchmark(i.e.,without speed control),the proposed IVSL strategy with a deterministic arriving pattern achieves signifcant improvements in both mobility and fuel efciency across diferent trafc levels with all three car-following models.The improvement of the IVSL with the Gipps’model is the most remarkable.When the vehicle arriving patterns are stochastic,the IVSL improves travel time,fuel consumption,and system cost by 8.95%,19.11%,and 11.37%,respectively,compared to the benchmark without speed control.
基金supported by the Science Foundation of Zhejiang Province of China(Grant No.LY22E080016)the National Natural Science Foundation of China(Grant No.51808499)the Fundamental Research Funds of Zhejiang Sci-Tech University(Grant No.24052126-Y).
文摘Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfieldmodeling for the efficient prediction of stochastic crack patterns and stress-strain responses.Atotal dataset of 500 samples,including crack propagation images and corresponding stress-strain curves,is generated via random Finite Element Method(FEM)simulations.This dataset is then partitioned into 400 training and 100 testing samples.Themodel demonstrates robust performance with Intersection overUnion(IoU)scores of 0.8438 and 0.8155 on training and testing datasets,and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions.By using these results,the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo(MC)Simulations to capture the key statistical characteristics such as crack density and spatial distribution.Compared to conventional FEM-based methods,this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy.The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials,balancing computational efficiency with physical fidelity in capturing material stochasticity.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61771177 and 61701454, the Natural Science Foundation of Jiangsu Province of China under Grant Nos. BK20160147 and BK20160148, and the Academy Project of Finland under Grant No. 310321.
文摘It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.
