| 51. |
- Zhang, Han, et al.
(författare)
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Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment
- 2016
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Ingår i: Journal of Alzheimer's Disease. - 1387-2877 .- 1875-8908. ; 54:3, s. 1095-1112
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Tidskriftsartikel (refereegranskat)abstract
- Temporal synchronization-based functional connectivity (FC) has long been used by the neuroscience community. However, topographical FC information may provide additional information to characterize the advanced relationship between two brain regions. Accordingly, we proposed a novel method, namely high-order functional connectivity (HOFC), to capture this second-level relationship using inter-regional resemblance of the FC topographical profiles. Specifically, HOFC first calculates an FC profile for each brain region, notably between the given brain region and other brain regions. Based on these FC profiles, a second layer of correlations is computed between all pairs of brain regions (i.e., correlation's correlation). On this basis, we generated an HOFC network, where "high-order" network properties were computed. We found that HOFC was discordant with the traditional FC in several links, indicating additional information being revealed by the new metrics. We applied HOFC to identify biomarkers for early detection of Alzheimer's disease by comparing 77 mild cognitive impairment patients with 89 healthy individuals (control group). Sensitivity in detection of group difference was consistently improved by similar to 25% using HOFC compared to using FC. An HOFC network analysis also provided complementary information to an FC network analysis. For example, HOFC between olfactory and orbitofrontal cortices was found significantly reduced in patients, besides extensive alterations in HOFC network properties. In conclusion, our results showed promise in using HOFC to comprehensively map the human brain connectome.
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| 52. |
- Zhao, Xiangmo, et al.
(författare)
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Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle
- 2020
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Ingår i: Transportation Research, Part C: Emerging Technologies. - : Elsevier BV. - 0968-090X. ; 114, s. 205-224
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Tidskriftsartikel (refereegranskat)abstract
- Although mixed traffic, including both autonomous vehicles (AV) and human-driven vehicles (HV), is expected to prevail in the foreseeable future, our current understanding of the longitudinal characteristics of mixed traffic is limited and, in particular, lacks evidence from field experiments. To bridge this gap, we designed and conducted a set of field experiments to reveal differences in car-following behaviors between a human driver following-AV and following-HV on both constant speed traffic characteristics with discrete speeds ({10,20,…,60}km/h) and dynamic car-following behaviors with continuous speeds (within 0–60 km/h) in both the indifferentiable and differentiable appearance settings of the AV. We recruited 10 drivers for the experiment (14 runs for each driver and collected position and speed data of the tested vehicles along their complete trajectories based on vehicle gaps, headways, and standard deviations of vehicle speed. A K-means clustering algorithm was applied to classify drivers based on their responses in following-AV vs. following-HV with both constant speed and dynamic speed characteristics. The analyses of the differentiable appearance setting show that different drivers exhibit different behaviors in following-AV vs. following-HV: some are AV-believers, some are AV-skeptics, and the others are insensitive. Yet in the indifferentiable appearance setting, there is no significant difference between following a lead AV and following a lead HV. This reveals that drivers’ response to the lead vehicle depends on their subjective trusts on AV technologies rather than the actual driving behavior. The results suggest that, depending on the characteristics and composition of the drivers, classic car-following behavior in pure HV traffic may need to be updated for modeling mixed traffic in the near future.
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| 53. |
- Zheng, Xiaobo, et al.
(författare)
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Inter-scale interaction in pipe flows at high Reynolds numbers
- 2022
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Ingår i: Experimental Thermal and Fluid Science. - : Elsevier BV. - 0894-1777 .- 1879-2286. ; 131
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Tidskriftsartikel (refereegranskat)abstract
- Hot-wire measurements of the streamwise velocity are conducted in the large-scale pipe-flow facility CICLoPE in the friction Reynolds number range of 7800 <= Re-tau < 40000. Measurements are performed both with a rake of five synchronised probes arranged at different radial locations, and through a radial scan with a single wire traversing the whole pipe radius. Correlation-based analysis is used to extract features of inter-scale modulation in turbulent pipe flows. The Re-tau-independence of geometric features is shown with the outer scaling. Very-large-scale motions keep the vertical coherence to the wall through the whole pipe radius, while for the large-scale motions, the local coherence gradually become isotropic as the structure centre moves far away from the wall. From results obtained with the one-point amplitude modulation (AM) correlation function map, the AM effect is characterised by positive correlations observed in the inner region, while an opposite AM effect characterised by negative correlations is observed in the outer region. The strongest AM effect (with the maximum correlations) and the zero net-modulation (with zero correlations) show that the phase difference between large and small scales has a linear relation with the logarithm of the outer-scaled wall distance, but the strongest opposite effect (with the negative maximum correlations) behaves with the phase difference independent of the outer scaled wall-normal distance. Two-point AM coefficient maps, which give richer spatial information in the outer region than the one-point map, present the Re-tau-independencefor AM and opposite effects within the present Re-tau range. In addition, the relative variation of the two effects in the coexisting wall-normal range is characterised by identifying the maximum gradient of the one-point AM coefficient and the peak-to-peak value of the two-point AM coefficient map.
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| 54. |
- Zhou, M. F., et al.
(författare)
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A recurrent neural network based microscopic car following model to predict traffic oscillation
- 2017
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Ingår i: Transportation Research, Part C: Emerging Technologies. - : Elsevier BV. - 0968-090X. ; 84, s. 245-264
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics.
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| 55. |
- Zhou, Mofan, et al.
(författare)
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On the impact of cooperative autonomous vehicles in improving freeway merging: A modified intelligent driver model based approach
- 2017
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 18:6, s. 1422-1428
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Tidskriftsartikel (refereegranskat)abstract
- Transport researchers and practitioners have long been seeking capable solutions to deal with the traffic oscillations caused by freeway merging. Although existing approaches based on ramp metering have improved the overall efficiency of on-ramps, their performance is still far below the theoretical capacity. The recently proposed detecting technology of autonomous vehicles (AVs) provides an alternative for maximizing the merging efficiency by developing and using appropriate controllers for AVs. In this paper, we develop a cooperative intelligent driver model in order to examine the system performance under different proportions of AVs. The results show that, with a proper vehicle-to-vehicle controlling mechanism, an increasing percentage of AVs will reduce the total travel time and smooth traffic oscillations.
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