Machine learning based fast multi-layer liquefaction disaster assessment

Bi, Chen; Fu, Bairan; Chen, Jian; Zhao, Yili; Lü, Yang; Duan, Yulin; Shi, Yun

doi:10.1007/s11280-018-0632-8

Cited by 34 publications

(13 citation statements)

References 39 publications

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“…According to the above analysis, consumers have greater expectations for intelligently connected vehicles than intelligent driving systems with single or simple functions such as the automatic single-lane driving system. This conclusion is consistent with the results of Bi et al [52], [53], [55] and Kong et al [54]. Overall, respondents are optimistic and welcome intelligent driving systems.…”

Section: Expectation Of Intelligent Driving Systemssupporting

confidence: 90%

Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City

et al. 2019

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This paper investigates the acceptance of intelligent driving vehicles in the Chinese market using Guangzhou City as an example and a field questionnaire investigation based on the innovation diffusion theory. First, we tested the reliability and validity of 2530 effective questionnaires in 2018. Second, the cognition, expectations, technical concerns, and prospective expectations data of consumers with respect to intelligent driving vehicles are analyzed statistically. Finally, T-test and Pearson's correlation analysis is carried out on the intelligent driving vehicle performance parameters and the respondents' basic information. The research shows that consumers are more focused on the developmental prospects of intelligent driving vehicles and express concerns about their technological safety. Furthermore, the acceptance of intelligent driving cars depends on age, the higher the expectation, the sooner to buy an intelligent, and so on. Ninety percentage of the respondents are willing to purchase intelligent driving vehicles and are willing to pay extra money to buy them. This result will provide an objective reference for the government to issue supporting policies and laws regarding intelligent driving vehicles, and it is useful to help the enterprises to adjust their research and developmental directions for intelligent driving vehicles.

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Section: Expectation Of Intelligent Driving Systemssupporting

confidence: 90%

Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City

et al. 2019

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“…Some researchers use driving simulators to analyze the relationship between the driver's mental load and driver's vision [13]. In addition, based on real driving scenarios, the research has focused on the impact of using mobile phones on driving behavior [14,15] and road safety [16].…”

Section: Related Workmentioning

confidence: 99%

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

Wang

Liu

Feng

2019

Journal of Advanced Transportation

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With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new tra c safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers' mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we nished nonparametric test of the drivers' psychological load and road test. e results show that in compassion with the daytime scenario, drivers should pay much e ort to driving skills and risk identi cation in the nighttime scenario. us, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions.

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“…With the development of HPC systems, simulations could generate larger amounts of data than before in aerodynamics, which means more powerful techniques should be adopted for data analyzing. Visualization aimed at aerodynamics has been playing an important role in scientific discovery, especially in massive data discovery [1][2][3][4][5][6]. The increase of data also challenges the traditional methods of scientific visualization.…”

Section: Introductionmentioning

confidence: 99%

mSwap: a large-scale image-compositing method with optimal m-ary tree

Hou

Wang

et al. 2021

Adv. Aerodyn.

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With the increasing of computing ability, large-scale simulations have been generating massive amounts of data in aerodynamics. Sort-last parallel rendering is the most classical image compositing method for large-scale scientific visualization. However, in the stage of image compositing, the sort-last method may suffer from scalability problem on large-scale processors. Existing image compositing algorithms tend to perform well in certain situations. For instance, Direct Send is well on small and medium scale; Radix-k gets well performance only when the k-value is appropriate and so on. In this paper, we propose a novel method named mSwap for scientific visualization in aerodynamics, which uses the best scale of processors to make sure its performance at the best. mSwap groups the processors that we can use with a (m,k) table, which records the best combination of m (the number of processors in subgroup of each group) and k (the number of processors in each group). Then in each group, using a m-ary tree to composite the image for reducing the communication of processors. Finally, the image is composited between different groups to generate the final image. The performance and scalability of our mSwap method is demonstrated through experiments with thousands of processors.

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Machine learning based fast multi-layer liquefaction disaster assessment

Cited by 34 publications

References 39 publications

Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City

Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

mSwap: a large-scale image-compositing method with optimal m-ary tree

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