This page has only limited features, please log in for full access.

Unclaimed
Tengfei Wang
School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 21 July 2021 in Sustainability
Reads 0
Downloads 0

Human error is a crucial factor leading to maritime traffic accidents. The effect of human–computer interaction (HCI) also plays a leading role in human error. The objective of this study is to propose a method of interaction strategies based on a cognitive-processing model in crews’ daily navigation tasks. A knowledge-based ship HCI framework architecture is established. It provides an extensible framework for the HCI process in the maritime domain. By focusing on the cognitive process of a crew in the context of accident and risk handling during ship navigation, based on the information, decision, and action in crew context (IDAC) model, in combination with the maritime accident dynamics simulation (MADS) system, the MADS-IDAC system was developed and enhanced by the HCI structure and function design of the dynamic risk analysis platform for maritime management. The results indicate that MADS enhanced by HCI can effectively generate a strategy set of various outcomes in preset scenarios. Moreover, it provides a new method and thought for avoiding human error in crew interaction and to lower the risk of ship collision as well as effectively improving the reliability of HCI.

ACS Style

Su Han; Tengfei Wang; Jiaqi Chen; Ying Wang; Bo Zhu; Yiqi Zhou. Towards the Human–Machine Interaction: Strategies, Design, and Human Reliability Assessment of Crews’ Response to Daily Cargo Ship Navigation Tasks. Sustainability 2021, 13, 8173 .

AMA Style

Su Han, Tengfei Wang, Jiaqi Chen, Ying Wang, Bo Zhu, Yiqi Zhou. Towards the Human–Machine Interaction: Strategies, Design, and Human Reliability Assessment of Crews’ Response to Daily Cargo Ship Navigation Tasks. Sustainability. 2021; 13 (15):8173.

Chicago/Turabian Style

Su Han; Tengfei Wang; Jiaqi Chen; Ying Wang; Bo Zhu; Yiqi Zhou. 2021. "Towards the Human–Machine Interaction: Strategies, Design, and Human Reliability Assessment of Crews’ Response to Daily Cargo Ship Navigation Tasks." Sustainability 13, no. 15: 8173.

Journal article
Published: 05 January 2021 in Applied Sciences
Reads 0
Downloads 0

A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly among all the effect quantities. However, due to the change of the external environment, the failure of monitoring instruments, and the existence of human errors, the obtained deformation monitoring data usually miss pieces, and sometimes the missing pieces are so critical that the remaining data fail to fully reflect the actual deformation patterns. In this paper, the composition, characteristics, and contamination of the concrete dam deformation monitoring information are analyzed. From the single-value missing data completion method based on the nonlocal average method, a multi-value missing data completion method using BP (back propagation) mapping of spatial adjacent points is proposed to improve the accuracy of analysis and pattern prediction of concrete dam deformation behaviors. A case study is performed to validate the proposed method.

ACS Style

Hao Gu; Tengfei Wang; Yantao Zhu; Cheng Wang; Dashan Yang; Lixian Huang. A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces. Applied Sciences 2021, 11, 463 .

AMA Style

Hao Gu, Tengfei Wang, Yantao Zhu, Cheng Wang, Dashan Yang, Lixian Huang. A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces. Applied Sciences. 2021; 11 (1):463.

Chicago/Turabian Style

Hao Gu; Tengfei Wang; Yantao Zhu; Cheng Wang; Dashan Yang; Lixian Huang. 2021. "A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces." Applied Sciences 11, no. 1: 463.

Journal article
Published: 29 October 2020 in Journal of Marine Science and Engineering
Reads 0
Downloads 0

It is expected that the prototypes of unmanned merchant ships will be deployed in the next few years. However, there is no specific research on whether the introduction of unmanned ships will reduce the risk of ship collision accidents in which communication between vessels is critical. This work constitutes an attempt to bridge the gap identified above by applying the Hybrid Causal Logic (HCL) methodology to model general-level collision scenarios of unmanned ships. The HCL methodology has been selected for its proven applicability to risk assessments, even when empirical data may be insufficient. Collision scenarios involving unmanned ships have been created in which manned ships of the conventional collision scenario HCL model are replaced with unmanned ships. Then, collision scenarios capturing the interactions between a manned ship and an unmanned ship were modeled. By comparing the qualitative and quantitative results of the different scenarios, we can see that the introduction of unmanned ships may effectively reduce the occurrence of ship collision accidents.

ACS Style

Qing Wu; Tengfei Wang; Mihai A. Diaconeasa; Ali Mosleh; Yang Wang. A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels. Journal of Marine Science and Engineering 2020, 8, 852 .

AMA Style

Qing Wu, Tengfei Wang, Mihai A. Diaconeasa, Ali Mosleh, Yang Wang. A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels. Journal of Marine Science and Engineering. 2020; 8 (11):852.

Chicago/Turabian Style

Qing Wu; Tengfei Wang; Mihai A. Diaconeasa; Ali Mosleh; Yang Wang. 2020. "A Comparative Assessment of Collision Risk of Manned and Unmanned Vessels." Journal of Marine Science and Engineering 8, no. 11: 852.

Journal article
Published: 30 June 2020 in Journal of Marine Science and Engineering
Reads 0
Downloads 0

A ship collision accident is one of the most dangerous and common types of maritime accidents. Traditional probabilistic risk assessment (PRA) of ship collision accidents is a methodology that can be adopted to ensure maritime safety. Nevertheless, a need for better approaches to model human behavior, such as risk identification, communication, and decision-making, has been identified. Such advanced PRA methods require a more explicit way of taking human factors into consideration than the traditional risk assessment methods. Hybrid causal logic (HCL) is an advanced PRA method due to its unique three-level framework that includes event sequence diagrams, fault trees, and Bayesian networks, which makes it suitable for modeling human behavior that is important to ship collision accidents. This paper discusses the applicability of the HCL methodology for the ship collision accident. Firstly, the event sequences of typical ship collision accidents are summarized based on the study of 50 accident investigation reports. Then, fault trees for mechanical failure events and the Bayesian networks for human error events are constructed to analyze the events in a structured way at a more detailed level. Finally, the three main end-state types of ship collision avoidance scenario have been quantified. The result of the probability of a ship collision accident is verified by estimating the annual frequency of collision accidents in the Singapore Strait. Comparing with the historical data, the estimation results are quite near to the real case. By taking advantage of the HCL methodology, the modeling of ship collision scenarios can be carried out at a deep logical level. At the same time, it is possible to combine a detailed analysis of various primary events with a comprehensive analysis at the system level.

ACS Style

Tengfei Wang; Qing Wu; Mihai A. Diaconeasa; Xinping Yan; Ali Mosleh. On the Use of the Hybrid Causal Logic Methodology in Ship Collision Risk Assessment. Journal of Marine Science and Engineering 2020, 8, 485 .

AMA Style

Tengfei Wang, Qing Wu, Mihai A. Diaconeasa, Xinping Yan, Ali Mosleh. On the Use of the Hybrid Causal Logic Methodology in Ship Collision Risk Assessment. Journal of Marine Science and Engineering. 2020; 8 (7):485.

Chicago/Turabian Style

Tengfei Wang; Qing Wu; Mihai A. Diaconeasa; Xinping Yan; Ali Mosleh. 2020. "On the Use of the Hybrid Causal Logic Methodology in Ship Collision Risk Assessment." Journal of Marine Science and Engineering 8, no. 7: 485.