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This study reports the results of a pilot study on spatiotemporal characteristics of drivers’ visual behavior while driving in three different luminance levels in a tunnel. The study was carried out in a relatively long tunnel during the daytime. Six experienced drivers were recruited to participate in the driving experiment. Experimental data of pupil area and fixation point position (at the tunnel’s interior zone: 1566 m long) were collected by non-intrusive eye-tracking equipment at three luminance levels (2 cd/m2, 2.5 cd/m2, and 3 cd/m2). Fixation maps (color-coded maps presenting distributed data) were created based on fixation point position data to quantify changes in visual behavior. The results demonstrated that luminance levels had a significant effect on pupil areas and fixation zones. Fixation area and average pupil area had a significant negative correlation with luminance levels during the daytime. In addition, drivers concentrated more on the front road pavement, the top wall surface, and the cars’ control wheels. The results revealed that the pupil area had a linear relationship with the luminance level. The limitations of this research are pointed out and the future research directions are also prospected.
Li Qin; Qi-Lei Cao; Arturo Leon; Ying-Na Weng; Xu-Hua Shi. Use of Pupil Area and Fixation Maps to Evaluate Visual Behavior of Drivers inside Tunnels at Different Luminance Levels—A Pilot Study. Applied Sciences 2021, 11, 5014 .
AMA StyleLi Qin, Qi-Lei Cao, Arturo Leon, Ying-Na Weng, Xu-Hua Shi. Use of Pupil Area and Fixation Maps to Evaluate Visual Behavior of Drivers inside Tunnels at Different Luminance Levels—A Pilot Study. Applied Sciences. 2021; 11 (11):5014.
Chicago/Turabian StyleLi Qin; Qi-Lei Cao; Arturo Leon; Ying-Na Weng; Xu-Hua Shi. 2021. "Use of Pupil Area and Fixation Maps to Evaluate Visual Behavior of Drivers inside Tunnels at Different Luminance Levels—A Pilot Study." Applied Sciences 11, no. 11: 5014.
Given that the main task of process monitoring (i.e., fault detection) is actually a classical one-class classification problem, the generalized regression neural network (GRNN) is directly inapplicable for handling process modeling and monitoring issues. Through the selection of only one variable to be the output while the others serve as the corresponding input, a GRNN model can then be constructed to approximate the nonlinear input to output relationship. The residuals, signifying the inconsistency between the actual measurement and the predicted output from the GRNN model, could be a good indicator for online fault detection. The proposed nonlinear process monitoring approach is termed decentralized GRNN (DGRNN), which applies the GRNN in an extremely decentralized manner and utilizes the squared Mahalanobis distance for the online monitoring of the abnormalities captured by the generated residuals. The effectiveness and superiority of the DGRNN-based nonlinear process monitoring approach over other state-of-the-art nonlinear process monitoring methods are investigated by comparisons in two nonlinear processes.
Ting Lan; Chudong Tong; Haizhen Yu; Xuhua Shi; Lijia Luo. Nonlinear process monitoring based on decentralized generalized regression neural networks. Expert Systems with Applications 2020, 150, 113273 .
AMA StyleTing Lan, Chudong Tong, Haizhen Yu, Xuhua Shi, Lijia Luo. Nonlinear process monitoring based on decentralized generalized regression neural networks. Expert Systems with Applications. 2020; 150 ():113273.
Chicago/Turabian StyleTing Lan; Chudong Tong; Haizhen Yu; Xuhua Shi; Lijia Luo. 2020. "Nonlinear process monitoring based on decentralized generalized regression neural networks." Expert Systems with Applications 150, no. : 113273.
Based on the immune mechanics and multi-agent technology, a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced. Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem, and combines the global and local search in the searching process. The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms. The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions. Furthermore, Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.
Xuhua Shi; Feng Qian. A Multi-agent Artificial Immune Network Algorithm for the Tray Efficiency Estimation of Distillation Unit. Chinese Journal of Chemical Engineering 2012, 20, 1148 -1153.
AMA StyleXuhua Shi, Feng Qian. A Multi-agent Artificial Immune Network Algorithm for the Tray Efficiency Estimation of Distillation Unit. Chinese Journal of Chemical Engineering. 2012; 20 (6):1148-1153.
Chicago/Turabian StyleXuhua Shi; Feng Qian. 2012. "A Multi-agent Artificial Immune Network Algorithm for the Tray Efficiency Estimation of Distillation Unit." Chinese Journal of Chemical Engineering 20, no. 6: 1148-1153.