Sensory evaluation is the most appropriate approach to describe the sensory perception of cosmetics, anddescriptive profiling has been a popular sensory technique for cognitive descriptions of products. This study eva...Sensory evaluation is the most appropriate approach to describe the sensory perception of cosmetics, anddescriptive profiling has been a popular sensory technique for cognitive descriptions of products. This study evaluated 33 models of BB/CC creams and analyzed the data with biostatistic method; thus, the results provided a better understanding of sensory characteristic for researchers.展开更多
Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive co...Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive combined tasks. Twenty-four graduate students （twelve females and twelve males） volunteered to participate in the study. The task requires participants to indicate when they perceive a change in the intensity of an auditory signal while simultaneously solving algebraic problems. Multivariate Analysis of Variance （MANOVA） results reveal no significant differences between genders when performing the combined tasks （p = 0.1831 and 2 = 0.7891） although the average number of false alarms made during the combined tasks by males is nearly 11% higher than the average number of false alarms made by females. However, （Multivariate Analysis of Variance） ANOVA results for the combined tasks show that males outperform females on the computational task while listening for changes in the auditory signal F（1, 22） - 5.09, p 〈 0.03, but there are no significant differences in their ability to detect noise intensity variation or in the number of false alarms made while multitasking. For the single task analysis the ANOVAs indicate no significant differences in signal detection task performance, computational task performance, or the number of false alarms made by males and females.展开更多
Most state-of-the-art robotic cars＇ perception systems are quite different from the way a human driver understands traffic environments. First, humans assimilate information from the traffic scene mainly through visu...Most state-of-the-art robotic cars＇ perception systems are quite different from the way a human driver understands traffic environments. First, humans assimilate information from the traffic scene mainly through visual perception, while the machine perception of traffic environments needs to fuse information from several different kinds of sensors to meet safety-critical requirements. Second, a robotic car requires nearly 100% correct perception results for its autonomous driving, while an experienced human driver works well with dynamic traffic environments, in which machine perception could easily produce noisy perception results. In this paper, we propose a vision-centered multi-sensor fusing framework for a traffic environment perception approach to autonomous driving, which fuses camera, LIDAR, and GIS information consistently via both geometrical and semantic constraints for efficient self- localization and obstacle perception. We also discuss robust machine vision algorithms that have been successfully integrated with the framework and address multiple levels of machine vision techniques, from collecting training data, efficiently processing sensor data, and extracting low-level features, to higher-level object and environment mapping. The proposed framework has been tested extensively in actual urban scenes with our self-developed robotic cars for eight years. The empirical results validate its robustness and efficiency.展开更多
文摘Sensory evaluation is the most appropriate approach to describe the sensory perception of cosmetics, anddescriptive profiling has been a popular sensory technique for cognitive descriptions of products. This study evaluated 33 models of BB/CC creams and analyzed the data with biostatistic method; thus, the results provided a better understanding of sensory characteristic for researchers.
文摘Women have been stereotyped as better multitaskers when compared to their male counterparts. The purpose of this study is to investigate whether there are differences in gender performance when performing cognitive combined tasks. Twenty-four graduate students （twelve females and twelve males） volunteered to participate in the study. The task requires participants to indicate when they perceive a change in the intensity of an auditory signal while simultaneously solving algebraic problems. Multivariate Analysis of Variance （MANOVA） results reveal no significant differences between genders when performing the combined tasks （p = 0.1831 and 2 = 0.7891） although the average number of false alarms made during the combined tasks by males is nearly 11% higher than the average number of false alarms made by females. However, （Multivariate Analysis of Variance） ANOVA results for the combined tasks show that males outperform females on the computational task while listening for changes in the auditory signal F（1, 22） - 5.09, p 〈 0.03, but there are no significant differences in their ability to detect noise intensity variation or in the number of false alarms made while multitasking. For the single task analysis the ANOVAs indicate no significant differences in signal detection task performance, computational task performance, or the number of false alarms made by males and females.
基金supported by the National Key Program Project of China(No.2016YFB1001004)the National Natural Science Foundation of China(Nos.91320301 and 61273252)
文摘Most state-of-the-art robotic cars＇ perception systems are quite different from the way a human driver understands traffic environments. First, humans assimilate information from the traffic scene mainly through visual perception, while the machine perception of traffic environments needs to fuse information from several different kinds of sensors to meet safety-critical requirements. Second, a robotic car requires nearly 100% correct perception results for its autonomous driving, while an experienced human driver works well with dynamic traffic environments, in which machine perception could easily produce noisy perception results. In this paper, we propose a vision-centered multi-sensor fusing framework for a traffic environment perception approach to autonomous driving, which fuses camera, LIDAR, and GIS information consistently via both geometrical and semantic constraints for efficient self- localization and obstacle perception. We also discuss robust machine vision algorithms that have been successfully integrated with the framework and address multiple levels of machine vision techniques, from collecting training data, efficiently processing sensor data, and extracting low-level features, to higher-level object and environment mapping. The proposed framework has been tested extensively in actual urban scenes with our self-developed robotic cars for eight years. The empirical results validate its robustness and efficiency.
