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Articles 1 - 5 of 5
Full-Text Articles in Social and Behavioral Sciences
Perception Of Mooney Faces By Young Infants: The Role Of Local Feature Visibility, Contrast Polarity And Motion, Yumiko Otsuka, Harold C. H Hill, So Kanazawa, Masami K. Yamaguchi, Branka Spehar
Perception Of Mooney Faces By Young Infants: The Role Of Local Feature Visibility, Contrast Polarity And Motion, Yumiko Otsuka, Harold C. H Hill, So Kanazawa, Masami K. Yamaguchi, Branka Spehar
Harold Hill
We examined the ability of young infants (3- and 4-month-olds) to detect faces in the two-tone images often referred to as Mooney faces. In Experiment 1, this performance was examined in conditions of high and low visibility of local features and with either the presence or absence of the outer head contour. We found that regardless of the presence of the outer head contour, infants preferred upright over inverted two-tone face images only when local features were highly visible (Experiment 1a). We showed that this upright preference disappeared when the contrast polarity of twotone images was reversed (Experiment 1b), reflecting …
An Investigation Of Mismatch Negativity In Current And Ex-Cannabis Users Using A Feature Controlled Method, Felicity Webster, Samantha Broyd, Lisa-Marie Greenwood, Rodney J. Croft, Juanita Todd, Patricia Michie, Stuart Johnstone, Benjamin Lee-Bates, Hannah Coyle, Nadia Solowij
An Investigation Of Mismatch Negativity In Current And Ex-Cannabis Users Using A Feature Controlled Method, Felicity Webster, Samantha Broyd, Lisa-Marie Greenwood, Rodney J. Croft, Juanita Todd, Patricia Michie, Stuart Johnstone, Benjamin Lee-Bates, Hannah Coyle, Nadia Solowij
Faculty of Social Sciences - Papers (Archive)
Abstract presented at the 23rd Australasian Society for Psychophysiology Conference, 20-22 Nov 2013, Wollongong, Australia
An Application Of Nonlinear Feature Extraction – A Case Study For Low Speed Slewing Bearing Condition Monitoring And Prognosis, Wahyu Caesarendra, Prabuono Buyung Kosasih, A K. Tieu, Craig Moodie
An Application Of Nonlinear Feature Extraction – A Case Study For Low Speed Slewing Bearing Condition Monitoring And Prognosis, Wahyu Caesarendra, Prabuono Buyung Kosasih, A K. Tieu, Craig Moodie
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents the application of four nonlinear methods of feature extraction in slewing bearing condition monitoring and prognosis: these are largest Lyapunov exponent, fractal dimension, correlation dimension, and approximate entropy methods. Although correlation dimension and approximate entropy methods have been used previously, the largest Lyapunov exponent and fractal dimension methods have not been used in vibration condition monitoring to date. The vibration data of the laboratory slewing bearing test-rig run at 1 rpm was acquired daily from February to August 2007 (138 days). As time progressed, a more accurate observation of the alteration of bearing condition from normal to …
A Scalable Unsupervised Feature Merging Approach To Efficient Dimensionality Reduction Of High-Dimensional Visual Data, Lingqiao Liu, Lei Wang
A Scalable Unsupervised Feature Merging Approach To Efficient Dimensionality Reduction Of High-Dimensional Visual Data, Lingqiao Liu, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix-based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an efficient way to reduce the dimensionality, which only requires a single scan of features to perform reduction. However, existing merging algorithms do not scale well with high-dimensional data, especially in the unsupervised case. To …
Condition Monitoring Of Slow Speed Slewing Bearing Based On Largest Lyapunov Exponent Algorithm And Circular-Domain Feature Extractions, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie
Condition Monitoring Of Slow Speed Slewing Bearing Based On Largest Lyapunov Exponent Algorithm And Circular-Domain Feature Extractions, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents a combined nonlinear and circular features extraction-based condition monitoring method for low speed slewing bearing. The proposed method employs the largest Lyapunov exponent (LLE) algorithm as a signal processing method based on vibration data. LLE is used to detect chaos existence in vibration data in discrete angular positions of the shaft. From the processed data, circular features such as mean, skewness and kurtosis are calculated and monitored. It is shown that the onset and the progressively deteriorating bearing condition can be detected more clearly in circular-domain features compared to time-domain features. The application of the method is …