Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance

Identification using Valanis model for beams with nonlinear bolted joint and boundary connection

Bolted joints are widely applied in assembled mechanical structures. This paper focuses on the nonlinear modeling and parameter identification of bolted beams. Two Valanis models are respectively used to describe the nonlinear behaviors of the bolted joint and the boundary connection. Experimental tests at low and high excitation levels are performed to reveal the dynamic characteristics of the bolted beams. The Young’s modulus of the beams is identified via experimental test with low excitation level; whereas the parameters of Valanis model are identified by using optimization technique in order to minimize the residual error between the measured and the simulation data at a high excitation level

On the Construction of a Safety Gap Prediction Model for Freeway Bus Lane-Changing Maneuver Using Driving Simulator Data

Lane-change crashes are not only responsible for an important portion of vehicular fatalities, but also for crash-caused traffic delays, often resulting in congestion. The type of discretionary lane change was the focus of this research, in which a safety gap prediction model was constructed for potential application in the development of lane-change support systems. Data for analysis and model fitting were collected from a fixed-based bus driving simulator. The experimental scene designed for the driving simulator consisted of a straight section of two-lane freeway mainline with daylight and vehicular flows traveling at different speed levels on the road. Ten professional coach drivers were recruited to perform lane-change experiments. Results of two-way ANOVA revealed a significant lane-change direction × vehicle speed on the target lane interaction, and further analyses demonstrated that there was a simple effect for vehicle speed on the target lane in the left-to-right group of the type of lane-change direction factor. A safety gap forecasting model with the time gap between lead and lag vehicle on the target lane as the forecasted variable was constructed, and tests of true out-of-sample forecast accuracy of the prediction model showed promising results for its potential application in the development of lane-change support systems