Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Accurate forecasts of passenger flow entering and leaving metro stations is an important work for Metro operation management, such as for the automatic adjustment of train operation diagrams or station passenger crowd regulation planning measures. In this study, Grey theory is introduced to develop a time series GM (1, 1) model for total passenger forecasting. Two modification factors determined by two minimum mean square error principles are proposed to decrease the discreteness of input data and thus improve the forecast accuracy. Moreover, the Markov chain approach is further used to optimize the residual error series. Passenger flow data entering and leaving the Xiaozhai station of Xi'an Metro Line 2 from September 1-30, 2015, were utilized to verify the effectiveness of the proposed method; the forecast results show that this novel Markov-Grey model performs well in terms of forecast accuracy with smaller SMSE and MAPE values. To this effect, the proposed method is especially well-suited to smooth passenger flow forecasting compared to other forecast techniques

Medical image retrieval with query-dependent feature fusion based on one-class SVM

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

How Expressway Geometry Factors Contribute to Accident Occurrence? A Binary Logistic Regression Study

Logistic regression and statistical method are combined to analyze accident data from “Traffic Accident Database System” (TADS) in order to find the relationship between expressway geometric factors and accident rate. A total of 2004 observations are used to illustrate the proposed model. A new concept mean angle of deflection (MAD) is also introduced to evaluate the effect of horizontal alignment. Accident rate (the dependent variable) in this study is a dichotomous variable, so a binary logistic regression is found suitable. Totally sixteen variables are proposed and fourteen are used in the model. Eight variables are found significantly associated with accident rate at the 0.05 significance. Each variable is interpreted with the results of SPSS 19.0 and the results provide the references for identifying unsafe locations and taking appropriate counteractive measures for expressways in mountainous areas

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

A Practical Anodic And Cathodic Curve Intersection Model To Understand Multiple Corrosion Potentials Of Fe-based Glassy Alloys In OH-contained Solutions

A practical anodic and cathodic curve intersection model, which consisted of an apparent anodic curve and an imaginary cathodic line, was proposed to explain multiple corrosion potentials occurred in potentiodynamic polarization curves of Fe-based glassy alloys in alkaline solution. The apparent anodic curve was selected from the measured anodic curves. The imaginary cathodic line was obtained by linearly fitting the differences of anodic curves and can be moved evenly or rotated to predict the number and value of corrosion potentials. © 2016 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Sensorless Control of Dual Three-Phase IPMSM Based on Frequency Adaptive Linear Extended State Observer

The sensorless control of interior permanent magnet synchronous motor (IPMSM) based on the conventional linear extended state observer (LESO) does not have sufficient capability to eliminate the steady-state position estimation error. To solve this issue, a frequency adaptive LESO (FA-LESO) is proposed to estimate the back electromotive force (BEMF) accurately. The gains of the proposed observer are designed according to the pre-designed transfer function of a second-order complex-coefficient filter, whose stability is guaranteed by the generalized Routh criterion. The linearized model of the proposed FA-LESO is established and the design guideline of the observer gains is presented. Compared with the conventional LESO, the proposed FA-LESO can eliminate the steady-state position estimation error without any phase compensation. Meanwhile, it exhibits better high-frequency noise immunity without additional filters being required. The feasibility and effectiveness of the proposed FA-LESO are verified by the comparative experiments on a dual three-phase IPMSM platform