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The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection.

A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset.

Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario.

To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability.

This chapter uses macro policy analysis conducted at the Center for Teacher Education Research (CTER), Beijing Normal University to analyze the decision-making concerning teacher internship from the perspective of national policy. Internship, a teacher preparation policy initiative in China, is both needed to ease the teacher shortages in rural and poor areas of China and to create new models of teaching practice. Attention is then given to two aspects in policy implementation – student teachers’ learning and teachers’ multiple forms of instruction – from the perspective of teacher preparation. To conclude, the chapter offers some summary statements having to do with the policy debate and the implementation of the internship in China.

Teacher education pedagogies face the complex challenge of attending to standards of professionalism while being sensitive to the local changing needs of professional learning. Encounters between these two aspects of professional work are manifested, for example, through the relations between innovative, “against the grain” pedagogies and standardized criteria and accountability to policy issues and measurement. This chapter characterizes various “contact zones” across participants, contexts and contents, called for by the four categories of pedagogies (working with multimodalities, partnerships and community learning, teacher assessment, and vehicles for dissemination) that comprise this volume of International Teacher Education: Promising Pedagogies (Part C). The four categories of pedagogies join five earlier categories of pedagogies (teacher leadership, diversity, family, social justice, and technology), which are found in International Teacher Education: Promising Pedagogies (Part B). These go in with yet another five categories of pedagogies (teacher selection, reflection, narrative knowing, teacher identity and mediation and mentoring), which are found in International Teacher Education: Promising Pedagogies (Part A).

This scholarly work analyzes the previous 17 chapters in the International Teacher Education: Promising Pedagogies (Part C) volume. The purpose of the analysis is to distill the essence of what constitutes promising international teacher education pedagogies and how those pedagogies can best be shared. The chapters are reviewed according to the sections in which they appeared: pedagogies of working with multimodalities; pedagogies of partnerships and communities; pedagogies of teacher assessment; and vehicles for teacher education research, and dissemination. Key knowledge contributions from each chapter are emphasized. Similarities between the featured pedagogies are also highlighted. Finally, overarching themes are pinpointed.

The purpose of this paper is to use the viscosity of fluid to transmit power known as the hydro-viscous drive (HVD) to research the effect of two-phase flow on transmission characteristics.

In this paper, a 3D computational model of oil film between friction pair was built to study the transmission characteristics of a two-phase oil film, and the distribution contours of pressure and temperature of oil film were investigated using the computational fluid dynamics technology.

The finding of the paper suggests that the distribution law of pressure and temperature of two-phase oil film is almost linear along the radial direction. However, since the physical phenomena near the outlet of the oil film are entirely different, there exists fluctuation. Meanwhile, the volume fraction of air was obtained at different rotation speeds, and the maximum value is 10.55 percent. Compared to the single-phase oil film, the torque transferred by the oil film is not linear with the rotation speed, its value decreases gradually.

This paper’s conclusions are very important for the study of HVD and its applications, which provide a new idea to further study the mechanism of oil film transmission and its cavitation. The development of fluid viscous speed clutch is dedicated to a large industrial fan and water pump speed regulation and energy saving. With the successful application of the technology, it will have more wide applications in different fields, such as, in steel, water, petrochemical, power plant of slag pump and exhaust fan.

The purpose of this paper is to study the temperature characteristics of hydro-viscous clutch with different structure of friction disks and obtain the distribution of film temperature.

The mathematical model of oil film between friction disks with radial grooves is established. Based on the flow rate equation, the temperature rise equation of oil film is deduced. Considering two-phase flow, the temperature distribution in the oil shear stage and the effects of the ratio of inner radius to outer radius on film temperature rise is studied by using computational fluid dynamics (CFD) technology.

The results show that when input speed is constant, the increase in the ratio of inner to outer radius leads to an increase in the peak temperature and the decrease in the ratio results in a larger increasing rate of temperature.

These results are of interest for the study of hydro-viscous drive and its applications. This study can also provide a theoretical basis for the mechanism of temperature rise by considering the effect of two-phase flow.

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

The correlation coefficient of prediction (r_p) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.