Soft-switching single inductor current-fed push-pull converter for PV applications

A switching control strategy is proposed for single inductor current-fed push-pull converter with a secondary side active voltage doubler rectifier or a voltage rectifier used in photovoltaic (PV) grid interfacing. The proposed switching control strategy helps to turn-on and turn-off the primary side power switches with zero-voltage and zero-current switching. The operation of the push-pull converter is analyzed for two modes of operation. The feasibility of the proposed switching control strategy is validated using simulation and experimental results.

Leadership to support quality improvement in Vietnamese higher education

The impetus for the study reported in this paper is the Higher Education (HE) reform agenda outlined by the Vietnamese Ministry of Education and Training (MOET). The paper reports on phase one of a mixed method research; a quantitative approach using the Multifactor Leadership Questionnaire (MLQ) to investigate the Vietnamese HE leaders’ leadership styles. The MLQ survey was administered to approximately 190 senior managers in State HE institutions in Mekong Delta region in Vietnam (nine of colleges). The psychometrics of the MLQ for the Vietnamese sample confirmed the reliability and validity of the instrument with a Cronbach’s alpha of 0.779. A CFA was conducted and all factor structures were stable and consistent. The demographic variables were used to analyse patterns of leadership behaviours by the different sub-groups. The findings suggest that leaders who have different educational background and different gender in Mekong Delta region, Vietnam do not differ significantly in their perceptions about leadership factors.

Watermarking capacity control for dynamic payload embedding

Despite significant improvements in capacity-distortion performance, a computationally efficient capacity control is still lacking in the recent watermarking schemes. In this paper, we propose an efficient capacity control framework to substantiate the notion of watermarking capacity control to be the process of maintaining “acceptable” distortion and running time, while attaining the required capacity. The necessary analysis and experimental results on the capacity control are reported to address practical aspects of the watermarking capacity problem, in dynamic (size) payload embedding.

Green: Exploring the aestheticized use of chroma-key techniques and technologies in two intermedial productions

Peggy Shaw’s RUFF, (USA 2013) and Queensland Theatre Company’s collaboration with Queensland University of Technology, Total Dik!, (Australia 2013) overtly and evocatively draw on an aestheticized use of the cinematic techniques and technologies of Chroma Key to reveal the tensions in their production and add layers to their performances. In doing so they offer invaluable insight where the filmic and theatrical approaches overlap. This paper draws on Eckersall, Grehan and Scheer’s New Media Dramaturgy (2014) to reposition the frame as a contribution to intermedial theatre and performance practices in light of increasing convergence between seemingly disparate discourses. In RUFF, the scenic environment replicates a chroma-key ‘studio’ which facilitates the reconstruction of memory displaced after a stroke. RUFF uses the screen and projections to recall crooners, lounge singers, movie stars, rock and roll bands, and an eclectic line of eccentric family members living inside Shaw. While the show pays tribute to those who have kept her company across decades of theatrical performance, use of non-composited chroma-key technique as a theatrical device and the work’s taciturn revelation of the production process during performance, play a central role in its exploration of the juxtaposition between its reconstructed form and content. In contrast Total Dik! uses real-time green screen compositing during performance as a scenic device. Actors manipulate scale models, refocus cameras and generate scenes within scenes in the construction of the work’s examination of an isolated Dictator. The ‘studio’ is again replicated as a site for (re)construction, only in this case Total Dik! actively seeks to reveal the process of production as the performance plays out. Building on RUFF, and other works such as By the Way, Meet Vera Stark, (2012) and Hotel Modern’s God’s Beard (2012), this work blends a convergence of mobile technologies, models, and green screen capture to explore aspects of transmedia storytelling in a theatrical environment (Jenkins, 2009, 2013). When a green screen is placed on stage, it reads at once as metaphor and challenge to the language of theatre. It becomes, or rather acts, as a ‘sign’ that alludes to the nature of the reconstructed, recomposited, manipulated and controlled. In RUFF and in Total Dik!, it is also a place where as a mode of production and subsequent reveal, it adds weight to performance. These works are informed by Auslander (1999) and Giesenkam (2007) and speak to and echo Lehmann’s Postdramatic Theatre (2006). This paper’s consideration of the integration of studio technique and live performance as a dynamic approach to multi-layered theatrical production develops our understanding of their combinatory use in a live performance environment.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.