Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Model-Predictive Control of Grid-Tied Four-Level Diode-Clamped Inverters for High-Power Wind Energy Conversion Systems

Abstract: In this paper, a model-predictive control of medium voltage, grid-tied four-level diode-clamped inverter for use in high-power wind energy conversion systems is presented. The control objectives such as regulation of net dc-link voltage, reactive power generation to meet the grid operator request, dc-link capacitor voltages balancing, and switching frequency minimization are included in the cost function. The latter objective is an important requirement for high-power converters, and this has been achieved without any modifications to the software or hardware configuration. The future behavior of the grid currents and dc-link voltages is predicted for all the possible switching states using the discrete-time model of the inverter, dc-link, inductive filter, and grid. The switching state that minimizes the cost function has been chosen and applied to the inverter directly. During different operating conditions, the switching frequency is regulated between 750 and 850 Hz with the help of a lookup-table-based weighting factor, and thus, the proposed methodology appears as a promising tool to control the high-power grid-tied inverters. The results obtained through the MATLAB simulations on 4 MVA/4000 V system and dSPACE DS1103-based experiments on 5 kW/208 V prototype are in a close relationship and thus validate the proposed control strategy.

Regularized common spatial patterns with generic learning for EEG signal classification

Abstract: The common spatial patterns (CSP) algorithm is commonly used to extract discriminative spatial filters for the classification of electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). However, CSP is based on a sample-based covariance matrix estimation. Therefore, its performance is limited when the number of available training samples is small. In this paper, the CSP method is considered in such a small-sample setting. We propose a regularized common spatial patterns (R-CSP) algorithm by incorporating the principle of generic learning. The covariance matrix estimation in R-CSP is regularized through two regularization parameters to increase the estimation stability while reducing the estimation bias due to limited number of training samples. The proposed method is tested on data set IVa of the third BCI competition and the results show that R-CSP can outperform the classical CSP algorithm by 8.5% on average. Moreover, the regularization introduced is particularly effective in the small-sample setting.

Workload generation for YouTube

Abstract: This paper introduces a workload characterization study of the most popular short video sharing service of Web 2.0, YouTube. Based on a vast amount of data gathered in a five-month period, we analyzed characteristics of around 250,000 YouTube popular and regular videos. In particular, we collected lists of related videos for each video clip recursively and analyzed their statistical behavior. Understanding YouTube traffic and similar Web 2.0 video sharing sites is crucial to develop synthetic workload generators. Workload simulators are required for evaluating the methods addressing the problems of high bandwidth usage and scalability of Web 2.0 sites such as YouTube. The distribution models, in particular Zipf-like behavior of YouTube popular video files suggests proxy caching of YouTube popular videos can reduce network traffic and increase scalability of YouTube Web site. YouTube workload characteristics provided in this work enabled us to develop a workload generator to evaluate the effectiveness of this approach.

Dominating biological networks.

Abstract: Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network. To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its “spine” that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

Clinical nurse specialists and nurse practitioners: title confusion and lack of role clarity.

Abstract: Title confusion and lack of role clarity pose barriers to the integration of advanced practice nursing roles (i.e., clinical nurse specialist [CNS] and nurse practitioner [NP]). Lack of awareness and understanding about NP and CNS roles among the healthcare team and the public contributes to ambiguous role expectations, confusion about NP and CNS scopes of practice and turf protection. This paper draws on the results of a scoping review of the literature and qualitative key informant interviews conducted for a decision support synthesis commissioned by the Canadian Health Services Research Foundation and the Office of Nursing Policy in Health Canada. The goal of this synthesis was to develop a better understanding of advanced practice nursing roles and the factors that influence their effective development and integration in the Canadian healthcare system. Specific recommendations from interview participants and the literature to enhance title and role clarity included the use of consistent titles for NP and CNS roles; the creation of a vision statement to articulate the role of CNSs and NPs across settings; the use of a systematic planning process to guide role development and implementation; the development of a communication strategy to educate healthcare professionals, the public and employers about the roles; attention to inter-professional team dynamics when introducing these new roles; and addressing inter-professionalism in all health professional education program curricula.