Concordia University

About: Concordia University is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Context (language use) & Control theory. The organization has 13565 authors who have published 31084 publications receiving 783525 citations. The organization is also known as: Sir George Williams University & Loyola College, Montreal.

When does quality of relationships with coworkers predict burnout over time? The moderating role of work motivation

Abstract: The present prospective study examines the interplay between the quality of relationships with coworkers and work motivation in predicting burnout. Considering self-determined motivation at work as a potential moderator, we investigated whether relationships with coworkers are equally important to all employees in preventing burnout. A total of 533 college employees participated in this study. Data were collected at two time points, two years apart. Results from structural equation modeling indicated negative main effects for high-quality relationships and self-determined motivation on burnout. A significant interaction effect between these two factors on burnout was also revealed, suggesting that high-quality relationships with coworkers is crucial for those employees who exhibit less self-determined work motivation. Implications for burnout research and management practices are discussed (Deci & Ryan, 1985). Copyright © 2009 John Wiley & Sons, Ltd.

A Reliability-Aware Network Service Chain Provisioning With Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

Abstract: Traditionally, service-specific network functions (NFs) (e.g., Firewall, intrusion detection system, etc.) are executed by installation-and maintenance-costly hardware middleboxes that are deployed within a datacenter network following a strictly ordered chain. NF virtualization (NFV) virtualizes these NFs and transforms them into instances of plain software referred to as virtual NFs (VNFs) and executed by virtual machines, which, in turn, are hosted over one or multiple industry-standard physical machines. The failure (e.g., hardware or software) of any one of a service chain’s VNFs leads to breaking down the entire chain and causing significant data losses, delays, and resource wastage. This paper establishes a reliability-aware and delay-constrained (READ) routing optimization framework for NFV-enabled datacenter networks. READ encloses the formulation of a complex mixed integer linear program (MILP) whose resolution yields an optimal network service VNF placement and traffic routing policy that jointly maximizes the achieved respective reliabilities of supported network services and minimizes these services’ respective end-to-end delays. A heuristic algorithm dubbed Greedy- ${k}$ -shortest paths (GSP) is proposed for the purpose of overcoming the MILP’s complexity and develop an efficient routing scheme whose results are comparable to those of READ’s optimal counterparts. Thorough numerical analyses are conducted to evaluate the network’s performance under GSP, and hence, gauge its merit; particularly, when compared to existing schemes, GSP exhibits an improvement of 18.5% in terms of the average end-to-end delay as well as 7.4% to 14.8% in terms of reliability.

Analysis and design of a three-phase offline DC-DC converter with high-frequency isolation

Abstract: Single-phase offline switch mode rectifiers (or offline DC-DC converters) face severe component stresses in higher than 10kW applications. The authors show that in three-phase, switch-mode rectifier (SMR) topologies' component stresses are reduced, and performance is improved substantially. These improvements include faster response times, reduced switching stresses of the power semiconductor devices, and reduced size and ratings of associated reactive components. The authors also present an analysis and design approach for three-phase SMR converters under large-input voltage and load variations. Output voltage control is achieved by varying the duty cycle of the inverter power semiconductor switches. Theoretical results are verified experimentally. >

Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines

Abstract: In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.