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.

Reinstatement of Drug-Taking Behavior as a Method of Assessing Incentive Motivational Properties of Drugs

Abstract: Noncontingent “priming” presentations of positive reinforcers (or incentive events) can enhance and reinstate previously acquired instrumental responding for these reinforcers We describe how this phenomenon may be used to study the motivational control of drug-taking behavior and the relapse to drug-taking in drug-free individuals

Communication security for smart grid distribution networks

Abstract: The operation and control of the next generation electrical grids will depend on a complex network of computers, software, and communication technologies. Being compromised by a malicious adversary would cause significant damage, including extended power outages and destruction of electrical equipment. Moreover, the implementation of the smart grid will include the deployment of many new enabling technologies such as advanced sensors and metering, and the integration of distributed generation resources. Such technologies and various others will require the addition and utilization of multiple communication mechanisms and infrastructures that may suffer from serious cyber vulnerabilities. These need to be addressed in order to increase the security and thus the greatest adoption and success of the smart grid. In this article, we focus on the communication security aspect, which deals with the distribution component of the smart grid. Consequently, we target the network security of the advanced metering infrastructure coupled with the data communication toward the transmission infrastructure. We discuss the security and feasibility aspects of possible communication mechanisms that could be adopted on that subpart of the grid. By accomplishing this, the correlated vulnerabilities in these systems could be remediated, and associated risks may be mitigated for the purpose of enhancing the cyber security of the future electric grid.

The long-term effect of increasing the albedo of urban areas

Abstract: Solar reflective urban surfaces (white rooftops and light-colored pavements) can increase the albedo of an urban area by about 0.1. Increasing the albedo of urban and human settlement areas can in turn decrease atmospheric temperature and could potentially offset some of the anticipated temperature increase caused by global warming. We have simulated the long-term (decadal to centennial) effect of increasing urban surface albedos using a spatially explicit global climate model of intermediate complexity. We first carried out two sets of simulations in which we increased the albedo of all land areas between 20 and 45 latitude respectively. The results of these simulations indicate a long-term global cooling effect of 3 10 15 K for each 1 m 2 of a surface with an albedo increase of 0.01. This temperature reduction corresponds to an equivalent CO2 emission reduction of about 7 kg, based on recent estimates of the amount of global warming per unit CO2 emission. In a series of additional simulations, we increased the albedo of urban locations only, on the basis of two independent estimates of the spatial extent of urban areas. In these simulations, global cooling ranged from 0.01 to 0.07 K, which corresponds to a CO2 equivalent emission reduction of 25‐150 billion tonnes of CO2.

Adaptive Neural Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form With Hysteresis Input

Abstract: In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.