Illinois Institute of Technology

About: Illinois Institute of Technology is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Electric power system & Wireless network. The organization has 10188 authors who have published 21062 publications receiving 554178 citations. The organization is also known as: IIT & Illinois Tech.

Combined Heat and Power Dispatch Considering Pipeline Energy Storage of District Heating Network

Abstract: The regional integration of variable wind power could be restricted by a strong coupling of electric power generation dispatch and heat supply of combined heat-and-power (CHP) units. The coupling in cold seasons precludes CHPs from providing the necessary flexibility for managing the wind power dispatch. The lack of flexibility problem can be tackled by exploiting the energy storage capability of a district heating network (DHN) which decouples the strong linkage of electric power and heat supplies. In this paper, a combined heat and power dispatch (CHPD) is formulated to coordinate the operation of electric power system (EPS) and district heating system (DHS). The proposed CHPD model which is solved by an iterative method considers the temperature dynamics of DHN for exploiting energy storage as an option for managing the variability of wind energy. The simulation results are discussed for several test systems to demonstrate the potential benefits of the proposed method in terms of operation economics, wind power utilization, as well as the potential benefits for real systems.

Relationships of Work Stressors with Aggression, Withdrawal, Theft and Substance Use: An Exploratory Study

Abstract: Based on findings from the domain of organizational frustration, the conceptual similarity between stress and frustration, and the functional similarity between frustrated events and work stressors, the relationships of behaviours (aggression, withdrawal, theft and substance use) with work stressors and affective reactions were investigated. Relations between reported stressors and behaviours were strongest for the more directly aggressive actions (sabotage, interpersonal aggression, and hostility and complaints), and for intention to quit. Relations with theft and absence were modest. None of the stressors correlated with reported substance use at work. Among the relations between affective reactions and the reported behaviours, anger and job satisfaction correlated with all behaviours except substance use at work. Hierarchical regression results further showed that the observed associations between stressors and behaviours were not attributed to affective variables for most cases.

Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data

Abstract: Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

Computational methods in authorship attribution

Abstract: Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant. © 2009 Wiley Periodicals, Inc.