Marjan Momtazpour

Coordinated clustering algorithms to support charging infrastructure design for electric vehicles

The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation.

Charging and Storage Infrastructure Design for Electric Vehicles

Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this article, we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple factors that can be simultaneously leveraged in our framework to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.

Secure and adaptive state estimation for a PMU-equipped smart grid

Modern power systems are constantly subjected to various disturbances, device failures, as well as data attacks. To improve the quality of monitoring and control in smart grid, researchers have conducted extensive studies in exploring the advantages of real-time digital meters such as the Phasor Measurement Units, combining with dynamic estimation methods such as Kalman filters. Standard Kalman filter assumes we have statistical knowledge regarding the uncertainty of the system under study. The reality is, the accurate system model is almost impossible to obtain, especially with the existence of malicious data attack. A lightweight and efficient adaptive Kalman filter algorithm is presented in this paper for its ability to alleviate the impact of incorrect system models and/or measurement data. Simulations demonstrate that it is resilient to suboptimal system modeling, sudden system dynamic changes and bad data injection.

Application of data mining in cryptanalysis

Cryptography is a popular method for information hiding and reaching confidentiality in digital world. On the other hand, cryptanalysis is an interesting and useful science from different viewpoints. All the digital materials that are processed by computers or transferred in data transmission systems have some information. However, the information, embodied in the data, is entangled with known or unknown patterns and features. As an example, each certain author uses certain writing patterns and techniques which are almost unique. While cryptography algorithms hide information from the eavesdroppers, this hiding is performed through recoding. Hence, information and the related features are still remained in the encrypted output of the cryptosystem. Then, discovering the hidden features in the encrypted texts can be used to analyze the output of a cryptography algorithm. In this paper, the application of data mining in cryptanalysis is explored. It is shown that how this attack may be employed to classify the cipher texts. Also, a number of methods to improve the security of cryptography systems will be introduced. Simulation results support the arguments of the paper.

New Routing Strategies for RSP Problems with Concave Cost

Multi-Constraint Path (MCP) and Restricted Shortest Path (RSP) are important problems studied in the field of QoS routing. Traditional versions of these problems are known to be NP-Complete ones. Various solutions have been proposed for RSP and MCP based on different heuristics, in practical situations. Restricted shortest path problem with concave route costs is studied in this paper. This is a special version of the traditional RSP problem and is widely applicable in wireless and mobile ad hoc networks. In this paper, we propose new algorithms for this kind of routing. The effectiveness and performance of our proposed solutions are shown through simulations.

The use of genetic algorithm for feature selection in video concept detection

Video semantic concept detection is considered as an important research problem by the multimedia industry in recent years. Classification is the most accepted method used for concept detection, where, the output of the classification system is interpreted as semantic concepts. These concepts can be employed for automatic indexing, searching and retrieval of video objects. However, employed features have high dimensions and thus, concept detection with the existing classifiers experiences high computation complexity. In this paper, a new approach is proposed to reduce the classification complexity and the required time for learning and classification by choosing the most important features. For this purpose genetic algorithms are employed as a feature selector. Simulation results illustrate improvements in the behavior of the classifier.

Installing Electric Vehicle Charging Stations City-Scale: How Many and Where?

Electric Vehicles (EVs) are touted as the sustainable alternative to reduce our over-reliance on fossil fuels and stem our excessive carbon emissions. As the use of EVs becomes more widespread, planners in large metropolitan areas have begun thinking about the design and installation of charging stations city-wide. Unlike gas-based vehicles, EV charging requires a significant amount of time and must be done more periodically, after relatively shorter distances. We describe a KDD framework to plan the design and deployment of EV charging stations over a city. In particular, we study this problem from the economic viewpoint of the EV charging station owners. Our framework integrates user route trajectories, owner characteristics, electricity load patterns, and economic imperatives in a coordinated clustering framework to optimize the locations of stations and assignment of user trajectories to (nearby) stations. Using a dataset involving over a million individual movement patterns, we illustrate how our framework can answer many important questions about EV charging station deployment and profitability.

Cipher-text classification with data mining

Cryptography plays an important role in information technology. With the explosive growth of Internet, the importance of information and network security is increased. The principle goal of cryptography is information hiding and block-cipher algorithms achieve this goal through information recoding. However, various kinds of information have distinctive features, which are employed in classification tasks. If these features are transformed to the cipher-text, cipher-texts may be classified to their original classes. In this paper, a classification strategy is proposed for cipher-text classification. The proposed strategy is evaluated through simulations. Different classifiers (SVM, KNN, and LDA) are used for this purpose.