Showing papers by "Boris Mirkin published in 2016"

Adaptive perimeter traffic control of urban road networks based on MFD model with time delays

Abstract: Summary In this work, we focus on two main themes: (i) developing a more realistic macroscopic fundamental diagram-based nonlinear control-oriented model of urban traffic networks with time delays incorporated into model structure; and (ii) based on its linearized form subject to input delay, designing two new perimeter control architectures for one aggregated urban traffic region under unknown bounded external disturbances and parameter uncertainties. The feedback control laws design is performed in the context of model reference adaptive control approach. Simulation results based on a linearized model are also presented, which demonstrate in this case desired closed-loop adaptive control system performance. Copyright © 2015 John Wiley & Sons, Ltd.

Qualitative Judgement of Research Impact: Domain Taxonomy as a Fundamental Framework for Judgement of the Quality of Research

Abstract: The appeal of metric evaluation of research impact has attracted considerable interest in recent times. Although the public at large and administrative bodies are much interested in the idea, scientists and other researchers are much more cautious, insisting that metrics are but an auxiliary instrument to the qualitative peer-based judgement. The goal of this article is to propose availing of such a well positioned construct as domain taxonomy as a tool for directly assessing the scope and quality of research. We first show how taxonomies can be used to analyse the scope and perspectives of a set of research projects or papers. Then we proceed to define a research team or researcher's rank by those nodes in the hierarchy that have been created or significantly transformed by the results of the researcher. An experimental test of the approach in the data analysis domain is described. Although the concept of taxonomy seems rather simplistic to describe all the richness of a research domain, its changes and use can be made transparent and subject to open discussions.

A Lattice-based Consensus Clustering Algorithm

Abstract: We propose a new FCA-based algorithm for consensus clustering, FCA-Consensus. As the input the algorithm takes T partitions of a certain set of objects obtained by k-means algorithm after its T different executions. The resulting consensus partition is extracted from an antichain of the concept lattice built on a formal context objects× classes, where the classes are the set of all cluster labels from each initial kmeans partition. We compare the results of the proposed algorithm in terms of ARI measure with the state-of-the-art algorithms on synthetic datasets. Under certain conditions, the best ARI values are demonstrated by FCA-Consensus.

Visualization of Dynamic Reference Graphs

Abstract: We present a tool for dynamic reference graph visualization. A reference graph is a graph based on key phrases retrieved from a timeindexed natural language text corpus. This tool may be useful for the analysis of connected pairs of latent topics, changes in the significance of these topics as well as in the relationship between them over various time periods.

A Clustering-Based Approach to Reduce Feature Redundancy

Abstract: Research effort has recently focused on designing feature weighting clustering algorithms. These algorithms automatically calculate the weight of each feature, representing their degree of relevance, in a data set. However, since most of these evaluate one feature at a time they may have difficulties to cluster data sets containing features with similar information. If a group of features contain the same relevant information, these clustering algorithms set high weights to each feature in this group, instead of removing some because of their redundant nature. This paper introduces an unsupervised feature selection method that can be used in the data pre-processing step to reduce the number of redundant features in a data set. This method clusters similar features together and then selects a subset of representative features for each cluster. This selection is based on the maximum information compression index between each feature and its respective cluster centroid. We present an empirical validation for our method by comparing it with a popular unsupervised feature selection on three EEG data sets. We find that our method selects features that produce better cluster recovery, without the need for an extra user-defined parameter.