arXiv subjects

Nirav Bhatt

Publications and source records attributed to Nirav Bhatt.

6 recordsLinked to original sources

arXiv2020

Learning Conserved Networks from Flows

A challenging problem in complex networks is the network reconstruction problem from data. This work deals with a class of networks denoted as conserved networks, in which a flow associated with every edge and the flows are conserved at all non-source and non-sink nodes. We propose a novel polynomial time algorithm to reconstruct conserved networks from flow data by exploiting graph theoretic properties of conserved networks combined with learning techniques. We prove that exact network reconstruction is possible for arborescence networks. We also extend the methodology for reconstructing networks from noisy data and explore the reconstruction performance on arborescence networks with different structural characteristics.

cs.LG↗

arXiv2018

An Algorithm for Integer Least-squares with Equality, Sparsity and Rank Constraints

In this work, we deal with rank-constrained integer least-squares optimization problems arising in low-rank matrix factorization related applications. We propose a solution for constrained integer least-squares problem subject to equality, sparsity, and rank constraints. The algorithm combines the Fincke-Pohst enumeration (or sphere decoding algorithm) with rank constraints and sparse solutions of Diophantine equations to arrive at an optimal solution. The proposed approach consists of two steps as follows: (i) find the solution set for Diophantine equations arising from the linear and sparsity constraints, (ii) find the matrix which minimizes the integer least-squares objective and satisfying the rank constraints using the solution set obtained in the step 1. The proposed algorithm is illustrated using a simple example. Then, we perform experiments to study the computational aspects of different steps of the proposed algorithm.

math.OC↗

arXiv2016

Identifying Topology of Power Distribution Networks Based on Smart Meter Data

In a power distribution network, the network topology information is essential for an efficient operation of the network. This information of network connectivity is not accurately available, at the low voltage level, due to uninformed changes that happen from time to time. In this paper, we propose a novel data--driven approach to identify the underlying network topology including the load phase connectivity from time series of energy measurements. The proposed method involves the application of Principal Component Analysis (PCA) and its graph-theoretic interpretation to infer the topology from smart meter energy measurements. The method is demonstrated through simulation on randomly generated networks and also on IEEE recognized Roy Billinton distribution test system.

eess.SY↗

arXiv2015

On Cooperative Behavior of Open Homogeneous Chemical Reaction Systems in the Extent Domain

Material balance equations describe the dynamics of the species in open reaction systems and contain information regarding reaction topology, kinetics and operation mode. For reaction systems, the state variables (the numbers of moles, or concentrations) have recently been transformed into decoupled reaction variants (extents of reaction), and reaction invariants (extents of flow) (Amrhein et al., AIChE Journal, 2010). This paper analyses the conditions under which an open homogeneous reaction system is cooperative in the extents domain. Further, it is shown that the dynamics of the extents of flow exhibit cooperative behavior. Further, we provide the conditions under which the dynamics of the extents of reaction exhibit cooperative behavior. Our results provide physical insights into cooperative and competitive nature of the underlying reaction system in the presence of material exchange with surrounding (i.e., inlet and outlet flows). The results of the article are demonstrated via examples.

eess.SY↗

arXiv2015

Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective

Data reconciliation (DR) and Principal Component Analysis (PCA) are two popular data analysis techniques in process industries. Data reconciliation is used to obtain accurate and consistent estimates of variables and parameters from erroneous measurements. PCA is primarily used as a method for reducing the dimensionality of high dimensional data and as a preprocessing technique for denoising measurements. These techniques have been developed and deployed independently of each other. The primary purpose of this article is to elucidate the close relationship between these two seemingly disparate techniques. This leads to a unified framework for applying PCA and DR. Further, we show how the two techniques can be deployed together in a collaborative and consistent manner to process data. The framework has been extended to deal with partially measured systems and to incorporate partial knowledge available about the process model.

cs.LG↗

arXiv2015

Monotonous (Semi-)Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be monotonous in nature. For example, in source separation problem, the source signals can be monotonously increasing or decreasing while the mixing matrix can have nonnegative entries. NMF methods may not be effective for such cases as it suffers from the ordering ambiguity. This paper proposes an approach to incorporate notion of monotonicity in NMF, labeled as monotonous NMF. An algorithm based on alternating least-squares is proposed for recovering monotonous signals from a data matrix. Further, the assumption on mixing matrix is relaxed to extend monotonous NMF for data matrix with real numbers as entries. The approach is illustrated using synthetic noisy data. The results obtained by monotonous NMF are compared with standard NMF algorithms in the literature, and it is shown that monotonous NMF estimates source signals well in comparison to standard NMF algorithms when the underlying sources signals are monotonous.

cs.LG↗