111 research outputs found
Entropy-based framework for combinatorial optimization problems and enabling the grid of the future
No full textThis thesis is divided into two parts. In the first part, I describe efficient meta-heuristic algorithms for a series of combinatorially complex optimization problems, while the second part is concerned with robust and scalable control architecture for a network of paralleled converter/inverter systems (DC/AC microgrids).
Combinatorial optimization problems arise in many applications in various forms in seemingly unrelated areas such as data compression, pattern recognition, image segmentation, resource allocation, routing, and scheduling, graph aggregation, and graph partition problems. These optimization problems are characterized by a combinatorial number of configurations, where a cost value can be assigned to each configuration, and the goal is to find the configuration that minimizes the cost. Moreover, these optimization problems are largely non-convex, computationally complex and suffer from multiple local minima that riddle the cost surface. Most heuristics to these optimization problems are very sensitive to initial guess solutions, and efforts to make them robust to initializations typically come at significant computational costs such that the algorithms lose practicality in many applications. In our work, we are motivated by solutions that are employed by nature to similar combinatorial optimization problems; well described in terms of laws such as maximum entropy principle (MEP) in statistical physics literature. We propose to use MEP in solving a variety of combinatorial optimization problems.
Our main current contributions are threefold - (i) First we provide a clustering or resource allocation viewpoint to several combinatorial optimization problems: (a) data clustering, (b) graph partitioning (such as clustering of power networks), (c) traveling salesman problem (TSP) and its variants, and (d) hard problems on graphs, such as multiway -cut. This viewpoint enables a unified approach to handle a broad class of problems, and therefore efficient MEP based heuristics can be leveraged to obtain high-quality solutions. (ii) Second, we explore MEP based ideas to clustering problems specified by pairwise distances. Many problems in graph theory are indeed specified in terms of the corresponding edge-weight matrices (and not in terms of the nodal locational coordinates). (iii) Finally, our framework allows for inclusion of several constraints in the clustering/resource allocation problems. These constraints may correspond to capacity constraints in case of resource allocations where capacity of each resource is limited, or minimum-tour length constraints in case of traveling salesman problems (TSPs) and its variants.
In the second part of this thesis, we describe a novel distributed, robust and optimal control architecture for both DC as well as AC microgrids. Microgrids are grid systems that allow integration of local power sources, such as photovoltaics (PVs), wind, battery and other distributed energy resources (DERs) with local loads connected at the DC-link or the point of common coupling (PCC). Microgrids are hypothesized as viable alternatives to the traditional electric grid. In a microgrid, the main goals of the control design are to regulate voltage and frequency at the PCC and ensuring a prescribed sharing of power among different sources; for instance, economic considerations can dictate that power provided by the sources should be in a certain proportion or according to a prescribed priority. The main challenges arise from the uncertainties in the size and the schedules of loads, the complexity of a coupled multi-converter network, the uncertainties in the model parameters at each converter, and the adverse effects of interfacing DC power sources with AC loads, such as the Hz ripple that must be provided by the DC sources. A systematic control design that addresses all the challenges and objectives for the multi-converter/inverter control is still lacking in the existing literature. The main contribution of the control architecture proposed by us is its capability to addresses all the primary objectives - a) voltage and frequency regulation at the PCC with guaranteed robustness margins, b) prescribed time-varying power sharing in a network of parallel converters, c) controlling the tradeoff between 120Hz ripple on the total current provided by the power sources and the ripple on the DC-link voltage. An important contribution of our work is that our control architecture allows for closed-loop analysis and robust control synthesis for the entire grid network. We introduce a structure in the control architecture, whereby, we show that analysis of the entire multi-component microgrid can be simplified to that of an equivalent {\em single-component} system. Besides analysis, this simplification facilitates using robust and optimal control tools for achieving multiple objectives simultaneously; in contrast in existing architectures, closed-loop analysis for entire networks is typically difficult, and posing optimal control and robustness objectives for the entire network practically untenable.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo termsThe student, Mayank Baranwal, accepted the attached license on 2018-06-12 at 14:47.The student, Mayank Baranwal, submitted this Dissertation for approval on 2018-06-12 at 15:02.This Dissertation was approved for publication on 2018-06-14 at 11:09.DSpace SAF Submission Ingestion Package generated from Vireo submission #12625 on 2018-09-27 at 10:44:46Made available in DSpace on 2018-09-27T16:17:25Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-06-1
Application of field programmable analog arrays (FPAAs) to fast scanning probe microscopy
No full textFor a long time, signal processing used to be accomplished by microprocessors and DSPs (Digital Signal Processors). The advent of reconfigurable computing devices, such as Complex Programmable Logic Devices (CPLDs) and Field Programmable Gate Arrays (FPGAs) has given a new dimension to signal processing applications by not only allowing users to customize the hardware to suit the specific requirements but also making high speed applications a possibility, too. More recently, Field Programmable Analog Arrays (FPAAs) have emerged as interesting alternatives to most signal processing based applications. Even though the use of FPAA devices is still limited due to small number of suppliers, a growing interest in using FPAAs for various engineering applications is expected. In this thesis, we exploit the FPAAs to demonstrate their usefulness and ease of implementation in developing fast and robust controllers for an Atomic Force Microscope (AFM) unit.
Atomic Force Microscopes (AFMs) are getting faster. However, video-rate imaging still remains a big challenge to the AFM community. Therefore AFMs are required to have very fast nanopositioning systems. However, high-bandwidth requirement on the positioning system poses fundamental limitations on the image resolution. The resolution of an image depends on the controller’s capabilities to attenuate the measurement noises. Tools from robust control theory are employed to not only quantify the measurement noises and parametric uncertainties, but also synthesize the controllers in an optimal setting.
However, implementation of such controllers require electronics that can support high-bandwidth operations. Field Programmable Analog Arrays (FPAAs), which have bandwidth up to 400 kHz, have been employed to demonstrate not only the direct implementation of these controllers in terms of transfer functions, but also high-bandwidth tracking performance, too, when compared to most other commercially available Digital Signal Processors (DSPs). A significant improvement in the closed-loop bandwidth (∼ 500Hz) has been demonstrated. A part of the work is dedicated to the Q-control of microcantilevers. Since, cantilevers are second-order flexible structures with high resonant frequencies (∼ 50kHz) , Q-control of cantilevers requires estimating velocity at resonant frequencies. High-bandwidth advantage of FPAAs can be exploited to achieve the desired Q-control.Item withdrawn by Laura Spradlin ([email protected]) on 2014-07-23T19:53:05Z
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University of Illinois Theses & Dissertations (ID: 1)
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Interaction of a railway tunnel with a deep slow landslide in clay shales
No full textThe Varco d’Izzo landslide system (Basilicata Region, Italy) develops at the suburbs of the city of Potenza, capital of the region, and is crossed by two transport infrastructures of local importance: the national highway Basentana and the national railway line. This paper is focused on the effects of slope movements on the railway tunnel which was built in the accumulation of an earthflow of the landslide system. The earthflow displacements were slow but continuous in the monitoring period 2005-2015 and in the order of one to several cm/year. They have led, not far from the railway tunnel area, to the eviction of a house, the dismantling of a pedestrian bridge, damages to roads and other structures. The tunnel was completely re-built in 1992 between two rows of piles, by the cut-and-cover method, after the previous tunnel had suffered severe damage due to the landslide. Available inclinometer data seem to suggest that, locally, the tunnel with its piles is hindering landslide displacements. In fact, measurements carried out in the vicinity of the tunnel, upslope from it, do not show a slip surface crossing the piles. On the other hand, landslide displacements are observed both farther, upslope from the tunnel, and downslope from it. The resultant of earth pressures acting on the tunnel is thus, probably, increasing with time. The distribution of landslide displacements around the tunnel until recent years is herein analyzed. Results of site surveys are reported. The causes of the current state of deformation of the tunnel, which was evaluated by laserscanning, are examined with the help of simplified calculations and FEM simulations
Topology-aware distributed graph processing for tightly-coupled clusters
No full textCloud applications have burgeoned over the last few years, but they are typically written for loosely-coupled clusters such as datacenters. In this thesis we investigate how one can run cloud applications in tightly-coupled clusters and network topologies, namely super-computers. Specifically, we look at a class of distributed machine learning systems called distributed graph processing systems, and run them on NCSA Blue Waters. Partitioning the graph is key to achieving performance in distributed graph processing systems. We present new topology-aware partitioning techniques that better exploit the structure of the network topologies in supercomputers. Compared to existing work, our new Restricted Oblivious and Grid Centroid partitioning approaches produce 25-33% improvement in makespan, along with a sizable reduction in network traffic. We also discuss optimizations such as smart network buffers that further amplify the improvement. To help operators select the best graph partitioning technique, we culminate our experimental results into a decision tree.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01The student, Mayank Bhatt, accepted the attached license on 2018-04-23 at 17:13.The student, Mayank Bhatt, submitted this Thesis for approval on 2018-04-23 at 17:20.This Thesis was approved for publication on 2018-04-24 at 15:21.DSpace SAF Submission Ingestion Package generated from Vireo submission #12435 on 2018-08-31 at 17:21:19Made available in DSpace on 2018-09-04T20:36:52Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-04-24Embargo set by: Seth Robbins for item 107297
Lift date: 2020-09-04T20:37:00Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 107297
Lift date: 2020-09-04T20:42:08Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 107297 on 2020-09-05T09:15:32Z
The Self-Destructive Nature of Human Divisions: When One Species Begins to See Itself as Many
No full textThis paper explores the paradoxical nature of human civilization, which, despite belonging to a single biological species (Homo sapiens), remains fundamentally fragmented by psychological, political, and ideological divisions. Author Mayank Singh argues that these constructed identities—ranging from religion and nationality to economic class and political ideology—are not harmless cultural differences but powerful structures that shape perception and foster systemic hostility. These divisions are largely maintained through unconscious conditioning and inherited social structures rather than rational choice.
The text highlights a critical existential risk: as modern humanity wields technologies capable of global destruction (such as nuclear weapons and AI), the continued reliance on "us versus them" mentalities threatens the survival of both the human species and the global biosphere. Singh posits that these rigid separations move civilization away from the interconnected patterns found in nature. Ultimately, the paper suggests that the future of civilization depends on a profound transformation in consciousness—transcending constructed identities to recognize a shared biological and cosmic existence before self-destructive logic leads to terminal conflict
Seeing Life as It Is: Beyond Human-Centered Existence
No full textThis paper critiques the anthropocentric foundations of modern civilization, arguing that human progress is frequently achieved through the systematic exploitation and destruction of non-human life. The author, Mayank Singh, explores the "illusion of human centrality," wherein animals and ecosystems are reduced to commodities, luxury products, or secondary participants in the planet's life system. A significant focus is placed on "conditioned demand"—consumption driven by luxury and convenience rather than survival—and the "economy of cruelty" that sustains it.
The work further identifies a contradiction in "symbolic respect," noting that many societies grant sacred status to specific animals based on religious mythology while remaining indifferent to the industrial-scale suffering of others. Singh proposes a shift toward "clear perception," a secular and non-utilitarian framework that recognizes all organisms as equivalent living processes. Ultimately, the paper suggests that a truly conscious civilization must redefine progress not through economic or technological growth, but through its capacity for ecological coexistence and respect for the full diversity of life
Accelerating Distributed Optimization via Fixed-time Convergent Flows: Extensions to Non-convex Functions and Consistent Discretization
No full textDistributed optimization has gained significant attention in recent years,
primarily fueled by the availability of a large amount of data and
privacy-preserving requirements. This paper presents a fixed-time convergent
optimization algorithm for solving a potentially non-convex optimization
problem using a first-order multi-agent system. Each agent in the network can
access only its private objective function, while local information exchange is
permitted between the neighbors. The proposed optimization algorithm combines a
fixed-time convergent distributed parameter estimation scheme with a fixed-time
distributed consensus scheme as its solution methodology. The results are
presented under the assumption that the team objective function is strongly
convex, as opposed to the common assumptions in the literature requiring each
of the local objective functions to be strongly convex. The results extend to
the class of possibly non-convex team objective functions satisfying only the
Polyak-\L ojasiewicz (PL) inequality. It is also shown that the proposed
continuous-time scheme, when discretized using Euler's method, leads to
consistent discretization, i.e., the fixed-time convergence behavior is
preserved under discretization. Numerical examples comprising large-scale
distributed linear regression and training of neural networks corroborate our
theoretical analysis.Comment: Under review. 10 pages, 1 figur
A Methodology Establishing Linear Convergence of Adaptive Gradient Methods under PL Inequality
No full textAdaptive gradient-descent optimizers are the standard choice for training neural network models. Despite their faster convergence than gradient-descent and remarkable performance in practice, the adaptive optimizers are not as well understood as vanilla gradient-descent. A reason is that the dynamic update of the learning rate that helps in faster convergence of these methods also makes their analysis intricate. Particularly, the simple gradient-descent method converges at a linear rate for a class of optimization problems, whereas the practically faster adaptive gradient methods lack such a theoretical guarantee. The Polyak-Łojasiewicz (PL) inequality is the weakest known class, for which linear convergence of gradient-descent and its momentum variants has been proved. Therefore, in this paper, we prove that AdaGrad and Adam, two well-known adaptive gradient methods, converge linearly when the cost function is smooth and satisfies the PL inequality. Our theoretical framework follows a simple and unified approach, applicable to both batch and stochastic gradients, which can potentially be utilized in analyzing linear convergence of other variants of Adam.Accepted for publication at the main track of 27th European Conference on Artificial Intelligence (ECAI-2024
PoliWAM: An Exploration of a Large Scale Corpus of Political Discussions on WhatsApp Messenger
No full textWhatsApp Messenger is one of the most popular channels for spreading information with a current reach of more than 180 countries and 2 billion people. Its widespread usage has made it one of the most popular media for information propagation among masses during any socially engaging event. In the recent past, several countries have witnessed its effectiveness and influence in political and social campaigns. We observe a high surge in information and propaganda flow during elections. To explore such activities, in this paper, we discuss challenges, methodology, and opportunities in data curation from WhatsApp for politics-based exploratory studies. As a use case, we study the period before, during, and after the Indian General Elections 2019, encompassing all major Indian political parties. We present several complementing insights into the investigative and sensational news stories from the same period. Exploratory data analysis and experiments showcase several exciting results and future research opportunities. To facilitate reproducible research, we make the anonymized datasets available in the public domain.
If you are using this dataset as part of your research, please cite the following paper
@article{srivastava2020poliwam,
title={PoliWAM: an exploration of a large scale corpus of political discussions on WhatsApp messenger},
author={Srivastava, Vivek and Singh, Mayank},
journal={arXiv preprint arXiv:2010.13263},
year={2020}
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