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Safe and Secure Optimization in Human-Cyber-Physical Systems
Get PDFIn our rapidly evolving technological landscape, the proliferation of enabling technologies for autonomous systems has given rise to a burgeoning realm of societal-scale smart systems. One noteworthy category within this domain is Human-Cyber-Physical Systems (H-CPS), which encompass physical systems controlled by a blend of computer-based algorithms and human inputs. Examples of H-CPS include the smart grid and autonomous transportation systems. These systems harness the potential of distributed computing units, fast communication channels, and real-time data collection, offering efficient mechanisms for their management. This requires a synthesis of tools from distributed optimization, machine learning, game theory, and stochastic control.However, the advent of H-CPS also presents novel challenges. Human decisions, often stochastic and beyond direct control, must be factored into the developed mechanisms. Moreover, the dependable operation of H-CPS hinges on secure communication between physical systems and computing units, raising concerns regarding user data privacy and system security. The growing number of humans and devices generates copious amounts of data from sensing units, necessitating computationally efficient data processing to ensure seamless H-CPS operation.This thesis aims to design network control, optimization, and learning frameworks that enhance safety, robustness, and efficiency in H-CPS, with practical applications in smart infrastructure systems like the power grid and transportation networks. Additionally, its relevance extends to diverse Internet of Things applications, emphasizing user data privacy, such as the development of language models from text data. The thesis unfolds in three interconnected chapters. In the first chapter, we introduce provably efficient and adversarially robust multi-agent optimization algorithms tailored for distributed resource allocation and distributed learning scenarios in the presence of malicious agents. Moving forward to the second chapter, we aim to design prices for shared resources that do not violate hard (mainly physical) constraints of the system, without any two-way communications with the users as common in distributed optimization based methods. The third chapter focuses on crafting and analyzing joint ride pricing and fleet management policies for the control of autonomous urban mobility fleets. Throughout these chapters, we not only analyze the theoretical performance of our proposed mechanisms but also substantiate their effectiveness through extensive simulations on real-world problems
Learning and Pricing Algorithms for Human-Cyber-Physical Systems
Get PDFNowadays with the growth of large-scale societal infrastructure systems, there has been significant research attention on improving efficiency, guaranteeing safety, reducing operational costs, and decreasing the carbon footprint of these systems. In particular, this thesis is focused on Human-Cyber-Physical Systems (H-CPS) (e.g. smart grid, electric transportation networks, autonomous driving). An H-CPS is any physical system in which a mechanism is controlled by both computer-based algorithms and human inputs. With the increasing complexity of human-machine interfaces, the traditional engineering and operating strategies are not adequate to manage. In fact, a mix of tools from stochastic control, distributed optimization, machine learning, and game theory is required. For example, in modern electric transportation systems, without appropriate demand management and coordination schemes, Electric Vehicle (EV) charging patterns could create problems for power transmission and distribution networks, and hence reduce the environmental benefits of transportation electrification.Furthermore, when managing demand to reduce costs in a power system, it is necessary to ensure that the operating constraints of the power grid are not violated as a result of our actions. Additionally, because of the availability of real-time data from these infrastructure systems, training a large-scale model over a vast amount of data requires sophisticated techniques that accelerate the training of learning models. Therefore, it becomes important to develop algorithms that are computationally efficient and consider the critical safety requirements of these systems.The aforementioned problems are characterized by many challenges including: How can we encourage customers to act in a way that is more likely to benefit society even when it may conflict with their own interests? How do we make sure that the infrastructure systems' safety criteria are not disregarded while we are learning the proper procedures to optimize customer behavior? How do we make sure that our proposed algorithms are computationally efficient? This thesis is focused on developing optimization and machine learning frameworks that promote efficiency and flexibility in large-scale societal infrastructure systems with the active involvement of humans. In the first part of the thesis, we focus on designing optimal price and routing mechanisms for a public charging stations network in electric transportation systems to coordinate customers (i.e., EV drives) towards a socially optimal behavior given their heterogeneity. In the second part of the thesis, we provide two theoretical learning guarantees for online decision-making problems in safety-constrained unknown linear systems. Moving on to the third part, we develop two methods to speed up the learning process of the online learning algorithms in new tasks based on their limited past experience with unknown linear systems. We also support our theoretical results in all three parts by significant improvement in numerical experiments
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Online Optimization and Learning for Sustainable Cyber-Human-Physical Systems
Get PDFDue to the increasing connectivity of modern technology and extensive data availability, large-scale societal systems have massive potential to improve reliability, reduce operational costs, ensure safety, and decrease their carbon footprint. However, the increasing complexity of these systems also brings forth new challenges that traditional engineering fields are not well equipped to handle. Rather, interdisciplinary solutions from many fields including optimization, control theory, communications, signal processing, economics, power engineering, and transportation engineering are required. Specifically, my research has focused on the upcoming Cyber-Human-Physical-Systems (CHPS) within this domain (e.g., transportation systems, power grids, and smart cities with cooperative building communities). A Cyber-Human-Physical System is any real-world system that involves physical components that are controlled by both algorithms and human input. For example, these systems operate by way of physical components and infrastructure, utilize algorithms for the control and optimization of the infrastructure, and rely on human input. Additionally, these societal systems seek to maximize profit or social welfare while operating subject to constraints and subject to inherently stochastic environments. This thesis is focused on developing optimization frameworks and machine learning strategies to improve the operation of these complex modern societal infrastructure systems in uncertain environments. Namely, 1) leveraging recent advancements in online optimization for system scheduling with applications to electric vehicle (EV) charging and community energy storage (CES) management, 2) using the machine learning framework called Thompson Sampling for the design of effective price signals for an electricity aggregator passively learning customers’ price sensitivities while running a load shaping program and providing theoretical safety guarantees on critical infrastructure constraints, and 3) optimizing real-world workplace EV charging in an online fashion and scheduling the charging/routing of a real electric bus fleet to minimize operational costs
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Safe Online Convex Optimization with Multi-Point Feedback
Get PDFMotivated by the stringent safety requirements that are often present in real-world applications, we study a safe online convex optimization setting where the player needs to simultaneously achieve sublinear regret and zero constraint violation while only using zero-order information. In particular, we consider a multi-point feedback setting, where the player chooses points in each round (where is the problem dimension) and then receives the value of the constraint function and cost function at each of these points. To address this problem, we propose an algorithm that leverages forward-difference gradient estimation as well as optimistic and pessimistic action sets to achieve regret and zero constraint violation under the assumption that the constraint function is smooth and strongly convex. We then perform a numerical study to investigate the impacts of the unknown constraint and zero-order feedback on empirical performance.20 pages, 1 figure. Published in the proceedings of the Learning for Dynamics and Control Conference (L4DC) 202
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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