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This chapter presents a game-theoretic solution to several challenges in electricity markets, e.g., intermittent generation; high levels of average prices; price volatility; and fundamental aspects concerning the environment, reliability, and affordability. It proposes a stochastic bi-level optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper-level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower-level problem using an extended stochastic (Bayesian) Cournot-based game.
Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic tools have been proposed for this purpose, the oldest one remains arguably the most versatile and widespread: Fano’s inequality. In this chapter, we provide a survey of Fano’s inequality and its variants in the context of statistical estimation, adopting a versatile framework that covers a wide range of specific problems. We present a variety of key tools and techniques used for establishing impossibility results via this approach, and provide representative examples covering group testing, graphical model selection, sparse linear regression, density estimation, and convex optimization.
Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.
We introduce the mathematical modeling process. We also set the stage for the rest of the book by discussing systems of linear equations and their solutions, matrices, Gauss-Jordan elimination, linear combinations of vectors, basis vectors of Euclidean space, and their connection to basic solutions of a linear system. We conclude with simple optimization problems with quadratic functions.
In this chapter, we discuss the application of edge caching to enhance the physical layer security of cellular networks with limited backhaul capacity. By proactively sharing the same content across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative multiple-input multiple-output (MIMO) transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. We formulate a two-stage nonconvex optimization problem for minimizing the total transmit power while providing quality of service (QoS) and guaranteeing communication secrecy during content delivery, where the caching and the cooperative MIMO transmission policy are optimized in an offline caching stage and an online delivery stage, respectively. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design.
Optimization and root finding are closely aligned techniques for determining the extremums and zeros, respectively, of a function. Newton's method is the workhorse of both types of algorithms for nonlinear functions, and the conjugate-gradient and GMRES methods are also covered. Optimization of linear, quadratic, and nonlinear functions are addressed with and without constraints, which may be equality or inequality. In the linear programming case, emphasis is placed on the simplex method.
Least-squares methods provide the mathematical foundation for optimization of algebraic systems. They can be applied to overdetermined systems, having more equations than unknowns, or undertermined systems, having fewer equations than unknowns. The optimization may involve constraints or be subject to a penalty function. Numerical methods, namely the conjugate-gradient and GMRES methods, that are based on least-squares optimization method are discussed in detail and put into the context of other Krylov-based methods.
Vector and matrix calculus provides a powerful set of tools for analying and manipulating scalars, vectors, and tensors in continuum mechanics. This includes transformations between coordinate systems and provides the foundation for optimization methods.
This paper proposes and evaluates swarming mechanisms of patrolling unmanned aerial vehicles (UAVs) that can collectively search a region for intruding UAVs. The main contributions include the development of multi-objective searching strategies and investigation of the required sensor configurations for the patrolling UAVs. Numerical results reveal that it is sometimes better to search through a region with a single swarm rather than multiple swarms deployed over sub-regions. Moreover, a large communication range does not necessarily improve search performances, and the patrolling swarm must have a speed close to the speed of the intruding UAVs to maximize the search performances.
Address vector and matrix methods necessary in numerical methods and optimization of linear systems in engineering with this unified text. Treats the mathematical models that describe and predict the evolution of our processes and systems, and the numerical methods required to obtain approximate solutions. Explores the dynamical systems theory used to describe and characterize system behaviour, alongside the techniques used to optimize their performance. Integrates and unifies matrix and eigenfunction methods with their applications in numerical and optimization methods. Consolidating, generalizing, and unifying these topics into a single coherent subject, this practical resource is suitable for advanced undergraduate students and graduate students in engineering, physical sciences, and applied mathematics.
The chapter introduces basic optimization concepts, and motivates the use of optimization models and methods to engineering and scientific practice applications. It establishes key concepts, such as the types of variables, arguments to an optimization problem as continuous, integer and control functions (for optimal control problems). Further, it introduces types of optimization problems according to their formulation (such as multiobjective, bilevel, stochastic optimization problems)
Impedance control is one of the interaction and force control methods that has been widely applied in the research of robotics. In this paper, a new position-based fractional-order impedance control scheme is proposed and applied to a 2 DOF serial manipulator. An RR robot manipulator with full arm dynamics and its environment were designed using Matlab/Simulink. The position control of the manipulator was utilized based on computed torque control to cancel out the nonlinearities existing on the dynamic model of the robot. Parameters of classical impedance controller (CIC) and proposed fractional-order impedance controller (FOIC) were optimized in order to minimize impact forces for comparison of the results in three conditions. In CIC condition: three constant parameters of the impedance controller were optimized: in Frac_λμ condition: Only non-integer parameters of the FOIC were re-optimized after the parameters in CIC had been accepted, and in Frac_all condition: all parameters of the FOIC were re-optimized. In order to show the effectiveness of the proposed method, simulations were conducted for all cases and performance indices were computed for the interaction forces. Results showed that impacts were reduced with an improvement of 26.12% from CIC to Frac_ λμ and an improvement of 47.21% from CIC to Frac_all. The proposed scheme improves the impedance behavior and robustness showing better impact absorption performance, which is needed in many challenging robotic tasks and intelligent mechatronic devices.
Modules are requisite for the realization of modular reconfigurable manipulators. The design of modules in literature mainly revolves around geometric aspects and features such as lengths, connectivity and adaptivity. Optimizing and designing the modules based on dynamic performance is considered as a challenge here. The present paper introduces an Architecture-Prominent-Sectioning (APS) strategy for the planning of architecture of modules such that a reconfigurable manipulator possesses minimal joint torques during its operations. Proposed here is the transferring of complete structure into an equivalent system, perform optimization and map the resulting arrangement into possible architecture. The strategy has been applied on a set of modular configurations considering three-primitive-paths. The possibility of getting advanced/complex shapes is also discussed to incorporate the idea of a modular library.
Changing practices in contemporary biosciences and biotechnology have altered definitions and understandings of what we call “life”—what we mean by this term, what constitutes life, and how human and nonhuman others experience life. This chapter explores the shifting definitions of life within the biosciences, and beyond—which have emerged due to specific bioscientific/biotech advancements and changing political-economic imperatives and governing logics that have emerged during the latter half of the twentieth century. Increasingly, now, life as we know it has come to be manipulated, customized, optimized, maximized, and commodified. The chapter considers how life is increasingly technologized, how life is now understood as an ‘open dynamism,’ how life is something now viewed as subject to self-governance, and how life has become a domain of biovalue. While concepts of life and life-making strategies might have altered, historic inequities have not receded but are instead re-secured and often exacerbated.
Understand both uncoded and coded caching techniques in future wireless network design. Expert authors present new techniques that will help you to improve backhaul, load minimization, deployment cost reduction, security, energy efficiency and the quality of the user experience. Covering topics from high-level architectures to specific requirement-oriented caching design and analysis, including big-data enabled caching, caching in cloud-assisted 5G networks, and security, this is an essential resource for academic researchers, postgraduate students and engineers working in wireless communications.
Usnic acid is a unique lichen metabolite of industrial importance, widely studied to explore its pharmacological potential and valued especially as an antibacterial agent in cosmetics. Although a vast number of papers describe usnic acid extraction from various lichen species, none has so far provided an unequivocal indication of the best extraction procedure for this compound. Thus, the current study was focused on the direct comparison of three commonly used usnic acid extraction methods (heat reflux, shaking, ultrasound-assisted extractions), which were optimized using fractional factorial design. Heat reflux extraction, shaking extraction and ultrasound-assisted extraction were first optimized in a series of experiments using fractional factorial design, with respect to three parameters: the extraction time, the solvent used and the number of extraction repetitions. HPLC was employed for usnic acid quantitative analysis. The best scores for each extraction method were statistically compared and the optimal conditions were indicated. The optimal set of parameters for usnic acid was established to be a single, 60 min heat reflux extraction with acetone. This extraction scheme provided 4.25 ±0.08 mg g−1 d.w. of usnic acid, while for ultrasound-assisted and shaking extractions the amount was two- or even four times lower (2.33 ±0.17 and 0.97 ±0.08 mg g−1 d.w., respectively). The optimal procedure for usnic acid extraction described here may be suitable for effective acquisition of this compound for scientific research purposes, but also for applications in the pharmaceutical or cosmetic industries.
This chapter proposes that globalization of multinational enterprises (MNEs) can be viewed as involving self-organizing swarms searching for optimal solutions to challenges presented by new and rapidly changing organizational ecologies. These challenges include developing innovative strategies for leadership, communication, training, management style, organizational design, and so forth. In this model, optimization most effectively occurs through evolutionary performance-based processes rather than more rational, analytic ones. This chapter applies recent theory and research on swarm intelligence to globalization, relates it to work on building microcultures in MNEs and suggests that the combination of these two processes can lead to more “intelligent swarming.” The latter involves a more detailed description of the past solution attempts of one’s own work team and those of other potentially comparable teams, including a fuller explication of key ecological characteristics, identification of potentially relevant comparison ecologies, and accurate evaluation of solution outcomes. It also involves better data storage and retrieval. Conditions which nurture intelligent swarming are discussed. Two training activities that have been used in a variety of organizational contexts and adaptable to enhancing intelligent swarming are described – “World Café” and “Smart Swarming.”
The Handbook of Behavior Change is the first wide-ranging compendium of theory- and evidence-based research and practice on behavior change. It provides scientists, students, and practitioners with the current evidence on behavior change and expert advice on how to develop, evaluate, and implement behavior change interventions. The handbook also sets an agenda for future research on behavior change theory and practice across multiple behaviors, contexts, and populations. This chapter outlines emerging issues and future research directions arising from the handbook. The chapter stresses the importance of theory development, including the need for greater emphasis on ecological and social theories; clearer descriptions and operationalizations of behavior change theories; and increased application of interdisciplinary approaches. Future research on intervention development should conduct more comprehensive intervention fidelity assessments; adopt novel means to improve the translation, feasibility, and optimization of interventions; ensure consideration of ethical issues in behavior change research; routinely evaluate mechanisms of action in behavior change interventions; and apply complex systems approaches to behavior change. “Best-practice” guidance on behavior change should consider emerging methods and approaches to behavior change; implement trials to evaluate the long-term maintenance of behavior change; and develop core curricula on behavior change to educate the next generation of scientists and practitioners.
Robust development of behavior change interventions is based on a sound understanding of the target group, the target behaviors that need to change, the context in which change will occur, the hypothesized mechanisms of change, and the behavior change techniques. Intervention development frameworks advocate a systematic approach to behavior change intervention development. Key tasks include (1) identify and analyze the problem addressed in behavioral terms; (2) identify intervention mechanisms, content, and delivery mode(s) and design a logic model or program theory; (3) develop materials or prototypes (e.g., interface); and (4) test the intervention iteratively through empirical optimization. The tasks apply to both developing new interventions and optimizing existing interventions. The tasks may differ somewhat for digital behavior change interventions (e.g., iterative testing and refinement of early prototypes during development). Depending on time and resources, the tasks can be completed relatively quickly or take considerable time. The current chapter presents key challenges in intervention development and describes potential solutions. Fidelity, feasibility, and acceptability should be considered during all development tasks. The chapter also provides recommendations for advancing the methodology of intervention development and the use of intervention development frameworks and approaches in practice and policy settings.