An Axiomatic Design Approach to Passenger Itinerary Enumeration in Reconfigurable Transportation Systems

Viswanath, Asha; Baca, E.; Farid, Amro M.

doi:10.1109/tits.2013.2293340

Cited by 25 publications

(16 citation statements)

References 32 publications

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“…Note that the scope of LFES spans multiple engineering application domains including production, power, water, and transportation systems [21,23,24,[27][28][29][30]63,91]. Furthermore, the choice of Axiomatic Design Theory rests in the realization that traditional graph theoretic methods only include an explicit description of system form and neglect system function [27][28][29]-thus hindering the study of resilience where both system function and form change.…”

Section: Definition 1 Lfesmentioning

confidence: 99%

“…While the full description of resilience measures is not feasible here, the underlying Axiomatic Design model [27][28][29][30] for LFES is included in order to provide a common foundation upon which the remainder of the discussion is based. The interested reader is referred to previous works [21,[23][24][25][27][28][29][30]63,91] for further discussion and illustrative examples of how this Axiomatic Design model has been applied to resilience and reconfigurability measurement. The second half of this section serves to ground the Axiomatic Design model, as a concise description of system structure, to traditional power systems models as descriptions of power system behavior.…”

Section: Definition 1 Lfesmentioning

confidence: 99%

“…The destination/location of one structural degree of freedom must occur at the origin/location of the subsequent one [21,23,24,[27][28][29][30]91]. Consequently, a new measure for sequence-dependent capabilities of the LFES can be defined.…”

Section: Definition 8 Lfes Rheonomic Knowledge Basementioning

confidence: 99%

“…At its foundation, Axiomatic Design for Large Flexible Engineering Systems is built upon a mapping of systems processes (P) to system resources (R) [21,23,24,[27][28][29][30]88,91]. Here, it is understood that both the processes and resources are cyber-physical.…”

Section: Introduction To Axiomatic Design For Large Flexible Engineermentioning

confidence: 99%

“…[21,23,24,[27][28][29][30]91]: A binary matrix J S of size σ (P) × σ (R) whose element J S (w, v) ∈ {0, 1} is equal to one when action e wv ∈ E S exists (where σ () gives the size of a set).…”

Section: Introduction To Axiomatic Design For Large Flexible Engineermentioning

confidence: 99%

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Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

Farid

2015

Intell Ind Syst

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Recently, the academic and industrial literature has coalesced around an enhanced vision of the electric power grid that is intelligent, responsive, dynamic, adaptive and flexible. One particularly emphasized "smart-grid" property is that of resilience where healthy regions of the grid continue to operate while disrupted and perturbed regions bring themselves back to normal operation. Multi-agent systems have recently been proposed as a key enabling technology for such a resilient control scheme. While the power system literature has often addressed multi-agent systems, many of these works did not have resilience as the central design intention. This paper now has a two-fold purpose. First, it seeks to identify a set of multi-agent system design principles for resilient coordination and control of future power systems. To that end, it draws upon an axiomatic design for large flexible engineering systems model which was recently used in the development of resilience measures. From this quantitative model, a set of design principles are easily distilled. Second, the paper assesses the adherence of existing multi-agent system implementations with respect to these design principles. The paper concludes that while many multi-agent systems have been developed for power grids, they have been primarily intended as the decentralization of a particular decision-making/control algorithm. Thus many of the works make only limited contributions to power grid resilience.B Amro M. Farid

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Section: Definition 1 Lfesmentioning

confidence: 99%

Section: Definition 1 Lfesmentioning

confidence: 99%

Section: Definition 8 Lfes Rheonomic Knowledge Basementioning

confidence: 99%

Section: Introduction To Axiomatic Design For Large Flexible Engineermentioning

confidence: 99%

Section: Introduction To Axiomatic Design For Large Flexible Engineermentioning

confidence: 99%

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Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

Farid

2015

Intell Ind Syst

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eIoT

Muhanji¹,

Flint²,

Farid³

2019

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adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

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The Development of IoT Within Energy Infrastructure

Muhanji

Flint

Farid

2019

eIoT

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The development of IoT within the energy infrastructure is best seen as a control loop. The control loop is composed of four functions: a physical process (such as the generation, transmission, or consumption of electricity), its measurement, decision making, and actuation. This control structure is shown in Fig. 3.1 where a sensor takes measurements of the states and outputs of a physical system. Wireless and wired communications are used to pass this information between the physical layer and other informatic components. This information is used to make decisions either independently in a decentralized fashion or in coordination with the informatic components of other devices. Decisions are sent back down to network-enabled actuators for implementation.In some cases, this control loop acts in near real-time; in other cases, some of the information is used as part of predictive applications that facilitate decisions at a longer timescale. Control algorithms implemented at different layers of this control loop enable the control of individual devices as well as the coordination of smart grid devices that make up other parts of eIoT. Given the connectivity between the functions of this control loop, its successful implementation requires architectures and standards that ensure interoperability between eIoT technologies.This chapter serves to summarize the most recent developments of IoT within the energy infrastructure. The discussion proceeds bottom-up by classifying these developments according to the generic control structure shown in Fig. 3.1.• Section 3.1 discusses some of the state of the art in network-enabled physical devices, whether they are network-enabled sensors or actuators in the control loop. • Section 3.2 focuses on the communication networks that send and receive data to and from these devices. • Section 3.3 discusses advancements in distributed control algorithms to coordinate the techno-economic performance.

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An Axiomatic Design Approach to Passenger Itinerary Enumeration in Reconfigurable Transportation Systems

Cited by 25 publications

References 32 publications

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

eIoT

The Development of IoT Within Energy Infrastructure

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