33 research outputs found

    Control Strategies for Multi-Controller Multi-Objective Systems

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    This dissertation\u27s focus is control systems controlled by multiple controllers, each having its own objective function. The control of such systems is important in many practical applications such as economic systems, the smart grid, military systems, robotic systems, and others. To reap the benefits of feedback, we consider and discuss the advantages of implementing both the Nash and the Leader-Follower Stackelberg controls in a closed-loop form. However, closed-loop controls require continuous measurements of the system\u27s state vector, which may be expensive or even impossible in many cases. As an alternative, we consider a sampled closed-loop implementation. Such an implementation requires only the state vector measurements at pre-specified instants of time and hence is much more practical and cost-effective compared to the continuous closed-loop implementation. The necessary conditions for existence of such controls are derived for the general linear-quadratic system, and the solutions developed for the Nash and Stackelberg controls in detail for the scalar case. To illustrate the results, an example of a control system with two controllers and state measurements available at integer multiples of 10% of the total control interval is presented. While both Nash and Stackelberg are important approaches to develop the controls, we then considered the advantages of the Leader-Follower Stackelberg strategy. This strategy is appropriate for control systems controlled by two independent controllers whose roles and objectives in terms of the system\u27s performance and implementation of the controls are generally different. In such systems, one controller has an advantage over the other in that it has the capability of designing and implementing its control first, before the other controller. With such a control hierarchy, this controller is designated as the leader while the other is the follower. To take advantage of its primary role, the leader\u27s control is designed by anticipating and considering the follower\u27s control. The follower becomes the sole controller in the system after the leader\u27s control has been implemented. In this study, we describe such systems and derive in detail the controls of both the leader and follower. In systems where the roles of leader and follower are negotiated, it is important to consider each controller\u27s leadership property. This property considers the question for each controller as to whether it is preferable to be a leader and let the other controller be a follower or be a follower and let the other controller be the leader. In this dissertation, we try to answer this question by considering two models, one static and the other dynamic, and illustrating the results with an example in each case. The final chapter of the dissertation considers an application in microeconomics. We consider a dynamic duopoly problem, and we derive the necessary conditions for the Stackelberg solution with one firm as a leader controlling the price in the marke

    The University Defence Research Collaboration In Signal Processing

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    This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

    Management: A bibliography for NASA managers

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    This bibliography lists 706 reports, articles, and other documents introduced into the NASA scientific and technical information system in 1984. Entries, which include abstracts, are arranged in the following categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs, and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy. Subject, personal author, corporate source, contract number, report number, and accession number indexes are included

    How does regulation affect innovation and technology change in the water sector in England and Wales?

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    This thesis examines the role of regulation in technological change in the water sector in England and Wales. Based on a combination of Social-Ecological Systems (SES) theory and the Multi-Level Perspective on technological transitions a Comparative Information-Graded Approach (CIGA) is developed in Part 1. As part of the CIGA, a series of tools is used for characterizing and evaluating the relationship between regulation and technology. In Part 2, the CIGA is applied to characterize the relationship between regulation and water innovation in England and Wales based on official publications, Environment Agency data, and interviews. In particular, 7 mechanisms are identified by which regulation affects innovation and 5 issues of trust negatively interact with innovation. As trust is established through these mechanisms, opportunities for innovation are at times sacrificed. Part 3 develops and analyses a set of models based on findings in Part 2. Dynamical systems and fictitious play analysis of a trustee game model of regulation exhibits cyclicality providing an explanation for observed cycles which create an inconsistent drive for innovation. Trustee and coordination models are evaluated in Chapter 7 highlighting how most tools struggle with the issue of technological lock-in. Chapter 8 develops a model of two innovators and a public good water technology over time, showing the role foresight plays in this context as well as the disincentive to develop it. Taken together, the CIGA characterization and modelling work provide a series of recommendations and insights into how the system of regulation affects technology change.Open Acces

    Multi-Agent Systems

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    A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

    Aeronautical Engineering: A Continuing Bibliography With Indexes

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    This supplemental issue of Aeronautical Engineering, A Continuing Bibliography with Indexes (NASA/SP-1999-7037) lists reports, articles, and other documents recently announced in the NASA STI Database. The coverage includes documents on the engineering and theoretical aspects of design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines) and associated components, equipment, and systems. It also includes research and development in aerodynamics, aeronautics, and ground support equipment for aeronautical vehicles. Each entry in the publication consists of a standard bibliographic citation accompanied, in most cases, by an abstract. Two indexes-subject and author are included after the abstract section

    The University Defence Research Collaboration In Signal Processing: 2013-2018

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    Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which youโ€™ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

    ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ๊ณต๊ธ‰์กฐ์ ˆ ์—ญํ• ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์™ธ๊ตํ•™๊ณผ, 2012. 8. ์œค์˜๊ด€.1960๋…„ ์ค‘๋™์˜ ์‚ฐ์œ ๊ตญ๋“ค์„ ์ค‘์‹ฌ์œผ๋กœ ํ•œ OPEC์˜ ์ฐฝ์„ค์€ ์„์œ  ๋…๊ณผ์ ์‹œ์žฅ ํ™˜๊ฒฝ ๋‚ด์—์„œ ์ƒ์‚ฐ๊ตญ ๊ฐ„์˜ ์œ ๊ธฐ์  ๋‹ดํ•ฉ์„ ๋…ธ๋ฆฌ๋Š” ์นด๋ฅดํ…”์˜ ํƒ„์ƒ์œผ๋กœ ๋น„์ถฐ์กŒ๊ณ , OPEC์˜ ์ด๊ธฐ์  ๋™๊ธฐ์— ๋Œ€ํ•œ ์„œ๋ฐฉ์„ธ๊ณ„์˜ ๋น„ํŒ์ด ์Ÿ์•„์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ OPEC ๊ฐ€์ž…๊ตญ ์ค‘, ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ๊ฒฝ์šฐ๋Š” OPEC์˜ ์ฐฝ์„ค๋ถ€ํ„ฐ ํ˜„์žฌ๊นŒ์ง€ OPEC์˜ ์„์œ  ์ƒ์‚ฐ๋Ÿ‰๊ณผ ์„ธ๊ณ„ ์œ ๊ฐ€๋ฅผ ์ ์ • ์ˆ˜์ค€์œผ๋กœ ์œ ์ง€ํ•˜๋Š”, ์†Œ์œ„ ๊ณต๊ธ‰์กฐ์ ˆ์ž(swing producer)์˜ ์—ญํ• ์„ ๋‹ด๋‹นํ•ด์™”๋‹ค๋Š” ์ ์—์„œ ์ฃผ๋ชฉ์„ ๋ฐ›์•„์™”๋‹ค. ์˜ˆ์ปจ๋Œ€ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„๋Š” OPEC ์›์œ ์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ๊ธ‰๊ฐํ•˜๊ณ  ์„์œ  ๊ณผ์ž‰๊ณต๊ธ‰์œผ๋กœ ์ธํ•œ ์œ ๊ฐ€ ํ•˜๋ฝ์ด ์˜ˆ์ƒ๋˜๋˜ 1980๋…„๋Œ€ ์ดˆ๋ฐ˜ OPEC ์„์œ  ์ƒ์‚ฐ๋Ÿ‰ ์ฟผํ„ฐ์ œ๋ฅผ ๋„์ž…ํ•˜๋Š” ๋ฐ ์ฃผ๋„์  ์—ญํ• ์„ ํ•˜์˜€๊ณ , ๋‹ค๋ฅธ ๊ตญ๊ฐ€๋ณด๋‹ค ๊ฐ€ํŒŒ๋ฅธ ๊ฐ์‚ฐ์ •์ฑ…์„ ์ทจํ•˜๋ฉด์„œ ์œ ๊ฐ€ ์œ ์ง€์— ๊ธฐ์—ฌํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ OPEC์— ๋ถˆ๋ฆฌํ•œ ์‹œ์žฅ ์ƒํ™ฉ์ด ์—ฌ์ „ํžˆ ๊ฐœ์„ ๋˜์ง€ ์•Š์€ 1985๋…„ 8์›” ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„๋Š” ๊ฐ‘์ž๊ธฐ ์ฆ์‚ฐ์ •์ฑ…์„ ๋‹จํ–‰ํ•˜์—ฌ ๊ฐ™์€ ํ•ด 12์›”๊นŒ์ง€ ์ผ์ผ ์›์œ ์ƒ์‚ฐ๋Ÿ‰์„ ์•ฝ 100% ๋Œ์–ด์˜ฌ๋ ธ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ 1985๋…„ ๋ง๊นŒ์ง€ ์•ฝ๊ฐ„์˜ ๊ฐ์†Œ์„ธ๋ฅผ ๋ณด์ด๋˜ ์œ ๊ฐ€๋Š” 1986๋…„ ์™„์ „ํžˆ ํญ๋ฝํ•˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ž๊ตญ์˜ ์ฆ์‚ฐ์œผ๋กœ ์„ธ๊ณ„ ์œ ๊ฐ€ ํญ๋ฝ์ด ์ถฉ๋ถ„ํžˆ ์˜ˆ์ƒ๋๋˜ ์ƒํ™ฉ์—์„œ๋„, ์ด ์‹œ๊ธฐ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„๊ฐ€ ๊ธฐ์กด์˜ ๊ณต๊ธ‰์กฐ์ ˆ์ž ์—ญํ• ์„ ๋Œ€ํญ ์ถ•์†Œํ•œ ๋ฐฐ๊ฒฝ์ด ๋ฌด์—‡์ธ๊ฐ€?๋ผ๋Š” ์—ฐ๊ตฌ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋Œ€๋‹ต์„ ์‹œ๋„ํ•˜๊ณ  ์žˆ๋‹ค. ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์„์œ  ์ •์ฑ…์— ๊ด€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ 1985๋…„์— ์ด๋ค„์ง„ ์ฆ์‚ฐ์ •์ฑ…์ด ๊ธ‰๊ฒฉํ•œ ์žฌ์ •๋‚œ์œผ๋กœ ํŒŒํƒ„๋œ ๊ตญ๋‚ด ๊ฒฝ์ œ์—์„œ ๋น„๋กฏ๋˜์—ˆ๋‹ค๊ณ  ์„ค๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์„ค๋ช…์ด ์ƒ๋‹นํ•œ ์ ํ•ฉ์„ฑ์„ ์ œ๊ณตํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์ฆ์‚ฐ์ •์ฑ…์„ ์ด๋Œ์–ด๋‚ธ ๊ฐ€์žฅ ๊ฒฐ์ •์ ์ธ ์š”์ธ์€ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์ •์น˜์  ๊ณ ๋ ค์˜€๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์ฆ‰, ์ •์น˜์  ์š”์ธ์ด ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์ •์ฑ…๊ฒฐ์ •๊ณผ์ •์— ์ž‘์šฉํ–ˆ๋‹ค๋Š” ๋ฐ ์ดˆ์ ์„ ๋‘๊ณ , ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์•ˆ๋ณด์— ๋Œ€ํ•œ ๊ตญ์ œ์ , ์ง€์—ญ์  ์œ„ํ˜‘์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„๊ฐ€ ์ฆ์‚ฐ์„ ํ†ตํ•ด ์„์œ  ์ˆ˜์ถœ ์ˆ˜์ต์„ ๋Š˜๋ ค ์•ˆ๋ณด์— ํž˜์„ ์Ÿ๋Š” ๋Œ€์‹  ๊ณต๊ธ‰์กฐ์ ˆ์ž์˜ ์—ญํ• ์„ ์ถ•์†Œํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค๋Š” ๊ฐ€์„ค์„ ์—ฐ๊ตฌ์งˆ๋ฌธ์— ๋Œ€ํ•œ ์ž ์ •์ ์ธ ๋Œ€๋‹ต์œผ๋กœ์„œ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์— ์˜ํ•˜๋ฉด ๋ƒ‰์ „์œผ๋กœ ์ธํ•œ ๊ตญ์ œ์  ๋ฏธ-์†Œ ๊ฐˆ๋“ฑ์ด ์ค‘๋™ ์ง€์—ญ์˜ ์—ญ๋‚ด ๊ธด์žฅ๊ด€๊ณ„๋ฅผ ์กฐ์„ฑ ๋ฐ ๊ฐ•ํ™”ํ•˜์˜€์œผ๋ฉฐ, 1970๋…„๋Œ€ ํ›„๋ฐ˜์—๋Š” ์ด๋ž€ํ˜๋ช…๊ณผ ์ด๋ž€โˆ™์ด๋ผํฌ ์ „์Ÿ์ด ๋ฐœ์ƒํ•˜์—ฌ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์•ˆ๋ณด์— ํฐ ์••๋ ฅ์œผ๋กœ ์ž‘์šฉํ•˜์˜€๋‹ค. ํŠนํžˆ ์ด๋ž€์€ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์นœ๋ฏธ์ •์ฑ…๊ณผ ์ข…๊ต์  ํƒ€๋ฝ์„ ๋ฌธ์ œ ์‚ผ์œผ๋ฉฐ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„๋ฅผ ์ž๊ทนํ•˜๋Š” ๋ฐ ์ด์–ด, 1984๋…„๊ณผ 1985๋…„์—๋Š” ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ํ•ต์‹ฌ ์ •์œ ์‹œ์„ค๊ณผ ์œ ์กฐ์„ ์„ ์ƒ๋Œ€๋กœ ๊ณต๊ฒฉ ๋ฐ ํ…Œ๋Ÿฌ๋ฅผ ๊ฑฐ๋“ญํ•˜์—ฌ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์•ˆ๋ณด์— ํฐ ์œ„ํ˜‘์„ ๊ฐ€ํ•˜์˜€๋‹ค. ์ด์— ๋Œ€ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„๋Š” ์ ๊ทน์ ์œผ๋กœ ์„œ๋ฐฉ์˜ ์„ ์ง„๋ฌด๊ธฐ ๊ตฌ์ž…์— ๋‚˜์„œ๋Š” ํ•œํŽธ, ๋ฏธ๊ตญ์˜ ๋ ˆ์ด๊ฑด ํ–‰์ •๋ถ€์™€ ๊ธด๋ฐ€ํ•œ ํ˜‘๋ ฅ๊ด€๊ณ„๋ฅผ ๋„๋ชจํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฌ์‹ค๋“ค์€ ์‚ฌ์šฐ๋””์•„๋ผ๋น„์•„์˜ ์ฆ์‚ฐ์ •์ฑ…์€ ์•ˆ๋ณด ๋Šฅ๋ ฅ ์ฆ๊ฐ•์„ ํ•˜๋Š” ๊ณผ์ •์—์„œ ๊ตญ๊ฐ€์ˆ˜์ž…์„ ์ฆ๊ฐ€์‹œํ‚ฌ ํ•„์š”๊ฐ€ ์žˆ์—ˆ๋‹ค๋Š” ๊ฐ€์„ค์„ ํ™•์ธ์‹œ์ผœ ์ฃผ์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฐ์œ ๊ตญ์˜ ์ƒ์‚ฐ ์ •์ฑ…์ด ๊ฐ€๊ฒฉ๊ณผ ๊ฐ™์€ ์‹œ์žฅ์š”์ธ์— ๋Œ€ํ•œ ๊ณ ๋ ค์—์„œ๋งŒ ๋น„๋กฏ๋˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ๊ตฐ์‚ฌ์  ์œ„ํ˜‘ ์ธ์‹๊ณผ ๊ฐ™์€ ์ •์น˜์  ์š”์ธ์— ์˜ํ•ด์„œ๋„ ํฐ ์˜ํ–ฅ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์นด๋ฅดํ…”๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์‚ฐ์œ ๊ตญ์˜ ๊ฒฝ์ œ์  ํ˜‘๋ ฅ์ด ๊ฐ€์ž…๊ตญ์˜ ์œ„ํ˜‘ ์ธ์‹ ๋•Œ๋ฌธ์— ๋ฌด์‚ฐ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์–ด, ๊ฐ€์Šค์ˆ˜์ถœ๊ตญํฌ๋Ÿผ๊ณผ ๊ฐ™์ด ํ˜„์žฌ ๊ตญ์ œ ์—๋„ˆ์ง€ ์‹œ์žฅ์— ํ˜•์„ฑ๋˜์–ด ์žˆ๋Š” ์นด๋ฅดํ…”์˜ ๋ฏธ๋ž˜๋ฅผ ๊ฐ€๋Š ํ•˜๋Š” ๋ฐ ์žˆ์–ด ์‹ค์ฆ์ ์œผ๋กœ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค.I. Introduction 1 1. Research Question 1 2. Literature Review 12 3. Research Hypothesis and Methodology 25 4. Organization of the Thesis 29 II. Historical Background 30 1. Foundation of OPEC and the Coalition between the Members 30 2. Market Change in the Early 1980s 37 3. Negotiations within OPEC 44 4. Broken Coalition 49 5. Saudi Arabias Determination to Increase its Oil Production 64 III. The Domestic Economy of Saudi Arabia 68 1. Saudi Arabias Economic Vulnerabilities 69 2. Saudi Arabias Economy in the mid-1980s: Examination of the Hypothesis (B) 84 IV. Saudi Arabias Politics in a Structural Context 98 1. Growing International Tensions in the Middle East 98 2. Regional Conflicts 107 3. Saudi Arabias Response to the Growing Tensions: Overproduction of its Crudes and Reduction of its Swing-Producer Role 123 V. Conclusions 139 Bibliography 145 ๊ตญ๋ฌธ์ดˆ๋ก 158Maste

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